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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

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