🃏 Becoming a Top 5% Winner in Today’s Las Vegas Poker Rooms

You have 20 years of experience. That’s a massive advantage.

But becoming a top 5% winner in modern Vegas poker rooms isn’t about more hours played — it’s about modernization, precision, discipline, and structured exploitation.

Below are the 6 pillars that separate strong regulars from elite crushers in 2026 Las Vegas cash games (1/3–5/10).

1️⃣ Preflop Is Now a Science, Not an Art

Top 5% players:

Use structured ranges (not vibes)
Adjust for rake structure
3-bet aggressively in position
Defend blinds correctly

What Changed in Vegas Rooms

More solver-studied younger players
Limp-heavy tables still exist — but regs isolate correctly
Rake at 1/3 & 2/5 punishes loose-passive play

You Must:

Eliminate open limping (unless exploit-based)
Increase 3-bet frequency in position
Attack capped ranges
Stop flatting hands that should 3-bet or fold

Edge comes from clean preflop construction.

If your preflop game hasn’t been solver-reviewed, there’s likely leakage.

2️⃣ Be Comfortable With GTO — Even If You Exploit

You don’t need to play robotic GTO.

But you must understand:

Minimum Defense Frequency (MDF)
Board texture advantages
Range vs range interaction
Polar vs merged strategies
Proper bet sizing distribution

Modern elite players study using:

PioSolver
GTO Wizard
Simple Postflop

You don’t need to memorize charts.

You need to understand why solver prefers certain lines — then exploit population deviations.

3️⃣ Vegas = Population Exploitation Gold Mine

Vegas 1/3–5/10 player pools tend to:

Under-bluff rivers
Over-call flop, over-fold turn
3-bet too tight
Play face-up postflop
Misunderstand polarized betting

Top 5% Winners:

Over-fold to river aggression
Double barrel scare cards relentlessly
Value bet thinner than average reg
Over-bluff tight old regs
Under-bluff sticky tourists

This isn’t pure GTO.

This is precision exploitation.

4️⃣ Elite Mental Game

After 20 years, subtle tilt patterns almost always exist.

Top 5% players:

Never revenge 3-bet
Never chase image
Quit when game quality drops
Track EV, not short-term results

5️⃣ Game Selection = 30–40% of Winrate

Vegas is unique because:

Table quality varies massively by casino
Time of day matters
Convention season matters
Weekends vs weekdays change player pool drastically

Elite players:

Table change aggressively
Leave reg-heavy tables
Track room tendencies (Aria vs Bellagio vs Wynn vs Venetian, etc.)
Know when tourists flood the room

If you’re not actively table-selecting, you’re sacrificing 2–5 BB/hr.

6️⃣ Bankroll & Stakes Strategy

To be top 5%, you must:

Avoid playing scared
Avoid over-shot-taking
Avoid ego-stakes

Ideal Structure:

40–60 buy-ins for live cash
Move up only when crushing over meaningful sample
Drop down quickly if confidence drops

What Separates Solid Regs from Top 5% Crushers

Solid Winning Reg	Top 5% Crusher
Good intuition	Structured ranges
Bets strong hands	Understands range advantage
Adjusts sometimes	Adjusts constantly
Reads players	Reads population
Studies occasionally	Studies weekly

Common Leaks for Experienced Players

Calling too wide in blinds
Not 3-betting enough
Under-bluffing rivers
Not value betting thin enough
Playing too many marginal hands multiway
Staying in mediocre games too long

If I Were Coaching You

Month 1

Rebuild preflop ranges (RFI, 3-bet, BB defense)
Study 3 board textures deeply

Month 2

Focus on turn barreling strategy
River decision study (over-bluff vs over-fold)

Month 3

Advanced exploit adjustments
Table selection mastery
Mental game leak audit

The Real Question

What stakes are you currently playing in Vegas?

1/3?
2/5?
5/10?

The path to top 5% differs significantly by stake.

Let’s get specific.

Melodic Techno & Deep Melodic House – Long Sets (20+ Minutes)

A curated list of longer sets with a similar dark, emotional, melodic techno style.

Anyma – Afterlife Barcelona 2023

Length: ~1 hour
Style: Emotional, cinematic melodic techno
Why listen: Signature Afterlife sound with hypnotic builds and powerful drops
Link: https://www.youtube.com/watch?v=turn0video1

Tale Of Us – Cercle, Paris

Length: ~1 hour
Style: Cinematic melodic techno
Why listen: One of the defining duos of the genre with a smooth, emotional progression
Link: https://www.youtube.com/watch?v=turn0video2

ARTBAT – Upperground Poland

Length: ~1 hour
Style: Energetic melodic techno
Why listen: Same melodic structure but more driving and club-oriented
Link: https://www.youtube.com/watch?v=turn0video3

Adriatique – Cercle at Hatshepsut Temple, Egypt

Length: ~1 hour
Style: Deep, hypnotic melodic techno
Why listen: Immersive sound with emotional progression and a stunning setting
Link: https://www.youtube.com/watch?v=turn0video4

Massano – Printworks London

Length: ~1 hour
Style: Modern melodic techno
Why listen: Big synth leads, strong drops, and a modern Afterlife-style sound
Link: https://www.youtube.com/watch?v=turn0video5
EZ’s Review: - I like it ! This set below is tight !
Listened to: - https://youtu.be/XQsj0_r9cxY?si=-1iHJC9ZdNunQRUg

Extra Long Mix Sources (1–2 hours)

Search these for longer continuous journeys:

Afterlife Radio episodes
Anyma live sets
Tale of Us Tomorrowland sets
Adriatique live mixes
Cercle melodic techno sets

20260212-PowerBI-notes

Posted on 2026-02-13 Edited on 2026-02-14 In Cheatsheets , Notes , Code

How it works

MAX(Events[Date]) = current date on the chart.

ALLSELECTED keeps slicer filters but ignores the current row context.

The filter counts all rows up to that date.

Use this measure on a line chart with:

Axis: Events[Date]

Values: Running Count

Running Count :=
CALCULATE(
    COUNTROWS(Events),
    FILTER(
        ALLSELECTED(Events[Date]),
        Events[Date] <= MAX(Events[Date])
    )
)

AI-personal-assistant

Posted on 2026-02-08 Edited on 2026-02-13 In Cheatsheets , Notes

Research

Mega prompt:

You are an expert research analyst. I need comprehensive research on [TOPIC].

Please provide:

Key findings from the last 12 months
Data and statistics with sources
Expert opinions and quotes
Emerging trends and predictions
Controversial viewpoints or debates
Practical implications for [INDUSTRY/AUDIENCE]

Format as an executive brief with clear sections. Include source links for all claims.

Writing white papers

Mega prompt:

You are a technical writer specializing in authoritative white papers.

Write a white paper on [TOPIC] for [TARGET AUDIENCE].

Structure:

Executive Summary (150 words)
Problem Statement with market data
Current Solutions and their limitations
Our Approach/Solution with technical details
Case Studies or proof points
Implementation framework
ROI Analysis
Conclusion and Call to Action

Tone: [Authoritative/Conversational/Technical]
Length: [2000-5000 words]

Include:

Relevant statistics and citations
Visual placeholders for charts/diagrams
Quotes from industry experts (mark as [NEEDS VERIFICATION])

Background context: [YOUR COMPANY/PRODUCT INFO]

Designing beautiful UIs

Mega prompt:

You are a senior product designer with expertise in [WEB/MOBILE] interfaces.

Design a [COMPONENT/PAGE] for [PRODUCT TYPE].

Requirements:

User goal: [WHAT USER WANTS TO ACCOMPLISH]
Design system: [MODERN/MINIMAL/BOLD/etc]
Color preferences: [COLORS OR “SURPRISE ME”]
Key elements: [LIST MUST-HAVE FEATURES]

Provide:

Detailed layout description with measurements
Component hierarchy and spacing
Interaction states (hover, active, disabled)
Responsive behavior for mobile
Accessibility considerations
React/Tailwind component code

Style: Clean, modern, follows [DESIGN TREND]
Inspiration: [REFERENCE SITES IF ANY]

Making social media content

Mega prompt:

You are a viral social media strategist specializing in [PLATFORM].

Create [NUMBER] posts about [TOPIC] for [TARGET AUDIENCE].

Post requirements:

Hook: Strong pattern interrupt in first line
Format: [THREAD/SINGLE POST/CAROUSEL]
Tone: [EDUCATIONAL/ENTERTAINING/CONTROVERSIAL]
Goal: [ENGAGEMENT/TRAFFIC/BRAND AWARENESS]

For each post provide:

Main post copy
3 alternative hooks to A/B test
Visual recommendations (screenshots, charts, memes)
Optimal posting time and hashtags
Engagement bait (question or CTA)

Context about my brand: [YOUR POSITIONING]
Recent viral posts in my niche: [EXAMPLES IF ANY]

Making presentations

Mega prompt:

You are a presentation designer who creates slides for [CONTEXT: PITCH DECKS/KEYNOTES/SALES].

Create a presentation on [TOPIC] for [AUDIENCE].

Presentation specs:

Length: [NUMBER] slides
Goal: [INFORM/PERSUADE/SELL]
Delivery method: [IN-PERSON/ZOOM/RECORDED]

For each slide provide:

Slide title
Key visual concept (chart type, image style, diagram)
Talking points (what to say)
Text on slide (minimal, headlines only)
Data/stats to include

Overall narrative arc: [PROBLEM-SOLUTION/STORY-BASED/DATA-DRIVEN]

Context: [COMPANY INFO, PRODUCT DETAILS]
Slides must build to: [FINAL CTA OR CONCLUSION]

Long-form writing (blogs, newsletters, and YouTube scripts)

Mega prompt:

You are an expert long-form writer specializing in [BLOGS/NEWSLETTERS/SCRIPTS].

Write a [FORMAT] on [TOPIC] for [AUDIENCE].

Specs:

Length: [WORD COUNT]
Structure: [LISTICLE/NARRATIVE/HOW-TO/ANALYSIS]
SEO keywords: [IF APPLICABLE]
Voice: [CONVERSATIONAL/AUTHORITATIVE/STORYTELLING]

Requirements:

Compelling hook that makes the problem visceral
Original insights, not generic advice
Specific examples and case studies
Actionable takeaways
Strong conclusion with clear next step

Include:

Subheadings every 300 words
Pull quotes or standout stats
Internal link opportunities [MARK AS PLACEHOLDER]
Meta description (155 characters)

Research I’ve done: [YOUR NOTES/DATA]
Unique angle: [YOUR CONTRARIAN TAKE]

Learning new skills or mastering a new subject

Mega prompt:

You are an expert educator specializing in [SUBJECT AREA].

Create a personalized learning plan for mastering [SKILL] in [TIMEFRAME].

My current level: [BEGINNER/INTERMEDIATE/ADVANCED]
My goal: [WHAT I WANT TO ACHIEVE]
Time available: [HOURS PER WEEK]
Learning style: [HANDS-ON/READING/VIDEO/MIXED]

Provide:

Learning roadmap with clear milestones
Week-by-week curriculum
Resources (free and paid) with links
Practice projects that build real skills
Common pitfalls and how to avoid them
Ways to validate learning (tests, projects, certifications)
5 specific exercises I can do today

Make it practical. I want to DO things, not just consume content.

Context: [WHY YOU’RE LEARNING THIS, YOUR BACKGROUND]

Competitor analysis

Mega prompt:

You are a competitive intelligence analyst.

Analyze [COMPETITOR] vs our product [YOUR PRODUCT] in [MARKET].

Research areas:

Product features and positioning
Pricing strategy and monetization
Target customers and use cases
Marketing channels and messaging
Recent product launches and roadmap signals
Team size and hiring patterns (LinkedIn)
Funding and financial health (if public)
Customer reviews and pain points
Technical architecture (if applicable)
Strengths we can’t match vs weaknesses we can exploit

Deliverable:

SWOT analysis
Feature comparison table
Pricing comparison
Positioning gaps we can own
3 tactical moves we should make this quarter

Be brutally honest about where they’re beating us.

Our context: [YOUR PRODUCT DETAILS]

Stock analysis

Mega prompt:

You are a financial analyst specializing in [SECTOR].

Analyze [STOCK TICKER] as a potential investment.

Analysis framework:

Business model and revenue streams
Financial health (revenue, profit, cash flow trends)
Competitive position and moat
Growth catalysts and headwinds
Valuation metrics vs peers (P/E, P/S, EV/EBITDA)
Technical analysis (chart patterns, support/resistance)
Insider trading and institutional ownership
Bear case: what could go wrong
Bull case: what could go right
Recommendation (buy/hold/sell) with price targets

Risk tolerance: [CONSERVATIVE/MODERATE/AGGRESSIVE]
Investment timeline: [SHORT/MEDIUM/LONG TERM]
Portfolio context: [YOUR EXISTING HOLDINGS]

Provide specific entry/exit points and position sizing.

Doing Taxes

Mega prompt:

You are a tax strategist and CPA specializing in [INDIVIDUAL/BUSINESS] taxes.

Help me maximize deductions and minimize tax liability for [TAX YEAR].

My situation:

Income sources: [W2/1099/BUSINESS/INVESTMENTS]
Filing status: [SINGLE/MARRIED/etc]
State: [YOUR STATE]
Dependents: [NUMBER]
Special situations: [STOCK OPTIONS/CRYPTO/RENTAL/etc]

Provide:

Checklist of all possible deductions I might qualify for
Documents I need to gather
Common mistakes to avoid
Estimated tax liability with different scenarios
Tax-saving strategies I can still implement
Whether I need a CPA or can use software
Quarterly estimated tax recommendations
State-specific considerations

Make it a step-by-step action plan.

Financial details: [ROUGH INCOME, EXPENSES, INVESTMENTS]

Dynamic View for All `E_` Tables in SQL Server

Problem

You want a SQL Server view that combines all tables whose names start with the prefix E_, even though those tables are being created and deleted over time.

Limitation

In SQL Server:

A view cannot be truly dynamic.
A view must be compiled against specific objects and columns.
If tables are added or removed, the view will not automatically update.

So the correct approach is to dynamically rebuild the view definition whenever the table list changes.

Requirements

You must choose a column strategy:

Option 1 – All `E_` tables have identical schema

Easiest scenario.

Option 2 – Use a template table

A table (for example dbo.E_Template) defines the canonical column list.
Only tables matching those columns are included.

Option 3 – Use only shared columns

More complex.
Only columns common across all tables are included.

The script below uses Option 2 (Template Table).

Stored Procedure: Rebuild the View

CREATE OR ALTER PROCEDURE dbo.Rebuild_v_E_All
    @ViewSchema sysname = N'dbo',
    @ViewName   sysname = N'v_E_All',
    @Prefix     nvarchar(10) = N'E_',
    @TemplateTable sysname = N'E_Template'
AS
BEGIN
    SET NOCOUNT ON;

    DECLARE @templateObjectId int =
        OBJECT_ID(QUOTENAME(@ViewSchema) + N'.' + QUOTENAME(@TemplateTable));

    IF @templateObjectId IS NULL
    BEGIN
        THROW 50001, 'Template table not found. Create dbo.E_Template (or pass @TemplateTable).', 1;
    END

    ;WITH TemplateCols AS (
        SELECT c.name, c.column_id
        FROM sys.columns c
        WHERE c.object_id = @templateObjectId
    ),
    TargetTables AS (
        SELECT t.object_id, s.name AS schema_name, t.name AS table_name
        FROM sys.tables t
        JOIN sys.schemas s ON s.schema_id = t.schema_id
        WHERE t.name LIKE @Prefix + N'%' ESCAPE N'\'
    ),
    ValidTables AS (
        -- keep only tables that contain ALL template columns
        SELECT tt.object_id, tt.schema_name, tt.table_name
        FROM TargetTables tt
        WHERE NOT EXISTS (
            SELECT 1
            FROM TemplateCols tc
            WHERE NOT EXISTS (
                SELECT 1
                FROM sys.columns c
                WHERE c.object_id = tt.object_id AND c.name = tc.name
            )
        )
    )
    SELECT 1;

    DECLARE @colList nvarchar(max) =
        STUFF((
            SELECT N',' + QUOTENAME(tc.name)
            FROM sys.columns c
            JOIN TemplateCols tc ON tc.name = c.name
            WHERE c.object_id = @templateObjectId
            ORDER BY tc.column_id
            FOR XML PATH(''), TYPE
        ).value('.', 'nvarchar(max)'), 1, 1, N'');

    DECLARE @sql nvarchar(max) = N'CREATE OR ALTER VIEW '
        + QUOTENAME(@ViewSchema) + N'.' + QUOTENAME(@ViewName) + N' AS' + CHAR(10);

    DECLARE @union nvarchar(max) =
        STUFF((
            SELECT CHAR(10) + N'UNION ALL' + CHAR(10)
                 + N'SELECT '
                 + QUOTENAME(vt.schema_name) + N'.' + QUOTENAME(vt.table_name) + N' AS source_table, '
                 + @colList
                 + N' FROM ' + QUOTENAME(vt.schema_name) + N'.' + QUOTENAME(vt.table_name)
            FROM (
                SELECT t.object_id, s.name AS schema_name, t.name AS table_name
                FROM sys.tables t
                JOIN sys.schemas s ON s.schema_id = t.schema_id
                WHERE t.name LIKE @Prefix + N'%'
            ) vt
            ORDER BY vt.schema_name, vt.table_name
            FOR XML PATH(''), TYPE
        ).value('.', 'nvarchar(max)'), 1, LEN(CHAR(10) + N'UNION ALL' + CHAR(10)), N'');

    IF @union IS NULL OR LTRIM(RTRIM(@union)) = N''
    BEGIN
        -- create empty view with correct structure
        SET @sql += N'SELECT CAST(NULL AS sysname) AS source_table, ' + @colList + N' WHERE 1=0;';
    END
    ELSE
    BEGIN
        SET @sql += @union + N';';
    END

    EXEC sys.sp_executesql @sql;
END
GO

20260205-PowerBI_Environment_Setup

Posted on 2026-02-05 Edited on 2026-02-06 In Plan

Power BI Dashboard Environment Setup

Construction Project – Planning & Standards Guide

This document outlines what to consider before designing Power BI dashboards** for a construction project. The goal is to create a scalable, consistent, and reusable Power BI environment—not just individual reports.

1. Define the Power BI “Environment”

Before any visuals are created, define the reporting ecosystem.

Key questions

Who are the primary audiences?
- Project Managers
- Construction Managers / Superintendents
- Cost Controls / Planning
- Executives / Client
What decisions are made:
- Daily
- Weekly
- Monthly
What systems are authoritative?
- Schedule (Primavera P6)
- Cost / ERP
- Progress tracking
- QA/QC systems

These answers drive layout, KPIs, and data modeling.

2. Create a Master Dashboard Template

Create a single master template used for all dashboards.

File type

Prefer .pbit (Power BI Template)
Use .pbix only if needed for shared datasets

Standard pages

Page	Purpose
Executive Summary	High-level KPIs
Schedule	Milestones, slippage, trends
Cost	Budget vs actuals, forecasts
Construction Progress	Quantities, earned value
QA/QC	NCRs, punch, inspections
Risks & Issues	Exceptions and flags
Data Definitions	KPI explanations

Layout standards

Fixed margins
Consistent header bar
Filters always in the same location
Uniform font sizes

Branding

Company logo
Project name
“Data as of” timestamp
Controlled color palette (avoid excessive colors)

3. Standardize the Data Model

Define modeling standards before connecting data.

Modeling principles

Star schema where possible
Separate facts and dimensions

Typical fact tables

FACT_Progress
FACT_Cost
FACT_Schedule
FACT_Quality

Shared dimensions

DIM_Date (single master date table)
DIM_WBS
DIM_CWP / IWP
DIM_Area / System

Naming conventions

Tables

Estimate Outline Cheatsheet

Posted on 2023-11-10 In Cheatsheets , Work , Estimate Outline

Creating an estimate for a construction project over three years, encompassing hardware costs, software licensing, and personnel expenses for setup and support, requires a detailed and organized approach. Here’s an outline that can guide you through this process:

Estimate Outline for Construction Project

I. Executive Summary

Brief overview of the project
Total estimated cost
Key highlights of the estimate

II. Project Overview

Description of the construction project
Scope and objectives
Duration of the project (3 years)
Key deliverables

III. Hardware Costs

Initial Setup Costs
- List of required hardware (computers, servers, networking equipment, etc.)
- Unit cost of each hardware item
- Total cost for initial setup
Maintenance and Upgrades
- Estimated maintenance costs over 3 years
- Potential upgrade costs and their triggers

IV. Software Licensing Costs

Initial Licensing Fees
- List of required software (CAD, project management tools, etc.)
- Cost of licenses (one-time or recurring)
- Total initial licensing fees
Ongoing Licensing Fees
- Annual or monthly fees over 3 years
- Potential cost increases or additional licenses

V. Personnel Costs

Setup Phase
- Roles required for setup (IT professionals, software specialists, etc.)
- Hourly rates or salaries
- Total personnel cost for the setup phase
Operational Support
- Ongoing support roles (technical support, software maintenance)
- Estimated hours of support per week/month
- Cost projections for 3 years

VI. Training Costs

Training programs for software or hardware
Cost per training session or package
Total training cost estimate

VII. Contingency Costs

Percentage of total costs to cover unforeseen expenses
Justification for the selected percentage

VIII. Summary of Costs

Table summarizing all costs:
- Hardware Costs
- Software Licensing Costs
- Personnel Costs
- Training Costs
- Contingency Costs
Total Estimated Cost

IX. Assumptions and Limitations

Assumptions made during estimation
Limitations or constraints of the estimate

X. Approval and Next Steps

Procedure for approving the estimate
Next steps upon approval

XI. Appendices

Detailed quotes from vendors
Resumes or qualifications of key personnel
Any other relevant documentation

Notes:

Accuracy: Regularly update the estimate as more detailed information becomes available.
Flexibility: Be prepared to adjust the estimate for changes in scope, unexpected delays, or changes in market prices.
Documentation: Keep detailed records of how each cost was estimated for transparency and future reference.
Review: Have the estimate reviewed by key stakeholders and financial experts.

This comprehensive estimate outline will help in effectively presenting and managing the financial aspects of your construction project over its three-year duration.

Python Style and Format Cheatsheet

Posted on 2023-11-10 In Programming Learning , Python , Cheatsheets

My Chosen Standard

I think PEP08 is the standard I want to use.
Link: PEP08 Guide

Naming Conventions

The naming conventions of Python’s library are a bit of a mess, so we’ll never get this completely consistent – nevertheless, here are the currently recommended naming standards. New modules and packages (including third party frameworks) should be written to these standards, but where an existing library has a different style, internal consistency is preferred.
Overriding Principle

Names that are visible to the user as public parts of the API should follow conventions that reflect usage rather than implementation.
Descriptive: Naming Styles

There are a lot of different naming styles. It helps to be able to recognize what naming style is being used, independently from what they are used for.

The following naming styles are commonly distinguished:

b (single lowercase letter)
B (single uppercase letter)
lowercase
lower_case_with_underscores
UPPERCASE
UPPER_CASE_WITH_UNDERSCORES
CapitalizedWords (or CapWords, or CamelCase – so named because of the bumpy look of its letters [4]). This is also sometimes known as StudlyCaps.

Note: When using acronyms in CapWords, capitalize all the letters of the acronym. Thus HTTPServerError is better than HttpServerError.
mixedCase (differs from CapitalizedWords by initial lowercase character!)
Capitalized_Words_With_Underscores (ugly!)

There’s also the style of using a short unique prefix to group related names together. This is not used much in Python, but it is mentioned for completeness. For example, the os.stat() function returns a tuple whose items traditionally have names like st_mode, st_size, st_mtime and so on. (This is done to emphasize the correspondence with the fields of the POSIX system call struct, which helps programmers familiar with that.)

The X11 library uses a leading X for all its public functions. In Python, this style is generally deemed unnecessary because attribute and method names are prefixed with an object, and function names are prefixed with a module name.

In addition, the following special forms using leading or trailing underscores are recognized (these can generally be combined with any case convention):

_single_leading_underscore: weak “internal use” indicator. E.g. from M import * does not import objects whose names start with an underscore.
single_trailing_underscore_: used by convention to avoid conflicts with Python keyword, e.g.

tkinter.Toplevel(master, class_='ClassName')

__double_leading_underscore: when naming a class attribute, invokes name mangling (inside class FooBar, __boo becomes _FooBar__boo; see below).
__double_leading_and_trailing_underscore__: “magic” objects or attributes that live in user-controlled namespaces. E.g. __init__, __import__ or __file__. Never invent such names; only use them as documented.

Prescriptive: Naming Conventions

Names to Avoid

Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase letter oh), or ‘I’ (uppercase letter eye) as single character variable names.

In some fonts, these characters are indistinguishable from the numerals one and zero. When tempted to use ‘l’, use ‘L’ instead.
ASCII Compatibility

Identifiers used in the standard library must be ASCII compatible as described in the policy section of PEP 3131.

Constants

1
2
3

- USERNAME =
- PASSWORD =
- Uses all caps to denote that the value is not to be changed or modified

Variables

1
2
3

- Names of type variables introduced in PEP 484 should normally use CapWords preferring short names: T, AnyStr, Num. It is recommended to add suffixes \_co or \_contra to the variables used to declare covariant or contravariant behavior correspondingly:

from typing import TypeVar

1 2	VT_co = TypeVar("VT_co", (covariant = True)); KT_contra = TypeVar("KT_contra", (contravariant = True));

Classes

1	- CapWords convention

Pandas DataFrames

1	- Uses all caps to denote that the value is not to be changed or modified

Lists

1	- Uses all caps to denote that the value is not to be changed or modified

Dictionaries

1	- Uses all caps to denote that the value is not to be changed or modified

Exception Names

1	- Because exceptions should be classes, the class naming convention applies here. However, you should use the suffix “Error” on your exception names (if the exception actually is an error).

Global Variable Names

(Let’s hope that these variables are meant for use inside one module only.) The conventions are about the same as those for functions.

Modules that are designed for use via from M import \* should use the **all** mechanism to prevent exporting globals, or use the older convention of prefixing such globals with an underscore (which you might want to do to indicate these globals are “module non-public”).
Function and Variable Names

Function names should be lowercase, with words separated by underscores as necessary to improve readability.

Variable names follow the same convention as function names.

mixedCase is allowed only in contexts where that’s already the prevailing style (e.g. threading.py), to retain backwards compatibility.
Function and Method Arguments

Always use self for the first argument to instance methods.

Always use cls for the first argument to class methods.

If a function argument’s name clashes with a reserved keyword, it is generally better to append a single trailing underscore rather than use an abbreviation or spelling corruption. Thus class\_ is better than clss. (Perhaps better is to avoid such clashes by using a synonym.)
Method Names and Instance Variables

Use the function naming rules: lowercase with words separated by underscores as necessary to improve readability.

Use one leading underscore only for non-public methods and instance variables.

To avoid name clashes with subclasses, use two leading underscores to invoke Python’s name mangling rules.

Python mangles these names with the class name: if class Foo has an attribute named **a, it cannot be accessed by Foo.**a. (An insistent user could still gain access by calling Foo.\_Foo\_\_a.) Generally, double leading underscores should be used only to avoid name conflicts with attributes in classes designed to be subclassed.

Note: there is some controversy about the use of \_\_names (see below).
Constants

Constants are usually defined on a module level and written in all capital letters with underscores separating words. Examples include MAX_OVERFLOW and TOTAL.
Designing for Inheritance

Always decide whether a class’s methods and instance variables (collectively: “attributes”) should be public or non-public. If in doubt, choose non-public; it’s easier to make it public later than to make a public attribute non-public.

Public attributes are those that you expect unrelated clients of your class to use, with your commitment to avoid backwards incompatible changes. Non-public attributes are those that are not intended to be used by third parties; you make no guarantees that non-public attributes won’t change or even be removed.

We don’t use the term “private” here, since no attribute is really private in Python (without a generally unnecessary amount of work).

Another category of attributes are those that are part of the “subclass API” (often called “protected” in other languages). Some classes are designed to be inherited from, either to extend or modify aspects of the class’s behavior. When designing such a class, take care to make explicit decisions about which attributes are public, which are part of the subclass API, and which are truly only to be used by your base class.

With this in mind, here are the Pythonic guidelines:

    Public attributes should have no leading underscores.
    If your public attribute name collides with a reserved keyword, append a single trailing underscore to your attribute name. This is preferable to an abbreviation or corrupted spelling. (However, notwithstanding this rule, ‘cls’ is the preferred spelling for any variable or argument which is known to be a class, especially the first argument to a class method.)

    Note 1: See the argument name recommendation above for class methods.
    For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax.

    Note 1: Try to keep the functional behavior side-effect free, although side-effects such as caching are generally fine.

    Note 2: Avoid using properties for computationally expensive operations; the attribute notation makes the caller believe that access is (relatively) cheap.
    If your class is intended to be subclassed, and you have attributes that you do not want subclasses to use, consider naming them with double leading underscores and no trailing underscores. This invokes Python’s name mangling algorithm, where the name of the class is mangled into the attribute name. This helps avoid attribute name collisions should subclasses inadvertently contain attributes with the same name.

    Note 1: Note that only the simple class name is used in the mangled name, so if a subclass chooses both the same class name and attribute name, you can still get name collisions.

    Note 2: Name mangling can make certain uses, such as debugging and __getattr__(), less convenient. However the name mangling algorithm is well documented and easy to perform manually.

    Note 3: Not everyone likes name mangling. Try to balance the need to avoid accidental name clashes with potential use by advanced callers.

Public and Internal Interfaces

Any backwards compatibility guarantees apply only to public interfaces. Accordingly, it is important that users be able to clearly distinguish between public and internal interfaces.

Documented interfaces are considered public, unless the documentation explicitly declares them to be provisional or internal interfaces exempt from the usual backwards compatibility guarantees. All undocumented interfaces should be assumed to be internal.

To better support introspection, modules should explicitly declare the names in their public API using the **all** attribute. Setting **all** to an empty list indicates that the module has no public API.

Even with **all** set appropriately, internal interfaces (packages, modules, classes, functions, attributes or other names) should still be prefixed with a single leading underscore.

An interface is also considered internal if any containing namespace (package, module or class) is considered internal.

Imported names should always be considered an implementation detail. Other modules must not rely on indirect access to such imported names unless they are an explicitly documented part of the containing module’s API, such as os.path or a package’s **init** module that exposes functionality from submodules.
Programming Recommendations

    Code should be written in a way that does not disadvantage other implementations of Python (PyPy, Jython, IronPython, Cython, Psyco, and such).

    For example, do not rely on CPython’s efficient implementation of in-place string concatenation for statements in the form a += b or a = a + b. This optimization is fragile even in CPython (it only works for some types) and isn’t present at all in implementations that don’t use refcounting. In performance sensitive parts of the library, the ''.join() form should be used instead. This will ensure that concatenation occurs in linear time across various implementations.
    Comparisons to singletons like None should always be done with is or is not, never the equality operators.

    Also, beware of writing if x when you really mean if x is not None – e.g. when testing whether a variable or argument that defaults to None was set to some other value. The other value might have a type (such as a container) that could be false in a boolean context!
    Use is not operator rather than not ... is. While both expressions are functionally identical, the former is more readable and preferred:

    # Correct:
    if foo is not None:

    # Wrong:
    if not foo is None:

    When implementing ordering operations with rich comparisons, it is best to implement all six operations (__eq__, __ne__, __lt__, __le__, __gt__, __ge__) rather than relying on other code to only exercise a particular comparison.

    To minimize the effort involved, the functools.total_ordering() decorator provides a tool to generate missing comparison methods.

    PEP 207 indicates that reflexivity rules are assumed by Python. Thus, the interpreter may swap y > x with x < y, y >= x with x <= y, and may swap the arguments of x == y and x != y. The sort() and min() operations are guaranteed to use the < operator and the max() function uses the > operator. However, it is best to implement all six operations so that confusion doesn’t arise in other contexts.
    Always use a def statement instead of an assignment statement that binds a lambda expression directly to an identifier:

    # Correct:
    def f(x): return 2*x

    # Wrong:
    f = lambda x: 2*x

    The first form means that the name of the resulting function object is specifically ‘f’ instead of the generic ‘<lambda>’. This is more useful for tracebacks and string representations in general. The use of the assignment statement eliminates the sole benefit a lambda expression can offer over an explicit def statement (i.e. that it can be embedded inside a larger expression)
    Derive exceptions from Exception rather than BaseException. Direct inheritance from BaseException is reserved for exceptions where catching them is almost always the wrong thing to do.

    Design exception hierarchies based on the distinctions that code catching the exceptions is likely to need, rather than the locations where the exceptions are raised. Aim to answer the question “What went wrong?” programmatically, rather than only stating that “A problem occurred” (see PEP 3151 for an example of this lesson being learned for the builtin exception hierarchy)

    Class naming conventions apply here, although you should add the suffix “Error” to your exception classes if the exception is an error. Non-error exceptions that are used for non-local flow control or other forms of signaling need no special suffix.
    Use exception chaining appropriately. raise X from Y should be used to indicate explicit replacement without losing the original traceback.

    When deliberately replacing an inner exception (using raise X from None), ensure that relevant details are transferred to the new exception (such as preserving the attribute name when converting KeyError to AttributeError, or embedding the text of the original exception in the new exception message).
    When catching exceptions, mention specific exceptions whenever possible instead of using a bare except: clause:

    try:
        import platform_specific_module
    except ImportError:
        platform_specific_module = None

    A bare except: clause will catch SystemExit and KeyboardInterrupt exceptions, making it harder to interrupt a program with Control-C, and can disguise other problems. If you want to catch all exceptions that signal program errors, use except Exception: (bare except is equivalent to except BaseException:).

    A good rule of thumb is to limit use of bare ‘except’ clauses to two cases:
        If the exception handler will be printing out or logging the traceback; at least the user will be aware that an error has occurred.
        If the code needs to do some cleanup work, but then lets the exception propagate upwards with raise. try...finally can be a better way to handle this case.
    When catching operating system errors, prefer the explicit exception hierarchy introduced in Python 3.3 over introspection of errno values.
    Additionally, for all try/except clauses, limit the try clause to the absolute minimum amount of code necessary. Again, this avoids masking bugs:

    # Correct:
    try:
        value = collection[key]
    except KeyError:
        return key_not_found(key)
    else:
        return handle_value(value)

    # Wrong:
    try:
        # Too broad!
        return handle_value(collection[key])
    except KeyError:
        # Will also catch KeyError raised by handle_value()
        return key_not_found(key)

    When a resource is local to a particular section of code, use a with statement to ensure it is cleaned up promptly and reliably after use. A try/finally statement is also acceptable.
    Context managers should be invoked through separate functions or methods whenever they do something other than acquire and release resources:

    # Correct:
    with conn.begin_transaction():
        do_stuff_in_transaction(conn)

    # Wrong:
    with conn:
        do_stuff_in_transaction(conn)

    The latter example doesn’t provide any information to indicate that the __enter__ and __exit__ methods are doing something other than closing the connection after a transaction. Being explicit is important in this case.
    Be consistent in return statements. Either all return statements in a function should return an expression, or none of them should. If any return statement returns an expression, any return statements where no value is returned should explicitly state this as return None, and an explicit return statement should be present at the end of the function (if reachable):

    # Correct:

    def foo(x):
        if x >= 0:
            return math.sqrt(x)
        else:
            return None

    def bar(x):
        if x < 0:
            return None
        return math.sqrt(x)

    # Wrong:

    def foo(x):
        if x >= 0:
            return math.sqrt(x)

    def bar(x):
        if x < 0:
            return
        return math.sqrt(x)

    Use ''.startswith() and ''.endswith() instead of string slicing to check for prefixes or suffixes.

    startswith() and endswith() are cleaner and less error prone:

    # Correct:
    if foo.startswith('bar'):

    # Wrong:
    if foo[:3] == 'bar':

    Object type comparisons should always use isinstance() instead of comparing types directly:

    # Correct:
    if isinstance(obj, int):

    # Wrong:
    if type(obj) is type(1):

    For sequences, (strings, lists, tuples), use the fact that empty sequences are false:

    # Correct:
    if not seq:
    if seq:

    # Wrong:
    if len(seq):
    if not len(seq):

    Don’t write string literals that rely on significant trailing whitespace. Such trailing whitespace is visually indistinguishable and some editors (or more recently, reindent.py) will trim them.
    Don’t compare boolean values to True or False using ==:

    # Correct:
    if greeting:

    # Wrong:
    if greeting == True:

    Worse:

    # Wrong:
    if greeting is True:

    Use of the flow control statements return/break/continue within the finally suite of a try...finally, where the flow control statement would jump outside the finally suite, is discouraged. This is because such statements will implicitly cancel any active exception that is propagating through the finally suite:

    # Wrong:
    def foo():
        try:
            1 / 0
        finally:
            return 42

Function Annotations

With the acceptance of PEP 484, the style rules for function annotations have changed.

    Function annotations should use PEP 484 syntax (there are some formatting recommendations for annotations in the previous section).
    The experimentation with annotation styles that was recommended previously in this PEP is no longer encouraged.
    However, outside the stdlib, experiments within the rules of PEP 484 are now encouraged. For example, marking up a large third party library or application with PEP 484 style type annotations, reviewing how easy it was to add those annotations, and observing whether their presence increases code understandability.
    The Python standard library should be conservative in adopting such annotations, but their use is allowed for new code and for big refactorings.
    For code that wants to make a different use of function annotations it is recommended to put a comment of the form:

    # type: ignore

    near the top of the file; this tells type checkers to ignore all annotations. (More fine-grained ways of disabling complaints from type checkers can be found in PEP 484.)
    Like linters, type checkers are optional, separate tools. Python interpreters by default should not issue any messages due to type checking and should not alter their behavior based on annotations.
    Users who don’t want to use type checkers are free to ignore them. However, it is expected that users of third party library packages may want to run type checkers over those packages. For this purpose PEP 484 recommends the use of stub files: .pyi files that are read by the type checker in preference of the corresponding .py files. Stub files can be distributed with a library, or separately (with the library author’s permission) through the typeshed repo [5].

Variable Annotations

PEP 526 introduced variable annotations. The style recommendations for them are similar to those on function annotations described above:

    Annotations for module level variables, class and instance variables, and local variables should have a single space after the colon.
    There should be no space before the colon.
    If an assignment has a right hand side, then the equality sign should have exactly one space on both sides:

    # Correct:

    code: int

    class Point:
        coords: Tuple[int, int]
        label: str = '<unknown>'

    # Wrong:

    code:int  # No space after colon
    code : int  # Space before colon

    class Test:
        result: int=0  # No spaces around equality sign

    Although the PEP 526 is accepted for Python 3.6, the variable annotation syntax is the preferred syntax for stub files on all versions of Python (see PEP 484 for details).

20241105-3D-CONSTRUCTION-PORTFOLIO

Posted on 2023-11-05 In AI , ChatGpT

Creating a portfolio website to display your 3D promotional materials for construction projects is a great way to showcase your skills and attract potential clients. Here’s a plan you can follow to create an effective portfolio:

1. Define Your Objectives:

Purpose: Showcase 3D construction project materials and your skills.
Target Audience: Construction firms, architects, real estate developers, and independent contractors.
Outcome: Generate inquiries and freelance job opportunities.

2. Gather Your Content:

3D Renders: High-resolution images and videos of your best work.
Descriptions: Brief explanations of each project, your role, tools used, and any unique challenges or solutions.
Testimonials: If available, include client testimonials to provide social proof of your skills.
Resume/CV: Outline your professional experience, skills, and any relevant education or certifications.
Contact Information: How clients can reach you, including email, phone number, and professional social media profiles or platforms where you’re active.
Services Offered: Detail the services you provide, such as 3D visualization, virtual walkthroughs, animation, etc.

3. Choose a Website Platform:

Custom Build: Hire a web developer or use your own skills if you’re proficient in web design.
Website Builders: Platforms like Squarespace, Wix, or WordPress with portfolio themes can be a cost-effective and user-friendly option.

4. Design Your Site:

Homepage: Create a visually impactful homepage with a carousel or grid of your featured work.
Portfolio Pages: Organize your 3D materials into categories if you have multiple services or project types.
About Page: Share your story, experience, and what sets you apart.
Contact Page: A form for inquiries as well as your direct contact information.
Responsive Design: Ensure your website is mobile-friendly for users on different devices.
SEO: Optimize your site for search engines to increase visibility.

5. Build the Website:

Homepage: Construct with a focus on your unique selling proposition and a showcase of your latest or most impressive projects.
Portfolio Gallery: Create a user-friendly gallery with options to view projects in detail.
Project Pages: Each project should have its own page with a gallery and the story behind the project.
Blog/Insights: Consider having a blog to share your insights, new trends in 3D visualization, and behind-the-scenes of your projects.
Call-to-Action (CTA): Encourage visitors to contact you for their projects on every page.

6. Optimize User Experience (UX):

Navigation: Make sure your website is easy to navigate with a clear menu.
Loading Times: Optimize image and video sizes to ensure quick loading times.
Interactivity: Include interactive elements like a virtual tour if applicable.

7. Test Your Website:

Cross-Browser Compatibility: Ensure it works across various web browsers.
Mobile Responsiveness: Test on different mobile devices.
Load Testing: Verify that your site can handle traffic without slowing down.

8. Launch the Website:

Soft Launch: Share your site with a small group for feedback.
Revise: Make necessary adjustments based on the feedback.
Official Launch: Announce the launch through your network, social media, and relevant online communities.

9. Marketing and Promotion:

Social Media: Use platforms like LinkedIn, Instagram, and Pinterest to promote your site.
Networking: Attend industry events and join online forums.
Content Marketing: Write articles or create videos that showcase your expertise and can help drive traffic to your site.

10. Maintain and Update:

Regular Updates: Keep your portfolio fresh with new projects and updates.
Analytics: Use tools like Google Analytics to track visitor behavior and make informed improvements.
Security: Regularly update your website’s security features to protect your work and your clients’ information.

11. Legalities:

Copyrights and Permissions: Ensure you have the rights to display all the content on your website.
Privacy Policy: If you collect user data, make sure you have a privacy policy in place.
Terms of Service: Clearly state the terms under which you provide your services.

By following this plan, you’ll be able to create a professional and compelling portfolio website that not only showcases your 3D promotional materials for construction projects but also effectively markets your skills to potential clients.

Construction Portfolio Website

Posted on 2023-11-05 Edited on 2023-11-10 In AI , ChatGpT

Creating a portfolio website to display your 3D promotional materials for construction projects is a great way to showcase your skills and attract potential clients. Here’s a plan you can follow to create an effective portfolio:

1. Define Your Objectives:

Purpose: Showcase 3D construction project materials and your skills.
Target Audience: Construction firms, architects, real estate developers, and independent contractors.
Outcome: Generate inquiries and freelance job opportunities.

2. Gather Your Content:

3D Renders: High-resolution images and videos of your best work.
Descriptions: Brief explanations of each project, your role, tools used, and any unique challenges or solutions.
Testimonials: If available, include client testimonials to provide social proof of your skills.
Resume/CV: Outline your professional experience, skills, and any relevant education or certifications.
Contact Information: How clients can reach you, including email, phone number, and professional social media profiles or platforms where you’re active.
Services Offered: Detail the services you provide, such as 3D visualization, virtual walkthroughs, animation, etc.

3. Choose a Website Platform:

Custom Build: Hire a web developer or use your own skills if you’re proficient in web design.
Website Builders: Platforms like Squarespace, Wix, or WordPress with portfolio themes can be a cost-effective and user-friendly option.

4. Design Your Site:

Homepage: Create a visually impactful homepage with a carousel or grid of your featured work.
Portfolio Pages: Organize your 3D materials into categories if you have multiple services or project types.
About Page: Share your story, experience, and what sets you apart.
Contact Page: A form for inquiries as well as your direct contact information.
Responsive Design: Ensure your website is mobile-friendly for users on different devices.
SEO: Optimize your site for search engines to increase visibility.

5. Build the Website:

Homepage: Construct with a focus on your unique selling proposition and a showcase of your latest or most impressive projects.
Portfolio Gallery: Create a user-friendly gallery with options to view projects in detail.
Project Pages: Each project should have its own page with a gallery and the story behind the project.
Blog/Insights: Consider having a blog to share your insights, new trends in 3D visualization, and behind-the-scenes of your projects.
Call-to-Action (CTA): Encourage visitors to contact you for their projects on every page.

6. Optimize User Experience (UX):

Navigation: Make sure your website is easy to navigate with a clear menu.
Loading Times: Optimize image and video sizes to ensure quick loading times.
Interactivity: Include interactive elements like a virtual tour if applicable.

7. Test Your Website:

Cross-Browser Compatibility: Ensure it works across various web browsers.
Mobile Responsiveness: Test on different mobile devices.
Load Testing: Verify that your site can handle traffic without slowing down.

8. Launch the Website:

Soft Launch: Share your site with a small group for feedback.
Revise: Make necessary adjustments based on the feedback.
Official Launch: Announce the launch through your network, social media, and relevant online communities.

9. Marketing and Promotion:

Social Media: Use platforms like LinkedIn, Instagram, and Pinterest to promote your site.
Networking: Attend industry events and join online forums.
Content Marketing: Write articles or create videos that showcase your expertise and can help drive traffic to your site.

10. Maintain and Update:

Regular Updates: Keep your portfolio fresh with new projects and updates.
Analytics: Use tools like Google Analytics to track visitor behavior and make informed improvements.
Security: Regularly update your website’s security features to protect your work and your clients’ information.

11. Legalities:

Copyrights and Permissions: Ensure you have the rights to display all the content on your website.
Privacy Policy: If you collect user data, make sure you have a privacy policy in place.
Terms of Service: Clearly state the terms under which you provide your services.

By following this plan, you’ll be able to create a professional and compelling portfolio website that not only showcases your 3D promotional materials for construction projects but also effectively markets your skills to potential clients.

Python Data Science; Numpy Pandas

Posted on 2023-08-15 In Programming Learning , Python , Data Science

[Data Visualisation With Python](“file:\M:_RESOURCES\LEARNING_PYTHON_DATA_SCIENCE\Data Analysis with Python NumPy & Pandas Masterclass”)

Teacher

Instructor Led

About this Course
Learn how to use Numpy and Pandas for Data Science.

Notes

General

Python Data Science Course

Posted on 2022-11-07 Edited on 2022-11-11 In Programming Learning , Python , Data Science

[Complete Data Wrangling & Data Visualisation With Python](“file:\M:_RESOURCES\LEARNING_PYTHON_DATA_SCIENCE\Complete Data Wrangling & Data Visualisation With Python”)

Teacher

Colt

About this Course
Learn how to make better decisions with data in this course on data analysis. We’ll start by looking at what data analysis is, and then we’ll see how we can use data analysis to create better outcomes.

Notes

General

Python2 vs Python3 - Python3 was a major overhaul and not all functions etc will be backward compatible. Python2 will be retired eventually, no longer maintained
Boolean ‘1’ is True, has truthiness, is not empty; while ‘0’ if False, has falsiness, is emptly
is vs == ; is false when not in same location in memory, even if value is equal
Data Types
- Lists

Libraries

MatPlotLib - plotting capabilites, creates static files
Seaborn - matplotlib backend, makes statistical plots
Pandas - matplotlib backend, uses .plot() calls to make static plots
Plotly - both a company and open source lib, for JS, React, R, Python. Creates interactive plats as .html files connected to static data sources.
Dash - allows for plots to interact with each other, components, or update in real time. Dash is open source lib to create full dashboard with multiple components, interactivity and multiple plots.

NumPy

In this course, Python manupulation of lists, arrays, matrices

Pandas

In this course, Pandas to read in datasets and select rows or columns of data

Plotly

In this course,
Documentation: Plotly Open Source Graphing Library for Python
Available Plots:
- Scatter
- line

Markdown Cheatsheet

Posted on 2022-10-17 Edited on 2023-11-10 In Programming Learning , Markdown , Cheatsheets

Markdown

Markdown is a text-to-HTML conversion tool for web writers. Markdown allows you to write using an easy-to-read, easy-to-write plain text format, then convert it to structurally valid XHTML (or HTML).

Documentation: Markdown Docs
RFC: RFC 7763
GitHub Documentation: Writing Markdown on GitHub

Cheat-Sheet

Headings

# Heading 1

## Heading 2

### Heading 3

#### Heading 4

##### Heading 5

###### Heading 6

Here is a heading: # Heading, don’t do this: #Heading

Emphasis

Emphasis, aka italics, with _asterisks_ or _underscores_.

Strong emphasis, aka bold, with **asterisks** or **underscores**.

Combined emphasis with **asterisks and _underscores_**.

Strikethrough uses two tildes. ~~Scratch this.~~

Line Breaks

1 2	First line with two spaces after. And the next line.

Lists

Ordered Lists

1
2
3

1. First item
2. Second item
3. Third item

Unordered Lists

1
2
3

- First item
- Second item
- Third item

Links

1	Link with text: [link-text](https://www.google.com)

Images

1
2
3

Image with alt text: ![alt-text](https://camo.githubusercontent.com/4d89cd791580bfb19080f8b0844ba7e1235aa4becc3f43dfd708a769e257d8de/68747470733a2f2f636e642d70726f642d312e73332e75732d776573742d3030342e6261636b626c617a6562322e636f6d2f6e65772d62616e6e6572342d7363616c65642d666f722d6769746875622e6a7067)

Image without alt text: ![](https://camo.githubusercontent.com/4d89cd791580bfb19080f8b0844ba7e1235aa4becc3f43dfd708a769e257d8de/68747470733a2f2f636e642d70726f642d312e73332e75732d776573742d3030342e6261636b626c617a6562322e636f6d2f6e65772d62616e6e6572342d7363616c65642d666f722d6769746875622e6a7067)

Code Blocks

Inline Code Block

1	Inline `code` has `back-ticks around` it.

Blocks of Code

1 2	var s = "JavaScript syntax highlighting"; alert(s);

1 2	s = "Python syntax highlighting" print s

1 2	No language indicated, so no syntax highlighting. But let's throw in a <b>tag</b>.

Tables

There must be at least 3 dashes separating each header cell.
The outer pipes (|) are optional, and you don’t need to make the raw Markdown line up prettily.

| Heading 1 | Heading 2 | Heading 3 |
| --------- | --------- | --------- |
| col1      | col2      | col3      |
| col1      | col2      | col3      |

Task list

To create a taksk lsit start line with square brackets with an empty space.
Ex: [ ] and add text for task.
To check the task replace the space between the bracket with “x”.

1
2
3

[x] Write the post
[ ] Update the website
[ ] Contact the user

Reference

Link: markdown guide

Timer to Database Software

Posted on 2022-10-13 Edited on 2022-10-17 In Custom Programs , Timer to Database , Programming Learning , Python

Purpose:

Update multiple databases based off of a multi-timer app to record completion of my activities

Requirements:

Metadata from tasks: [datetime, durration, completed(y/n), ]
Multi-timer
Notification with sound
Add and start more timers on the fly

To Do (short term)**

[ ] Add Task Name field to app
[ ] Time started
[ ] Time finished
[ ] Keep on top
[ ] restart timer
[ ] dock to other timer and reshuffle order

To Do (long term)**

[ ] Backend to Database
[ ] Make it a free standing executable app

Intro to Folium Mapping

Posted on 2022-10-06 Edited on 2022-10-13 In Programming Learning , Python

Introduction to Folium

Data Science with Streamlit

Posted on 2022-10-05 Edited on 2022-10-13 In Programming Learning , Python , Web Dev

Youtube followed:

12 Data Science Projects Using Python & Streamlit

Purpose:

I found a youtube video titled

Requirements:

Completed Projects

01 - Simple Stock Price
02 - Simple Bioinformatics DNA Count

Today Projects

03 - EDA Basketball
04 - EDA Football

Future Projects

05 - EDA SP500 Stock Price
06 - EDA Cryptocurrency
07 - Classification Iris
08 - Classification Penguins
09 - Regression Boston Housing
10 - Regression Bioinformatics Solubility
11 - Deploy to Heroku
12 - Deploy to Streamlit Sharing

HEXO Blog Cheatsheet

Posted on 2022-10-03 Edited on 2024-04-10 In Cheatsheets

My tech blog ERC.cloud

hexo commands

Purpose:

Create a Repository for everything I have been and will be learning

Requirements:

Simple, archivable and able to portable(ability to move to different platform in future)
Lightweight, and able to remain speedy and responsive
Manaage large amount of entries and attackments(code snippets, images)
Provide a place for me archive up info on my projects
Provide a good directory for github code
Owned by me, no company has ability to shut me down and jeopardize my info

Why I chose HEXO

HEXO meets all of the requirements above, I was testing WordPress as well and it is a far second in most of my requirements

Instructions and Go-Bys

how to run local HEXO

In Powershell - nav to hexo source location Users/User/eri-cloud-blog
Enter command: Hexo server
Location: C:\Users\prime\erc-cloud-blog\source_posts

How to create new page

In Powershell - hexo new <new filename>

How to publish

In Powershell - hexo generate --watch (This extracts the blog by comparing and only exporting pages with changes)
Then use FileZilla to FTP to HostGator

Still To DO:

How to deploy to HostGator, I need more configuration for this
- command: hexo generate --deploy
Create workstation to be more blog ready
- set up VScode with markdown ice(installed, need to learn)
- PlantUML for workflow editor(installed, need to learn)
- image importer(installed, need to learn)
[ ]

Modern Python 3 Bootcamp Review

Posted on 2022-10-03 Edited on 2022-10-29 In Programming Learning , Python

Introduction to Python

Teacher

Colt

About this Course
Learn how to make better decisions with data in this course on data analysis. We’ll start by looking at what data analysis is, and then we’ll see how we can use data analysis to create better outcomes.

Notes

General

Python2 vs Python3 - Python3 was a major overhaul and not all functions etc will be backward compatible. Python2 will be retired eventually, no longer maintained
Boolean ‘1’ is True, has truthiness, is not empty; while ‘0’ if False, has falsiness, is emptly
is vs == ; is false when not in same location in memory, even if value is equal
Data Types
- Lists
  - List functions(.append-adds 1 item, .extend-adds multiple items)(.insert(2,”purple”- inserts purple in seat 2))
  - Defined: thislist = ["apple", "banana", "cherry"]
  - Retrieved: listitem1 = thislist[0]
  - .pop - pop() method removes the element at the specified position.
  - Slices - List[start:stop:step]
  - List Comprehension is used whe iterating over lists, strings, ranges and more data types
  - nested listat are essential for building more complex data structures like matrices, board games, mazes
  - swapping is useful when shuffling or sorting
- Dictionaries
  - Get loaded by item - items = {"name": "Eric", "age": 47, "isCool": False} or using dict()
  - Defined: thisdict = {"brand": "Ford", "model": "Mustang", "year": 1964}
  - Has methods for pulling data(.keys, .values, .items)
- Tuples
  - Ordered and unchangeable(immutable), used for protecting data, good example are GPS coordinates
  - Defined: thistuple = ("apple", "banana", "cherry") OR tuple()
  - Retrieved: tupleitem1 = thistuple[0]
- Sets - Unordered and unique; faster than lists - Defined: thisset = {"apple", "banana", "cherry"} OR set() - Useful for removing duplicates
Functions
- Allows us to stary DRY(Don’t Repeat Yourself)
- ‘return’ Returns results of the funtion
Modules and Methods
- Built-in modules are available for import with standard Python installations
- Built-In modules
OOP(Object Oriented Programming)
- Encapsulation - refers to encapsulating of code and methods into a class that is virtually separate from the rest of the program
- Abstraction - refers to the exercise of feeding data into and getting responses from the class
- Instance attributes
  1
  2
  3
  4
  5
  class Comment():
  def __init__(self, username, text, likes=0):
  self.username = username
  self.text = text
  self.likes = likes
Instance Methods - instanciated per class
Class attributes
Class methods -
Iterator - an object that can bne iterated upon an returns data. One element at at time using next()
Iterable - Object which will return an Iterator when iter() is called on it.
Generator Functions: uses yield, can yield multiple times, when invoked returns a generator.
- ie.sum([x for x in range(100)]) =
Decorator - ‘@’are higher order functions wrapping other functions to enhoance their behavior

Debugging

while True:
         try:      # this might be necessary
             pass  #
         except:   # ValueError as err  and can print err
             pass  # there was a blank
         else:
             pass  # input(f"Please enter some info: ")
         finally:
             pass  # this runs no matter what,so remove if not necessary
             break

Powershell

mkdir <new directory name> - creates directory
ls - list directory, same as dir
pwd - outputs current location
echo $null >> <new file name>.py - creates new .py file, THEN you still have to change the file to UDP-8
New-Item -ItemType file <new file name>.py - creates new .py file
rm -r -fo <directory name> - deletes ENTIRE directory; -r(Recursive for all child direcories); -f(Force prevents verifications & warnings)

Course Progress

Course Introduction
WINDOWS Command Line Fundamentals
WINDOWS Python Setup
Numbers, Operators, and Comments
Variables and Strings
Boolean and Conditional Logic
Rock, Paper, Scissors
Looping in Python
Guessing Game
Lists
Lists Comprehensions
Dictionaries
Dictionary Exercises
Tuples and Sets
Functions Part I
Functions Exercises
Functions Part II
Lambdas and Built-In Functions
Debugging and Error Handling
Modules
OPTIONAL SECTION Making HTTP Requests with Python
Object Oriented Programming
Deck Of Cards Exercise
OOP Part 2 (did not complete 7-11)
Iterators & Generators
Decorators (did not complete 7-14)
Testing With Python (did not complete 3-11)
File IO (did not complete any)
Working With CSV and Pickling (did not complete any)
Web Scraping with BeautifulSoup
Web Scraping Project
Regular Expressions
Python + SQL
Massive Section of Challenges

TO DO:

What did I learn / what do I need to review

[x] Print statements and variable assigning
[x] Variables and simple variable types(Numbers, String, List, Tuple, Dictionary)
[x] Complex variables(Long, Float, List, Tuple, Dictionary)
[x] Input and Ouput
[x] Loops(For, While)
[x] Conditional if statements
[x] Lists
[x] Functions and Methods(for lists)
[ ] Review “Comprehensions”, they seem useful and I don’t totally understand
[ ] Find Youtube on debugging in VScode
[ ] Go back and finish the (Did on completes)

Sample MarkDown page

Posted on 2022-10-03 Edited on 2022-10-13 In Programming Learning , Markdown

Data Analysis Basics

75-minute Data Analysis Course

Teacher

Ben Deitch

Ben is an Android teacher at Treehouse with a long history of creating and tinkering with Android apps. He’s an avid learner, loves playing sports, and is a fairly average chef.

About this Course

Learn how to make better decisions with data in this course on data analysis. We’ll start by looking at what data analysis is, and then we’ll see how we can use data analysis to create better outcomes.

What you’ll learn

Cleaning and preparing data in spreadsheets
Summarizing data with formula
Normal distributions and standard deviations
Simple visualizations in spreadsheets
Presenting findings

Introducing Data Analysis

From the daily weather forecast to the nightly sports results, data is all around us.
Let’s take a look at how we can analyse data to draw conclusions and uncover hidden insights!

Making Better Decisions with Data Analysis (3:17)
Data is Everywhere (3:13)
Creating a Spreadsheet (3:54)
Getting Good Data is Hard (4:28)
Introducing Data Analysis 5 questions (Quiz)

Getting to Know Your Data

There’s a lot of ways we can look at our data. We can look at it as rows in columns in a spreadsheet, or we can try and represent it visually as a chart. We can even summarize our data into just a few numbers to help us make decision making less of a chore and more of a step-by-step process!

Summarizing Data Maximum, Minimum, Range (4:02)
Summarizing Data Mean, Median, Mode (2:35)
Getting Friendly with Data 5 questions (Quiz)
Analyzing Data Spread (3:53)
Is Our Data Normal (5:15)
Visualizing Data (3:47)
Charting Our Data Part 1 (3:06)
Charting Our Data Part 2 (4:30)
Becoming the Data 5 questions (Quiz)

The Data Analysis Process

There are a lot of ways to perform an analysis of data. In this stage, we’ll talk about metrics and look at one way to work through the data analysis process.

Understanding Metrics (4:03)
Defining Terms (4:38)
Analyzing the Data (6:34)
Presenting Your Findings (4:04)
Mastering Data Analysis 5 questions (Quiz)

🃏 Becoming a Top 5% Winner in Today’s Las Vegas Poker Rooms

1️⃣ Preflop Is Now a Science, Not an Art

What Changed in Vegas Rooms

You Must:

2️⃣ Be Comfortable With GTO — Even If You Exploit

3️⃣ Vegas = Population Exploitation Gold Mine

Top 5% Winners:

4️⃣ Elite Mental Game

5️⃣ Game Selection = 30–40% of Winrate

6️⃣ Bankroll & Stakes Strategy

Ideal Structure:

What Separates Solid Regs from Top 5% Crushers

Common Leaks for Experienced Players

If I Were Coaching You

Month 1

Month 2

Month 3

The Real Question

Melodic Techno & Deep Melodic House – Long Sets (20+ Minutes)

Anyma – Afterlife Barcelona 2023

Tale Of Us – Cercle, Paris

ARTBAT – Upperground Poland

Adriatique – Cercle at Hatshepsut Temple, Egypt

Massano – Printworks London

Extra Long Mix Sources (1–2 hours)

Dynamic View for All E_ Tables in SQL Server

Problem

Limitation

Recommended Solution (Option A)

Stored Procedure that Rebuilds the View

Requirements

Option 1 – All E_ tables have identical schema

Option 2 – Use a template table

Option 3 – Use only shared columns

Stored Procedure: Rebuild the View

Power BI Dashboard Environment Setup

Construction Project – Planning & Standards Guide

1. Define the Power BI “Environment”

Key questions

2. Create a Master Dashboard Template

File type

Standard pages

Layout standards

Branding

3. Standardize the Data Model

Modeling principles

Typical fact tables

Shared dimensions

Naming conventions

Estimate Outline for Construction Project

I. Executive Summary

II. Project Overview

III. Hardware Costs

IV. Software Licensing Costs

V. Personnel Costs

VI. Training Costs

VII. Contingency Costs

VIII. Summary of Costs

IX. Assumptions and Limitations

X. Approval and Next Steps

XI. Appendices

Notes:

My Chosen Standard

Naming Conventions

Prescriptive: Naming Conventions

Constants

Variables

Classes

Pandas DataFrames

Lists

Dictionaries

Exception Names

Global Variable Names

Public and Internal Interfaces

Function Annotations

Variable Annotations

1. Define Your Objectives:

2. Gather Your Content:

3. Choose a Website Platform:

4. Design Your Site:

Dynamic View for All `E_` Tables in SQL Server

Option 1 – All `E_` tables have identical schema