Trending December 2023 # How To Draw A Hexagon With Polyline Class Using Fabricjs? # Suggested January 2024 # Top 13 Popular

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We can create a Polyline object by creating an instance of fabric.Polyline. A polyline object can be characterised by a set of connected straight-line segments. Since it is one of the basic elements of FabricJS, we can also easily customize it by applying properties like angle, opacity etc. A hexagon is a closed two-dimensional polygon with six sides.

Syntax new fabric.Polyline(points: Array, options: Object) Parameters

points − This parameter accepts an Array which denotes the array of points that make up the polyline object.

options (optional) − This parameter is an Object which provides additional customizations to our object. Using this parameter origin, stroke width and a lot of other properties can be changed related to the Polyline object.

Example 1: Creating an Instance of fabric.Polyline() and Adding it to our Canvas

Before creating a start, let’s see a code example of how we can add a polyline object to our canvas. The only required parameter is the points Array whereas the second argument is the optional options object.

var canvas = new fabric.Canvas(“canvas”); canvas.setWidth(document.body.scrollWidth); canvas.setHeight(250);

var points = [ { x: 30, y: 50 }, { x: 0, y: 0 }, { x: 60, y: 0 }, ];

var polyline = new fabric.Polyline(points, { left: 100, top: 40, fill: “white”, strokeWidth: 4, stroke: “teal”, });

canvas.add(polyline);

Example 2: Creating a hexagon with Polyline

In this example, we will create a hexagon using the Polyline instance. We can select the coordinates in such a way that the shape forms a hexagon as given below.

var canvas = new fabric.Canvas(“canvas”); canvas.setWidth(document.body.scrollWidth); canvas.setHeight(250);

var points = [ { x: 50, y: 0 }, { x: 25, y: 43.30}, { x: -25, y: 43.301 }, { x: -50, y: 0}, { x: -25, y: -43.301}, { x: 25, y: -43.301 }, { x: 50, y: 0 }, ];

var polyline = new fabric.Polyline(points);

polyline.set(“stroke”, “teal”); polyline.set(“strokeWidth”, 3); polyline.set(“fill”, “white”); polyline.set(“top”, 50); polyline.set(“left”, 100); polyline.set(“scaleX”, 0.75); polyline.set(“scaleY”, 0.75);

canvas.add(polyline);

You're reading How To Draw A Hexagon With Polyline Class Using Fabricjs?

How To Draw A Rectangle With Polygon Object Using Fabricjs?

We can create a Polygon object by creating an instance of fabric.Polygon. A polygon object can be characterized by any closed shape consisting of a set of connected straight line segments. Since it is one of the basic elements of FabricJS, we can also easily customize it by applying properties like angle, opacity etc.

Syntax new fabric.Polygon( points: Array, options: Object ) Parameters

points − This parameter accepts an Array which denotes the array of points that make up the polygon object.

Example 1: Default Appearance of Polygon Object

Let’s see a code example of how we can draw any general polygon object. We need to specify an array of points where each point is an object with x and y. Specifying the array of points is crucial without which our polygon object would not be rendered onto the canvas. We can also customize the polygon object by using various properties.

Here, we have customized our polygon object by assigning fill colour, stroke colour and setting the strokeWidth to 2.

var canvas = new fabric.Canvas(“canvas”); canvas.setWidth(document.body.scrollWidth); canvas.setHeight(250);

var polygon = new fabric.Polygon( [ { x: 500, y: 20 }, { x: 550, y: 60 }, { x: 550, y: 200 }, { x: 350, y: 200 }, { x: 350, y: 60 }, { x: 500, y: 20 }, ], { fill: “black”, stroke: “blue”, strokeWidth: 2, } );

canvas.add(polygon);

Example 2: Drawing a Rectangle using Polygon

Let’s see a code example to see how we draw a rectangle using polygon. Since it is a rectangle, we need only four coordinates

var canvas = new fabric.Canvas(“canvas”); canvas.setWidth(document.body.scrollWidth); canvas.setHeight(250);

var rectangle = new fabric.Polygon( [ { x: -240, y: 90 }, { x: 240, y: 90 }, { x: 240, y: -90 }, { x: -240, y: -90 }, ], { stroke: “red”, left: 140, top: 10, strokeWidth: 2, strokeLineJoin: “bevil”, } );

canvas.add(rectangle);

Conclusion

In this tutorial, we used two simple examples to demonstrate how you can draw a rectangle with Polygon using FabricJS.

Using Pbl In Environmental Science Class

A few years ago, my students became bothered by the number of plastic bags showing up in the Guyandotte River, which winds behind our school and through our rural southern West Virginia towns. They believed that recycling and other waste management options would decrease littering, but we didn’t know where to start—our rural county had no recycling program.

As an AmeriCorps alumna, I was familiar with launching community programs without a budget. By merging apprenticeships and project-based learning (PBL) in my environmental science class, we were able to create our county’s first recycling program.

The Setup

Our students initially started an after-school recycling program, which rapidly evolved into our county’s only recycling center within one year. We grew so quickly that we needed outside help, fast. PepsiCo Recycling Rally provides curriculum and equipment to jump-start recycling collection at your school, so we started to use those resources.

Merging PBL with the apprenticeship model provided a framework for designing units with learning outcomes that build critical thinking and creative problem-solving skills. Operating a recycling center does not work if our student body and community do not know our recycling procedures, what can be recycled, or how recycling can save our streams. Students share their knowledge by organizing schoolwide recycling pep rallies featuring recycling games they develop. They organize school assemblies and create videos, theatrical performances, and rap songs about recycling procedures.

To determine the effectiveness of our outreach programs within our school, we conduct waste audits, analyzing data to see the percentage of recyclables and trash in correct bins. My students design educational activities for local fairs and festivals, teaching students why it’s important to understand where our waste goes and how to best manage it. They work with our communities to assess microplastic levels along our riverbank and launched a Spotify podcast, Waste in Our Waters. They also create and deliver presentations to our town councils and county commission because our ultimate goal is to create a countywide recycling network.

Our program is unique because there are both curricular and extracurricular components. Plastic pollution and waste management are only two units in the environmental science curriculum, so it’s challenging to dedicate the time to complete all the tasks for running a recycling program and addressing plastic pollution within a classroom. If we don’t complete our weekly requirements of collecting and sorting recyclables during class, which happens frequently due to teaching other content standards, then the after-school program picks up the slack. It takes seven to 10 students to stay on top of the recycling demands.

Transforming students into environmental leaders does not happen overnight. It requires time and intentional planning, but the outcomes are what we hope for as teachers: confident, engaged, and civically minded students.

Growing Student Environmental Leaders

Here are eight steps for creating environmental change makers. Although some of these features are standard in PBL, there is much more of an emphasis on building community relationships when using the apprenticeship model.

Make observations: Instruct students to record observations about the environment while walking around campus. Are there invasive species, sources of pollution, or suitable habitats for specific species?

Find patterns: Discuss patterns that emerge from your students’ observations. Record these ideas, and let students prioritize topics.

Identify community experts: Specialists may be found at museums, parks, and/or natural resource and environmental agencies. National Geographic’s Explorer Classroom and Exploring by the Seat of Your Pants YouTube channels connect classrooms to experts across the globe. The expert’s role is to extend the students’ background knowledge about the selected environmental issue. Ask students how they felt and what interested them after a session with an expert. Are there additional questions or ideas for solving their environmental issue?

Determine the environmental project: Tell students that local problems are often global problems, and instruct them to research ways that other organizations, states, and countries solve related environmental problems. Ask students to share what they learned. Are there feasible projects for the students to modify or replicate? Is there a stand-out project that clearly fits your students’ interests?

Identify stakeholders: Instruct students to brainstorm individuals and organizations in your community that have a vested interest in helping fix this environmental problem. Reach out to these stakeholders for help.

Create a step-by-step plan: Guide students through enumerating all actions required to complete their project. What materials do they need? What is the time frame for completing the project? Who can complete each task? Allow students to express their interests and self-select tasks.

Work alongside community mentors: While meeting with an expert provides environmental content knowledge, the mentor guides the students through tasks to complete the project. Sending a survey home to see if guardians have related skill sets and are willing to help out is a way to build connections with your students’ families.

Achieve goals: What are low-hanging fruits for the students to accomplish first to feel successful? Some projects take time, and their efforts may be the first steps toward a larger project. After a step from your plan is achieved, identify the next step, and create an associated goal within a realistic time frame. Celebrate your success as each goal is completed.

A Closer Look at Apprenticeships

The apprenticeship model helps intentionally build long-lasting mentorships with community partners and experts in the field in order to improve our program and student learning outcomes. In the beginning, our students secured community volunteers to help haul recyclables and worked alongside them to learn unloading procedures. My students began meeting with our neighboring county’s Solid Waste Authority’s director of education, taking tours of their large-scale recycling operations in order to learn the recycling ropes to create a sustainable operation in our county.

One of our students’ grandmothers became a board member of our county’s Solid Waste Authority, and she continues to work with our students biweekly to solve logistical problems and determine new outreach possibilities with our students. Other businesses, like Alpha Metallurgical Resources, reached out to us, and several students work directly with their environmental compliance manager to plan biannual Adopt-A-Highway litter clean-up events. Working alongside community members and experts in the field to solve a critical community issue nurtured my students’ leadership capabilities and confidence.

Creating a Lasting Legacy

Middle and high school students can develop ingenious solutions to problems such as air and water pollution, threatened species, and the lack of green space. At the same time, taking students outdoors jump-starts learning by awakening the senses and increasing connectedness and happiness. Through goal setting, hard work, and problem-solving, our recycling program grew and now serves as the only plastic recycling location in our entire county.

If a recycling program isn’t a good fit for your school, there are myriad other projects that students can pursue, such as doing a survey of microplastics or coming up with technological solutions to environmental challenges. Both the EPA’s Microplastic Beach Protocol for freshwater or marine waters and The Big Microplastic Survey provide citizen science opportunities for students to collect and report data, and Samsung’s Solve for Tomorrow gives students a chance to win classroom technology.

How To Deal With Missing Data Using Python

This article was published as a part of the Data Science Blogathon

Overview of Missing Data

Real-world data is messy and usually holds a lot of missing values. Missing data can skew anything for data scientists and, A data scientist doesn’t want to design biased estimates that point to invalid results. Behind, any analysis is only as great as the data. Missing data appear when no value is available in one or more variables of an individual. Due to Missing data, the statistical power of the analysis can reduce, which can impact the validity of the results.

This article will help you to a guild the following topics.

The reason behind missing data?

What are the types of missing data?

Missing Completely at Random (MCAR)

Missing at Random (MAR)

Missing Not at Random (MNAR)

Detecting Missing values

Detecting missing values numerically

Detecting missing data visually using Missingno library

Finding relationship among missing data

Using matrix plot

Using a Heatmap

Treating Missing values

Deletions

Pairwise Deletion

Listwise Deletion/ Dropping rows

Dropping complete columns

Basic Imputation Techniques

Imputation with a constant value

Imputation using the statistics (mean, median, mode)

K-Nearest Neighbor Imputation

let’s start…..

What are the reasons behind missing data?

Missing data can occur due to many reasons. The data is collected from various sources and, while mining the data, there is a chance to lose the data. However, most of the time cause for missing data is item nonresponse, which means people are not willing(Due to a lack of knowledge about the question ) to answer the questions in a survey, and some people unwillingness to react to sensitive questions like age, salary, gender.

Types of Missing data

Before dealing with the missing values, it is necessary to understand the category of missing values. There are 3 major categories of missing values.

Missing Completely at Random(MCAR):

A variable is missing completely at random (MCAR)if the missing values on a given variable (Y) don’t have a relationship with other variables in a given data set or with the variable (Y) itself. In other words, When data is MCAR, there is no relationship between the data missing and any values, and there is no particular reason for the missing values.

Missing at Random(MAR):

Let’s understands the following examples:

Women are less likely to talk about age and weight than men.

Men are less likely to talk about salary and emotions than women.

familiar right?… This sort of missing content indicates missing at random.

MAR occurs when the missingness is not random, but there is a systematic relationship between missing values and other observed data but not the missing data.

Let me explain to you: you are working on a dataset of ABC survey. You will find out that many emotion observations are null. You decide to dig deeper and found most of the emotion observations are null that belongs to men’s observation.

Missing Not at Random(MNAR):

The final and most difficult situation of missingness. MNAR occurs when the missingness is not random, and there is a systematic relationship between missing value, observed value, and missing itself. To make sure, If the missingness is in 2 or more variables holding the same pattern, you can sort the data with one variable and visualize it.

Source: Medium

‘Housing’ and ‘Loan’ variables referred to the same missingness pattern.

Detecting missing data

Detecting missing values numerically:

First, detect the percentage of missing values in every column of the dataset will give an idea about the distribution of missing values.

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings # Ignores any warning warnings.filterwarnings("ignore") train = pd.read_csv("Train.csv") mis_val =train.isna().sum() mis_val_per = train.isna().sum()/len(train)*100 mis_val_table = pd.concat([mis_val, mis_val_per], axis=1) mis_val_table_ren_columns = mis_val_table.rename( columns = {0 : 'Missing Values', 1 : '% of Total Values'}) mis_val_table_ren_columns = mis_val_table_ren_columns[ mis_val_table_ren_columns.iloc[:,:] != 0].sort_values( '% of Total Values', ascending=False).round(1) mis_val_table_ren_columns

Detecting missing values visually using Missingno library :

Missingno is a simple Python library that presents a series of visualizations to recognize the behavior and distribution of missing data inside a pandas data frame. It can be in the form of a barplot, matrix plot, heatmap, or a dendrogram.

To use this library, we require to install and import it

pip install missingno import missingno as msno msno.bar(train)

The above bar chart gives a quick graphical summary of the completeness of the dataset. We can observe that Item_Weight, Outlet_Size columns have missing values. But it makes sense if it could find out the location of the missing data.

The msno.matrix() is a nullity matrix that will help to visualize the location of the null observations.

The plot appears white wherever there are missing values.

Once you get the location of the missing data, you can easily find out the type of missing data.

Let’s check out the kind of missing data……

Both the Item_Weight and the Outlet_Size columns have a lot of missing values. The missingno package additionally lets us sort the chart by a selective column. Let’s sort the value by Item_Weight column to detect if there is a pattern in the missing values.

sorted = train.sort_values('Item_Weight') msno.matrix(sorted)

The above chart shows the relationship between Item_Weight and Outlet_Size.

Let’s examine is any relationship with observed data.

data = train.loc[(train["Outlet_Establishment_Year"] == 1985)]

data

The above chart shows that all the Item_Weight are null that belongs to the 1985 establishment year.

The Item_Weight is null that belongs to Tier3 and Tier1, which have outlet_size medium, low, and contain low and regular fat. This missingness is a kind of Missing at Random case(MAR) as all the missing Item_Weight relates to one specific year.

msno. heatmap() helps to visualize the correlation between missing features.

msno.heatmap(train)

Item_Weight has a negative(-0.3) correlation with Outlet_Size.

After classified the patterns in missing values, it needs to treat them.

Deletion:

The Deletion technique deletes the missing values from a dataset. followings are the types of missing data.

Listwise deletion:

Listwise deletion is preferred when there is a Missing Completely at Random case. In Listwise deletion entire rows(which hold the missing values) are deleted. It is also known as complete-case analysis as it removes all data that have one or more missing values.

In python we use dropna() function for Listwise deletion.

train_1 = train.copy() train_1.dropna()

Listwise deletion is not preferred if the size of the dataset is small as it removes entire rows if we eliminate rows with missing data then the dataset becomes very short and the machine learning model will not give good outcomes on a small dataset.

Pairwise Deletion:

Pairwise Deletion is used if missingness is missing completely at random i.e MCAR.

Pairwise deletion is preferred to reduce the loss that happens in Listwise deletion. It is also called an available-case analysis as it removes only null observation, not the entire row.

All methods in pandas like mean, sum, etc. intrinsically skip missing values.

train_2 = train.copy() train_2['Item_Weight'].mean() #pandas skips the missing values and calculates mean of the remaining values.

Dropping complete columns

If a column holds a lot of missing values, say more than 80%, and the feature is not meaningful, that time we can drop the entire column.

Imputation techniques:

The imputation technique replaces missing values with substituted values. The missing values can be imputed in many ways depending upon the nature of the data and its problem. Imputation techniques can be broadly they can be classified as follows:

Imputation with constant value:

As the title hints — it replaces the missing values with either zero or any constant value.

We will use the SimpleImputer class from sklearn.

from sklearn.impute import SimpleImputer train_constant = train.copy() #setting strategy to 'constant' mean_imputer = SimpleImputer(strategy='constant') # imputing using constant value train_constant.iloc[:,:] = mean_imputer.fit_transform(train_constant) train_constant.isnull().sum()

Imputation using Statistics:

The syntax is the same as imputation with constant only the SimpleImputer strategy will change. It can be “Mean” or “Median” or “Most_Frequent”.

“Mean” will replace missing values using the mean in each column. It is preferred if data is numeric and not skewed.

“Median” will replace missing values using the median in each column. It is preferred if data is numeric and skewed.

“Most_frequent” will replace missing values using the most_frequent in each column. It is preferred if data is a string(object) or numeric.

Before using any strategy, the foremost step is to check the type of data and distribution of features(if numeric).

train['Item_Weight'].dtype sns.distplot(train['Item_Weight'])

Item_Weight column satisfying both conditions numeric type and doesn’t have skewed(follow Gaussian distribution). here, we can use any strategy.

from sklearn.impute import SimpleImputer train_most_frequent = train.copy() #setting strategy to 'mean' to impute by the mean mean_imputer = SimpleImputer(strategy='most_frequent')# strategy can also be mean or median train_most_frequent.iloc[:,:] = mean_imputer.fit_transform(train_most_frequent) train_most_frequent.isnull().sum()

Advanced Imputation Technique:

Unlike the previous techniques, Advanced imputation techniques adopt machine learning algorithms to impute the missing values in a dataset. Followings are the machine learning algorithms that help to impute missing values.

K_Nearest Neighbor Imputation:

The KNN algorithm helps to impute missing data by finding the closest neighbors using the Euclidean distance metric to the observation with missing data and imputing them based on the non-missing values in the neighbors.

train_knn = train.copy(deep=True) from sklearn.impute import KNNImputer knn_imputer = KNNImputer(n_neighbors=2, weights="uniform") train_knn['Item_Weight'] = knn_imputer.fit_transform(train_knn[['Item_Weight']]) train_knn['Item_Weight'].isnull().sum()

The fundamental weakness of KNN doesn’t work on categorical features. We need to convert them into numeric using any encoding method. It requires normalizing data as KNN Imputer is a distance-based imputation method and different scales of data generate biased replacements for the missing values.

Conclusion

There is no single method to handle missing values. Before applying any methods, it is necessary to understand the type of missing values, then check the datatype and skewness of the missing column, and then decide which method is best for a particular problem.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

How To Set The Border Colour Of Image In Fabricjs?

In this tutorial, we are going to learn how to set the border colour of Image in FabricJS. We can create an Image object by creating an instance of fabric.Image. Since it is one of the basic elements of FabricJS, we can also easily customize it by applying properties like angle, opacity etc. In order to set the border colour of Image, we use the borderColor property.

Syntax Parameters

element − This parameter accepts HTMLImageElement, HTMLCanvasElement, HTMLVideoElement or String which denotes the image element. The String should be a URL and would be loaded as an image.

callback (optional) − This parameter is a function which is to be called after eventual filters are applied.

Options Keys

borderColor − This property accepts a String which specifies the colour of the border when our image object is selected. Its default value is rgb(178,204,255).

Passing borderColor key with a String value Example

Let’s see a code example of how we can assign a value to the borderColor property. We have assigned the value “red” to the borderColor key which helps to create the red border on selection of our image object.

var

canvas

new

fabric

Canvas

(

“canvas”

)

;

canvas

setWidth

(

document

body

scrollWidth

)

;

canvas

setHeight

(

250

)

;

var

imageElement

document

getElementById

(

“img1”

)

;

var

image

new

fabric

Image

(

imageElement

{

top

left

110

borderColor

“red”

}

)

;

canvas

add

(

image

)

;

Passing a rgba value to the borderColor key Example

Instead of passing simple colour names as a String, we can also use RGBA values, whose components specify the amount of Red, Green, Blue and Alpha, where alpha denotes the opacity. In this example we have used rgba(164,0, 0,1) which is the rgb value for the colour dark red.

You can select the image object to see the border colour added using rgba value

var

canvas

new

fabric

Canvas

(

“canvas”

)

;

canvas

setWidth

(

document

body

scrollWidth

)

;

canvas

setHeight

(

250

)

;

var

imageElement

document

getElementById

(

“img1”

)

;

var

image

new

fabric

Image

(

imageElement

{

top

left

110

borderColor

“rgba(164,0,0,1)”

}

)

;

canvas

add

(

image

)

;

How To Draw Curved Lines In Photoshop (2 Easy Ways)

The Line Tool is great, but it doesn’t offer much when you need to draw a curved line in your Photoshop projects. Luckily there are two simple ways to create curved lines in Photoshop with the help of a path. The first method you’ll learn here is the fastest way of doing things, while the second gives you some more creative freedom with how your curved line looks with the help of brushes.

How To Draw A Curved Line In Photoshop

The first method we’ll use to curve a line is with the Pen Tool (P) set to Shape Mode. This is the easiest method and will allow you to quickly create simple curved lines and make some basic adjustments to the appearance.

Step 1: Activate The Pen Tool (P)

Start with an open project or new document and head to the Pen Tool (P) in the Toolbar.

Step 2: Change The Mode To Shape

You’ll see the Options Bar change to reflect the Shape settings.

Step 3: Set The Shape Fill To Transparent

This will ensure the shape fill is transparent.

Step 4: Set The Stroke To A Solid Color

You can then set the line’s thickness in pixels by entering a number or dragging the toggle along the width slider.

You can go back and further edit the appearance of the line once you’ve drawn it on your document.

Step 5: Add An Anchor Point Step 6: Add A Second Anchor Point

The line will appear with the stroke settings you chose.

Step 7: Change The Line’s Appearance Within The Stroke Settings (Optional)

You can alter the appearance of the line once it’s created by heading to the Options bar and further adjusting the stroke settings.

For instance, if I want to change the color and thicken the line, I can change the stroke fill and increase the thickness within the stroke width setting box. Now the line looks totally different despite still following the same path as before.

How To Curve A Line Using The Brush Tool In Photoshop

The other method of curving a line uses the Brush Tool (B) to set the brush’s appearance. While the first method is generally simpler and faster to use, using the brush tool will give you more options for how your line looks.

For instance, lines made with the Brush Tool can have a rounded end, while otherwise, you’d only have a boxy end. You can also decrease the brush’s Hardness to give the line a softer edge.

Step 1: Activate The Pen Tool (P) Step 2: Set The Mode To Path

Now, head to the Options bar. Rather than setting the mode to Shape as in our previous example, we want to set it to Path for this method (if it isn’t already).

This will allow you to create the line as a Path, which you can then apply brush settings to.

Step 3: Draw The Curved Line

Then, add additional anchor points to create the curve you want. For my curve, I’ll add two more anchor points parallel to the first and drag them up, then down, to make the curve.

Step 4: Select The Brush Tool (B)

Next, head to the Brush Tool (B) in the Toolbar.

The Options Bar will change to show the brush settings.

Step 5: Set The Brush’s Appearance

For my purposes, I’ll choose a Hard Round brush with the Hardness set to 100. Feel free to adjust this if you’d like a more feathered line.

You also want to ensure the line doesn’t end up too thick by setting the width of the brush stroke. You want something relatively small. This will dictate the thickness of your line.

Step 6: Open The Path Panel And Stroke The Path

The Paths Panel will open in your workspace.

Step 7: Adjust The Appearance (Optional)

If you’d like to, you can further adjust the look of your line by returning to the Brush Settings and adjusting the appearance of the brush. This allows you to further change the color, hardness, brush type, and size.

Creating curved lines has nothing to do with the regular Shape Tools in Photoshop, which has always surprised me. Luckily with the help of the Pen Tool, they are still quick and easy to draw on any project!

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