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How to Create a 2D Character Controller in Unity: Part 1

কিভাবে ঐক্যে একটি 2D ক্যারেক্টার কন্ট্রোলার তৈরি করবেন: পার্ট 1

So erstellen Sie einen 2D-Charakter-Controller in Unity: Teil 1

Cómo crear un controlador de personajes 2D en Unity: Parte 1

Comment créer un contrôleur de personnage 2D dans Unity : partie 1

यूनिटी में 2D कैरेक्टर कंट्रोलर कैसे बनाएं: भाग 1

Unity で 2D キャラクター コントローラーを作成する方法: パート 1

Unity에서 2D 캐릭터 컨트롤러를 만드는 방법: 1부

Como criar um controlador de personagem 2D no Unity: Parte 1

Как создать контроллер 2D-персонажа в Unity: часть 1

Unity'de 2D Karakter Kontrol Cihazı Nasıl Oluşturulur: Bölüm 1

Cách tạo bộ điều khiển nhân vật 2D trong Unity: Phần 1

如何在 Unity 中创建 2D 角色控制器：第 1 部分

Python Bokeh is one of the best Python packages for data visualization. Today, we are going to see some Python Bokeh Examples. I have also provided the Python Bokeh project source code on GitHub. Learn this easy visualization tool and add it to your Python stack. What is Python Bokeh? Python is a data visualization tool, or we can also say Python Bokeh is used to plot various types of graphs. There are various other graph plotting libraries like matplotlib, but Python Bokeh graphs are dynamic in nature, which means you can interact with the generated graph. See the below examples… Bokeh Installation 💻: Python Bokeh can be easily installed using PIP. You can install the Python Bokeh easily by running the command: pip install bokeh Now everything is ready, let’s go through the examples 🏃‍♂️… 1. LinePlot from bokeh.plotting import figure, show, output_notebook
x = [1, 2, 3, 4, 5]
y = [4, 5, 5, 7, 3]
p = figure(title="{LinePlot Python Bokeh Example")
p.line(x, y, line_width=2)
output_notebook()
show(p) | | Live Preview Source Code 🌿 Contribute 2. Scatter Plot from bokeh.plotting import figure, show, output_notebook
x = [1, 2, 3, 4, 5]
y = [4, 5, 5, 7, 3]
p = figure(title="Scatter Plot Python Bokeh Example by PratikPathak.com")
p.circle(x, y, size=10, color="navy", alpha=0.5)
output_notebook()
show(p) 3. Bar Chart from bokeh.plotting import figure, show, output_notebook
categories = ["A", "B", "C", "D", "E"]
values = [10, 15, 8, 12, 6]
p = figure(x_range=categories, title="Bar Chart Python Bokeh Example by PratikPathak.com")
p.vbar(x=categories, top=values, width=0.9)
output_notebook()
show(p) 4. Histogram from bokeh.plotting import figure, show, output_notebook
import numpy as np
data = np.random.normal(0, 1, 1000)
p = figure(title="Histogram Python Bokeh Example by PratikPathak.com")
p.hist(data, bins=30, color="navy", alpha=0.5)
output_notebook()
show(p) 5. Pie Chart from bokeh.plotting import figure, show, output_notebook
labels = ["A", "B", "C", "D"]
values = [10, 15, 8, 12]
p = figure(title="Pie Chart Python Bokeh Example by PratikPathak.com")
p.wedge(x=0, y=0, radius=0.4, start_angle=0.6, end_angle=2.6, color=["red", "green", "blue", "yellow"], legend_label=labels)
output_notebook()
show(p) | | Live Preview Source Code 🌿 Contribute 6. Time Series Plot from bokeh.plotting import figure, show, output_notebook
from datetime import datetime, timedelta
start = datetime(2023, 1, 1)
end = start + timedelta(days=30)
x = [start + timedelta(days=i) for i in range((end-start).days)]
y = [10, 15, 8, 12, 6, 18, 9, 14, 7, 11, 5, 16, 8, 13, 6]
p = figure(x_axis_type="datetime", title="Time Series Plot Python Bokeh Example by PratikPathak.com")
p.line(x, y, line_width=2)
output_notebook()
show(p) 7. Linked Brushing from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource
x1 = [1, 2, 3, 4, 5]
y1 = [4, 5, 5, 7, 3]
x2 = [2, 3, 4, 5, 6]
y2 = [2, 4, 6, 8, 4]
source = ColumnDataSource(data=dict(x1=x1, y1=y1, x2=x2, y2=y2))
p1 = figure(title="Scatter Plot 1")
p1.circle('x1', 'y1', source=source)
p2 = figure(title="Scatter Plot 2 Python Bokeh Example by PratikPathak.com")
p2.circle('x2', 'y2', source=source)
output_notebook()
show(p1, p2) 8. Hover Tooltips from bokeh.plotting import figure, show, output_notebook
from bokeh.models import HoverTool
x = [1, 2, 3, 4, 5]
y = [4, 5, 5, 7, 3]
p = figure(title="Hover Tooltips Python Bokeh Example by PratikPathak.com")
p.circle(x, y, size=15, fill_color="navy", line_color="white", alpha=0.5)
hover = HoverTool(tooltips=[("(x,y)", "(@x, @y)")])
p.add_tools(hover)
output_notebook()
show(p) 9. Annotations from bokeh.plotting import figure, show, output_notebook
from bokeh.models import Arrow, VectorRenderer, Label
x = [1, 2, 3, 4, 5]
y = [4, 5, 5, 7, 3]
p = figure(title="Annotations Python Bokeh Example by PratikPathak.com")
p.circle(x, y, size=15, fill_color="navy", line_color="white", alpha=0.5)
arrow = Arrow(x_start=2, y_start=4, x_end=3, y_end=5, line_width=2, line_color="red")
label = Label(x=3, y=6, text="This is a label", render_mode='css', border_line_color='black', border_line_alpha=1.0, background_fill_color='white', background_fill_alpha=0.5)
p.add_layout(arrow)
p.add_layout(label)
output_notebook()
show(p) 10. Custom Glyphs from bokeh.plotting import figure, show, output_notebook
from bokeh.models.glyphs import Asterisk
x = [1, 2, 3, 4, 5]
y = [4, 5, 5, 7, 3]
p = figure(title="Custom Glyphs Python Bokeh Example by PratikPathak.com")
p.add_glyph(x, y, Asterisk(size=20, line_color="red", fill_color="yellow"))
output_notebook()
show(p) | | Live Preview Source Code 🌿 Contribute 11. Gridlines and Axes from bokeh.plotting import figure, show, output_notebook
x = [1, 2, 3, 4, 5]
y = [4, 5, 5, 7, 3]
p = figure(title="Gridlines and Axes Python Bokeh Example by PratikPathak.com", x_range=(0, 6), y_range=(0, 8))
p.grid.grid_line_color = "grey"
p.grid.grid_line_dash = [6, 4]
p.xaxis.axis_label = "X-axis"
p.yaxis.axis_label = "Y-axis"
p.line(x, y, line_width=2)
output_notebook()
show(p) 12. Legend from bokeh.plotting import figure, show, output_notebook
x1 = [1, 2, 3, 4, 5]
y1 = [4, 5, 5, 7, 3]
x2 = [2, 3, 4, 5, 6]
y2 = [2, 4, 6, 8, 4]
p = figure(title="Legend Python Bokeh Example by PratikPathak.com")
p.line(x1, y1, line_width=2, color="red", legend_label="Line 1")
p.line(x2, y2, line_width=2, color="blue", legend_label="Line 2")
p.legend.location = "top_left"
output_notebook()
show(p) 13. Categorical Plots from bokeh.plotting import figure, show, output_notebook
fruits = ["Apples", "Pears", "Nectarines", "Plums", "Grapes", "Strawberries"]
counts = [5, 3, 4, 2, 4, 6]
p = figure(x_range=fruits, title="Categorical Plots Python Bokeh Example by PratikPathak.com")
p.vbar(x=fruits, top=counts, width=0.9)
p.xaxis.axis_label = "Fruit"
p.yaxis.axis_label = "Count"
p.xaxis.axis_label_text_font_size = "12pt"
p.yaxis.axis_label_text_font_size = "12pt"
output_notebook()
show(p) 14. Subplots from bokeh.plotting import figure, show, output_notebook
from bokeh.layouts import gridplot
x1 = [1, 2, 3, 4, 5]
y1 = [4, 5, 5, 7, 3]
x2 = [2, 3, 4, 5, 6]
y2 = [2, 4, 6, 8, 4]
p1 = figure(title="Scatter Plot 1 Python Bokeh Example by PratikPathak.com")
p1.circle(x1, y1, size=10, color="navy", alpha=0.5)
p2 = figure(title="Scatter Plot 2 Python Bokeh Example by PratikPathak.com")
p2.circle(x2, y2, size=10, color="red", alpha=0.5)
grid = gridplot([[p1, p2]], plot_width=400, plot_height=400)
output_notebook()
show(grid) 15. Interactive Plots from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource, HoverTool, BoxSelectTool
import numpy as np
x = np.random.random(1000)
y = np.random.random(1000)
source = ColumnDataSource(data=dict(x=x, y=y))
p = figure(title="Interactive Plots Python Bokeh Example by PratikPathak.com", tools="hover,box_select")
p.circle('x', 'y', source=source, size=3, color="navy", alpha=0.5)
hover = HoverTool(tooltips=[("(x,y)", "(@x, @y)")])
p.add_tools(hover)
output_notebook()
show(p) | | Live Preview Source Code 🌿 Contribute 16. Linked Panning and Zooming from bokeh.plotting import figure, show, output_notebook
from bokeh.models import Range1d
x1 = [1, 2, 3, 4, 5]
y1 = [4, 5, 5, 7, 3]
x2 = [2, 3, 4, 5, 6]
y2 = [2, 4, 6, 8, 4]
p1 = figure(title="Scatter Plot 1 Python Bokeh Example by PratikPathak.com", x_range=Range1d(0, 6), y_range=Range1d(0, 8))
p1.circle(x1, y1, size=10, color="navy", alpha=0.5)
p2 = figure(title="Scatter Plot 2 Python Bokeh Example by PratikPathak.com", x_range=p1.x_range, y_range=p1.y_range)
p2.circle(x2, y2, size=10, color="red", alpha=0.5)
output_notebook()
show(p1, p2) 17. Linked Axis Ranges from bokeh.plotting import figure, show, output_notebook
from bokeh.models import Range1d
x1 = [1, 2, 3, 4, 5]
y1 = [4, 5, 5, 7, 3]
x2 = [2, 3, 4, 5, 6]
y2 = [2, 4, 6, 8, 4]
p1 = figure(title="Scatter Plot 1", x_range=(0, 6), y_range=(0, 8))
p1.circle(x1, y1, size=10, color="navy", alpha=0.5)
p2 = figure(title="Scatter Plot 2", x_range=p1.x_range, y_range=p1.y_range)
p2.circle(x2, y2, size=10, color="red", alpha=0.5)
output_notebook()
show(p1, p2) 18. DateTime Axis from bokeh.plotting import figure, show, output_notebook
from datetime import datetime, timedelta
start = datetime(2023, 1, 1)
end = start + timedelta(days=30)
x = [start + timedelta(days=i) for i in range((end-start).days)]
y = [10, 15, 8, 12, 6, 18, 9, 14, 7, 11, 5, 16, 8, 13, 6]
p = figure(x_axis_type="datetime", title="Datetime Axis Python Bokeh Example by PratikPathak.com")
p.line(x, y, line_width=2)
output_notebook()
show(p) 19. Heatmap from bokeh.plotting import figure, show, output_notebook
import numpy as np
data = np.random.rand(10, 10)
p = figure(title="Heatmap Python Bokeh Example by PratikPathak.com")
p.image(image=[data], x=0, y=0, dw=10, dh=10, palette="Viridis256")
output_notebook()
show(p) 20. Streamgraph from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource
import numpy as np
# Generate some sample data
x = np.linspace(0, 10, 100)
y1 = np.cumsum(np.random.randn(100))
y2 = np.cumsum(np.random.randn(100))
y3 = np.cumsum(np.random.randn(100))
y4 = np.cumsum(np.random.randn(100))
y5 = np.cumsum(np.random.randn(100))
source = ColumnDataSource(data=dict(x=x, y1=y1, y2=y2, y3=y3, y4=y4, y5=y5))
p = figure(title="Streamgraph Python Bokeh Example by PratikPathak.com")
p.area(x='x', y1='y1', y2='y2', y3='y3', y4='y4', y5='y5', source=source, line_color=None)
output_notebook()
show(p) | | Live Preview Source Code 🌿 Contribute 21. Polar Plot from bokeh.plotting import figure, show, output_notebook
import numpy as np
r = np.linspace(0.1, 1, 10)
theta = np.linspace(0, 2*np.pi, 10)
p = figure(title="Polar Plot Python Bokeh Example by PratikPathak.com", plot_width=600, plot_height=600, x_range=(-1.2, 1.2), y_range=(-1.2, 1.2), toolbar_location=None)
p.polar(r, theta, color="navy", alpha=0.5, line_width=2)
output_notebook()
show(p) 22. Donut Chart from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource
labels = ["A", "B", "C", "D"]
values = [10, 15, 8, 12]
source = ColumnDataSource(data=dict(labels=labels, values=values))
p = figure(title="Donut Chart Python Bokeh Example by PratikPathak.com", x_range=labels)
p.wedge(x=0, y=1, radius=0.4, start_angle=0.6, end_angle=2.6, color=["red", "green", "blue", "yellow"], legend_field="labels")
p.wedge(x=0, y=1, inner_radius=0.3, outer_radius=0.4, start_angle=0.6, end_angle=2.6, color="white")
p.axis.visible = False
p.grid.grid_line_color = None
output_notebook()
show(p) 23. Treemap from bokeh.plotting import figure, show, output_notebook
from bokeh.models import TreemapRenderer, ColumnDataSource
import pandas as pd

# Sample data
data = pd.DataFrame({
    'label': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
    'parent': ['', 'A', 'A', 'B', 'B', 'C', 'C', 'D'],
    'value': [10, 5, 4, 3, 2, 1, 1, 1]
})

source = ColumnDataSource(data)
p = figure(title="Treemap Python Bokeh Example by PratikPathak.com")
treemap = TreemapRenderer(
    source=source,
    label='label',
    parent='parent',
    value='value',
    color_discrete_sequence=["#e6550d", "#3182bd", "#31a354", "#756bb1", "#636363", "#9e9ac8", "#de2d26", "#fd8d3c"],
)
p.add_layout(treemap)
output_notebook()
show(p) If you get any error, make sure you have installed Python pandas by using the “pip install pandas” command 24. Network Plot from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource, HoverTool, NodesAndLinkedEdges
import networkx as nx

# Sample data
nodes = ['A', 'B', 'C', 'D', 'E', 'F']
edges = [('A', 'B'), ('A', 'C'), ('B', 'D'), ('B', 'E'), ('C', 'F')]
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)

node_positions = nx.spring_layout(G)
node_x = [node_positions[node][0] for node in G.nodes()]
node_y = [node_positions[node][1] for node in G.nodes()]
node_color = ['red', 'green', 'blue', 'yellow', 'purple', 'orange']

source = ColumnDataSource(data=dict(
    x=node_x,
    y=node_y,
    color=node_color,
    node=nodes
))

p = figure(title="Network Plot Python Bokeh Example by PratikPathak.com", x_range=(-1.1, 1.1), y_range=(-1.1, 1.1))
p.add_tools(HoverTool(tooltips=[("Node", "@node")]))
p.circle('x', 'y', size=15, color='color', source=source)

edge_x = []
edge_y = []
for start, end in edges:
    x0, y0 = node_positions[start]
    x1, y1 = node_positions[end]
    edge_x.append(x0)
    edge_x.append(x1)
    edge_x.append(None)
    edge_y.append(y0)
    edge_y.append(y1)
    edge_y.append(None)

p.multi_line(edge_x, edge_y, color="black", alpha=0.5, line_width=1)

output_notebook()
show(p) 25. Sankey Diagram from bokeh.plotting import figure, show, output_notebook
from bokeh.models import Sankey, ColumnDataSource

# Sample data
flows = [
    ('A', 'X', 5),
    ('B', 'X', 3),
    ('A', 'Y', 2),
    ('B', 'Y', 4),
    ('X', 'C', 4),
    ('X', 'D', 3),
    ('Y', 'C', 2),
    ('Y', 'D', 4)
]

source = ColumnDataSource(data=dict(
    source=[flow[0] for flow in flows],
    target=[flow[1] for flow in flows],
    value=[flow[2] for flow in flows]
))

p = figure(plot_width=800, plot_height=400, title="Sankey Diagram Python Bokeh Example by PratikPathak.com")
sankey = Sankey(source=source, label_text_color="white", label_text_font_size="8pt")
p.renderers.append(sankey)

output_notebook()
show(p) | | Live Preview Source Code 🌿 Contribute 26. Chord Diagram from bokeh.plotting import figure, show, output_notebook
from bokeh.models import ColumnDataSource, CategoricalColorMapper
import numpy as np

# Sample data
nodes = ['A', 'B', 'C', 'D', 'E', 'F']
connections = [
    ('A', 'B', 0.5), ('A', 'C', 0.3), ('A', 'D', 0.2),
    ('B', 'C', 0.4), ('B', 'D', 0.3), ('B', 'E', 0.3),
    ('C', 'D', 0.2), ('C', 'E', 0.3), ('C', 'F', 0.5),
    ('D', 'E', 0.2), ('D', 'F', 0.3), ('E', 'F', 0.4)
]

source = ColumnDataSource(data=dict(
    start=np.array([con[0] for con in connections]),
    end=np.array([con[1] for con in connections]),
    value=np.array([con[2] for con in connections])
))

p = figure(title="Chord Diagram Python Bokeh Example by PratikPathak.com", plot_width=800, plot_height=800)
chord = p.chord(
    source=source, 
    start_angle=np.pi / 2,
    direction="clockwise",
    colors=CategoricalColorMapper(factors=nodes, palette="Spectral11")
)

output_notebook()
show(p) How to Contribute? Feel free to open a PR request on our GitHub repo. Steps to contribute: Fork the repo Make changes in the Forked repo and save Open a Pull Request That’s it 😄! 🌿 Contribute Conclusion In this Repo, I have shared 25+ Python Bokeh examples that can help you learn Python Bokeh. Feel free to contribute to our GitHub repo and keep it updated. Also published . here

Getting Started With Python Bokeh: 25+ Data Visualization Examples With Source Code

Want to Master Javascript Design Patterns? Here's Everything You Need to Know!

জাভাস্ক্রিপ্ট ডিজাইন প্যাটার্নস আয়ত্ত করতে চান? আপনার যা জানা দরকার তা এখানে!

Möchten Sie Javascript-Designmuster beherrschen? Hier finden Sie alles, was Sie wissen müssen!

¿Quieres dominar los patrones de diseño de Javascript? ¡Aquí está todo lo que necesita saber!

Vous voulez maîtriser les modèles de conception Javascript ? Voici tout ce que vous devez savoir !

क्या आप जावास्क्रिप्ट डिज़ाइन पैटर्न में महारत हासिल करना चाहते हैं? यहां वह सब कुछ है जो आपको जानना आवश्यक है!

JavaScript デザインパターンをマスターしたいですか?知っておくべきことはすべてここにあります!

Javascript 디자인 패턴을 마스터하고 싶으십니까? 여기에 당신이 알아야 할 모든 것이 있습니다!

Quer dominar os padrões de design Javascript? Aqui está tudo o que você precisa saber!

Хотите освоить шаблоны проектирования Javascript? Вот все, что вам нужно знать!

Javascript Tasarım Desenlerinde Uzmanlaşmak İster misiniz? İşte Bilmeniz Gereken Her Şey!

Bạn muốn nắm vững các mẫu thiết kế Javascript? Đây là tất cả mọi thứ bạn cần biết!

想掌握 Javascript 设计模式吗？这是您需要了解的一切！

Hey there, have you ever needed to programmatically add a class to an element in an application? Like, maybe when a button is disabled, we need a class, but then once it’s enabled, we don’t. Or how about when a form goes from an invalid state to a valid one? Well, good news, this is actually pretty easy to do in angular. In this post, I’m going to show you four different ways. Angular First, we’ll use a method called class binding. Next, we’ll use the ngClass directive. After that, we’ll use the @HostBinding decorator. And finally, we’ll use the Renderer2 addClass() and removeClass() methods. Let’s get to it! https://youtu.be/sAa8QyFkVkI?embedable=true Using Angular Class Binding So class binding is something that you'd want to use in more simplistic cases where we just either want a class on an element or not. For example, we may want to add a disabled class to a button in a form when the form is invalid. To do this we simply add the word class in square brackets, followed by a dot and then our disabled class name. Then, we will bind this when our control is invalid. <form>
  ...
  <input [formControl]="emailControl" type="email" />
  ...
  <button [class.disabled]="emailControl.invalid">Subscribe</button>
</form> Using the Angular ngClass Directive When we encounter more complex scenarios, we may find ourselves needing to use the directive instead of Angular class binding. The directive gives us more flexibility and allows us to use more advanced logic for adding and removing class names. In this example, we want to switch between two different classes on our wrapping depending on whether the form is valid or not. ngClass fieldset To use the directive, we still use the square brackets, but this time we're going to use a ternary operator and when our control is invalid we'll add a class of 'invalid' and when it's not we'll add a class of 'valid'. ngClass <form>
  <fieldset [ngClass]="emailControl.invalid ? 'invalid' : 'valid'">
    ...
    <input [formControl]="emailControl" type="email" />
    ...
  </fieldset>
</form> Using Angular @HostBinding What if we need to conditionally bind a class on the host of our Angular Component or Directive? Well, this is where the Angular decorator comes into play. To do this we add the decorator within our component class and this will look much like our class binding example. We'll add the class name followed by a dot and then the class. Then we use a field, we'll call this one 'isValid' and we'll initialize it to false. @HostBinding @HostBinding To update this field, we'll need to subscribe to our email control status changes event and then simply update the value based on the status. ...
export class FormComponent implements OnInit {
  @HostBinding('class.valid') isValid = false;
  ...
  constructor(private destroyRef: DestroyRef) {}
    
  ngOnInit() {
    this.emailControl.statusChanges
      .pipe(takeUntilDestroyed(this.destroyRef))
      .subscribe((status) => this.isValid = status === 'VALID');
  }
} Using Angular Renderer2 addClass and removeClass Methods When we need to get really crazy and do something like add a class to the body element from within our component, this may seem near impossible in Angular. But, fortunately, it's not. We can reach for the Angular Renderer2 class which allows us to do several different things. For our purposes for conditionally adding classes to elements, we are only concerned with the add and remove class methods. First, we need to make sure that we inject the Renderer2 into our component via the constructor. Then, we will add a ternary operator within our status changes observable subscription for our control. If its status is valid, we will want to add the class to our element, and if it's invalid, we will want to be sure to remove the valid class. document.body ...
export class FormComponent implements OnInit {
  ...
  constructor(private destroyRef: DestroyRef, private renderer: Renderer2) {}
    
  ngOnInit() {
    this.emailControl.statusChanges
      .pipe(takeUntilDestroyed(this.destroyRef))
      .subscribe((status) => {
        ...
        status === 'VALID'
          ? this.renderer.addClass(document.body, 'valid')
          : this.renderer.removeClass(document.body, 'valid');
      });
  }
} In Conclusion Okay, so we now have four different ways to conditionally add and remove classes in Angular. First, we used class binding for more simple, straightforward cases. Then we used the directive where more advanced logic was needed. After that, we used the @HostBinding decorator to conditionally bind classes to our component host element. And finally, when all else failed and we needed something else, we used the Renderer2 add and remove class methods to bridge the gap. ngClass So hopefully that helps you out along your way and gives you some options when you need to programmatically add or remove classes. Want to See It in Action? Check out the demo code and examples of these techniques in the stackblitz example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://stackblitz.com/edit/stackblitz-starters-gn5o3d?ctl=1&embed=1&file=src%2Fform%2Fform.component.ts&embedable=true Also published here.

4 Ways to Dynamically Add Classes in Angular

"The most effective debugging tool is still careful thought, coupled with judiciously placed print statements." -- Brian Kernighan. Cutting a patient open and using print for debugging used to be the best way to diagnose problems. If you still advocate either one of those as the superior approach to troubleshooting, then you're either facing a very niche problem or need to update your knowledge. This is a frequent occurrence, e.g., this recent tweet: This specific tweet got to the HN front page, and people chimed in with that usual repetitive nonsense. No, it’s not the best way for the vast majority of developers. It should be discouraged just as surgery should be avoided when possible. Fixating on print debugging is a form of a mental block; debugging isn’t just stepping over code. It requires a completely new way of thinking about issue resolution. A way that is far superior to merely printing a few lines. Before I continue, my bias is obvious. I and I . This is a pet peeve of mine. , wrote a book about debugging blog about it a lot I want to start with the exception to the rule, though: when do we need to print something... Logging is NOT Print Debugging! One of the most important debugging tools in our arsenal is a logger, but it is not the same as print debugging in any way: Logger Print Permanence of output Permanent Ephemeral Permanence in code Permanent Should be removed Globally Toggleable Yes No Intention Added as part of design Added ad-hoc A log is something we add with forethought. We want to keep the log for future bugs and might even want to expose it to the users. We can control its verbosity often at the module level and can usually disable it entirely. It’s permanent in code and usually writes to a permanent file we can review at our leisure. Print debugging is code we add to locate a temporary problem. If such a problem has the potential of recurring, then a log would typically make more sense in the long run. This is true for almost every type of system. We see developers adding print statements and removing them constantly instead of creating a simple log to track frequent problems. There are special cases where print debuggings makes some sense, in mission critical embedded systems a log might be impractical in terms of device constraints. Debuggers are awful in those environments, and print debugging is a simple hack. Debugging system-level tools like a kernel, compiler, debugger, or JIT can be difficult with a debugger. Logging might not make sense in all of these cases, e.g., I don’t want my JIT to print every bytecode it’s processing and the metadata involved. Those are the exceptions, not the rules. Very few of us write such tools. I do, and even then, it’s a fraction of my work. For example, when working at Lightrun, I was working on a production debugger. Debugging the agent code that’s connected to the executable was one of the hardest things to do. A mix of C++ and JVM code that’s connected to a completely separate binary... Print debugging of that portion was simpler, and even then, we tried to aim towards logging. However, the visual aspects of the debugger within the server backend and the IDE were perfect targets for the debugger. Why Debug? There are three reasons to use a debugger instead of printouts or even logs: Features - modern debuggers can provide spectacular capabilities that are unfamiliar to many developers. Sadly, there are very few debugging courses in academia since it’s a subject that’s hard to test. Low overhead - in the past, running with the debugger meant slow execution and a lot of overhead. This is no longer true. Many of us use the debug action when launching an application instead of running, and there’s no noticeable overhead for most applications. When there is overhead, some debuggers provide means to improve performance by disabling some features. Library code - a debugger can step into a library or framework and track the bug there. Doing this with print debugging will require compiling code that you might not want to deal with. I dug into the features I mentioned in my book and series on debugging (linked above), but let’s pick a few fantastic capabilities of the debugger that I wrote about in the past. For the sake of positive dialog, here are some of my top features of modern debuggers. Tracepoints Whenever someone opens the print debugging discussion, all I hear is, “I don’t know about tracepoints”. They aren’t a new feature in debuggers, yet so few are aware of them. A tracepoint is a breakpoint that doesn’t stop. It just keeps running. Instead of stopping, you can do other things at that point, such as print to the console. This is similar to print debugging, only it doesn’t suffer from many of the drawbacks: no runtime overhead, no accidental commit to the code base, no need to restart the application when changing it, etc. https://www.youtube.com/watch?v=eXRqKqSp7x0&embedable=true Grouping and Naming The previous video/post included a discussion of grouping and naming. This lets us group tracepoints together, disable them as a group, etc. This might seem like a minor feature until you start thinking about the process of print debugging. We slowly go through the code, adding a print and restarting. Then suddenly, we need to go back, or if a call comes in and we need to debug something else... When we package the tracepoints and breakpoints into a group, we can set aside a debugging session, much like we set aside a branch in version control. It makes it much easier to preserve our train of thought and jump right back to the applicable lines of code. Object Marking When asked about my favorite debugging feature I’m always conflicted, ... It seems like a simple thing; we can mark an object, and it gets saved with a specific name. Object Marking is one of my top two features However, this is a powerful and important feature. I used to write down the pointers to objects or memory areas while debugging. This is valuable as sometimes an area of memory would look the same but would have a different address, or it might be hard to track objects with everything going on. Object Marking allows us to save a global reference to an object and use it in conditional breakpoints or for visual comparison. https://www.youtube.com/watch?v=DGjVVKCNosM&embedable=true Renderers My other favorite feature is ; it lets us define how elements look in the debugger watch area. Imagine you have a sophisticated object hierarchy but rarely need that information... A renderer lets you customize the way IntelliJ/IDEA presents the object to you. the renderer https://www.youtube.com/watch?v=oaUf8KXHsd0&embedable=true Tracking New Instances One of the often overlooked capabilities of the debugger is . A Java debugger can show you a searchable set of all object instances in heap, that is a fantastic capability that can expose unintuitive behavior But it can go further, it can track new allocations of an object and provide you with the stack to the applicable object allocation. memory tracking https://www.youtube.com/watch?v=dFOFOEg2W4k&embedable=true Tip of the Iceberg I wrote a lot about debugging, so there’s no point in repeating all of it in this post. If you’re a person who feels more comfortable using print debugging, then ask yourself this: why? Don’t hide behind an out-of-date Brian Kernighan quote. Things change. Are you working in one of the edge cases where print debugging is the only option? Are you treating logging as print debugging or vice versa? Or is it just that print debugging was how your team always worked, and it stuck in place? If it’s one of those, then it might be time to re-evaluate the current state of debuggers. Also published . here

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Overview Digital fingerprinting is a technique used to identify users across different websites based on their unique device and browser characteristics. These characteristics - fingerprint parameters, can include various software, hardware (CPU, RAM, GPU, media devices - cameras, mics, speakers), location, time zone, IP, screen size/resolution, browser/OS languages, network, internet provider-related and other attributes. The combination of these parameters creates a unique identifier - fingerprint, that can be used to track a user's online activity. Fingerprints play a crucial role in online security, enabling services to identify and authenticate unique users. They also make it possible for users to trick such systems to stay anonymous online. However, if you can manipulate your fingerprints, you can run tens or hundreds or more different accounts to pretend that they are unique, authentic users. While this may sound cool, it has serious implications as it can make it possible to create an army of bots that can spread spam and fakes all over the internet, potentially resulting in fraudulent actions. Note: Obviously, I won’t discuss here how you can do "bad" things; you must always be careful, stay away from the “dark side,” and avoid committing illegal actions. This article is about the technology behind this, so use the information wisely. This is the first (and maybe not the last) article in this domain, so it will be more like an overview. The content offers an intro to the topic. There are dozens of tools and ways to detect, collect, and spoof fingerprints, and there are many different parameters and technologies that can reveal or hide your real identity or the fact that you're spoofing fingerprints (meaning you're not an authentic user). Parameters in Digital Fingerprints Let's consider some of them, some examples of obvious, simple, or well-known params, some that are rarer, less-know, and difficult to spoof. this string provides information about the user's browser, operating system, and device. User-Agent: it reveals the user's network and geo-location. Services use IP addresses for security and to prevent malicious activities. If you use one account from different IPs or many different accounts from the same IP, some services may view this activity (in combination with other params) as suspicious and use some level of bot protection against you. This can also be triggered if you use IPs that are already used or in some kind of banned list (proxies). IP: Information about installed plugins and extensions can be used to create a unique fingerprint - it helps in identifying users based on their browser's additional functionalities. Browser Plugins and Extensions: different users often have different display characteristics. Screen Resolution and Color Depth: they are important factors in fingerprinting - if you have random values, e.g., Japan timezone Norwegian language this is a bit unusual. Timezone and Language Settings: it involves rendering hidden graphics in the user's browser to gather information about the graphics hardware. Quite a tricky one because if you spoof your hardware info, then you can’t have the proper canvas value - you can’t render it on your different real hardware. Canvas: it exploits the unique capabilities and limitations of the user's graphics hardware in rendering 3D graphics - additional information about the user's device. WebGL Fingerprinting: the list of fonts installed on a user's system can be used as a fingerprinting parameter. This information is accessible through JavaScript. The list of fonts has to be realistic - so e.g. you can’t have Win fonts on macOS or you can’t have just 1 or 2 fonts, this is a very suspicious indicator. Fonts: it allows websites to determine the device's battery level and charging status, the combination of battery attributes can be used for fingerprinting. Battery status API: websites can use the web audio API to generate unique audio fingerprints by analyzing the audio processing characteristics of the device. Audio Fingerprinting: This information includes details about the software and hardware components of the device, such as the graphics card, network adapter, and operating system. This adds to the uniqueness of the fingerprint. Hardware Concurrency: This information includes details about the network connection, such as the IP address, ISP, and DNS server. Network Information: some users may use some software that listens to specific ports which also might be useful for some specific cases. Open and used ports: Navigating the Web incognito Digital fingerprint manipulation is a difficult task that demands a proactive approach to avoid detection. The are lots of strategies and tools to hide your identity (fingerprints) or pretend that you are someone else. Use privacy-focused browsers Opting for privacy-focused browsers, such as Brave, Ghostery, Tor, Octo Browser, or Vivaldi with enhanced privacy settings, provides a fundamental defense against common fingerprinting techniques. These browsers prioritize user privacy and incorporate features designed to hide your real fingerprints, making it harder to track your activities. For example, the Tor Browser, grounded in principles of anonymity, routes internet traffic through the Tor network. This strategic routing obscures the user's identity by bouncing connections through a series of volunteer-operated servers, enhancing overall online anonymity. Browser Extensions Privacy-centric browser extensions, including AdBlock, uBlock Origin, Privacy Badger, or CanvasBlocker, could be active defenses against tracking scripts, cookies, and fingerprinting attempts. These tools operate in the background, protecting and preserving user anonymity. VPN and Proxies The use of VPNs or proxy services adds an additional layer of protection by masking the user's real IPs (location, timezone, language, etc) and encrypting internet traffic. This is not only used to change your fingerprints but also to provide a more anonymous online presence and a higher security level. Motivations behind fingerprint spoofing Understanding the Whys Individuals concerned about personal privacy and the growing digital surveillance often spoof fingerprints to shield themselves from relentless online tracking. Privacy: Spoofing fingerprints prove useful for circumventing geo-restrictions, allowing users to access content restricted to specific regions. VPN and proxy services play a crucial role here by not only hiding the user's identity but also providing access to servers in different geographic locations. Geo-restriction: Avoiding targeted advertising and online profiling serves as a compelling motive for individuals to manipulate their digital fingerprints. Some tools can block 3rd-party tracking scripts and cookies, disrupting the profiling process. Ads: Web scraping, multi-accounting, e-commerce, bounty&airdrop, bonus-hunting, social networks bots, and affiliate marketing are often sources of income or bases for medium businesses. Digital agencies, individuals, and influencers engaged in these activities may require fingerprint spoofing to navigate through complicated bot detection systems. Avoiding detection becomes vital for some services, ensuring that legitimate activities are not mistakenly flagged or restricted (though quite often, such activities and accounts are not really legit but pretend to be so). Some of the most popular tools are Multilogin, X-Browser, Octo Browser, AdsPower, Incogniton, Scrapy, Surfsky, Web Scraper.io, ScrappingBee, etc. Strategic needs in the digital arena, bot detection, and protection systems: Collecting Users' Fingerprints Websites use JS (obviously) to harvest details about users to construct comprehensive digital portraits. This involves probing screen resolution, device orientation, mouse movements, keystroke dynamics, etc. Sophisticated fingerprinting scripts enumerate a wide range of browser and hardware attributes. JavaScript: Persistent cookies and data stored in local storage work to track users across sessions and platforms. Techniques include leveraging browser cookies and storing unique identifiers for user tracking. Depending on your purposes you need to know when you need an "empty" browser with unique fingerprints or when you need to trick a service showing that you have particular cookies and the same fingerprints. Cookies and local storage: The inclusion of 3rd-party scripts for analytics and advertising embeds invisible trackers often used to adjust systems behavior for a specific user based on collected fingerprints. You can use the aforementioned tools and approaches to selectively block or trick such scripts to get the needed behavior. 3rd-party scripts: Understanding Your Online Identity Panopticlick (EFF), Pixelscan, deviceinfo.me, and BrowserLeaks show your browser's fingerprint parameters such as User-Agent, canvas fingerprinting, fonts, etc. These checkers provide insights into the uniqueness and stability of your digital fingerprint across different browsing sessions. Online checkers: they allow users to inspect network requests, cookies, and other fingerprinting parameters, fostering a deeper understanding of their digital imprint and how websites detect bots and unique legit users and collect fingerprints. Examining the Network and Application tabs in browsers provides a real-time view of the data exchanged between the browser, websites, and servers. Browser developer tools: Here are some examples of how you can get some info about users' fingerprints in the browser. GEO: navigator.geolocation.getCurrentPosition(function(position) { var userLocation = position.coords; }); Microphone: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var microphones = devices.filter(device => device.kind === 'audioinput'); }); Camera: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var cameras = devices.filter(device => device.kind === 'videoinput'); }); Speakers: navigator.mediaDevices.enumerateDevices() .then(function(devices) { var speakers = devices.filter(device => device.kind === 'audiooutput'); }); Audio: var audioContext = new (window.AudioContext || window.webkitAudioContext)(); var oscillator = audioContext.createOscillator(); var analyser = audioContext.createAnalyser(); oscillator.connect(analyser); analyser.connect(audioContext.destination); GPU: var canvas = document.createElement('canvas'); var gl = canvas.getContext('webgl') || canvas.getContext('experimental-webgl'); var renderer = gl.getParameter(gl.RENDERER); console.log(renderer); Fonts: var fonts = []; var fontList = document.fonts.forEach(function(font) { fonts.push(font.family); }); console.log(fonts); Canvas (hashing): var canvas = document.createElement('canvas'); var context = canvas.getContext('2d'); var dataURL = canvas.toDataURL(); var canvasHash = MurmurHash3(dataURL); console.log(canvasHash); RAM and CPU: function getCPUInfo() { return navigator.hardwareConcurrency || 0; } function getDeviceMemory() { return navigator.deviceMemory || 0; } var cpuInfo = getCPUInfo(); var deviceMemory = getDeviceMemory(); console.log(`CPU Cores: ${cpuInfo}`); console.log(`Device Memory (GB): ${deviceMemory}`); Bot Detection Systems: Defending Against Automation A couple of examples of such systems: It is a global leader in bot detection and mitigation. It provides a proactive and precise way to verify that legitimate human users are accessing your website, mobile app, and APIs. Distil Networks uses a combination of techniques to detect bots. It studies a range of variables such as cursor movement, click patterns, and web browsing patterns across other websites. It uses , which checks all visitors against hundreds of different characteristics, focusing on their behavior. Distil Networks defends against web scraping, competitive data mining, account takeovers, transaction fraud, unauthorized vulnerability scans, spam, click fraud, denial of service, and API abuse. It can automatically block 99.9% of malicious traffic without impacting legitimate users. It also offers Distil Bot Defense for Web and API, which protect your website and API servers respectively. Distil Networks: Bot Detection: device fingerprints and Are You a Human technology It actively pulls additional data from the browser to identify devices with precision. When a browser request comes in, Distil interrogates the headers to see if the visitor is lying about their identity. This unique identifier can be used to fully or partially identify individual devices even when cookies cannot be read or stored in the browser, the client IP address is hidden, or one switches to another browser on the same device. It uses machine learning to detect biometric patterns in mouse activity and scrolling, as well as looking at browsers, devices, and other factors for clues. It can detect many simple or medium complexity bots that run JS from a web page. Resource Protection: It's a comprehensive cybersecurity platform that integrates fingerprint-based identification to discern legitimate users from potential threats. Utilizing advanced behavioral analysis and anomaly detection, Imperva builds profiles based on a combination of factors, including IP reputation, user-agent, different fingerprint params, and behavioral characteristics. This allows the system to detect suspicious activities indicative of bot traffic and mitigate potential threats effectively. Imperva: It's a prominent content delivery and cloud service provider that incorporates robust bot detection mechanisms within its security offerings. Leveraging a combination of fingerprint-based detection, behavior analysis, and machine learning, Akamai identifies and mitigates various bots. Akamai's global network allows for real-time threat intelligence, enabling proactive defense against evolving bot tactics. Akamai: It's a widely used content delivery network and security service that employs a multifaceted approach to bot detection. Cloudflare distinguishes between human users and bots by analyzing parameters such as IP reputation, user-agent characteristics, and behavioral patterns. The platform also utilizes threat intelligence and community-driven insights to stay ahead in the battle against emerging bot threats. Cloudflare uses to profile SSL/TLS clients and block potential bot requests. It also uses to apply rules and route traffic based on HTTP request information. is used to identify specific files and prevent data loss. These techniques help in detecting not real users. Cloudflare: JA3 Fingerprinting HTTP Filtering Data Fingerprinting Role of Bot Detection Systems: They play a pivotal role in enhancing bot detection capabilities. With servers strategically positioned around the globe, they can leverage geographical insights and real-time threat intelligence to identify and mitigate bot traffic effectively. The platform's WAF and bot managers contribute to a comprehensive defense against automated "users.” In this ongoing cat-and-mouse game, Bot Detection Systems continue to evolve, leveraging advanced technologies to stay ahead of fingerprint spoofing tech. The collaboration between security providers, businesses, and the wider online community remains crucial in defense against the tactics of automated threats but also, in a way, used against users' privacy, anonymity, and experience online, bringing intrusive ads and sophisticated user tracking tools that are only beneficial for businesses not for users. However, privacy protection, fingerprint hiding and spoofing, and ads and trackers blocking tools are also developing to evade even the most sophisticated user tracking systems. Websites' additional verification steps If a fingerprint appears suspicious, such as frequent changes or unusual combinations of data points, the website may trigger additional verification steps. Suspicious behavior: Websites may present users with captcha challenges to verify their identity. This involves solving puzzles or identifying distorted text, tasks that are difficult to perform for primitive bots. Captcha: Websites may request users to verify their mobile phone numbers to establish a stronger connection between a unique identity and a real-world entity. This can help prevent fraudulent activities. Mobile verification: the idea is the same as with mobile phones, but you need to submit your ID ID verification: Examples: Google doesn't require additional verification for new users with clear, consistent fingerprints. If a user's fingerprint changes frequently or resembles those used by known bots (or just already known for Google), Google may ask for a mobile phone number or ask to deal with a captcha. Financial websites often enforce stricter verification measures due to the sensitive nature of transactions. They may require users to provide additional personal information, verify their identity through secure channels, or pass more complex captcha challenges. Opinion: spoofing is better than hiding Hiding fingerprints can enhance privacy, security, and online anonymity, but spoofing them to appear as a unique, legitimate user offers even greater advantages. By blending in with the crowd and avoiding suspicion from systems that detect anti-detection tools, you can maintain credibility and trust. This approach allows you to enjoy the benefits of being perceived as a unique, legitimate user, minimizing the chance of facing additional protecting measures or obstacles. and, obviously, it helps in preventing leaking of your real data and identity. Also published . here

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Introduction is a foundational class in UIKit and . Everything we see on the screen is constructed from `s and their subclasses. UIView iOS development UIView One topic often considered basic is the geometry of . It's easy to grasp initially, but complete understanding is often elusive, even for very experienced iOS engineers. In this article, we will dive deeply into the world of geometry. We will explain the main concepts, such as , , , , , how they relate to each other, and answer more advanced questions about computing the and Autolayout specifics. UIView UIView frame bounds center anchorPoint transform frame Playground To better understand the geometry, I've also developed a small iOS app: . https://github.com/psharanda/UIViewGeometry In the app, you can experiment with different properties in real time by adjusting sliders. Such an interactive approach is really helpful for grasping the nuances. UIView frame The definition of in Apple documentation is: UIView.frame The frame rectangle, which describes the view’s location and size in its superview’s coordinate system. Most of the time, we operate just with frames, either during manual layout or when printing the view's debug description. The designated initializer for also uses the frame as the only param. UIView This seems simple and straightforward. However, the reality is more complex. The frame is actually a computed property of . It is not the definitive source of the view's geometry. So, what is it exactly? UIView bounds Apparently, has many more properties related to geometry, and we’re now going to check the real, non-computed ones. Let’s examine the first of them: . UIView bounds The definition of is: UIView.bounds The bounds rectangle, which describes the view’s location and size in its own coordinate system. Usually, both and are simply equal to , and is the same as (however, it is not always true). bounds.origin.x bounds.origin.y 0.0 bounds.size frame.size What happens if is not equal to ? In that case, bounds basically act as a viewport, defining which part of the view coordinate system is visible to the outside world (superview). can also be seen as an additional translation (with a minus sign) for all subviews and view’s content rendered using . bounds.origin .zero bounds.origin drawRect This might remind you of . It is for a reason: the property of a is directly linked to its . UIScrollView contentOffset UIScrollView bounds.origin It's important to note that border, background, and shadow are rendered without considering . bounds.origin Previously, we mentioned that sometimes cannot be equal to . It may happen if transform is not equal to . frame.size bounds.size .identity According to the documentation, setting to any value when is not is considered undefined behavior (getting is still fine). However, setting when is not is valid.  In that case, describes the rectangle that fits the view after all transformations in its superview’s coordinate system. frame transform .identity bounds transform .identity frame center The definition of : UIView.center The center point of the view's frame rectangle in its superview’s coordinate system. However, the term "center" can be a bit misleading. It only represents the actual center of the view if the (more on this in the next section) is positioned at the center, specifically at ( , ). A more accurate definition for would be: view.anchorPoint 0.5 0.5 center The coordinates of the view’s anchor point in its superview’s coordinate system. UIView’s is exactly the same as . center CALayer.position The combination of and can fully replace the use of bounds center frame transform The transform property in iOS development is a fundamental concept that enables developers to apply geometric transformations to views. UIView The transform property of is of the type , which represents a matrix used for affine transformations. Affine transformations include: UIView CGAffineTransform Translation To move a view, you set the transform property with . For instance, moves the view 50 points to the right and 75 points down. CGAffineTransform(translationX:y:) view.transform = CGAffineTransform(translationX: 50, y: 75) Scaling To scale a view, use . For example, halves the size of the view in both width and height. CGAffineTransform(scaleX:y:) view.transform = CGAffineTransform(scaleX: 0.5, y: 0.5) Rotation To rotate a view, apply . The angle is in radians, so to rotate 30 degrees, you would use . CGAffineTransform(rotationAngle:) view.transform = CGAffineTransform(rotationAngle: .pi / 6) Combining Transforms One of the powerful aspects of the transform property is the ability to combine transformations. You can combine scaling, rotation, and translation into a single transform using concatenation, for example: var t = CGAffineTransform.identity // 0
t = t.concatenating(CGAffineTransform(scaleX: 0.5, y: 0.5)) //1
t = t.concatenating(CGAffineTransform(rotationAngle: .pi / 6)) //2
t = t.concatenating(CGAffineTransform(translationX: 50, y: 150)) //3
view.transform = t To reset a transformation, set the property to , which is also the default for a newly created . transform CGAffineTransform.identity UIView Perspective ’s transform is baked by which has the type and can be used for even more advanced transformations like changing the perspective UIView CALayer.transform CATransform3D var transform = CATransform3DIdentity;
transform.m34 = 1.0 / 500.0;
view.layer.transform = CATransform3DRotate(transform, .pi / 4, 0, 1, 0) anchorPoint The anchor point defines how a view behaves during transformations. It's specified using the unit coordinate space, where ( , ) is the top-left corner of the view's bounds rectangle, and ( , ) is the bottom-right corner. By default, the anchor point of a is set to ( , ), which is the center of the view’s bounds rectangle. 0 0 1 1 UIView 0.5 0.5 Interestingly, the property was only added to in iOS 16, but it has always been accessible through . anchorPoint UIView view.layer.anchorPoint Geometric manipulations, like rotation or scaling, occur around the anchor point. For example, if you apply a rotation transform to a view with the default anchor point, the view will rotate around its center. If you change the anchor point to a different location, the view will rotate around this new point. How to calculate the frame? We mentioned in the beginning that is a computed property. Now, with enough knowledge, let’s compile all the observations into actual code that computes the frame. frame First, let’s follow the positioning logic that is used during the render process. We start with a rectangle, with set to and set to . is not involved in calculations since it only affects subviews and the output. origin .zero size bounds.size bounds.origin drawRect Before applying the transform, we must translate the view according to its . We apply the transform only when the view, with its , is aligned with the starting point coordinates ( , ). anchorPoint anchorPoint 0 0 Now, we can apply the transform. The last step is to translate the view by its value, positioning it correctly. center These transformations can be concatenated into a single transform (i.e., absolute one), which is then applied to zero-origin bounds by using function. CGRectApplyAffineTransform Here is the final function which computes the frame: func computeFrame(bounds: CGRect,
                  center: CGPoint,
                  anchorPoint: CGPoint,
                  tranform: CGAffineTransform) -> CGRect {

    // set the initial rectangle to bounds with origin set to (0, 0)
    let zeroOriginBounds = CGRect(origin: .zero, size: bounds.size) // 1

    // combine transformations
    let absoluteTransform = CGAffineTransform(
            translationX: -anchorPoint.x * bounds.width,
            y: -anchorPoint.y * bounds.height
        ) // 2
        .concatenating(tranform) // 3
        .concatenating(CGAffineTransform(translationX: center.x, y: center.y)) // 4

    // apply transform to the initial rectangle
    return CGRectApplyAffineTransform(zeroOriginBounds, absoluteTransform)
} How can this knowledge be useful for us? Apart from just satisfying curiosity and gaining a better understanding of the internals, this algorithm has some real-world applications. A classic example is an image editor. Imagine you are building an application that creates custom stories. You want to place stickers on top of a photo, move, scale, and rotate them with your finger. For the editing mode, you use the actual and layout it by modifying its properties like center, bounds, and transform. Eventually, you’d like to render your story project into a bitmap and share it in high resolution, and that’s where understanding how geometry works becomes really useful. The image rendering code can look like the following: UIImageView UIView let renderer = UIGraphicsImageRenderer(size: imageSize)
let image = renderer.image { context in
    let cgContext = context.cgContext
    // render photo…
    // render other things…
    
    // render our sticker
    let absoluteTransform = CGAffineTransform(
        translationX: -sticker.anchorPoint.x * sticker.bounds.width,
        y: -sticker.anchorPoint.y * sticker.bounds.height
    )
        .concatenating(sticker.transform)
        .concatenating(
            CGAffineTransform(translationX: sticker.center.x, y: sticker.center.y)
        )
    
    cgContext.saveGState()
    cgContext.concatenate(absoluteTransform)
    sticker.image.draw(in: CGRect(origin: .zero, size: sticker.bounds.size))
    cgContext.restoreGState()
} What about Autolayout? Autolayout doesn't work with . It also ignores the and properties during its calculations. For each view, Autolayout calculates and sets only the and . The and are still used, but only for the final render on screen, and are applied afterward. frame transform anchorPoint bounds center transform anchorPoint To reiterate, in the Autolayout world, , , and don't exist. The Autolayout process is: constraints in, and out. frame transform anchorPoint bounds center Additional Resources UIView Documentation – https://developer.apple.com/documentation/uikit/uiview View Programming Guide – https://developer.apple.com/library/archive/documentation/WindowsViews/Conceptual/ViewPG_iPhoneOS/WindowsandViews/WindowsandViews.html#//apple_ref/doc/uid/TP40009503-CH2-SW5 Core Animation Basics – https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/CoreAnimation_guide/CoreAnimationBasics/CoreAnimationBasics.html#//apple_ref/doc/uid/TP40004514-CH2-SW3 Understanding UIKit Rendering – https://www.wwdcnotes.com/notes/wwdc11/121/ Understanding UIKit: CALayer Anchor Point – https://ikyle.me/blog/2022/understanding-uikit-calayer-anchor-point Understanding UIScrollView – https://oleb.net/blog/2014/04/understanding-uiscrollview/

Mastering UIView Geometry: A Dive into UIKit's Foundational Class and Geometry Concepts for iOS

Video is not just a story; it is the storyteller. It weaves narratives, captures moments, and connects us in ways words alone cannot. The ever-increasing demand for video content in marketing, streaming and OTT platforms, social media, and mobile video consumption has increased the demand for bandwidth and server capacity. processing has revolutionized the media industry, enhancing accessibility to video content beyond previous boundaries. The continuously increasing appetite for multimedia content and the growing prominence of deep learning have driven industry leaders like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform to offer GPU-enabled instances tailored for parallel processing excellence. Cloud media Installing FFmpeg on CPU for Optimal Performance FFmpeg is an indispensable multimedia innovation champion, consistently delivering eminence in audio-video processing and transcoding. If you are new to FFmpeg or someone who wants to make the most out of FFmpeg and set it up for optimal performance on CPU, you can refer to, , where we discussed FFmpeg and provided a step-by-step guide to make the most out of FFmpeg on CPU. How to install FFmpeg on Linux from Source Now, we will take a step further and look into how FFmpeg can leverage the power of hardware acceleration, which can significantly reduce processing time and help deliver content to users faster than ever before. This How-to guide will show you how to set up FFmpeg for multimedia transcoding and other multimedia task with hardware acceleration. Problem Traditional CPU-based video processing on cloud servers can be time-consuming and inefficient. As videos increase in resolution and size, the processing demand grows exponentially. Even with multi-core CPU configurations, the computational overhead can cause extended processing times, leading to delays in workflows and increased costs. Given cloud infrastructure providers charge per instance-hour, the longer your server runs, the more expensive your bill becomes. Failing to embrace GPU-powered machines means underutilizing their potential, especially since GPUs are specifically designed for parallel computational tasks. Thus, there is a pressing need to harness the GPU's power to optimize FFmpeg's performance and make the most out of it. The Remedy Know your Hardware The first step is to figure out the GPU for your required architecture. For the scope of this guide, we will focus on and available with AWS Graviton2 and Intel-based instances on AWS, but you can follow this guide for any Nvidia GPU by installing the desired driver yourself or by following and getting the appropriate driver from the . Nvidia Tesla T4 Nvidia Tesla T4G G4g G4dn this guide Nvidia driver download center Update and Install System Utilities The following snippet updates your existing libraries and other utilities. It uses some variables that are defined in the final script. echo "Installing utilities..."
dnf -y update
dnf -y groupinstall "Development Tools"
dnf install -y openssl-devel cmake3 amazon-efs-utils htop iotop yasm nasm jq Setup Nvidia GPU, CUDA, and CUDNN As the scope of this article is to demonstrate a working GPU-powered FFmpeg on AWS, the following will check whether this is the Graviton2-powered instance which is based on architecture or an Intel-based instance. Once selected, script will install the appropriate drivers for the architecture. G4g ARM/AARCH64 G4dn if [ "$(uname -m)" = "aarch64" ]; then
    echo "System is running on ARM / AArch64"
    DRIVE_URL="https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-aarch64-535.104.05.run"
    CUDA_SDK_URL="https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run"
    CUDNN_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.5.29_cuda12-archive.tar.xz"
else
    DRIVE_URL="https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-x86_64-535.104.05.run"
    CUDA_SDK_URL="https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux.run"
    CUDNN_ARCHIVE_URL="https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-8.9.5.29_cuda12-archive.tar.xz"
fi
echo "Setting up GPU..."
DRIVER_NAME="NVIDIA-Linux-driver.run"
wget -O "$DRIVER_NAME" "$DRIVE_URL"
TMPDIR=$LOCAL_TMP sh "$DRIVER_NAME" --disable-nouveau --silent

CUDA_SDK="cuda-linux.run"
wget -O "$CUDA_SDK" "$CUDA_SDK_URL"
TMPDIR=$LOCAL_TMP sh "$CUDA_SDK" --silent --override --toolkit --samples --toolkitpath=$USR_LOCAL_PREFIX/cuda-12.2 --samplespath=$CUDA_HOME --no-opengl-libs

CUDNN_ARCHIVE="cudnn-linux.tar.xz"
EXTRACT_PATH="$SRC_DIR/cudnn-extracted"
mkdir -p "$EXTRACT_PATH"

wget -O "$CUDNN_ARCHIVE" "$CUDNN_ARCHIVE_URL"
tar -xJf "$CUDNN_ARCHIVE" -C "$EXTRACT_PATH"
CUDNN_INCLUDE=$(find "$EXTRACT_PATH" -type d -name "include" -print -quit)
CUDNN_LIB=$(find "$EXTRACT_PATH" -type d -name "lib" -print -quit)
cp -P "$CUDNN_INCLUDE"/* $CUDA_HOME/include/
cp -P "$CUDNN_LIB"/* $CUDA_HOME/lib64/
chmod a+r $CUDA_HOME/lib64/*
ldconfig By now, you should have a working system with an Nvidia device driver, which can be checked by running in the terminal. nvidia-smi Install FFmpeg Prerequisites Before diving into FFmpeg's installation, it's crucial to understand and set up its dependencies so FFmpeg can process most general process tasks for different formats and get an understanding of how to expand or limit the scope of FFmpeg for your multimedia task. System Dependencies FFmpeg is a great tool for text processing and text rendering in videos as well. To enable this feature, we will need to install libraries that are used for text manipulation, embedding subtitles, and other fancy operations like embedding the watermark of your brand in the video. These are essential system libraries that FFmpeg requires. dnf install -y freetype-devel fribidi-devel harfbuzz-devel fontconfig-devel bzip2-devel Installing Audio & Video Codecs At its core, FFmpeg functions like a versatile framework, ready to be extended with various plugins and modules. One of the primary ways this extensibility shines is through its support for many audio and video codecs. These codecs are the building blocks that allow FFmpeg to decode and encode media in various formats. Now, we will install different codecs. FFmpeg is like a template that is built in a way anyone can write a plugin like a video codec and that can be used with FFmpeg : Since the target instance is powered with GPU, codec enables FFmpeg to leverage NVIDIA GPU acceleration, significantly speeding up video processing tasks for and / ffnvcodec ffnvcodec av1, H.264, HEVC H.265 : FFmpeg can use for subtitle renderer to handle subtitles in various video formats. LIBASS LIBASS : FFmpeg uses LIBAOM to decode and encode videos, if you want to use software transcoding for . LIBAOM (AV1 Codec Library) AV1 AV1 : These include libraries for various audio and video formats (e.g., for MP3 audio, for the Opus audio codec, and for the Ogg container). In this build we will also enable software encoding of and using and libraries . Other Codecs libmp3lame opus libogg H.264 HEVC/H.265 x264 x265 By ensuring all these prerequisites are installed and correctly set up, FFmpeg can provide its robust set of features, and users can harness the desired power out of this great utility. Installing FFmpeg Finally, we will compile GPU-accelerated FFmpeg from the source, with enabled support of all the codecs and libraries we have compiled or installed in previous steps. wget https://ffmpeg.org/releases/ffmpeg-snapshot.tar.bz2 &&
tar -jxf ffmpeg-snapshot.tar.bz2 &&
pushd ffmpeg &&
PKG_CONFIG_PATH="$USR_LOCAL_PREFIX/lib/pkgconfig:/usr/lib64/pkgconfig:/usr/share/pkgconfig:/usr/lib/pkgconfig:$USR_LOCAL_PREFIX/lib/pkgconfig" \
    ./configure \
    --prefix="$USR_LOCAL_PREFIX" --disable-static --enable-shared \
    --extra-cflags="-I$USR_LOCAL_PREFIX/include $NVIDIA_CFLAGS" \
    --extra-ldflags="-L$USR_LOCAL_PREFIX/lib $NVIDIA_LDFLAGS" \
    --extra-libs='-lpthread -lm' --bindir="$USR_LOCAL_PREFIX/bin" \
    --enable-gpl --enable-libaom --enable-libass --enable-libfdk-aac \
    --enable-libfreetype --enable-libmp3lame --enable-libopus \
    --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 \
    --enable-nonfree --enable-openssl $NVIDIA_FFMPEG_OPTS &&
make -j $CPUS &&
make install Verifying GPU Acceleration Support in FFmpeg Once you've set up FFmpeg with GPU acceleration capabilities, it's a good idea to ensure that it recognizes and can utilize the GPU for tasks like video encoding and decoding. The command below checks for the presence of NVIDIA's NVENC (NVIDIA Video Encoder) in the list of supported codecs within FFmpeg: ffmpeg -hide_banner -codecs | grep nvenc If you see the output codecs with the label (e.g., , , ), it indicates that FFmpeg has been correctly configured to support GPU-accelerated encoding for those specific codecs using NVIDIA's hardware. nvenc h264_nvenc hevc_nvenc av1_nvenc The Complete Script Building upon the various segments we've discussed, here's the consolidated FFmpeg installation script along with the Nvidia GPU installation. This script has been thoroughly tested on and running on AWS instances. It's not just limited to those; its flexibility is its strength. Whether you're on Debian, Red Hat, CentOS, Fedora, openSUSE, or even the sleek Alpine, this script is crafted to serve both and architectures seamlessly. Dive in and experience the universality of our FFmpeg installation script, bringing the power of multimedia to every corner of the Linux world. Amazon Linux 2023 Ubuntu 22.04 G5g ARM/aarch64 x86_64 https://gist.github.com/bilalmughal/cb89936bc947fa727a8ec66e3ddf768a?embedable=true Hands-On: Converting H.264 to H.265 using Hardware Acceleration In this section, we'll take a hands-on approach to show you how to convert an H.264 encoded video to H.265 using the power of NVIDIA's GPU acceleration. This way we can test the installation and experience the power of GPU acceleration in action. To perform this conversion, use the following command: ffmpeg -y -hide_banner -hwaccel cuvid -c:v h264_cuvid -i input.mp4 -vf "scale_cuda=720:480" -c:v hevc_nvenc output.mp4 In this command: and uses GPU-based decoding of the H.264 video. -hwaccel cuvid -c:v h264_cuvid scales the video using GPU acceleration. -vf "scale_cuda=1920:1080" instructs FFmpeg to encode the output video using the H.265 codec with NVIDIA's GPU acceleration. -c:v hevc_nvenc After executing the command, you'll get an encoded video. output.mp4 H.265 1080p Save up to 400% on Video-Transcoding. We conducted a comprehensive cost-performance-centric benchmark on AWS with CPU and GPU-powered instances, and the findings changed our perspective about the narrative “CPU is slow, but GPU is expensive.” The results were astonishing and enabled us to make decisions that enabled us to save up to 400% cost on video-transcoding jobs. We recommend you to check that out as well at CPU vs GPU for Video Transcoding: Challenging the Cost-Speed Myth Conclusion By integrating FFmpeg with GPU hardware acceleration on AWS or any other infra-provider or architecture, you can optimize costs and reduce processing times, but it also future-proofs your workflow for higher-resolution media processing tasks down the line. By compiling from the source, we are also addressing the outdated libraries and generalized package issues, which are not fully optimized for your architecture, as discussed in " " and some aspects are also discussed in the article as well. The evolution of multimedia is ceaseless, but with tools like FFmpeg combined with AWS's GPU prowess and cost-effectiveness, you are well-equipped to handle whatever challenges come next. Whether you're a media company looking to scale or a hobbyist aiming for maximum efficiency, GPU-accelerated FFmpeg on AWS is a game-changer. How to install FFmpeg on Linux from source "Why your AWS Deep learning AMI is Holding you back and how to fix Resources : Follow the detailed instructions on . Building FFmpeg from Source Master the Compilation Process! https://mirzabilal.com/how-to-install-ffmpeg-on-linux-from-source : See for yourself at CPU vs GPU Benchmark for video Transcoding on AWS Debunking the CPU-GPU myth! https://mirzabilal.com/cpu-vs-gpu-for-video-transcoding-challenging-the-cost-speed-myth : Learn How to Break Free at . Dark Truth about AWS Deep Learning AMIs The Silent Roadblock to Your Success! https://mirzabilal.com/why-your-aws-deep-learning-ami-is-holding-you-back-and-how-to-fix : Read this article and more at . Deep Learning with AWS Graviton2 and Nvidia Tensor T4g Discover Cutting-Edge Insights! https://mirzabilal.com/deep-learning-with-aws-graviton2-nvidia-tensor-t4g-for-as-low-as-free-with-cuda-12-2-56d8457a6f6d This guide was originally published at . https://mirzabilal.com/how-to-install-ffmpeg-with-harware-accelaration-on-aws Also published . here

Enabling GPU-Accelerated FFmpeg on AWS for Optimal Video Processing

The world of blockchain gaming has witnessed nothing short of a revolution in recent years, with a greater focus on bringing functionality to a familiar Web2 gaming experience. One of these innovations, asset interoperability, is an exciting premise, offering players and developers the ability to transfer their in-game items between different games and platforms. Web3 In this interview, I had the pleasure of speaking with Anik Dang, the CEO of ReneVerse, who strives to bring unmatched fidelity and control to the interoperable gaming experience. We’ll look into the significance of interoperability in gaming, the role of non-fungible tokens (NFTs), and the interesting implementations of interoperability technology that have emerged with the potential to infiltrate the mainstream gaming industry. Let’s see how these innovations are revolutionizing the gaming experience and what lies ahead for this dynamic field. Please introduce yourself and tell us what you do. Hello, I’m Anik Dang, the CEO of ReneVerse, the first scalable, immersive in-game advertising infrastructure designed to modularly enable the next generation of non-intrusive advertisements and in-game product placements in any game. Built on top of a proprietary interoperability technology that we’ve named the Borderless Interoperability Engine, ReneVerse enables any in-game assets to traverse the borders of a single game seamlessly. Hence, our belief is that the future of gaming is “borderless.” Our mission is simply to empower players, developers, and brands to bring their assets with them on their own gaming journeys, and Borderless Advertising is just the beginning. It’s an exciting venture, and I’m honored to be leading the charge. Can you provide a brief overview of what interoperability means in the context of blockchain gaming and why it's important? Interoperability can refer to one of two things. The first is interoperability – the functionality that allows an on-chain asset to move from one blockchain network to another. Many projects out there offer various solutions to facilitate this, and while the concept certainly is related to gaming, it is not a feature exclusive to it. cross-chain The other, specific to the context of the gaming industry, is interoperability – the ability for, say, Asset A from Game 1 to be transferred and used in Game 2. This asset could be an in-game character, vehicle, or anything else made by the game’s developer. cross-game Both kinds of interoperability are important and gaining attention in the world of web3 gaming. Many projects that integrate cross-chain interoperability have seen success in giving players more autonomy over their gaming items and helping developers create multi-title game ecosystems that offer experiences with enough variety to keep players in their playpen (and thus their revenue). What we have witnessed here at ReneVerse is that blockchain-powered interoperability in gaming is received incredibly well by web2-native game developers and players, as long as its integration does not interfere with its end-user experience and precludes the possibility of unfairness in the play space, including situations like pay-to-win scenarios. Ultimately, interoperability that gets baked into games under the hood brings about tons of opportunities for open collaboration, connected universes, and exciting new ways to play. I think that it will produce many unexpected innovations for games that will prove refreshing and well-received – that’s why we are excited to facilitate the path forward! What is the difference between traditional in-game items and those powered by ReneVerse technology? What advantages do they offer to players and developers? I think there are actually two degrees of separation between traditional in-game items and those we’re working on. There’s first a difference between these traditional items from typical Web2 games and existing blockchain gaming assets, and that comes down to ownership. Traditional items are those that are inextricably tied to the game they come from; for the most part, they cannot be uniquely identified, owned, or transferred. Blockchain gaming assets, on the other hand, are unique, ownable, and tradable. Then, there’s a distinction between these blockchain gaming assets and those powered by ReneVerse, which is a matter of scalability. Normal blockchain gaming items still reside within the “walled garden” franchises that they belong to. However, we’re developing a way for assets to go outside their normal bounds, packaged up nicely with all their usability intact. Currently, our solution is geared toward the context of immersive in-game advertising. Through the ReneVerse SDK, which is available for Unity, Java, and soon Unreal Engine, game developers can integrate what we call “Branded Assets,” which are in-game items with dynamically-served advertisement spaces, or ad surfaces, on them. These effectively serve as interactive product placements and can enter any game that integrates our API. For developers, it's a win-win. These assets are essentially ad surfaces that developers can rent to interested brands for high-touch marketing in their games. This introduces more monetization without interrupting the player experience. And since these Branded Assets are also blockchain-enabled, it will be possible for players to purchase and own them. Players who do this will receive ad revenue from participating brands, much like how people wrap their real-life cars with ads to supplement their income. This mechanism of ownership allows players to make their specific gaming experience lucrative without altering the gameplay for anyone else. How do non-fungible tokens (NFTs) play a role in enabling such assets within blockchain gaming ecosystems? Non-fungible tokens (NFTs) are crucial in making branded assets a reality for us. NFTs are uniquely identified with their own token address, and an NFT can be an in-game asset. For the purpose of identifying to whom revenue is distributed and dynamically targeting ad content in-game, it’s essential to have the token address, especially if the asset is stored on different blockchains or is transferred to other games. There are a slew of other purposes that asset identification serves, so while our current focus is on ad-tech implementation, NFTs are a fantastic basis for many different kinds of interoperable experiences, both in and out of gaming. In terms of user experience, how do you see the future of interoperable assets evolving in blockchain gaming? What improvements or innovations can gamers expect? The future of interoperability in gaming holds a lot of promise for enhancing the user experience. In fact, I believe that this is where different interoperability projects will attempt to differentiate amongst themselves. Firstly, we're likely to see smoother transitions between games. Moving assets between games can be a bit clunky, but we can expect more seamless interoperability as technology evolves. This means you can hop from one game to another with your assets, and the experience will feel as fluid as changing levels within a single game. Secondly, I think we'll see more dynamic in-game economies. With interoperable assets, players can engage in cross-game trading and activities, which could lead to vibrant and interconnected virtual economies. We fervently believe that Borderless Ads will be part of this phenomenon. It will also more firmly establish the concept of player-owned assets and virtual property rights. In the same way, that online display ads became a “go-to” option for reaching tech-forward audiences back in the day, interoperable advertising objects have the potential to accomplish the same. Lastly, as more games adopt them, interoperability solutions will likely package themselves to be hidden. I believe that no matter how expansive games might become with this technology, they will only find success if players don’t have to learn to navigate third-party, add-on software. The experience must remain immersive and uninterrupted. In short, things are promising. It's all about making gaming more flexible, dynamic, and player-centric. How can developers balance maintaining the uniqueness and scarcity of in-game assets (NFTs) while allowing them to be interoperable across different gaming platforms? This is an interesting and challenging question that lots of interoperability projects need to wrestle with. I think that there are two suitable ways to approach this, but of course, other methods may better suit certain developers based on their goals. The first is simply to limit interoperability to function within an ecosystem consisting of multiple games. A good example is Nintendo’s game collection. Games like Super Smash Bros combine multiple IPs within a single game to bring fans a new experience, and obviously, it saw massive success. With NFT technology, it becomes possible for game studios to recreate this phenomenon at a fraction of the cost and in less than half the time. By tokenizing these assets, developers allow users to control the transfer of assets between different games of the same “IP universe”. Of course, this is, by definition, not fully open-ended interoperability. It is more akin to a walled garden with multiple pods within it that have better player autonomy and cross-game communication than previously seen. The second approach, which we hope will become more apparent to game developers, is to build asset ecosystems whose values are predicated on utility instead of rarity. NFTs are, by definition, unique. Since they are non-fungible, it stands to reason that the value of NFTs is determined by their rarity. However, NFTs are a really recent development, and as new innovations arise, NFTs themselves are becoming more advanced, taking different forms and offering new functionalities. It can be argued that, in the context of gaming, the value of an NFT should not be based on how rare it is but on how useful it is to a particular user. As such, in-game assets like our Branded Assets could change the way NFTs in gaming are appraised. The chance that players opt for Branded Item A, with a quantity of 100, over Non-Branded Item B, with a quantity of 10, is high, in my opinion. It gives players an opportunity to earn money while playing without the chance for the asset itself to give any competitive advantages. In turn, I believe developers will also see that the more compatible their branded assets are with other games, the greater the revenue they will generate. In summary, whether it is through the controlled distribution of assets within a closed ecosystem or through the introduction of better NFTs that retain their value through utility regardless of where they are, developers have more options than ever to open their borders without harming their ecosystem. Are there any notable efforts or projects focused on creating protocols to enhance interoperability in blockchain gaming? What are the challenges they face, and what progress has been made? Yes, there are indeed notable efforts and projects in the blockchain gaming world that are dedicated to creating protocols to enhance interoperability. One of the most prominent initiatives is the Ethereum-based ERC-1155 standard. It's gained widespread recognition and adoption as it allows developers to create NFTs (non-fungible tokens) with multiple properties, making it easier for these assets to move between different games. ERC-1155 has laid a solid foundation for interoperability. One of the biggest challenges that most projects face in the context of interoperability in gaming is how to map universal attributes to in-game assets originating from various genres and designed with unique mechanics. Overall, the process must be simplified to a degree; ReneVerse’s Interoperability Suite is such a tool that makes mapping of game objects to sets of attributes easy while offering developers the ability to compose customized assets for their own environments as they please. How do you see the future of interoperability evolving in the crypto gaming industry, and what potential benefits or disruptions can we expect? The future of interoperability in the crypto gaming industry is incredibly promising. We will likely see a more seamless integration of in-game assets across various games and platforms. One significant benefit is that players will have greater control and ownership of their digital assets. This increased versatility could make various in-game activities more viable, and players could have a stronger sense of ownership over their virtual possessions, especially if they provide unique utilities to their owners. Naturally, with truly open interoperability, there could be some challenges. It’s possible that certain developers will be wary of sharing their assets outside of their game, especially if they don’t believe that they measure up to the competition. It will require games to be far more creative and thoughtful about how assets interact with the games they reside in. Other challenges, mainly regarding security and the need for a robust, scalable infrastructure to handle cross-chain and cross-game asset transactions at scale, are also top of mind for the industry. There are projects building even faster layer-2 networks and even layer-3 networks. Others are building the means for players to log into blockchain games through familiar Web2 social accounts like email, Twitter, and Facebook. The projects building for these concerns are literally building the foundations for the future of the gaming industry.

Exploring Borderless Ads – A Unique Use Case of Interoperability Tech in Web3 Gaming

In this article, we will learn how to generate with by taking inputs from users. Some of the use cases include generating , , , based on data received, etc. dynamic PDFs React invoices certificates resumes reports To enable PDF downloading, we will use package which provides useful components like , , , , , , etc. react-pdf Document Page View Image Text PDFDownloadLink PDFViewer Let's check each component: This tag represents the PDF document itself and must be the root of our PDF. Document: It represents a single page inside the PDF documents and should always be rendered inside the Document component only. Page: This component helps to build UI for the PDFs. It can be nested inside other views. View: It's used for displaying network or local (Node only) JPG or PNG images in PDFs. Image: Used to display text in PDFs. It also supports the nesting of other Text components. Text: It allows you to generate and download PDF documents. PDFDownloadLink: It is used for rendering client-side generated documents. PDFViewer: Required Installations Create react app using the following cmd: pdf-invoice npx create-react-app react-pdf-invoice After the successful creation of the app, use the following commands to go to a directory and start the project - cd react-pdf-invoice
npm start Command to install in react app: react-pdf Using npm npm install @react-pdf/renderer --save Using yarn yarn add @react-pdf/renderer Folder structure: Creating Invoice Form Since our PDF is dynamic in nature, there will be options to add/delete items, change the price/quantity of the products, computation of total amount based on the items mentioned. As a result, we need to take inputs from users and show the data accordingly. src > components > createInvoice > InvoiceForm.js import React, { useState } from 'react';
import InvoicePDF from '../getPDF/InvoicePDF';
import { PDFDownloadLink } from '@react-pdf/renderer';
import './styles.css';

const InvoiceForm = () => {

// state for storing info about user creating Invoice
  const [billFrom, setBillFrom] = useState({
    name: '',
    address: '',
    invoiceNumber: '',
  })

// state for capturing info of person who needs to pay
  const [client, setClient] = useState({
    clientName: '',
    clientAddress: '',
  })

// items description containing name, price and quantity
  const [items, setItems] = useState([{ name: '', quantity: 0, price: 0}]);

  const handleBillFromData = (e) => {
    e.preventDefault();
    const {name, value} = e.target;  
    setBillFrom({
      ...billFrom,
      [name] : value
    })
  }

  const handleClientData = (e) => {
    e.preventDefault();
    const {name, value} = e.target;  
    setClient({
      ...client,
      [name] : value
    })
  }
  
  const handleItemChange = (e, index, field, value) => {
    e.preventDefault();
    const updatedItems = [...items];
    updatedItems[index][field] = value; // updating the item field (using index) according to user's input

    setItems(updatedItems);  // updating the items array
  };

  const handleAddItem = () => {
    setItems([...items, { name: '', quantity: 0, price: 0}]);  // adding new item to items array
  };

  const handleRemoveItem = (index) => {
    const updatedItems = [...items];
    updatedItems.splice(index, 1); // removing the selected item
    
    setItems(updatedItems);  // updating the items array 
  };

// to compute the items' total amount
  const total = () => {
    return items.map(({price, quantity}) => price * quantity).reduce((acc, currValue) => acc + currValue, 0);
  }

  return (
    <div className="invoice">
      <div>
        <h1 className='title'>Invoice</h1>

        <div className='firstRow'>
          <div className='inputName'>
            <label>Invoice Number:</label>
            <input name="invoiceNumber" className="input" type="text" value={billFrom.invoiceNumber} onChange={handleBillFromData} />
          </div>
        </div>

        <div className='firstRow'>
          <div className='inputName'>
            <label>Name:</label>
            <input name="name" className="input" type="text" value={billFrom.name} onChange={handleBillFromData} />
          </div>
          <div className='inputName'>
            <label>Address:</label>
            <textarea name="address" className="textarea" type="text" value={billFrom.address} onChange={handleBillFromData} />
          </div>
        </div>

        <hr/>

        <h2>Bill To:</h2>
        
        <div className='firstRow'>
          <div className='inputName'>
            <label>Client Name:</label>
            <input name="clientName" className="input" type="text" value={client.clientName} onChange={handleClientData} />
          </div>
          <div className='inputName'>
            <label>Address:</label>
            <textarea name="clientAddress" className="textarea" type="text" value={client.clientAddress} onChange={handleClientData} />
          </div>
        </div>

        <h2 className='title'>Add Details</h2>
        <div className='subTitleSection'>
          <h2 className='subTitle item'>Item</h2>
          <h2 className='subTitle quantity'>Quantity</h2>
          <h2 className='subTitle price'>Price</h2>
          <h2 className='subTitle action'>Amount</h2>
        </div>

        {items?.map((item, index) => (
          <div key={index} className='firstRow'>
            <input className="input item"
              type="text"
              value={item.name}
              onChange={(e) => handleItemChange(e, index, 'name', e.target.value)}
              placeholder="Item Name"
            />
            <input className="input quantity"
              type="number"
              value={item.quantity}
              onChange={(e) => handleItemChange(e, index, 'quantity', e.target.value)}
              placeholder="Quantity"
            />
            <input className="input price"
              type="number"
              value={item.price}
              onChange={(e) => handleItemChange(e, index, 'price', e.target.value)}
              placeholder="Price"
            />
            <p className='amount'>$ {item.quantity * item.price}</p>
            <button className='button' onClick={() => handleRemoveItem(index)}>-</button>
          </div>
        ))}
        <button className='button' onClick={handleAddItem}>+</button>
        <hr/>
        
        <div className='total'>
          <p>Total:</p>
          <p>{total()}</p>
        </div>
        <hr/>

        <PDFDownloadLink document={<InvoicePDF billFrom={billFrom} client={client} total={total} items={items} />} fileName={"Invoice.pdf"} >
          {({ blob, url, loading, error }) =>
                loading ? "Loading..." : <button className='button'>Print Invoice</button>
              }
        </PDFDownloadLink>
      </div>
    </div>
  );
};

export default InvoiceForm; Storing User's Info const handleBillFromData = (e) => {
    e.preventDefault();
    const {name, value} = e.target;  
    setBillFrom({
      ...billFrom,
      [name] : value
    })
  } In the above-mentioned code, we are storing the information (name, invoice number, address) of the person who is creating an Invoice. Storing Client's Info const handleClientData = (e) => {
    e.preventDefault();
    const {name, value} = e.target;  
    setClient({
      ...client,
      [name] : value
    })
  } In the above code block, the function will update the Client's information (name, address) who needs to pay the total amount based on the user's input. Create/Update/Delete operations on Items const handleItemChange = (e, index, field, value) => {
    e.preventDefault();
    const updatedItems = [...items];
    updatedItems[index][field] = value; // updating the item field (using index) according to user's input

    setItems(updatedItems);  // updating the items array
  };

  const handleAddItem = () => {
    setItems([...items, { name: '', quantity: 0, price: 0}]);  // adding new item to items array
  };

  const handleRemoveItem = (index) => {
    const updatedItems = [...items];
    updatedItems.splice(index, 1); // removing the selected item
    
    setItems(updatedItems);  // updating the items array 
  }; will add a new item having , , as input fields whenever user clicks on button. handleAddItem() itemName quantity price + will remove the selected item when user clicks on button. handleRemoveItem() - will update the selected item by grabbing index of that particular item with the values (input by the user). handleItemChange() Props of PDFDownloadLink <PDFDownloadLink document={<InvoicePDF billFrom={billFrom} client={client} total={total} items={items} />} fileName={"Invoice.pdf"} >
          {({ blob, url, loading, error }) =>
                loading ? "Loading..." : <button className='button'>Print Invoice</button>
              }
        </PDFDownloadLink> The above-mentioned code snippet is responsible for generating a PDF document using all the inputs taken by the user. : To implement PDF document functionality document : Name of the PDF once downloaded filename : Tag for adding styling style Adding Styling in Invoice Form src > components > createInvoice > styles.css .invoice {
    display: flex;
    padding: 10px;
    margin: 20px;
    border-radius: 12px;
    justify-content: center;
    width: 1200px;
    box-shadow: rgba(0, 0, 0, 0.35) 0px 5px 15px;
}

.title {
    font-size: 30px;
}

.firstRow {
    display: flex;
    justify-content: space-between;
    flex-grow: 1;
}

.inputName {
    display: flex;
    flex-direction: column;
}

.total {
    display: flex;
    justify-content: space-between;
    font-size: 20px;
}

.total > p {
    align-items: center;
    justify-content: center;
    font-weight: 700;
}

.inputName > label {
    font-size: 20px;
    font-weight: 600;
    margin-right: 5px;
    text-align: left;
}

.input, .textarea{
    font-size: 20px;
    border-radius: 5px;
    padding-left: 10px;
    margin: 10px 0px;
}

.subTitleSection {
    background: bisque;
    display: flex;
    justify-content: space-between;
    border-radius: 12px;
    flex-grow: 1;
    padding-right: 15px;
}

.subTitle {
    font-size: 20px;
    font-weight: 700;
    margin: 10px 10px;
    text-align: left;
}

.item {
    width: 50%;
}

.quantity {
    width: 15%;
}

.price {
    width: 15%;
}

.action {
    width: 10%;
}

.amount {
    font-size: 18px;
    font-weight: 700;
}

.remove {
    border-radius: 12px;
    height: 20px;
    width: 20px;
}

.button {
    background-color: #405cf5;
    border-radius: 6px;
    border-width: 0;
    box-sizing: border-box;
    color: #fff;
    cursor: pointer;
    font-size: 18px;
    height: 44px;
    padding: 0 25px;
} Invoice Form UI Invoice with multiple items: Generating PDF Documents Based on Invoice Data Once we get the required data from the user's end, we feed the data to the component responsible for generating the PDF document. In our case, InvoicePDF is that component. src > components > getPDF > InvoicePDF.js import React from 'react';
import { Page, Text, View, Document, StyleSheet } from '@react-pdf/renderer';
import ItemsTable from './ItemsTable';

const styles = StyleSheet.create({
  page: {
    flexDirection: 'column',
    padding: 20,
  },
  name: {
    flexDirection: 'column',
    justifyContent: 'flex-start',
    fontSize: 22,
    marginBottom: 5
  },
  invoiceNumber: {
    flexDirection: 'column',
    justifyContent: 'flex-start',
  },
  section: {
    margin: 10,
    padding: 10,
    flexGrow: 1,
  },
  header: {
    fontSize: 24,
    marginBottom: 10,
    textAlign: 'center'
  },
  label: {
    fontSize: 12,
    marginBottom: 5,
  },
  input: {
    marginBottom: 10,
    paddingBottom: 5,
  },
  client: {
    borderTopWidth: 1,
    marginTop: 20,
    marginBottom: 10
  },
});

const InvoicePDF = ({ billFrom, client, total, items }) => { // destructuring props
  return (
    <Document>
      <Page size="A4" style={styles.page}>
        <View>
          <Text style={styles.header}>Invoice Form</Text>
          <View>
            <Text style={styles.name}>{billFrom.name}</Text>
          </View>

          <View style={styles.invoiceNumber}>
            <Text style={styles.label}>INVOICE NO.</Text>
            <Text style={styles.input}>{billFrom.invoiceNumber}</Text>
          </View>

          <View style={styles.invoiceNumber}>
            <Text style={styles.label}>ADDRESS</Text>
            <Text style={styles.input}>{billFrom.address}</Text>
          </View>
          
          <View style={styles.client}></View>
          
          <Text style={styles.label}>BILL TO</Text>
          
          <View>
            <Text style={styles.name}>{client.clientName}</Text>
          </View>

          <View style={styles.invoiceNumber}>
            <Text style={styles.label}>CLIENT ADDRESS</Text>
            <Text style={styles.input}>{client.clientAddress}</Text>
          </View>

          <ItemsTable items={items} total={total} />

        </View>
      </Page>
    </Document>
  );
};

export default InvoicePDF; Create an Items Table listing all the products src > components > getPDF > ItemsTable.js import React from 'react'
import { Text, View, StyleSheet } from '@react-pdf/renderer';

const styles = StyleSheet.create({
    row: {
        flexDirection: 'row',
        borderBottomWidth: 1,
        backgroundColor: '#D3D3D3',
        borderTopColor: 'black',
        borderTopWidth: 1,
        borderBottomColor: 'black',
        fontStyle: 'bold',
        alignItems: 'center',
        height: 22,
      },
      quantity: {
        width: '10%',
        borderRightWidth: 1,
        textAlign: 'right',
        borderRightColor: '#000000',
        paddingRight: 10,
      },
      description: {
          width: '60%',
          borderRightColor: '#000000',
          borderRightWidth: 1,
          textAlign: 'left',
          paddingLeft: 10,
      },
      price: {
        width: '15%',
        borderRightColor: '#000000',
        borderRightWidth: 1,
        textAlign: 'right',
        paddingRight: 10,
      },
      amount: {
        width: '15%',
        textAlign: 'right',
        paddingRight: 10,
      },
      total: {
        display: 'flex',
        flexDirection: 'row',
        justifyContent: 'space-between',
        borderBottomColor: 'black',
        borderBottomWidth: 1,
      }
})

const ItemsTable = ({ items, total }) => { // destructuring props
  return (
    <View>
        <View style={styles.row}>
            <Text style={styles.description}>Item Description</Text>
            <Text style={styles.quantity}>Qty</Text>
            <Text style={styles.price}>Price</Text>
            <Text style={styles.amount}>Amount</Text>
        </View>

        { 
            items.map((item, index) => (
            <View key={index} style={styles.row}>
                <Text style={styles.description}>{item.name}</Text>
                <Text style={styles.quantity}>{item.quantity}</Text>
                <Text style={styles.price}>{item.price}</Text>
                <Text style={styles.amount}>$ {item.quantity * item.price}</Text>
            </View>
        ))}

        <View style={styles.total}>
            <Text>Total: </Text>
            <Text>$ {total()} </Text>
        </View>
    </View>

  )
}

export default ItemsTable; Updated App.js src > App.js import './App.css';
import InvoiceForm from './components/createInvoice/InvoiceForm';

function App() {
  return (
    <div className="App">
      <InvoiceForm />
    </div>
  );
}

export default App; Output On clicking , a PDF document named gets downloaded with the following structure: Print Invoice Invoice.pdf Conclusion With this article, we got to know how to download a dynamic PDF document with the help of based on the user's inputs. To gain more insights, play around with the other components. react-pdf References https://react-pdf.org/ Also published here.

Learn How to Create a Dynamic PDF Generator with React

In this article, we will tackle a question: Does Vue.js support multiple event listeners? Our journey will take us deep into the mechanics of Vue.js, unraveling some intriguing undocumented behaviors along the way. Let's begin with a closer look at the official documentation on "Event Handling" in Vue.js. The primary method of attaching an event listener is through the syntax, which can also be simplified as . v-on:click="handler" @click="handler" In this syntax, the refers to a reference to a function. Additionally, in the "Inline Handlers" section, it's highlighted that you can employ arbitrary JavaScript code directly within the attribute. handler For instance, you can use to increment a variable. An important note is provided in the "Method vs. Inline Detection" section, which indicates that: @click="count++" The template compiler detects method handlers by checking whether the value string is a valid JavaScript identifier or a property access path. v-on So, does Vue support multiple listeners? The answer seems to lean towards no, but it's not entirely clear-cut. Let recap it with the code: <script setup>
import { ref } from 'vue';

const count = ref(0);
function inc() { count.value += 1; }
</script>

<template>
  <h3>{{ count }}</h3>
  <button @click="count++">Incremenet by count++</button>
  <button @click="inc">Incremenet by ref</button>
  <button @click="inc()">Incremenet by call</button>
  <button @click="() => inc()">Incremenet by lambda</button>
</template> Now, let's within the Vue SFC Playground to closely examine how the Vue.js compiler has compiled these listeners. take a plunge into the JS tab We will encounter the following code snippet (I've omitted the portions for the sake of readability): _cache[0] || (_cache[0] = $event => (count.value++)) _createElementVNode("h3", null, _toDisplayString(count.value), 1 /* TEXT */);

// @click="count++" will be compiled to...
_createElementVNode(
  "button",
  {
    onClick: ($event) => count.value++,
  },
  "Incremenet by count++",
);

// @click="inc" will be compiled to...
_createElementVNode("button", { onClick: inc }, "Incremenet by ref");

// @click="inc()" will be compiled to...
_createElementVNode(
  "button",
  {
    onClick: ($event) => inc(),
  },
  "Incremenet by call",
);

// @click="() => inc()" will be compiled to...
_createElementVNode(
  "button",
  {
    onClick: () => inc(),
  },
  "Incremenet by lambda",
); The behavior is indeed intriguing. When a reference to is passed, the compiler simplifies it to . However, for , and , the compiler follows a distinct route. It encapsulates the code enclosed within the template's into a lambda function and then proceeds to execute it exactly as it's written. inc { onClick: inc } count++ inc() () => inc() " This observation offers valuable insight: if the compiler wraps code within a lambda, we can leverage native JavaScript capabilities to call multiple functions within a single expression using or . Let's try it. fn1(); fn2() fn1(), fn2() We will introduce another function, , which will invoke the native function and pass into it. You can access the updated Playground . showAlert() alert() count.value here Here is the code: // @click="count++, showAlert()" will be compiled to...
_createElementVNode(
  "button",
  {
    onClick: ($event) => (count.value++, showAlert()),
  },
  "Increment by count++",
);

// How to pass multiple refs?
_createElementVNode("button", { onClick: inc }, "Increment by ref");

// @click="inc(); showAlert()" will be compiled to...
_createElementVNode(
  "button",
  {
    onClick: ($event) => {
      inc();
      showAlert();
    },
  },
  "Increment by call",
);

// @click="() => (inc(), showAlert())" will be compiled to...
_createElementVNode(
  "button",
  {
    onClick: () => (inc(), showAlert()),
  },
  "Increment by lambda",
); For , , and , everything works fine, allowing us to call multiple functions for a single event. @click="count++, showAlert()" @click="inc(); showAlert()" @click="() => (inc(), showAlert())" The issue arises when dealing with the case. How can we pass multiple into the handler? The official documentation is notably silent on the topic of passing multiple references. ref refs @click="..." It appears that this feature might not be supported, leaving us unable to achieve this behavior directly. To explore this further, let's experiment with the two initial approaches that come to mind: and , and observe how they are compiled by Vue.js. fn1, fn2 [fn1, f2] // @click="inc, showAlert" will be complied to...
_createElementVNode("button", {
  onClick: $event => (inc, showAlert)
}, "Multiple refs 1");

// @click="[inc, showAlert]" will be complied to...
_createElementVNode("button", {
  onClick: $event => ([inc, showAlert])
}, "Multiple refs 2") Unfortunately, both attempts did not yield success. Vue.js compiles these expressions in a manner that involves encapsulating the code enclosed within the template's . This approach is consistent with the behavior we previously uncovered. " Let's take a step back and examine the scenario where we simply pass a function identifier without any accompanying braces in the event handler. () // @click="inc" will be compiled to...
_createElementVNode(
  "button",
  { onClick: inc },
  "Incremenet by ref",
); Vue simply maps to . Now, let's recap the rule we extracted from the documentation. inc onClick The template compiler detects method handlers by checking whether the value string is a valid JavaScript identifier or a property access path. v-on Incorporating the insights gained from the above, we can rephrase this rule as follows: If the string within the template's or , is recognized as a valid JavaScript identifier, Vue.js compiler will directly map it to . v-on @event { onEvent: <Valid JS Identifier> } Or like this: Using only the name of a variable or a function will result in direct mapping. Our revised definition omits any reference to a "method" handler; it purely states that when a valid identifier is used, it is directly passed as is. This implies that you can even pass a numeric value like to an handler, provided you first create an identifier (in other words, a variable) that's bound to the value. 1337 onClick Passing a number as a handler clearly won't yield the desired results. However, as we recall, our aim is to pass multiple handlers as an array of refs. Given our newly established understanding, this is achievable. However, the prerequisite is to create a "valid JS Identifier (variable)" to store the reference to the array. Let's put this into action and see the results. . An interesting observation unfolds. Take a look here Firstly, using a "valid JS Identifier (variable)" named , we successfully pass an array to , and it gets mapped accordingly. multiple onClick However, TypeScript expresses its discontent. It raises an error stating: Type '(() => void)[]' is not assignable to type '(payload: MouseEvent) => void'. Type '(() => void)[]' provides no match for the signature '(payload: MouseEvent): void'.ts(2322) In essence, the types within Vue.js prevent us from passing an array of functions as a click listener. Let's set this aside for now, and simply click on the button to observe whether both listeners will be invoked. And yes, they are. We witness the counter value incrementing, followed by the appearance of the alert. But hold on, there's a puzzle to solve. Why is this functioning? What's going on behind the scenes? To comprehend this, we must delve deeper and grasp the mechanics underlying the translation of Vue's , created by the function, into a native DOM element. The key lies in exploring the source code of Vue.js itself! VNode _createElementVNode When we call the function in our main or , it triggers a chain of events that leads to the execution of the function (look for function ). createApp() app.js index.js createRenderer() createApp here This sequence results in the formation of an instance, complete with a method. This method establishes an association with the renderer (see ). This renderer's primary task is to convert our s into the native DOM elements we interact with. app mount() ensureRenderer() here VNode Here's an overview of the key steps: We compile our template, resulting in a series of calls. _createElementVNode() These calls build our Virtual DOM, generating s. VNode The renderer then traverses these nodes, converting them into native DOM elements. As the renderer transforms s into native DOM elements, it performs additional tasks using the object of a through the method. VNode props VNode patchProp Additionally, note that the function is invoked with , encompassing a "patched" . Let's delve into this for further understanding. createRenderer(rendererOptions) extended rendererOptions patchProp method export const patchProp: DOMRendererOptions['patchProp'] = (
  // Omitted params...
) => {
  if (key === 'class') {
    patchClass(el, nextValue, isSVG)
  } else if (key === 'style') {
    patchStyle(el, prevValue, nextValue)
    // Keep in mind that we provide an object containing on<EventName> keys.
    // `isOn(key)` will return true for these keys.
  } else if (isOn(key)) {
    if (!isModelListener(key)) {
      // If the listener isn't intended for `v-model`, we utilize the `patchEvent` mechanism.
      patchEvent(el, key, prevValue, nextValue, parentComponent)
    }
  } // ... We can interpret the code as follows: "If a prop is , apply special handling based on values. If a prop is , implement special handling based on values. And if a prop begins with , perform specific actions using ." class class style style on patchEvent Let's direct our attention to the . We've reached the bottom where Vue establishes native event bindings through the browser's method. However, prior to this step, there are additional operations in play. patchEvent method addEventListener() The high-level call chain is as follows: is invoked. patchEvent() It proceeds to call in order to generate an invoker function. createInvoker() Inside the , we invoke , passing a wrapped version (altered by ) of the value provided in the event handler. invoker callWithAsyncErrorHandling patchStopImmediatePropagation @click="..." Now, let's examine to uncover the answer to the question: "Why does passing multiple refs to a function work?" patchStopImmediatePropagation function patchStopImmediatePropagation(
  e: Event,
  value: EventValue
): EventValue {

  // If the value is an array, there's even more to explore! 
  // We can call $event.stopImmediatePropagation()
  // and other functions within the array won't be invoked.
  if (isArray(value)) {
    const originalStop = e.stopImmediatePropagation
    e.stopImmediatePropagation = () => {
      originalStop.call(e)
      ;(e as any)._stopped = true
    }

    // This is where the actual function calls occur.
    return value.map(fn => (e: Event) => !(e as any)._stopped && fn && fn(e))
  } else {
    return value
  }
} And here we are, fully informed. Even though the official documentation and TypeScript might not explicitly endorse it, we've found a code segment that effectively allows us to pass event listeners using an array of function references. There is the that introduced this functionality. It appears that at some point in the past, there might have been an intention to enable the capability of passing multiple listeners. However, as it stands now, this remains an undocumented feature. commit Lastly, let's address the question we initially posed: Does Vue support multiple listeners? The answer hinges on your interpretation of "supports". To summarize: We can invoke multiple functions using , and there's a for that. fn1(); fn2() test We can also invoke them using . fn1(), fn2() We can pass it through an array if stored in a variable. Alternatively, given the newfound knowledge, we can even call them like so: <template>
  <button @click="[fn1, fn2].forEach((fn) => fn($event))">
    Click!
  </button>
</template> Also published here

Unlocking the Puzzle: Investigating Multiple Event Listeners in Vue.js

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In an era where and computer graphics are becoming increasingly vital in industries ranging from gaming to architecture, finding ways to streamline the creation of complex 3D shapes can feel like a herculean task. Enter , an AI model that transforms input conditions into detailed 3D shapes, making the creative process both seamless and efficient. 3D modeling Shap-e In this guide, we'll take you on a tour of the Shap-e model, explaining its unique capabilities, how to use it, and even how to compare it with similar models on AIModels.fyi. Shap-e offers promising features for designers, artists, developers, and anyone looking to explore the possibilities of AI-assisted 3D modeling. So, let's embark on this fascinating journey. About Shap-e Created by cjwbw, Shap-e is a model that generates conditional 3D implicit functions. These functions form the basis for creating intricate 3D shapes based on input conditions. By employing neural networks, Shap-e understands the relationship between input conditions and the shape of the object. This makes it an invaluable tool for a broad range of applications including 3D modeling, computer graphics, virtual reality, and even scientific simulations. Whether it's generating realistic and customized 3D objects for video games, animations, and virtual reality environments, or creating complex architectural designs, Shap-e opens up a world of creative possibilities. It even allows for scientific simulations to generate accurate 3D representations of objects or environments. Understanding the Inputs and Outputs of Shap-e Before diving into how to use Shap-e, it's crucial to understand the inputs it takes and the outputs it produces. Knowing this can significantly enhance your user experience and help you get the most out of the model. Note: you may also want to check out our guide on , a text-to-3d model generator from the same creator. We also have Point-e a guide for AdaMPI - a model that turns images into 3D scenes. Inputs Shap-e receives several inputs that dictate the 3D shape that will be generated. These include: : This is a text prompt for generating the 3D model, but it's ignored if an image is provided. prompt : A synthetic view image used for generating the 3D model. For optimal results, it's recommended to remove the background from the input image. image : This sets the scale for guidance, with the default value set at 15. guidance_scale : Specifies the number of outputs. The default value is 1. batch_size : Allows you to choose a render mode, either 'nerf' or 'stf'. The default value is 'nerf'. render_mode : This sets the size of the renderer, and higher values take longer to render. The default value is 128. render_size : If set to true, this saves the latent as meshes. save_mesh Outputs In response to the inputs, Shap-e provides an array of URIs corresponding to the generated 3D models. The raw schema of the model's output structure is as follows: JSON {
  "type": "array",
  "items": {
    "type": "string",
    "format": "uri"
  },
  "title": "Output"
} Step-by-step Guide to Using Shap-e After understanding the inputs and outputs, it's time to explore how to use Shap-e. This section will provide a step-by-step guide to using this model for creating intricate 3D shapes. If coding isn't your cup of tea, Shap-e also comes with a demo (not available yet). It's a great way to experiment with the model's parameters and receive instant feedback. For those keen on coding, the steps below will guide you on how to interact with Shap-e's Replicate API. Step 1: Install the Client First, install the Node.js client with the following command: npm install replicate Step 2: Set API Token Next, authenticate by setting your API token as an environment variable: export REPLICATE_API_TOKEN=r8_************************************* This is your Default API token, which should be kept private. Step 3: Run the Model Then, run the model: import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

const output = await replicate.run(
  "cjwbw/shap-e:5957069d5c509126a73c7cb68abcddbb985aeefa4d318e7c63ec1352ce6da68c",
  {
    input: {
      prompt: "..."
    }
  }
); You can also set a webhook URL to be called when the prediction is complete. Check out the for details on setting that up. webhook docs Comparing Shap-e with Similar Models AIModels.fyi offers a platform where you can find a multitude of AI models for diverse applications. On the website, you can also compare models like Shap-e with other similar models. To do this: and navigate to the list of models. Visit AIModels.fyi Choose Shap-e and any other model(s) that you want to compare it with. Select the Models for Comparison: Click on the "Compare" button to view a side-by-side comparison of the models. This will provide insights into the capabilities, inputs and outputs, and performance of the models. Compare: This comparison feature can help you choose the most suitable model for your particular use case. Conclusion Shap-e offers a novel approach to generating detailed 3D shapes. Its use of AI not only streamlines the process of 3D modeling but also opens up the potential for creating complex and customizable shapes. Whether you're a developer working on a video game or an architect designing a new building, Shap-e provides a powerful tool that can enhance your creativity and productivity. It's certainly a model worth exploring and experimenting with on your AI journey. Also published here.

Exploring the Shap-e 3D Model Generator 

Recently, we took over a data integration project where the upstream data was delivered to Kafka. Initially, we chose the SpringBoot+Flink approach to process the incoming data (referred to as Solution 1 below). However, after facing efficiency issues in the production environment, we turned our attention to the open-source project SeaTunnel. We discovered that SeaTunnel supports Kafka as a source and started exploring and testing it. In our development environment, we experimented with over 5 million records and achieved an efficiency of around 10,000 records per second. Consequently, we abandoned Solution 1 and adopted SeaTunnel for integrated data processing (referred to as Solution 2 below). During our exploration of SeaTunnel, we identified two methods. Method 2, compared to Method 1, offers a more comprehensive solution that handles various scenarios without concerns about data misalignment caused by unexpected characters within field values. Version Information: SeaTunnel: apache-seatunnel-2.3.2-SNAPSHOT Engines: Flink 1.16.2 Zeta: Consistent with SeaTunnel Comparison Based on Solution 2, two derived methods were developed: After extensive exploration and validation in our production environment, we recommend using Method 2 of Solution 2. Now, let's dive into the main content and explore the two methods in Solution 2, allowing you to experience the magic of SeaTunnel. Solution 1: Kafka Complex JSON Parsing with Spring Boot + Flink There are many online examples available for this approach, so we won't go into much detail here. Solution 2: Kafka Complex JSON Parsing with SeaTunnel Before we proceed, let's take a look at a sample JSON data delivered to Kafka by the upstream party (part of the data has been redacted): "type": "D***",
"headers": null,
"messageSchemaId": null,
"messageSchema": null,
"message": {
    "data": {
        "LSH": "187eb13****l0214723",
        "NSRSBH": "9134****XERG56",
        "QMYC": "01*****135468",
        "QMZ": "1Zr*****UYGy%2F5bOWtrh",
        "QM_SJ": "2023-05-05 16:42:10.000000",
        "YX_BZ": "Y",
        "ZGHQ_BZ": "Y",
        "ZGHQ_SJ": "2023-06-26 16:57:17.000000",
        "SKSSQ": 202304,
        "SWJG_DM": "",
        "SWRY_DM": "00",
        "CZSJ": "2023-05-05 16:42:10.000000",
        "YNSRSBH": "9134****XERG56",
        "SJTBSJ": "2023-06-26 19:29:59.0,00",
        "SJCZBS": "I"
    },
    "beforeData": null,
    "headers": {
        "operation": "INSERT",
        "changeSequence": "12440977",
        "timestamp": "2023-06-26T19:29:59.673000",
        "streamPosition": "00****3.16",
        "transactionId": "000***0006B0002",
        "changeMask": "0FFF***FF",
        "columnMask": "0F***FFFF",
        "transactionEventCounter": 1,
        "transactionLastEvent": false
    }
} Method 1: Parsing without UDF Functions Issue: Field values may contain delimiters, such as ','. This can lead to data misalignment during storage. This method primarily utilizes various transformation plugins available in the official transform-v2. It includes the , , and implementations. Replace Split SQL SeaTunnel Script: (ybjc_qrqm.conf) job.mode = "STREAMING"
job.name = "kafka2mysql_ybjc"
execution.checkpoint.interval = 60000

source {
  Kafka {
    result_table_name = "DZFP_***_QRQM1"
    topic = "DZFP_***_QRQM"
    bootstrap.servers = "centos1:19092,centos2:19092,centos3:19092"
    schema = {
      fields {
        message =  {
            data = {
                LSH = "string",
                NSRSBH =  "string",
                QMYC =  "string",
                QMZ =  "string",
                QM_SJ =  "string",
                YX_BZ =  "string",
                ZGHQ_BZ =  "string",
                ZGHQ_SJ =  "string",
                SKSSQ =  "string",
                SWJG_DM = "string",
                SWRY_DM = "string",
                CZSJ = "string",
                YNSRSBH = "string",
                SJTBSJ = "string",
                SJCZBS = "string"
            }
        }
      }  
    }
	  start_mode = "earliest"
    kafka.config = {
      auto.offset.reset = "earliest"
      enable.auto.commit = "true"
      max.partition.fetch.bytes = "5242880"
      session.timeout.ms = "30000"
      max.poll.records = "100000"
    }
  }
}

transform {
 Replace {
    source_table_name = "DZFP_***_QRQM1"
    result_table_name = "DZFP_***_QRQM2"
    replace_field = "message"
    pattern = "[["
    replacement = ""
  }
  Replace {
    source_table_name = "DZFP_***_QRQM2"
    result_table_name = "DZFP_***_QRQM3"
    replace_field = "message"
    pattern = "]]"
    replacement = ""
  }
  
  Split {
    source_table_name = "DZFP_***_QRQM3"
    result_table_name = "DZFP_***_QRQM4"
    separator = ","
    split_field = "message"
    output_fields = [zwf1,zwf2,zwf3,zwf4,zwf5,nsrsbh,qmyc,qmz,qm_sj,yx_bz,zghq_bz,zghq_sj,skssq,swjg_dm ,swry_dm ,czsj ,ynsrsbh ,sjtbsj ,sjczbs]
  }
  
  sql{
   source_table_name = "DZFP_***_QRQM4"
   query = "select replace(zwf5 ,'fields=[','') as lsh,nsrsbh,trim(qmyc) as qmyc,qmz,qm_sj,yx_bz, zghq_bz,zghq_sj,skssq,swjg_dm ,swry_dm ,czsj ,ynsrsbh ,sjtbsj ,replace(sjczbs,']}]}','') as sjczbs  from DZFP_DZDZ_QRPT_YWRZ_QRQM4 where skssq <> ' null'"
   result_table_name = "DZFP_***_QRQM5"
  }
  
}

sink {
	Console {
		source_table_name = "DZFP_***_QRQM5"
	}
	
    jdbc {
	    source_table_name = "DZFP_***_QRQM5"
        url = "jdbc:mysql://localhost:3306/dbname?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        user = "user"
        password = "pwd"
        batch_size = 200000
        database = "dbname"
        table = "tablename"
        generate_sink_sql = true
        primary_keys = ["nsrsbh","skssq"]
    } 
} Data can be successfully written: Kafka Source Data: Target Data in TiDB: However, when we simulate sending data to Kafka, specifically setting a ',' as part of the 'SJTBSJ' field: Original value: Modified value: 2023-06-26 19:29:59.000 2023-06-26 19:29:59.0,00 Command to produce data to the topic: kafka-console-producer.sh --topic DZFP_***_QRQM --broker-list centos1:19092,centos2:19092,centos3:19092 Data sent: "type": "D***",
"headers": null,
"messageSchemaId": null,
"messageSchema": null,
"message": {
    "data": {
        "LSH": "187eb13****l0214723",
        "NSRSBH": "9134****XERG56",
        "QMYC": "01*****135468",
        "QMZ": "1Zr*****UYGy%2F5bOWtrh",
        "QM_SJ": "2023-05-05 16:42:10.000000",
        "YX_BZ": "Y",
        "ZGHQ_BZ": "Y",
        "ZGHQ_SJ": "2023-06-26 16:57:17.000000",
        "SKSSQ": 202304,
        "SWJG_DM": "",
        "SWRY_DM": "00",
        "CZSJ": "2023-05-05 16:42:10.000000",
        "YNSRSBH": "9134****XERG56",
        "SJTBSJ": "2023-06-26 19:29:59.0,00",
        "SJCZBS": "I"
    },
    "beforeData": null,
    "headers": {
        "operation": "INSERT",
        "changeSequence": "12440977",
        "timestamp": "2023-06-26T19:29:59.673000",
        "streamPosition": "00****3.16",
        "transactionId": "000***0006B0002",
        "changeMask": "0FFF***FF",
        "columnMask": "0F***FFFF",
        "transactionEventCounter": 1,
        "transactionLastEvent": false
    }
} After writing the data, we notice that the data is misaligned: Conclusion: This issue can still occur in production environments when fields contain delimiters like commas in addresses or remarks. Therefore, this approach is not recommended as it poses significant risks to the data. It can be used for handling simple data as an alternative approach. Method 2: Parsing with UDF Functions This method extends SeaTunnel's capabilities using UDF functions ( ), enabling nested JSON source data parsing from Kafka. It simplifies the ST script configuration significantly. https://seatunnel.apache.org/docs/2.3.2/transform-v2/sql-udf ST Script: (ybjc_qrqm_yh.conf) execution.parallelism = 5
job.mode = "STREAMING"
job.name = "kafka2mysql_ybjc_yh"
execution.checkpoint.interval = 60000
}

source {
    Kafka {
        result_table_name = "DZFP_***_QRQM1"
        topic = "DZFP_***_QRQM"
        bootstrap.servers = "centos1:19092,centos2:19092,centos3:19092"
        schema = {
        fields {
             message =  {
                data = "map<string,string>"
             }
        }  
        }
        start_mode = "earliest"
        kafka.config = {
        auto.offset.reset = "earliest"
        enable.auto.commit = "true"
        max.partition.fetch.bytes = "5242880"
        session.timeout.ms = "30000"
        max.poll.records = "100000"
        }
    }
}
transform {
    sql{
        source_table_name = "DZFP_***_QRQM1"
        result_table_name = "DZFP_***_QRQM2"
        query = "select qdmx(message,'lsh') as lsh,qdmx(message,'nsrsbh') as nsrsbh,qdmx(message,'qmyc') as qmyc,qdmx(message,'qmz') as qmz,qdmx(message,'qm_sj') as qm_sj,qdmx(message,'yx_bz') as yx_bz,qdmx(message,'zghq_bz') as zghq_bz,qdmx(message,'zghq_sj') as zghq_sj,qdmx(message,'skssq') as skssq,qdmx(message,'swjg_dm') as swjg_dm,qdmx(message,'swry_dm') as swry_dm,qdmx(message,'czsj') as czsj,qdmx(message,'ynsrsbh') as ynsrsbh, qdmx(message,'sjtbsj') as sjtbsj,qdmx(message,'sjczbs') as sjczbs  from  DZFP_DZDZ_QRPT_YWRZ_QRQM1"
    }
}

    
sink {
    Console {
        source_table_name = "DZFP_***_QRQM2"
    }
    jdbc {
        source_table_name = "DZFP_***_QRQM2"
        url = "jdbc:mysql://localhost:3306/dbname?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        user = "user"
        password = "pwd"
        batch_size = 200000
        database = "dbname"
        table = "tablename"
        generate_sink_sql = true
        primary_keys = ["nsrsbh","skssq"]
    } 
} Executing the script and checking the results, we can see that the data is not misaligned and remains in the original field ('sjtbsj'): The specific UDF function is implemented as follows. Maven dependencies are as follows: <dependency>
    <groupId>org.apache.seatunnel</groupId>
    <artifactId>seatunnel-transforms-v2</artifactId>
    <version>2.3.2</version>
    <scope>provided</scope>
  </dependency>

  <dependency>
    <groupId>org.apache.seatunnel</groupId>
    <artifactId>seatunnel-api</artifactId>
    <version>2.3.2</version>
  </dependency>

  <dependency>
    <groupId>cn.hutool</groupId>
    <artifactId>hutool-all</artifactId>
    <version>5.8.20</version>
  </dependency>

  <dependency>
    <groupId>com.google.auto.service</groupId>
    <artifactId>auto-service-annotations</artifactId>
    <version>1.1.1</version>
    <optional>true</optional>
    <scope>compile</scope>
  </dependency>

  <dependency>
    <groupId>com.google.auto.service</groupId>
    <artifactId>auto-service</artifactId>
    <version>1.1.1</version>
    <optional>true</optional>
    <scope>compile</scope>
  </dependency>
</dependencies> UDF-specific implementation in Java: import cn.hutool.core.util.StrUtil;
import cn.hutool.json.JSONObject;
import cn.hutool.json.JSONUtil;
import com.google.auto.service.AutoService;
import org.apache.seatunnel.api.table.type.BasicType;
import org.apache.seatunnel.api.table.type.SeaTunnelDataType;
import org.apache.seatunnel.transform.sql.zeta.ZetaUDF;

import java.util.HashMap;
import java.util.List;
import java.util.Map;

@AutoService(ZetaUDF.class)
public class QdmxUDF implements ZetaUDF {

    @Override
    public String functionName() {
        return "QDMX";
    }

    @Override
    public SeaTunnelDataType<?> resultType(List<SeaTunnelDataType<?>> list) {
        return BasicType.STRING_TYPE;
    }

    // list Example of parameters: (which is the data parsed by Kafka)
    //SeaTunnelRow{tableId=, kind=+I, fields=[{key1=value1,key2=value2,.....}]}
    @Override
    public Object evaluate(List<Object> list) {
        String str = list.get(0).toString();
        //1 Remove the prefix
        str = StrUtil.replace(str, "SeaTunnelRow{tableId=, kind=+I, fields=[{", "");
        //2 Remove the suffix
        str = StrUtil.sub(str, -3, 0);
        // 3 build Map key value
        Map<String, String> map = parseToMap(str);
        if ("null".equals(map.get(list.get(1).toString())))
            return "";
        // 4 return the value of the key
        return map.get(list.get(1).toString());
    }

    public static Map<String, String> parseToMap(String input) {
        Map<String, String> map = new HashMap<>();
        // Remove the curly brackets and remove them during the string stage.
        // input = input.replaceAll("[{}]", "");
        // Split key-value pairs
        String[] pairs = input.split(", ");

        for (String pair : pairs) {
            String[] keyValue = pair.split("=");
            if (keyValue.length == 2) {
                String key = keyValue[0].trim().toLowerCase();
                String value = keyValue[1].trim();
                map.put(key, value);
            }
        }
        return map;
    }
} Then, for packaging, use the following command: mvn -T 8 clean install -DskipTests -Dcheckstyle.skip -Dmaven.javadoc.skip=true Check the directory to see if the @AutoService annotation generates the corresponding SPI interface. META-INF/services If it is as shown below, then the packaging is successful! If not, the packaging fails, and the UDF function cannot be used. You can refer to my packaging plugin: <plugins>
    <plugin>
      <groupId>org.apache.maven.plugins</groupId>
      <artifactId>maven-site-plugin</artifactId>
      <version>3.7</version>
      <dependencies>
        <dependency>
          <groupId>org.apache.maven.doxia</groupId>
          <artifactId>doxia-site-renderer</artifactId>
          <version>1.8</version>
        </dependency>
      </dependencies>
    </plugin>
    <plugin>
      <groupId>org.apache.maven.plugins</groupId>
      <artifactId>maven-compiler-plugin</artifactId>
      <version>3.8.1</version>
      <configuration>
        <annotationProcessorPaths>
          <path>
            <groupId>com.google.auto.service</groupId>
            <artifactId>auto-service</artifactId>
            <version>1.1.1</version>
          </path>
        </annotationProcessorPaths>
      </configuration>
    </plugin>
  </plugins>
</build> Finally, place the packaged JAR file in the $ directory. Since my UDF function depends on a third-party JAR, it should be uploaded together. If using the Zeta cluster, you need to restart the Zeta cluster for it to take effect. For other engines, it will take effect in real time. {SEATUNNEL_HOME}/lib The successful upload will look like this: Note: The "hutool-all" JAR file can be included in the project. I uploaded two for convenience. java_study In conclusion, it is recommended to to parse the nested JSON data from Kafka sources. use the extension of UDF functions Also published . here

Resolving Complex JSON in Kafka Source Using Apache SeaTunnel

249 Stories To Learn About Web Development

The most used chart library for writing chart-related requirements is . echarts The performance of echarts on the website is quite mature, and the official solution is provided for the applet side, but there is no corresponding support in RN. On the market, most of the search is still based on the essence of the webview implementation, and I prefer the RN-based program. After all, the native experience will be better than the Web. Later, I found to meet me needs, so try it out; the results are not bad. For those interested in the principle of implementation, click @wuba/react-native-echarts here Tips If you already have an APP package, you can ignore the previous packaging process and start directly from step 4. The full code for the trial is on GitHub at: https://github.com/iambool/TestApp The steps to use it are as follows Step 1. Development environment setup This is the process of building a local RN development environment which is already available on the Internet, so I won't go over it again. You can search for it on Google :) Step 2. Creating an RN project As it was a trial, I used the expo to newly initialize an rn project called TestApp. npx create-expo-app TestApp Step 3. Building an app on mobile devices Generate ios and android app packages with a command line. iOS is recommended to use the emulator (no need to match the certificate). While on Android, I was connected to the real machine. yarn android
yarn ios After generating the package, the app like the one below is already installed on the phone, which means it is successful. Step 4. Install related dependencies yarn add @wuba/react-native-echarts echarts
yarn add @shopify/react-native-skia
yarn add react-native-svg Note: if you are installing in an existing project, you have to build a new package after the installation is complete, otherwise the lack of native dependencies will report an error. Step 5. Try the Skia model @wuba/react-native-echarts supports two , try a simple chart with Skia first. rendering modes (Skia and Svg) It's divided into these small steps： Introduce echarts, chart components and other dependencies. Registering chart components. Create a chart instance and set an option. Synchronized destruction of chart instances when the page is destroyed. The specific code is as follows: import { useRef, useEffect } from 'react';
import { View } from 'react-native';
/**
 * 1. Import the echarts dependency, this example first tries the line chart
 */
import * as echarts from 'echarts/core';
import { LineChart } from 'echarts/charts';
import { GridComponent } from 'echarts/components';
import { SVGRenderer, SkiaChart } from '@wuba/react-native-echarts';

/**
 * 2. Register the required components
 * SVGRenderer: it is required to register
 * LineChart: because we want to show the line chart, we have to import LineChart
 *      - If you don't know which components to import, just look at the error report and add whatever the error says is missing
 * GridComponent: This is the prompt when the error is reported, and then I added the, ha ha
 */
echarts.use([SVGRenderer, LineChart, GridComponent]);

export default () => {
  const skiaRef = useRef(null); // Ref for saving chart instances
  useEffect(() => {
    /**
     * 3. chart option
     */
    const option = {
      xAxis: {
        type: 'category',
        data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'],
      },
      yAxis: {
        type: 'value',
      },
      series: [
        {
          data: [150, 230, 224, 218, 135, 147, 260],
          type: 'line',
        },
      ],
    };
    let chart;
    if (skiaRef.current) {
      /**
       * 4. Initialize the chart, specifying the lower width and height
       */
      chart = echarts.init(skiaRef.current, 'light', {
        renderer: 'svg',
        width: 400,
        height: 400,
      });
      chart.setOption(option);
    }
    /**
     * 5. To destroy the chart instance after the page is closed
     */
    return () => chart?.dispose();
  }, []);
  return (
    <View className='index'>
      <SkiaChart ref={skiaRef} />
    </View>
  );
}; After writing the code, shaking the phone and reloading the bundle package, an error was reported: ERROR Invariant Violation: requireNativeComponent: "SkiaDomView" was not found in the UIManager. I googled it and it says it requires a . It should correspond to the expo version, there will be a similar prompt when installing the dependency, install the prompted version and it will be fine. version downgrade So I followed the instructions and did a version downgrade: @shopify/react-native-skia@0.1.157
react-native-svg@13.4.0 It loaded up after rebuilding the app, which was nice. (but Android covers up the point, it seems that the screen width should be adaptive.) iOS Android Step 6. Try the Svg model Write a more complex dynamic sorting bar chart with Svg mode, and compare Svg and Skia. The full code is 。 here // ...Some unimportant code is omitted here

// Register the required components, such as BarChart and LegendComponent
echarts.use([SVGRenderer, BarChart, LegendComponent, GridComponent]);

export default () => {
  const skiaRef = useRef(null);
  const svgRef = useRef(null);

  useEffect(() => {
    // Skia mode
    const skiaChartData = getData(); // Generate chart bar data
    let skiaChart;
    let skiaInter;
    if (skiaRef.current) {
      skiaChart = echarts.init(skiaRef.current, 'light', {
        renderer: 'svg',
        width: 300,
        height: 300,
      });
      skiaChart.setOption(getDefaultOption(skiaChartData));
      setTimeout(function () {
        run(skiaChart, skiaChartData);
      }, 0);
      skiaInter = setInterval(function () {
        run(skiaChart, skiaChartData);
      }, 3000);
    }

    // Svg mode
    const svgChartData = getData();
    let svgChart;
    let svgInter;
    if (svgRef.current) {
      svgChart = echarts.init(svgRef.current, 'light', {
        renderer: 'svg',
        width: 300,
        height: 300,
      });
      svgChart.setOption(getDefaultOption(svgChartData));
      setTimeout(function () {
        run(svgChart, svgChartData);
      }, 0);
      svgInter = setInterval(function () {
        run(svgChart, svgChartData);
      }, 3000);
    }

    return () => {
      skiaChart?.dispose();
      svgChart?.dispose();
      // The timer has to be cleaned up, otherwise it will still run after exiting the page
      clearInterval(skiaInter);
      clearInterval(svgInter);
    };
  }, []);
  return (
    <View>
      <Text>skia</Text>
      <SkiaChart ref={skiaRef} />
      <Text>svg</Text>
      <SvgChart ref={svgRef} />
    </View>
  );
}; I can't see the difference between these two modes with my own eyes. iOS Android Step 7. Wrapping Chart Components So far the effect was quite good, but every time I used a bunch of things to import, It bothered me. Let's wrap it up simply: import { useRef, useEffect } from 'react';
import * as echarts from 'echarts/core';
import { BarChart, LineChart, PieChart } from 'echarts/charts';
import {
  DataZoomComponent,
  GridComponent,
  LegendComponent,
  TitleComponent,
  ToolboxComponent,
  TooltipComponent,
} from 'echarts/components';
import {
  SVGRenderer,
  SvgChart as _SvgChart,
  SkiaChart as _SkiaChart,
} from '@wuba/react-native-echarts';
import { Dimensions } from 'react-native';

// Register the required components
echarts.use([
  DataZoomComponent,
  SVGRenderer,
  BarChart,
  GridComponent,
  LegendComponent,
  ToolboxComponent,
  TooltipComponent,
  TitleComponent,
  PieChart,
  LineChart,
]);

// Default width and height of the chart
const CHART_WIDTH = Dimensions.get('screen').width; // Default with the phone screen width
const CHART_HEIGHT = 300;

const Chart = ({
  option,
  onInit,
  width = CHART_WIDTH,
  height = CHART_HEIGHT,
  ChartComponent,
}) => {
  const chartRef = useRef(null);

  useEffect(() => {
    let chart;
    if (chartRef.current) {
      chart = echarts.init(chartRef.current, 'light', {
        renderer: 'svg',
        width,
        height,
      });
      option && chart.setOption(option);
      onInit?.(chart);
    }
    return () => chart?.dispose();
  }, [option]);
  return <ChartComponent ref={chartRef} />;
};

const SkiaChart = (props) => <Chart {...props} ChartComponent={_SkiaChart} />;
const SvgChart = (props) => <Chart {...props} ChartComponent={_SvgChart} />;
// Just export these two guys
export { SkiaChart, SvgChart }; Step 8. Using multiple charts Once it’s wrapped, let’s write a page with multiple charts and see how it works. Here is a page for “e-commerce data analysis”, including a line chart, bar chart and pie chart. Below is the main code written with svg mode, click for detailed code. here import { SkiaChart } from '../../components/Chart';
import { ScrollView, Text, View } from 'react-native';
import { StatusBar } from 'expo-status-bar';
import { useCallback, useEffect, useState } from 'react';
import {
  defaultActual,
  lineOption,
  salesStatus,
  salesVolume,
  userAnaly,
  getLineData,
} from './contants';
import styles from './styles';
// Turn on chart loading
const showChartLoading = (chart) =>
  chart.showLoading('default', {
    maskColor: '#305d9e',
  });
// Close chart loading
const hideChartLoading = (chart) => chart.hideLoading();

export default () => {
  const [actual, setActual] = useState(defaultActual); // Recording real-time data

  useEffect(() => {
    // Assuming a recurring request for data
    const interv = setInterval(() => {
      const newActual = [];
      for (let it of actual) {
        newActual.push({
          ...it,
          num: it.num + Math.floor((Math.random() * it.num) / 100),
        });
      }
      setActual(newActual);
    }, 200);
    return () => clearInterval(interv);
  }, [actual]);

  const onInitLineChart = useCallback((myChart) => {
    showChartLoading(myChart);
    // Simulation of data requests
    setTimeout(() => {
      myChart.setOption({
        series: getLineData,
      });
      hideChartLoading(myChart);
    }, 1000);
  }, []);

  const onInitUserChart = useCallback((myChart) => {
    // Simulate data request, similar to onInitLineChart
  }, []);
  const onInitSaleChart = useCallback((myChart) => {
    // Simulate data request, similar to onInitLineChart
  }, []);
  const onInitStatusChart = useCallback((myChart) => {
    // Simulate data request, similar to onInitLineChart
  }, []);

  const chartList = [
    ['订单走势', lineOption, onInitLineChart],
    ['用户统计', userAnaly, onInitUserChart],
    ['各品类销售统计', salesVolume, onInitSaleChart],
    ['订单状态统计', salesStatus, onInitStatusChart],
  ];

  return (
    <ScrollView style={styles.index}>
      <StatusBar style='light' />
      <View>
        <View style={styles.index_panel_header}>
          <Text style={styles.index_panel_title}>实时数据</Text>
        </View>
        <View style={styles.index_panel_content}>
          {actual.map(({ title, num, unit }) => (
            <View key={title} style={styles.sale_item}>
              <View style={styles.sale_item_cell}>
                <Text style={styles.sale_item_text}>{title}</Text>
              </View>
              <View style={[styles.sale_item_cell, styles.num]}>
                <Text style={styles.sale_item_num}>{num}</Text>
              </View>
              <View style={[styles.sale_item_cell, styles.unit]}>
                <Text style={styles.sale_item_text}>{unit}</Text>
              </View>
            </View>
          ))}
        </View>
      </View>
      {chartList.map(([title, data, callback]) => (
        <View key={title}>
          <View style={styles.index_panel_header}>
            <Text style={styles.index_panel_title}>{title}</Text>
          </View>
          <View style={styles.index_panel_content}>
            <SkiaChart option={data} onInit={callback} />
          </View>
        </View>
      ))}
    </ScrollView>
  );
}; Reload the bundle and see the result iOS Android After rendering, the interaction on iOS is very smooth, while the interaction on Android feels occasionally laggy (not because my phone is too bad, right?...) Trying the Skia mode again… Well, although it can, it seems that Chinese can not be displayed properly, Android Chinese is not displayed, and iOS is a mess of code. After reading the documentation, skia currently does not support Chinese on the Android side, We can display Chinese on iOS by setting the font to ‘PingFang SC’, for example: const option = {
  title: {
    text: '我是中文',
    textStyle: {
      fontFamily: 'PingFang SC', // setting the font type
    },
  },
}; But every place that displays Chinese has to set the font... that or use Svg first, I'm lazy. Summary After using it for a while, I summarized the following: In terms of support, @wuba/react-native-echarts supports all types of charts except GL series and map charts which are not yet supported, which is very enough for daily business. The code to implement the various charts in echarts can be found in . taro-playground Interaction, iOS is very silky smooth, Android sometimes there are cases of frame drops. Performance: Performance is officially reported as better than other solutions. I tried it, not a very large amount of data will not have any problems, but when the amount of data is too large (such as drawing a large amount of data heat map), the rendering speed significantly decreased a lot, which is a point waiting for the official to optimize. In addition, if there are many charts on the page, the loading speed will be slow when debugging on the real machine, so it is recommended to use the simulator first. Chinese support, Svg mode supports Chinese, but Skia mode is not available yet. The above is only a personal view, any questions and comments are welcome below!

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