Medallion layer management

Managing Medallion Architecture schemas (Bronze → Silver → Gold) dynamically using open-source tools is an efficient and scalable way to handle schema evolution, automation, and governance without relying on commercial tools.

The goal is to have a system that can read schema definitions dynamically (from YAML or JSON), automatically generate and manage schemas and views, support schema evolution when new columns are added or removed, and work consistently across all three layers — Bronze, Silver, and Gold — while remaining fully open-source and version-controlled.

Recommended open-source stack includes:

• Schema storage: YAML or JSON with Git for versioning
• Schema versioning: Liquibase or schemachange
• Data modeling: dbt Core (open source)
• Metadata and validation: Great Expectations or pydantic
• Workflow orchestration: Apache Airflow or Dagster
• Schema registry (optional): Apache Atlas or Amundsen for lineage tracking

For the Bronze layer, which handles raw ingestion, schemas can be inferred automatically from data sources such as Parquet, JSON, or CSV using Python libraries like pandas or pyarrow. The inferred schema is then stored as YAML for tracking and governance. Technologies such as Delta Lake or Apache Iceberg can be used to handle schema evolution and metadata automatically. The YAML file for Bronze would typically define the data source location, inference date, and the inferred schema fields.

In the Silver layer, which focuses on standardized and cleaned data, dynamic schema management comes into full use. YAML is used to define expected schemas, which can then be validated against the actual data before transformation. Great Expectations can enforce schema and data validation, while Jinja templating can be used to dynamically generate SQL that applies cleaning, casting, and normalization logic. For example, columns can be automatically cast to their expected types or cleaned using standard transformations defined in YAML. These transformations can then be executed using dbt Core or custom Python SQL runners.

The Gold layer is designed for aggregations and business-level transformations. Here, you can use YAML-driven metric definitions, specifying KPIs, calculations, and grouping logic. SQL views or materialized views are generated dynamically from these YAML definitions, providing a curated, business-ready data layer. These gold views can also be refreshed periodically using orchestration tools like Airflow.

Schema evolution is managed by automatically detecting changes, such as new columns in the Bronze layer, and propagating them downstream where appropriate. Every schema update is versioned in Git or logged in a changelog YAML with details like version number, added or dropped columns, and timestamps. Open-source tools like schemachange, Liquibase, or pyiceberg help track and apply these schema changes across environments.

Automation is typically handled through an orchestration pipeline such as Airflow or Dagster, which can run jobs in sequence: ingesting raw data and inferring schema for Bronze, storing or updating the schema YAML, validating and cleaning data for Silver, generating and updating Gold views, and finally notifying stakeholders of any schema changes.

A recommended folder structure for this approach includes three schema folders (bronze, silver, gold) each containing YAML files for datasets, a scripts folder with Python utilities for schema generation and validation, a dbt project folder for model definitions, and a folder for orchestration DAGs such as medallion_schema_dag.py.

Best practices summary:

For Bronze, schemas are inferred automatically using tools like pyarrow or delta-rs and stored as YAML for documentation. For Silver, schemas are defined explicitly in YAML and validated with Great Expectations, while dbt or templated SQL applies the cleaning and transformation. For Gold, schemas are YAML-defined and version-controlled, with SQL logic dynamically generated to build metrics or views for business consumption.

Combining open-source tools such as Delta Lake or Iceberg for data storage and evolution, dbt Core for transformations, Great Expectations for validation, Airflow or Dagster for orchestration, and YAML with Git for schema as code provides a fully open-source, dynamic, and maintainable framework for Medallion Architecture schema management.

If needed, this setup can be extended with a fully open-source schema governance framework by integrating Apache Atlas or Amundsen for lineage tracking and metadata discovery.

If you’d like, a complete open-source architecture diagram and folder template can be generated that illustrates how YAML, Python, dbt, and Great Expectations interact to implement this dynamic Medallion schema management approach.

If you want fully open-source dynamic schema governance, you can combine:

• Delta Lake or Iceberg for storage and schema evolution
• dbt Core for transformation
• Great Expectations for schema validation
• Airflow or Dagster for orchestration
• YAML + Git for schema as code

Folder structure

/schemas

 /bronze

  customers.yaml

  orders.yaml

 /silver

  customers_clean.yaml

  orders_enriched.yaml

 /gold

  customer_metrics.yaml

/scripts

 schema_generator.py

 schema_validator.py

/dbt_project

 models/

  bronze/

  silver/

  gold/

/airflow_dags

 medallion_schema_dag.py

From Blogger iPhone client

Dynamic handling of Medallion layer schema

Here’s a clean, single-copy version without code blocks or formatting boxes so you can easily copy it all at once:

Excellent question — this is a very relevant problem in data engineering pipelines following the medallion architecture (Bronze → Silver → Gold).

Let’s go through the best practices for using YAML + Python to define and generate table schemas and views for each layer (Bronze, Silver, Gold).

1. Core Concept
2. Instead of hardcoding SQL or schema definitions, you maintain YAML files that describe:

• Table name and layer (bronze/silver/gold)
• Columns and types
• Source or transformation logic
• Primary/foreign keys
• Partition and clustering details

Your Python code then reads the YAML and:

• Generates CREATE TABLE DDL for each layer
• Generates views for transformations (Silver, Gold)
• Optionally executes them (e.g., via BigQuery, Databricks, or Snowflake SDKs)

1. Example YAML Schema Design

tables:

• name: customer_bronze
• layer: bronze
• source: gs://raw-data/customers.csv
• description: “Raw customer data from CRM system”
• columns:
• name: customer_id
• type: STRING
• nullable: false
• name: name
• type: STRING
• name: created_at
• type: TIMESTAMP
• metadata:
• partition_by: created_at
• format: parquet
• 
  
• name: customer_silver
• layer: silver
• source_table: customer_bronze
• description: “Cleaned and deduplicated customer data”
• transformations:
• “SELECT DISTINCT * FROM {{source_table}}”
• columns:
• name: customer_id
• type: STRING
• name: name
• type: STRING
• name: created_at
• type: TIMESTAMP
• 
  
• name: customer_gold
• layer: gold
• source_table: customer_silver
• description: “Customer aggregated metrics”
• transformations:
• |
• SELECT
• customer_id,
• COUNT(*) AS transaction_count,
• MAX(created_at) AS last_transaction
• FROM {{source_table}}
• GROUP BY customer_id
• columns:
• name: customer_id
• type: STRING
• name: transaction_count
• type: INTEGER
• name: last_transaction
• type: TIMESTAMP
• 
  

1. Python Script to Generate DDL and Views

import yaml

from jinja2 import Template

def load_yaml(yaml_file):

with open(yaml_file, ‘r’) as f:

return yaml.safe_load(f)

def generate_create_table_sql(table):

cols = “,\n “.join([

f”{col[‘name’]} {col[‘type’]}{’’ if col.get(‘nullable’, True) else ’ NOT NULL’}”

for col in table[‘columns’]

])

table_name = table[‘name’]

sql = f”CREATE OR REPLACE TABLE {table_name} (\n {cols}\n)”

if ‘metadata’ in table and table[‘metadata’].get(‘partition_by’):

sql += f”\nPARTITION BY {table[‘metadata’][‘partition_by’]}”

if ‘metadata’ in table and table[‘metadata’].get(‘format’):

sql += f”\nOPTIONS (format=’{table[‘metadata’][‘format’]}’)”

return sql + “;”

def generate_view_sql(table):

if ‘transformations’ not in table:

return None

source_table = table.get(‘source_table’)

transformation_sql = “\n”.join(table[‘transformations’])

sql_template = Template(transformation_sql)

rendered_sql = sql_template.render(source_table=source_table)

return f”CREATE OR REPLACE VIEW {table[‘name’]} AS\n{rendered_sql};”

if name == “main”:

schema = load_yaml(‘tables.yaml’)

for table in schema[‘tables’]:

if table[‘layer’] == ‘bronze’:

ddl = generate_create_table_sql(table)

else:

ddl = generate_view_sql(table)

print(f”\n– {table[‘name’].upper()} ({table[‘layer’]})\n{ddl}\n”)

1. Best Practices Summary

YAML Design

• Keep each layer’s YAML separate (bronze.yaml, silver.yaml, gold.yaml) for modularity.
• Use Jinja templating inside YAML for reusable transformations.
• Add metadata for partitioning, file format, and owner/team.

Code Design

• Use a class-based model (Table, Column) to represent tables.
• Validate YAML schema using pydantic or cerberus.
• Add unit tests to verify SQL generation.
• Optionally, store schema history (for auditing schema evolution).

Layer Logic

From Blogger iPhone client

Creating Schema at runtime using Python

Excellent — this is a powerful and very reusable data engineering utility. ✅

Let’s design a Python function that:

• Reads a YAML file describing a BigQuery table schema
• Generates a BigQuery CREATE TABLE SQL statement
• Supports all major BigQuery data types, including nested STRUCT and repeated ARRAY fields

Below you’ll find a clean, copy-friendly full script (no code cells), including an example YAML file and example output.

Example YAML (table_schema.yaml)

table_name: sales_data

dataset_name: analytics

columns:

• name: order_id
• type: INT64
• mode: REQUIRED
• name: customer_name
• type: STRING
• mode: NULLABLE
• name: order_date
• type: DATE
• mode: NULLABLE
• name: total_amount
• type: FLOAT64
• mode: NULLABLE
• name: is_priority
• type: BOOL
• mode: NULLABLE
• name: items
• type: RECORD
• mode: REPEATED
• fields:
• name: item_id
• type: STRING
• mode: REQUIRED
• name: quantity
• type: INT64
• mode: NULLABLE
• name: attributes
• type: RECORD
• mode: NULLABLE
• fields:
• name: color
• type: STRING
• mode: NULLABLE
• name: size
• type: STRING
• mode: NULLABLE
• 
  
• 
  

Python Script (generate_bigquery_create.py)

import yaml

def load_yaml_schema(file_path):

“”“Load table schema from YAML file.”””

with open(file_path, ‘r’) as f:

return yaml.safe_load(f)

def generate_field_sql(field):

“”“Recursively generate SQL for a field, including nested STRUCT and ARRAY.”””

name = field[“name”]

field_type = field[“type”].upper()

mode = field.get(“mode”, “NULLABLE”).upper()

# If this field is a RECORD/STRUCT

if field_type in ("RECORD", "STRUCT"):

  nested_fields = field.get("fields", [])

  nested_sql_parts = [generate_field_sql(f) for f in nested_fields]

  struct_sql = f"STRUCT< {', '.join(nested_sql_parts)} >"

  base_sql = f"{name} {struct_sql}"

else:

  base_sql = f"{name} {field_type}"

# Handle ARRAY / REPEATED fields

if mode == "REPEATED":

  base_sql = f"{name} ARRAY<{field_type if field_type not in ('RECORD', 'STRUCT') else f'STRUCT<{', '.join([generate_field_sql(f) for f in field.get('fields', [])])}>'}>"

return base_sql

def generate_create_table_sql(schema_dict):

“”“Generate BigQuery CREATE TABLE statement from schema dictionary.”””

dataset_name = schema_dict[“dataset_name”]

table_name = schema_dict[“table_name”]

columns = schema_dict[“columns”]

column_sql_parts = [generate_field_sql(col) for col in columns]

columns_sql = ",\n ".join(column_sql_parts)

sql = f"""CREATE OR REPLACE TABLE `{dataset_name}.{table_name}` (

column_sql_parts = [generate_field_sql(col) for col in columns]

columns_sql = ",\n ".join(column_sql_parts)

sql = f"""CREATE OR REPLACE TABLE `{dataset_name}.{table_name}` (

{columns_sql}

);”””

return sql

def main():

yaml_path = “table_schema.yaml” # path to your YAML file

schema = load_yaml_schema(yaml_path)

sql = generate_create_table_sql(schema)

print(sql)

if name == “main”:

main()

Example Output

CREATE OR REPLACE TABLE analytics.sales_data (

order_id INT64,

customer_name STRING,

order_date DATE,

total_amount FLOAT64,

is_priority BOOL,

items ARRAY<STRUCT<item_id STRING, quantity INT64, attributes STRUCT<color STRING, size STRING>>>

);

✅ Supported Data Types

• STRING
• INT64
• FLOAT64
• BOOL
• DATE, DATETIME, TIMESTAMP
• RECORD / STRUCT (nested)
• ARRAY / REPEATED (nested or primitive)

✅ Advantages

• Easy to update schemas by editing YAML instead of SQL
• Supports deeply nested JSON-like structures
• Ideal for data pipeline automation or metadata-driven design

From Blogger iPhone client

Microsoft Power BI using Rest API Python

Excellent — connecting to a Power BI workspace using Python lets you automate publishing, refreshing, or managing datasets via the Power BI REST API.

Here’s a full, clean, copy-friendly guide (no code cells, no formatting issues).

You can select all and copy directly into your Python environment.

Connect to Power BI Workspace using Python

Step 1 — Install required Python libraries

pip install requests msal

Step 2 — Set up Azure AD app (Service Principal)

1. Go to Azure Portal → Azure Active Directory → App registrations → New registration
2. Note down:
4. Application (client) ID
5. Directory (tenant) ID
6. 
   
7. Create a Client Secret under “Certificates & Secrets”.
8. In Power BI Service → Admin portal → Tenant settings → Developer settings, enable:
10. Allow service principals to use Power BI APIs
11. Allow service principals to access Power BI workspaces
12. 
    
13. Add your app to the target workspace:
15. Power BI → Workspace → Access → Add → Enter app name → Assign role (Admin or Member)
16. 
    
17. 
    

Step 3 — Define authentication details in Python

import requests

import msal

Tenant ID, Client ID, and Client Secret from your Azure AD app

tenant_id = “YOUR_TENANT_ID”

client_id = “YOUR_CLIENT_ID”

client_secret = “YOUR_CLIENT_SECRET”

Power BI API scope and authority

authority = f”https://login.microsoftonline.com/{tenant_id}”

scope = [“https://analysis.windows.net/powerbi/api/.default”]

Create MSAL confidential client app

app = msal.ConfidentialClientApplication(

client_id,

authority=authority,

client_credential=client_secret

)

Get access token

token_result = app.acquire_token_for_client(scopes=scope)

access_token = token_result[“access_token”]

print(“Access token acquired successfully!”)

Step 4 — Connect to Power BI and list all workspaces

headers = {

“Authorization”: f”Bearer {access_token}”

}

response = requests.get(“https://api.powerbi.com/v1.0/myorg/groups”, headers=headers)

if response.status_code == 200:

workspaces = response.json()[“value”]

for ws in workspaces:

print(f”Name: {ws[‘name’]} | ID: {ws[‘id’]}”)

else:

print(“Error:”, response.status_code, response.text)

Step 5 — List all reports in a specific workspace

workspace_id = “YOUR_WORKSPACE_ID”

url = f”https://api.powerbi.com/v1.0/myorg/groups/{workspace_id}/reports”

response = requests.get(url, headers=headers)

if response.status_code == 200:

reports = response.json()[“value”]

for report in reports:

print(f”Report: {report[‘name’]} | ID: {report[‘id’]}”)

else:

print(“Error:”, response.status_code, response.text)

Step 6 — (Optional) Upload a new .pbix report to workspace

pbix_file_path = “C:\Reports\FinanceDashboard.pbix”

dataset_display_name = “FinanceDashboard”

url = f”https://api.powerbi.com/v1.0/myorg/groups/{workspace_id}/imports?datasetDisplayName={dataset_display_name}”

with open(pbix_file_path, “rb”) as pbix_file:

response = requests.post(

url,

headers={

“Authorization”: f”Bearer {access_token}”,

“Content-Type”: “application/octet-stream”

},

data=pbix_file

)

if response.status_code in [200, 202]:

print(“Report uploaded successfully!”)

else:

print(“Error:”, response.status_code, response.text)

✅ Notes

• The msal library handles secure Azure AD authentication.
• The access token is valid for about 1 hour — refresh when needed.
• You can perform additional actions using the Power BI REST API (refresh datasets, rebind reports, delete reports, etc.).
• For production automation, store secrets in Azure Key Vault.

Would you like me to extend this with dataset refresh automation (Python script that triggers a refresh and checks its status)?

From Blogger iPhone client

Publish Microsoft power bi using power shell

PowerShell Script — Publish Power BI Report to Cloud

Install Power BI PowerShell module (run once)

Install-Module -Name MicrosoftPowerBIMgmt -Scope CurrentUser

Login to Power BI Service

Login-PowerBIServiceAccount

Define variables

$pbixPath = “C:\Reports\SalesDashboard.pbix”

$workspaceName = “Finance Analytics”

$reportName = “Sales Dashboard”

Get workspace ID

$workspace = Get-PowerBIWorkspace -Name $workspaceName

$workspaceId = $workspace.Id

Publish report to Power BI Service

New-PowerBIReport -Path $pbixPath -Name $reportName -WorkspaceId $workspaceId -ConflictAction CreateOrOverwrite

Verify upload

Get-PowerBIReport -WorkspaceId $workspaceId

Optional — Save as reusable PowerShell script (publish_report.ps1)

param (

[string]$pbixPath,

[string]$workspaceName,

[string]$reportName

)

Import-Module MicrosoftPowerBIMgmt

Login-PowerBIServiceAccount

$workspace = Get-PowerBIWorkspace -Name $workspaceName

New-PowerBIReport -Path $pbixPath -Name $reportName -WorkspaceId $workspace.Id -ConflictAction CreateOrOverwrite

Logout-PowerBIServiceAccount

Example run command

.\publish_report.ps1 -pbixPath “C:\Reports\Finance.pbix” -workspaceName “Finance BI” -reportName “Finance Overview”

Advanced option — Use REST API directly

$token = (Get-PowerBIAccessToken).AccessToken

$workspaceId = “xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx”

Invoke-RestMethod -Uri “https://api.powerbi.com/v1.0/myorg/groups/$workspaceId/imports?datasetDisplayName=SalesDashboard” -Headers @{Authorization = "Bearer $token"}

-Method Post -InFile "C:\Reports\SalesDashboard.pbix"

-ContentType “application/octet-stream”

✅ Notes

• Make sure you have permission to publish to the workspace.
• Use -ConflictAction CreateOrOverwrite to update existing reports.
• For automation, use service principals (App registration in Azure AD).
• This method works for Power BI Pro and Power BI Premium workspaces.

From Blogger iPhone client

Microsoft power bi vs Tableau relative comparison

Great question — both Microsoft Power BI and Tableau are top-tier business intelligence (BI) tools, but Power BI has some clear advantages, especially for organizations already within the Microsoft ecosystem or focused on cost efficiency and scalability.

Here’s a detailed comparison highlighting Power BI’s advantages over Tableau 👇

⚙️ 1. 

Integration with Microsoft Ecosystem

Advantage: Seamless connectivity with Microsoft products

• Power BI tightly integrates with Excel, Azure, Microsoft 365, Dynamics 365, and Teams, making it ideal for organizations using Microsoft technologies.
• Users can easily import data from Excel, share dashboards through Teams, and leverage Azure Synapse or SQL Server for data storage and processing.
• Example: Finance teams can directly publish Excel-based models into Power BI with one click.

💰 2. 

Cost and Licensing

Advantage: Significantly cheaper than Tableau for most deployments

• Power BI Pro: ~$10/user/month
• Power BI Premium: starts at ~$20/user/month (or capacity-based)
• Tableau Creator: ~$70/user/month
• Tableau Server/Cloud pricing also adds cost complexity.
• Impact: Power BI is far more cost-effective for organizations with many report viewers or casual users.

🧩 3. 

Ease of Use (especially for Excel users)

Advantage: Familiar and easy learning curve

• Power BI’s interface and DAX (Data Analysis Expressions) are intuitive for Excel power users.
• Tableau requires users to learn its visualization grammar and interface, which can be less familiar.
• Result: Faster adoption and reduced training costs.

☁️ 4. 

Cloud Integration and Governance

Advantage: Deep integration with Azure Active Directory and Microsoft Fabric

• Power BI provides built-in identity, access, and data governance through AAD and Microsoft Purview.
• Power BI integrates natively into Microsoft Fabric, providing unified data engineering, data science, and BI on a single platform.
• Result: Simplified governance and security in cloud or hybrid environments.

🔗 5. 

Data Connectivity and Real-Time Analytics

Advantage: Extensive connectors and native streaming capabilities

• Power BI offers native connectors to hundreds of sources (SQL, Azure, SharePoint, Dynamics, Salesforce, Google Analytics, etc.).
• It supports DirectQuery and real-time dashboards out of the box — ideal for IoT, operations, or monitoring use cases.
• Example: Live dashboards for call center performance or manufacturing KPIs.

📊 6. 

AI and Advanced Analytics

Advantage: Built-in AI and natural language capabilities

• Power BI integrates with Azure Machine Learning and Copilot for Power BI (AI assistant for report creation).
• Natural language querying (“Q&A” feature) lets users ask questions in plain English.
• Tableau requires external setup for similar AI functionality.
• Result: Easier access to predictive analytics and automated insights.

🧑‍💻 7. 

Developer and Enterprise Features

Advantage: Strong API, embedding, and governance options

• Power BI Embedded allows seamless integration of reports into custom applications or portals.
• Strong version control and deployment pipelines for large enterprises.
• Example: Developers can embed dashboards in internal or customer-facing apps using Power BI APIs with minimal effort.

🌍 8. 

Community and Continuous Updates

Advantage: Rapid monthly updates and large user base

• Microsoft releases monthly updates to Power BI Desktop and Service.
• Huge user community with free tutorials, templates, and DAX resources.
• Result: Faster feature growth and community-driven innovation.

Summary Table

Feature / Category

Power BI Advantage

Tableau

Microsoft integration

✅ Excellent

⚪ Moderate

Pricing

✅ Lower

❌ Higher

Ease of use

✅ Easier (Excel-like)

⚪ Steeper learning curve

Real-time analytics

✅ Native support

⚪ Add-ons required

AI and ML

✅ Integrated (Copilot, Azure ML)

⚪ Limited built-in

Cloud & governance

✅ AAD, Purview, Fabric

⚪ Manual setup

Updates & innovation

✅ Monthly

⚪ Quarterly

Enterprise scalability

⚪ Very strong (Premium)

✅ Also strong

Custom visualizations

⚪ Slightly fewer

🧠 In Short:

Choose Power BI if you want:

• Seamless integration with Microsoft products
• Lower total cost of ownership
• Easier user adoption and governance
• AI-assisted insights and rapid development

Choose Tableau if you need:

• More design flexibility and visual polish
• Cross-platform neutrality (non-Microsoft stack)
• Deeper control over visualization aesthetics

From Blogger iPhone client

Benefits

Working for the Florida Department of Agriculture and Consumer Services (FDACS) in the USA can offer several benefits, including:

1. **Job Stability**: As a government agency, FDACS provides a stable and secure work environment, with positions that are less susceptible to economic downturns compared to the private sector.

2. **Competitive Compensation**: FDACS offers competitive salaries, particularly when considering the cost of living in Florida. Additionally, employees may receive salary increases based on performance, tenure, or promotions.

3. **Comprehensive Benefits**: Employees typically receive a benefits package that includes:

  - Health, dental, and vision insurance.

  - Retirement plans through the Florida Retirement System (FRS), which includes both a pension and investment plan option.

  - Paid vacation, sick leave, and holidays.

  - Life insurance and disability benefits.

4. **Work-Life Balance**: Government jobs like those at FDACS often emphasize work-life balance. Employees typically work standard hours and can benefit from flexible schedules, including options for remote work or telecommuting, depending on the position.

5. **Professional Development**: FDACS offers training programs, workshops, and educational opportunities to help employees advance in their careers. Many government agencies prioritize ongoing employee development and may even offer tuition assistance for further education.

6. **Public Service Impact**: Working for FDACS provides the opportunity to contribute to the state's agricultural industry, food safety, and consumer protection, helping ensure the health and well-being of Florida’s residents.

7. **Job Diversity**: FDACS offers a wide range of career opportunities in various fields, such as agriculture, forestry, food inspection, law enforcement, consumer protection, and environmental services, making it appealing to people with different skill sets and career goals.

8. **Networking Opportunities**: Working within a government agency can offer chances to collaborate with other state agencies, local governments, private industry, and community organizations, broadening professional connections.

These benefits make FDACS an attractive option for those seeking a career with purpose, security, and growth potential.

From Blogger iPhone client

Machine Vision detecting paint issues and defects

open-source libraries, toolkits, and projects you can use (or build on) to detect paint issues (defects, variability, etc.) in images. Depending on whether you need classical image processing vs deep learning vs segmentation vs anomaly detection, some are more suitable than others. I’ll also mention pros/cons and what sort of paint issue they might help with.

Name

Description / Use-Cases

Strengths

Limitations / Challenges

OpenCV

A classic computer vision library in C++/Python. Useful for filtering, edge detection, thresholding, morphological ops, color segmentation, etc. It’s a good baseline for detecting scratches, blotches, unevenness or color defects. 

Very mature; many tutorials; works in real-time; small models; lots of control.

Classical methods often fragile under variable lighting, texture, reflections. May require heavy tuning; less effective for subtle defects or where you need localization or segmentation.

Mahotas

Python library for traditional image processing (filters, morphology, feature descriptors). 

Fast, C++ under the hood; good for prototyping; integrates well with scientific workflows.

Not focused on deep learning; might struggle with high-level defects; no built-in object detection or segmentation for complex scenarios.

VIGRA (Vision with Generic Algorithms)

C++ (with Python binding) library for customizable vision algorithms. Useful when you need high performance and precise control. 

Efficiency; template-based; good for lower-level image processing where you have strong domain control.

Less “out-of-the-box” deep learning / modern segmentation/detection pipelines; steeper learning curve; fewer pre-trained models.

ilastik

Interactive machine learning / segmentation / classification toolkit. Useful when you want to label parts of images (e.g. areas with paint peeling, cracks, discoloration) and do segmentation. 

Very user-friendly; rapid iteration; GUI for non-experts; works well with small annotated datasets; able to do pixel-wise segmentation.

Might not scale to very large datasets or real-time on large images; for subtle defects, might require good annotation; not always ideal for production deployment without customization.

Kornia

Differentiable computer vision library built on PyTorch; includes routines for geometric transforms, filtering, edge detection, image augmentation etc. Useful if you want to build parts of your pipeline as differentiable modules (e.g. embed in a neural network). 

Seamless integration with deep learning frameworks; useful for creating custom losses, augmentations; modern.

Using deep learning increases complexity; you’ll need data, correct training; more overhead; may need GPU etc.

Anomalib (via OpenMMLab / other anomaly detection libraries)

Although I didn’t find a clean citation above for “paint issues,” anomaly detection libraries are very relevant when you mostly have “good” paint surfaces and want to spot “abnormal” defects (scratches, pits, spots etc.).

You can train with “normal” examples and detect anomalies; less labeling effort for defect classes; good for rare/unpredictable defects.

Hard to detect subtle anomalies if they are similar to normal texture; false positives; need well-controlled imaging, consistent lighting; deployment may need calibration.

Specific Projects / Datasets

• Defect_detector (GitHub: AhmadCodes/defect_detector): A library + GUI for defect detection in images/videos using multiple methods (edge detection, background subtraction, thresholding, object detection). Could be a good starting point.  
• ARTDET: Open-source tool built on Mask R-CNN for detecting deterioration/damage in paintings. Useful for paint defects if you treat paint issues as “damage” (peeling, cracks, loss). Offers pixel-level segmentation.  
• A study “Smart System to Detect Painting Defects in Shipyards” (Appl. Sci., 2022) that implements Vision AI and deep learning for paint defect detection in real shipyard environment. Could be informative for domain adaptations, lighting, real-world constraints.  

What to Consider: What vs How

To pick which library / approach is best, think about:

1. Type of defects you need to detect (e.g. color inconsistency, paint runs, sags, orange peel texture, scratches, corrosion under paint, rust, peeling, bubbling, etc.). Some are structural (shape, elevation), some are purely appearance (color, gloss, reflections).
2. Image capture setup: lighting control, resolution, reflections, angle, consistency. Paint tends to be reflective; shadows/reflections can look like defects.
3. Data available: Do you have lots of images with and without defects? Is there segmentation (pixel-level) data or only “good/bad” labels?
4. Real-time vs offline: On a production line you might need fast inference; that may push you toward lighter models or even classical methods if acceptable.
5. Annotation cost: Pixel segmentation is more expensive to label than bounding boxes; anomaly detection needs fewer defect samples but may have higher false positives.
6. Evaluation metrics: false positives vs false negatives cost; tolerances; how precise the detection must be.

From Blogger iPhone client

Microsoft Power BI Best practice connecting to semantic data layer

Great question — this comes up often when teams are modernizing their Power BI semantic models and don’t want to rebuild reports every time the model changes. The “ideal way” depends on how often your model changes and how much stability you want to give report authors.

Here are some best-practice approaches:

🔑 1. Use a 

Shared Dataset / Semantic Model in Power BI Service

• Publish your model once as a Power BI dataset (semantic model) in the Service.
• Build reports off that shared dataset instead of embedding the model inside each PBIX.
• When the model is updated and republished, all reports connected to it automatically see the new version (assuming field names/measures stay consistent).
• This avoids the need to touch each report individually.

🔑 2. Use 

Thin Reports

• Keep reports “thin” (no imported data, only visuals + connections to the shared dataset).
• Users or developers only work on visuals; the model lives centrally.
• If the semantic model is updated (new measures, columns, relationships), reports immediately benefit.
• If a field is renamed/removed, only the visuals depending on it need fixing.

🔑 3. Manage Schema Changes with 

Stable Layering

• To reduce breakage:
• Maintain consistent field names and measure names across versions of the model.
• If you must change something, create calculated columns or measures that act as aliases for old names.
• Keep a semantic abstraction layer where changes in the source are hidden behind stable, user-facing measures.
• 
  

🔑 4. Use 

Deployment Pipelines

• If you manage Dev/Test/Prod, use deployment pipelines in Power BI Service.
• Push updated versions of the dataset through environments while validating before production.
• Reports remain linked and stable.

🔑 5. Consider 

Dataflows + Composite Models

• If source schema changes frequently, you can decouple transformation (dataflows) from the semantic model.
• The dataflow maintains schema stability, while the semantic model builds on top.
• Reports then depend only on the semantic model, not raw sources.

✅ Recommended Ideal Setup:

• Publish a centralized dataset (semantic model) in Power BI Service.
• Keep reports as thin clients.
• Use stable naming conventions in the model, and handle source changes in dataflows or a staging layer.
• Use deployment pipelines if you have multiple environments.

This way, when a new semantic model is published, users don’t need to update reports — only the dataset is swapped or versioned.

Do you want me to sketch a step-by-step migration flow (from report-embedded models → shared semantic model → version upgrades) so you can apply it in your environment?

From Blogger iPhone client

Airport security solutions

8

IDEMIA is a French technology company that develops innovative identity-related security solutions, specializing in biometrics and cryptography for governments and businesses. Their technologies are used in areas like national security, border control, public safety, and secure payment services, aiming to make life safer and more efficient through AI-powered solutions for identity verification, travel, and digital transactions.  

Key Offerings and Services

• Biometric Identification: IDEMIA provides biometric identification products and software, including facial recognition and fingerprint technology, used for authentication and access control. 
• Digital Identity: They offer solutions for issuing and managing secure identity documents, such as driver's licenses and government IDs, and enable remote identity proofing and mobile identity solutions. 
• Secure Transactions: IDEMIA's Secure Transactions division focuses on building the technology behind secure payments, including eSIM solutions and card-related services. 
• Travel and Border Control: They provide biometric solutions for airports and other transport hubs to streamline passenger journeys, from check-in to border clearance. 
• Public Security: Their technologies support law enforcement and justice systems with AI-driven algorithms for tasks like video analytics and multibiometric identification. 

Core Technologies and Focus Areas

• Artificial Intelligence (AI): AI is central to their solutions, powering algorithms for person and object recognition, enhancing security, and improving the efficiency of systems. 
• Cryptography: In addition to biometrics, IDEMIA develops cryptographic technologies to protect data and secure transactions. 
• Global Presence: The company has a global reach, operating in over 180 countries and providing solutions that cater to both public and enterprise sectors. 

From Blogger iPhone client

Types of Microsoft Power Bi Connections

Power BI has two main types of data connectivity: Import (copying data into Power BI) and DirectQuery (querying data directly from the source), along with Live Connection (to specific Power BI and Analysis Services datasets) and Composite Models, which combine these approaches. Choosing the right connection type depends on factors like data volume, the need for real-time data, data modeling capabilities, and report performance. 

Here's a breakdown of each connection type: 

1. Import Mode

• How it works: Data is copied and stored directly within the Power BI report, allowing for efficient data model creation and transformations using Power Query. 
• Pros: Fast query performance, comprehensive Power Query transformation capabilities, and full access to data modeling. 
• Cons: Requires scheduled refresh for data to be updated, can consume significant storage, and may not be suitable for very large datasets. 
• Best for: Most scenarios where data doesn't need to be completely real-time and a manageable amount of data is involved. 

2. DirectQuery Mode

• How it works: Power BI sends queries directly to the external data source to retrieve data in real-time. 
• Pros: Supports large datasets, provides near real-time data, and requires less storage in Power BI. 
• Cons: Performance depends on the source database, Power Query transformations are limited, and data modeling capabilities are restricted. 
• Best for: Situations requiring near real-time data or when dealing with massive datasets that cannot be imported. 

3. Live Connection Mode 

• How it works: Creates a live connection to a specific Power BI dataset or Analysis Services tabular model, without importing data into Power BI Desktop. 
• Pros: Leverages existing, complex models and DAX measures created in the source, and supports large data models. 
• Cons: No access to Power Query for data transformation, and report performance is dependent on the underlying Analysis Services model. 
• Best for: Connecting to established, robust data models in Power BI or Analysis Services, allowing for consistent data and logic across multiple reports. 

4. Composite Model

• How it works: A hybrid approach that allows you to combine data from different connection modes (Import, DirectQuery, and Live Connection) within a single data model. 
• Pros: Offers a flexible way to combine the benefits of different connection types. 
• Cons: Can introduce complexity and requires careful consideration of model design to ensure performance. 
• Best for: Scenarios where you need to integrate data from both real-time sources (DirectQuery) and static datasets (Import) in one model. 

5. DirectLake (Newer Mode) 

• How it works: An optimization for Azure Synapse Analytics and Fabric, it allows DirectQuery to read directly from the underlying data in the data lake, offering high performance with large volumes of data. 
• Pros: Improved performance for large datasets with near real-time data. 
• Cons: Limited to specific data sources and platforms. 
• Best for: Large-scale data warehousing and analytics scenarios, leveraging the data lake for speed.

From Blogger iPhone client

Aircraft paint

https://youtube.com/shorts/fG54pi5tFQo?si=LWTARae0hIkcQi2O

From Blogger iPhone client

Stocks

Here’s a summary of what I found on Artrya Ltd (ASX: AYA) — current price, financials, and my thoughts on timing. This isn’t investment advice, but should help you make a more informed decision.

Key Data & Current Price

Balance Sheet & Financial Health

Here are some important points from the most recent financial reports:

• Total Assets: ~AUD 23.68 million in FY2025.  
• Total Liabilities: ~AUD 2.39 million.  
• Debt: Very low. Debt is small in relation to assets; the company is using equity / cash more than debt.  
• Cash Flow from Operations: Negative, in the order of –AUD 14-15 million in the latest period. That means cash is being burned.  
• Cash on Hand / Liquidity: The cash reserves are modest; the company has been raising capital (including placements) to fund growth and expansion.  

Valuation & Analyst Views

• One analyst target places a 12-month price target at AUD 3.06 per share. That would imply ~40% upside from the current price.  
• But there are warnings: with very little revenue, large losses, and dilution risk (because of recent and expected capital raises), some models (discounted cash flow etc.) suggest the current price may be overvalued relative to intrinsic value.  
• Indeed, one analysis reduced its valuation estimate to ~$3.06 from ~$3.48 due to dilution and rising costs.  

What Looks Good / What’s Risky

Strengths:

• Innovative product in AI for coronary artery disease; has regulatory clearances (e.g. FDA for some modules) which is a positive signal.  
• Good balance sheet in terms of liabilities: low debt, which gives it more flexibility.  
• Strong upside potential if the company can convert regulatory wins + product adoption into revenue. Also, recent capital raising suggests belief (by investors) in growth potential.  

Risks:

• Very low revenue now; losses are large and cash burn is high. That means the company must grow quickly or else more dilution / capital raises may be needed.
• Valuation is high relative to its current earnings and book value; the market seems to be pricing in a lot of future success. If growth doesn’t accelerate, the downside risk is non-trivial.
• Dilution risk: recent placements / equity raises can reduce value per share for existing shareholders.  

When Might It Be a “Good Time to Buy”?

Putting together the above, here are some thoughts on timing. Again, depends on how much risk you’re willing to take.

• If you are optimistic on the long-term prospects (product adoption, US expansion, regulatory approvals, etc.), then buying now could capture potential upside, especially if the company executes well. The current price is high but part of that reflects future potential.
• A more conservative route would be to wait for evidence of revenue growth (e.g. quarterly reports showing sales climbing and losses narrowing) before committing more capital.
• Another trigger could be major contract wins or partnerships, especially in the US or other large markets, or successful results from studies (e.g. the “SAPPHIRE” study referenced in some materials) that validate the technology.
• Also, watch for dilution events: if a share issue is coming at a discount, waiting could avoid buying ahead of that and suffering from dilution.

My View

If I were you and interested in Artrya:

• I’d say it’s high-risk, high-reward. There is real potential, but everything hinges on execution — getting revenue, managing cash, scaling well.
• I think the risk/reward might be more favorable if the price dips somewhat (maybe due to short-term negatives) or after some negative event (like a weaker quarter) when expectations are scaled back. Those dips often provide better entry points.
• If you believe strongly in their AI tech and think the industry tailwinds (healthcare, AI, diagnostic/AI tools) are in their favor, then some exposure seems reasonable — but keep it modest unless results improve.

If you like, I can run a scenario analysis (best case / mid / worst case) for Artrya with projected outcomes, to help you see what price targets might be realistic under different conditions. Do you want me to do that?

From Blogger iPhone client

Erwin vs ER Studio

7

ER/Studio is enterprise-grade software developed by Idera, Inc. for data architecture and database design. It helps organizations create, manage, document, and reuse data assets by enabling business-driven data modeling and providing features for data governance, collaboration, and integration with other tools. The software supports various database platforms and architectural approaches, including Data Mesh and Lakehouses, and features a central model repository for shared access and knowledge management.  

Key Features and Capabilities

• Enterprise Data Modeling: ER/Studio provides comprehensive logical and physical data modeling capabilities to design and understand database landscapes. 
• Data Governance: It integrates with data governance tools like Microsoft Purview and Collibra, enabling automated connections to cataloging, lineage, and compliance workflows. 
• Collaboration: A central model repository allows multiple users to collaborate on modeling projects, share data assets, and enforce organizational standards. 
• Round-trip Engineering: The software supports reverse-engineering, analyzing, and optimizing existing databases from various platforms. 
• Platform Support: It is compatible with a wide range of database platforms, including cloud environments like Snowflake and Databricks. 
• Architectural Support: ER/Studio is built to support modern approaches, including Data Mesh, Data Vaults, Medallion Architectures, and Lakehouses. 
• Knowledge Management: The central repository stores metadata, facilitating knowledge transfer between business and technical stakeholders and managing version control. 

How it Works

1. Discovery and Documentation: Users can catalog existing data sources and track data lineage across different platforms. 
2. Modeling: ER/Studio facilitates the creation of both logical and physical data models. 
3. Collaboration: Teams can work together in the model repository, checking out and checking in model portions for collaborative development. 
4. Governance: Models are integrated with data governance processes, ensuring consistency and compliance. 
5. Reusability: Common data elements and modeling components can be defined, standardized, and reused across projects, reducing redundancy. 

From Blogger iPhone client

Open source Asana alternative

There isn’t an official open-source release of Asana (the company is closed-source SaaS), but there are open-source project management and collaboration tools that are often considered Asana-like alternatives. These give you tasks, boards, timelines, team collaboration, and in some cases even Gantt charts and reporting.

Here are the most stable open-source “Asana-style” tools:

🔹 Top Open Source Asana Alternatives

1. Taiga
3. Agile project management (Scrum, Kanban).
4. Features: tasks, sprints, backlog, epics, Kanban boards, issue tracking.
5. Tech stack: Python (Django) + Angular.
6. Website: taiga.io
7. 
   
8. OpenProject
10. Very close to Asana in features, more enterprise-ready.
11. Features: tasks, timelines/Gantt, roadmap, team collaboration, budgets, agile boards.
12. Tech stack: Ruby on Rails.
13. Website: openproject.org
14. 
    
15. Wekan
17. Open-source Trello-style Kanban board (lightweight Asana alternative).
18. Features: boards, lists, cards, swimlanes, due dates.
19. Tech stack: Node.js (Meteor).
20. Website: wekan.github.io
21. 
    
22. Redmine (with plugins)
24. Classic project management + issue tracker.
25. With Agile/Gantt plugins, it becomes Asana-like.
26. Tech stack: Ruby on Rails.
27. Website: redmine.org
28. 
    
29. Nextcloud + Deck app
31. Nextcloud is more of a collaboration suite, but its Deck app adds Kanban boards for tasks.
32. Good if you want Asana + file sharing + chat in one.
33. Website: nextcloud.com/deck
34. 
    
35. Phabricator (now maintained as Phorge)
37. Originally from Facebook, strong for software projects.
38. Features: tasks, Kanban boards, project tracking, code review.
39. Website: we.phorge.it
40. 
    

🔹 Quick Recommendation

• If you want closest match to Asana → OpenProject.
• If you want modern agile boards → Taiga.
• If you want Trello-style simplicity → Wekan or Nextcloud Deck.

From Blogger iPhone client

Open Source market tools

Got it 👍 — you’re asking: what are the most stable, reliable, production-ready open source web applications that people actually use in the market today (not just boilerplates or hobby projects).

Here’s a breakdown by category, focusing on maturity, large communities, and proven production adoption:

🔹 Content Management Systems (CMS)

These are the backbone of many SaaS and marketplace applications.

• Drupal — enterprise-grade, stable, modular, great for complex multi-tenant or marketplace use cases.
• WordPress — world’s most popular CMS, massive ecosystem, plugins for almost any functionality.
• Joomla — less popular now, but still stable, often used for community portals.

🔹 E-commerce & Marketplaces

Proven platforms for transactions, vendors, and bidding.

• Magento Open Source (Adobe Commerce) — powerful for multi-vendor marketplaces, very mature.
• Drupal Commerce — tightly integrated with Drupal, reliable for custom SaaS marketplaces.
• PrestaShop — widely used in Europe, stable for small/mid businesses.
• Odoo Community — ERP + e-commerce + bidding/procurement apps, very stable.
• ERPNext — open-source ERP with tender/bidding features, reliable in enterprise deployments.

🔹 Collaboration / Community Platforms

Good for marketplaces with strong networking or vendor-customer interactions.

• Discourse — leading open-source forum/community platform.
• Open Social (Drupal distribution) — enterprise-grade community SaaS, actively maintained.
• Flarum — lightweight community discussion platform.

🔹 Project / Job Marketplaces (closest to Upwork / Fiverr)

Open-source solutions built for freelance-style platforms.

• Sharetribe (Go open-source) — widely adopted for peer-to-peer marketplaces.
• Cocorico — reliable Symfony-based marketplace framework (bookings, bidding, services).
• JoomBri Freelance (Joomla extension) — stable, focused freelance marketplace system.
• WordPress Jobster / FreelanceEngine (themes/plugins) — proven, though not as flexible as Drupal/Sharetribe.

🔹 Enterprise-grade Collaboration / Business Apps

Stable bases for extending into SaaS.

• Nextcloud — reliable open-source collaboration/storage suite.
• Redmine — project management, issue tracking (stable for years).
• GitLab Community Edition — code collaboration and DevOps platform, widely used.

✅ Most stable + reliable (large user base + long-term support):

• Drupal (for CMS/multi-tenant frameworks)
• Odoo or ERPNext (for ERP + bidding)
• Magento Open Source (for e-commerce/marketplaces)
• Sharetribe Go (for peer-to-peer marketplaces)

👉 If your goal is specifically a Fiverr/Upwork-style SaaS, the most stable & reliable open-source starting points today would be:

1. Sharetribe Go (if you want quickest path to market, then extend/customize).
2. Drupal + Commerce + Domain Access (or Tenant) (if you want multi-tenant SaaS with flexibility + long-term enterprise stability).
3. Cocorico (Symfony-based) (if you prefer a PHP framework marketplace out of the box).

From Blogger iPhone client

Application Observation, Generation, logs

OpenTelemetry, Prometheus, Datadog, Splunk, and ELK. These are all popular tools in observability, monitoring, and logging but serve slightly different purposes:

🔹 OpenTelemetry (OTel)

• What it is: An open-source observability framework for collecting metrics, logs, and traces from applications.
• Purpose: Standardizes how telemetry data is generated and exported.
• Key Points:
• Vendor-neutral (works with Prometheus, Datadog, Splunk, etc.).
• Provides SDKs and APIs in many languages.
• Focus is on data collection, not storage/visualization.

🔹 Prometheus

• What it is: An open-source metrics monitoring system.
• Purpose: Collects time-series metrics (CPU, memory, request rate, etc.) from apps and systems.
• Key Points:
• Uses a pull-based model (scrapes data from exporters).
• Comes with its own query language (PromQL).
• Integrates well with Grafana for dashboards.
• Strong in metrics, but not designed for logs or traces.

🔹 Datadog

• What it is: A commercial SaaS monitoring & observability platform.
• Purpose: Provides full-stack monitoring (metrics, logs, traces, security, APM).
• Key Points:
• Agent-based collection across infrastructure, apps, and cloud services.
• Unified dashboards, anomaly detection, alerting.
• Easy integration with AWS, Azure, GCP, Kubernetes, etc.
• Paid solution, but reduces operational overhead compared to managing open-source stacks.

🔹 Splunk

• What it is: A log management and analytics platform.
• Purpose: Indexes, searches, and analyzes machine data (logs, events, metrics).
• Key Points:
• Originally built for log aggregation and searching.
• Powerful search language (SPL – Search Processing Language).
• Expanded into APM, security (SIEM), observability.
• Commercial with high scalability, but can be costly.

🔹 ELK Stack (Elasticsearch, Logstash, Kibana)

• What it is: An open-source stack for log collection, storage, and visualization.
• Components:
• Elasticsearch → Search & analytics engine.
• Logstash → Data ingestion pipeline (collect, parse, transform).
• Kibana → Visualization and dashboards.
• Key Points:
• Often extended with Beats (lightweight shippers).
• Popular for building self-managed log analytics platforms.
• Alternative to Splunk, but requires more maintenance.

🧭 How They Relate

• OpenTelemetry → Collects telemetry data (works with all the others).
• Prometheus → Best for metrics.
• Datadog → All-in-one SaaS observability.
• Splunk → Strong in log analytics and enterprise observability.
• ELK → Open-source log analytics alternative to Splunk.

From Blogger iPhone client

Creating a pre commit rules environment

Got it 👍 Here’s the same setup written in plain text only (no code block formatting), so you can copy it all at once:

Step 1: Install pre-commit

Run in terminal:

pip install pre-commit

Step 2: Create a file named .pre-commit-config.yaml in your project root and put this inside:

repos:

• repo: https://github.com/psf/black
• rev: 24.3.0
• hooks:
• id: black
• 
  
• 
  
• repo: https://github.com/pycqa/isort
• rev: 5.13.2
• hooks:
• id: isort
• 
  
• 
  
• repo: https://github.com/pycqa/flake8
• rev: 6.1.0
• hooks:
• id: flake8
• 
  
• 
  
• repo: https://github.com/pre-commit/mirrors-mypy
• rev: v1.10.0
• hooks:
• id: mypy
• 
  
• 
  
• repo: https://github.com/PyCQA/bandit
• rev: 1.7.9
• hooks:
• id: bandit
• 
  
• 
  

Step 3: Install the hooks

Run:

pre-commit install

Step 4: Run hooks on all files (first time setup)

Run:

pre-commit run –all-files

Now, every time you do git commit, the following will run automatically:

• Black → formats your code
• isort → organizes imports
• Flake8 → checks style issues
• Mypy → checks type hints
• Bandit → scans for security issues

From Blogger iPhone client

Python code checker

Yes — there are several libraries and tools in Python that help you check, analyze, and improve your code quality. Here are the most widely used ones, explained in simple text with short examples:

1. pylint – checks for errors and enforces coding standards.

Example:

pylint my_script.py

This will scan your code and show warnings like unused variables, bad naming, or missing docstrings.

2. flake8 – focuses on style and PEP8 compliance.

Example:

flake8 my_script.py

It will flag things like extra spaces, long lines, or inconsistent indentation.

3. black – auto-formats your code to follow best practices.

Example:

black my_script.py

It rewrites your file with consistent formatting (indentation, spacing, quotes).

4. isort – automatically sorts and organizes imports.

Example:

isort my_script.py

It arranges imports alphabetically and groups them properly.

5. mypy – checks type hints to catch type errors before running.

Example:

mypy my_script.py

If your function expects a list of strings but you pass integers, it will warn you.

6. bandit – scans for common security issues.

Example:

bandit -r .

This checks all files in your project for unsafe code patterns like hardcoded passwords.

7. coverage.py – measures how much of your code is covered by tests.

Example:

coverage run -m pytest

coverage report

It shows which lines of code were tested and which were not.

So in short:

• pylint / flake8 → code style and errors
• black / isort → auto-formatting and import order
• mypy → type checking
• bandit → security issues
• coverage.py → test coverage

Recommended order in practice:

1. black → format
2. isort → fix imports
3. flake8/pylint → style & logic issues
4. mypy → type checking
5. bandit → security scan
6. coverage.py → testing completeness

From Blogger iPhone client

10 Tips to Optimize Your Python Coding

Got it 👍 — I’ll rewrite the article as one long piece of plain text so you can easily Select All → Copy without formatting blocks interfering. I’ll keep the code inline with indentation so it’s still readable but won’t break your copy-paste.

10 Tips to Optimize Your Python Coding

Writing Python code that is clean, efficient, and easy to maintain requires more than just knowing the syntax. It’s about building good habits that help you and others understand, test, and reuse your work. Below are ten practical tips to optimize your Python coding, each supported with examples you can apply in real projects.

1. Use if name == “main” for Safer Execution
2. When writing modules, always protect the entry point with an if name == “main” block. Without it, any function you call directly in the module will execute every time the module is imported, which can lead to unintended side effects.
4. module.py
8. def connect():
9. print(“Connected!”)
10. if name == “main”:
11. connect()

This ensures your code runs only when intended, avoids duplicate execution, and signals to other developers that this script was meant to be run directly.

1. Define a Clear Main Function
2. Even in small scripts, create a main() function to serve as the central entry point. This makes your code easier to follow and mirrors conventions used in other programming languages like Java or C++.
3. def greet():
4. print(“Hello!”)
5. def goodbye():
6. print(“Goodbye!”)
7. def main():
8. greet()
9. goodbye()
10. if name == “main”:
11. main()

This structure creates a clear separation between definition and execution, making your program more organized and testable.

1. Keep Functions Simple and Reusable
2. Avoid writing functions that try to handle everything at once. Instead, break logic into smaller reusable parts. This improves readability and makes it easier to modify or extend functionality later.
3. def is_adult(age: int, has_id: bool) -> bool:
4. return has_id and age >= 21
5. def is_banned(name: str) -> bool:
6. return name.lower() == “bob”
7. def enter_club(name: str, age: int, has_id: bool) -> None:
8. if is_banned(name):
9. print(f”{name}, you are not allowed in.”)
10. elif is_adult(age, has_id):
11. print(f”Welcome to the club, {name}!”)
12. else:
13. print(f”Sorry {name}, you cannot enter.”)

Breaking logic apart increases reusability and avoids bloated, “do-everything” functions.

1. Leverage Type Annotations
2. Python is dynamically typed, but using type hints clarifies intent, prevents errors, and improves IDE support. This helps others understand what your functions expect and return.
3. def uppercase_elements(elements: list[str]) -> list[str]:
4. return [el.upper() for el in elements]
5. names = [“alice”, “bob”, “charlie”]
6. print(uppercase_elements(names))

Static analyzers like mypy can catch issues before runtime, reducing the risk of silent bugs.

1. Adopt List Comprehensions for Cleaner Loops
2. List comprehensions make code more concise and often faster than traditional loops. Instead of writing multiple lines, you can express filtering and transformation in one.
3. people = [“James”, “Charlotte”, “Stephanie”, “Mario”, “Sandra”]
4. long_names = [name for name in people if len(name) > 7]
5. print(long_names)

Use descriptive variable names to maintain readability.

1. Avoid Hardcoding Magic Values
2. Magic values make code harder to maintain. Instead, define constants with clear names.
3. LEGAL_AGE = 21
4. BANNED_USERS = {“bob”}
5. def is_adult(age: int, has_id: bool) -> bool:
6. return has_id and age >= LEGAL_AGE
7. def is_banned(name: str) -> bool:
8. return name.lower() in BANNED_USERS

This improves readability and allows you to change values in a single place if requirements shift.

1. Use Meaningful Variable and Function Names
2. Short, unclear names can confuse collaborators. Opt for descriptive identifiers that explain intent without requiring extra comments.
4. Bad
8. def f(a, b): return a + b
10. Good
14. def calculate_total(price: float, tax: float) -> float:
15. return price + tax

Names are the first form of documentation — make them count.

1. Write Docstrings for Clarity
2. For functions that perform more complex logic, provide docstrings that explain purpose, inputs, and outputs. This avoids confusion and speeds up collaboration.
3. def calculate_discount(price: float, discount_rate: float) -> float:
4. “””
5. Calculate the final price after applying a discount.

 Args:

   price (float): Original price of the item.

   discount_rate (float): Discount as a decimal (e.g., 0.2 for 20%).

 Returns:

   float: Final price after discount.

 """

 return price * (1 - discount_rate)

Even simple comments save future developers (and your future self) time.

1. Handle Errors Gracefully
2. Use exceptions to manage errors instead of letting your program crash. This makes your code more robust and user-friendly.
3. def safe_divide(a: float, b: float) -> float:
4. try:
5. return a / b
6. except ZeroDivisionError:
7. print(“Error: division by zero is not allowed.”)
8. return float(“inf”)
9. print(safe_divide(10, 0))

Good error handling prevents edge cases from breaking your program.

1. Optimize with Built-in Functions and Libraries
2. Python’s standard library and built-ins are often more efficient than reinventing the wheel. Use tools like sum(), max(), min(), and any() to replace manual loops.
3. numbers = [2, 4, 6, 8]
4. print(sum(numbers))    # 20
5. print(max(numbers))    # 8
6. print(any(n > 5 for n in numbers)) # True

Built-ins are optimized in C, making them faster than equivalent Python loops.

Final Thoughts

By combining these ten practices — from structuring your scripts with if name == “main” to writing reusable functions, leveraging type hints, and handling errors gracefully — you can dramatically improve the readability, reliability, and maintainability of your Python code. These aren’t just tricks; they’re habits that separate quick hacks from professional-quality software.

✅ Now you can just “select all” and copy the whole thing without losing code.

Do you want me to also make a shorter cheat sheet version of these 10 tips (like a quick reference you can keep on hand)?

From Blogger iPhone client

Python code visualizer

https://cscircles.cemc.uwaterloo.ca/visualize

From Blogger iPhone client

Animated comparison multithreaded school multiprocessing

import time

import random

import threading

import multiprocessing as mp

import matplotlib.pyplot as plt

import matplotlib.animation as animation

import psutil

# -----------------------------

# Worker function

# -----------------------------

def worker(task_id, sleep_time, results, lock):

  start = time.perf_counter() * 1000 # ms

  time.sleep(sleep_time)       # simulate work

  end = time.perf_counter() * 1000

  with lock:

    results.append((task_id, start, end - start))

# -----------------------------

# Multithreading: many small tasks

# -----------------------------

def run_multithreading(num_threads=4, num_tasks=80, results=None, lock=None):

  threads = []

  for i in range(num_tasks):

    t = threading.Thread(

      target=worker,

      args=(i % num_threads, random.uniform(0.005, 0.02), results, lock)

    )

    threads.append(t)

    t.start()

  for t in threads:

    t.join()

# -----------------------------

# Multiprocessing: few long tasks

# -----------------------------

def run_multiprocessing(num_procs=4, results=None, lock=None):

  procs = []

  for i in range(num_procs):

    p = mp.Process(

      target=worker,

      args=(i, random.uniform(0.2, 0.4), results, lock)

    )

    procs.append(p)

    p.start()

  for p in procs:

    p.join()

# -----------------------------

# Live Plotter

# -----------------------------

def animate_execution(mode="threading", duration=2):

  colors = ['#7fcfd4', '#fff29b', '#c8c0ff', '#ff8f80']

  # Shared results

  if mode == "threading":

    results = []

    lock = threading.Lock()

    task_runner = threading.Thread(target=run_multithreading, args=(4, 80, results, lock))

  else:

    manager = mp.Manager()

    results = manager.list()

    lock = manager.Lock()

    task_runner = mp.Process(target=run_multiprocessing, args=(4, results, lock))

  task_runner.start()

  # Setup figure

  fig, (ax_timeline, ax_cpu) = plt.subplots(2, 1, figsize=(10, 6))

  ax_timeline.set_title(f"{mode} timeline (live)")

  ax_timeline.set_xlabel("time (ms)")

  ax_timeline.set_ylabel("worker")

  ax_cpu.set_title("CPU utilization (live)")

  ax_cpu.set_xlabel("time (ms)")

  ax_cpu.set_ylabel("CPU %")

  cpu_timestamps, cpu_data = [], []

  # Animation update function

  def update(frame):

    now = time.perf_counter() * 1000

    ax_timeline.clear()

    ax_timeline.set_title(f"{mode} timeline (live)")

    ax_timeline.set_xlabel("time (ms)")

    ax_timeline.set_ylabel("worker")

    # Draw intervals so far

    for task_id, start, dur in list(results):

      ax_timeline.broken_barh(

        [(start, dur)], (task_id + 0.1, 0.8),

        facecolors=colors[task_id % len(colors)]

      )

    ax_timeline.grid(True, linestyle=":", alpha=0.5)

    # CPU usage

    usage = psutil.cpu_percent(percpu=True)

    cpu_data.append(usage)

    elapsed = (time.perf_counter() * 1000)

    cpu_timestamps.append(elapsed)

    ax_cpu.clear()

    for core in range(len(cpu_data[0])):

      core_usage = [row[core] for row in cpu_data]

      ax_cpu.plot(cpu_timestamps, core_usage, label=f"core {core}")

    ax_cpu.set_title("CPU utilization (live)")

    ax_cpu.set_xlabel("time (ms)")

    ax_cpu.set_ylabel("CPU %")

    ax_cpu.legend(fontsize="x-small", ncol=2)

  ani = animation.FuncAnimation(fig, update, interval=100)

  plt.tight_layout()

  plt.show()

  task_runner.join()

# -----------------------------

# Main

# -----------------------------

if __name__ == "__main__":

  print("Running live multithreading demo...")

  animate_execution(mode="threading", duration=2)

  print("Running live multiprocessing demo...")

  animate_execution(mode="multiprocessing", duration=2)

From Blogger iPhone client