digitalarzengine 0.3.9.1

DigitalArzEngine for GEE, raster and vector data processing

DigitalArzEngine

DigitalArzEngine is a Python library designed to streamline raster data processing by extending the capabilities of the rasterio library. It provides a suite of tools for efficient geospatial transformations, mosaicing, and analysis, making it ideal for researchers, analysts, and developers working with geospatial raster data.

🚀 Features

• Mosaicing: Seamlessly merge multiple raster datasets into a unified output.
• Summary Statistics: Extract key metrics such as minimum, maximum, mean, and standard deviation from raster layers.
• Reprojection & Resampling: Transform raster datasets to different coordinate systems and resolutions with ease.
• Clipping & Masking: Apply geometric masks or clip rasters to specific regions of interest.
• Pixel-wise Analysis: Enable pixel-level operations for customized raster computations.
• Efficient I/O Handling: Support for reading, writing, and converting between various raster formats.

📦 Installation

To install the library using pip:

pip install digitalarzengine

---

📚 DataManager Utility Class

The DataManager class is a powerful and modular tool for handling geospatial data using SQLite databases. It supports structured storage of JSON records alongside geometric data (as WKB), with metadata tracking, querying, and integration with GeoPandas.

✅ Benefits and Usage

• Structured Storage: Stores geospatial records (geometry + attributes) in a portable .db format.
• Metadata Management: Tracks field names, geometry columns, and record counts.
• Geometry Support: Accepts and stores geometries as WKB with support for reprojection to EPSG:4326.
• Integration with GeoPandas: Easily convert the stored data into DataFrames and GeoDataFrames.
• Custom Query Support: Run filtered SQL queries and retrieve results as structured pandas objects.
• Extendable Schema: Dynamically add and update fields in your dataset.

🔧 Example Use-Cases

• Saving extracted geospatial features from remote sensing workflows.
• Iteratively storing geospatial model outputs with spatial context.
• Lightweight local spatial database for machine learning input.

🔄 Core Methods

• add_record, update_record, delete_record, get_record
• get_data_as_df, get_data_as_gdf, get_gdf_list_under_aoi
• record_exists, change_key, add_column, update_column
• Context manager support (with DataManager(...) as dm:)

This utility class is designed to complement the raster processing tools in DigitalArzEngine, making it easier to work with both raster and vector data consistently.

---

🗃️ DBManager Utility Class

The DBManager class provides a secure and flexible way to interact with PostgreSQL/PostGIS databases. It builds SQLAlchemy engines from settings and supports reading data directly into Pandas or GeoPandas.

✅ Benefits and Usage

• Secure Configuration: Pulls database credentials from a centralized encrypted config using CryptoUtils.
• Flexible Access: Supports multiple environments or databases through the from_config method.
• SQLAlchemy Integration: Simplifies connection management and avoids hardcoding sensitive details.
• Read GeoData: Loads spatial tables directly into GeoDataFrame using read_postgis.
• Exclude Geometry When Needed: Supports reading attribute-only tables as plain DataFrames.

🔧 Example Use-Cases

    import os
from digitalarzengine.settings import DATA_DIR
from digitalarzengine.adapters.data_manager import DataManager

output_dir = os.path.join(DATA_DIR, 'pak', 'snow_cover/stats')
data_manager = DataManager(output_dir, base_name="snow_cover_normal_data",
                           purpose="snow cover normal data from 2001 to 2024")
data_manager.add_record("key_value", {})

• Connecting securely to remote geospatial databases for querying.
• Reading and processing large PostGIS tables as GeoDataFrames.
• Integrating web dashboards or data pipelines with PostgreSQL/PostGIS backends.

🔄 Core Methods

• from_config(db_key): Load credentials and settings from DATABASES
• get_engine(): Return SQLAlchemy engine object
• read_as_geo_dataframe(): Read spatial data into GeoDataFrame
• read_as_dataframe(): Read tabular data with option to exclude geometry
• get_geometry_columns(): Identify spatial fields in the schema

⚙️ Configuration Example

Below is a sample DATABASES configuration dictionary to be placed in digitalarzengine/settings.py:

DATABASES = {
    "drm": {
        "ENGINE": "postgresql+psycopg2",
        "NAME": "drm",
        "USER": "dafast",
        "PASSWORD": "***********************************",
        "HOST": os.getenv("DB_HOST", "localhost"),
        "PORT": "5432",
    }
}

⚠️ Note: The password here is shown encrypted. It should be decrypted using CryptoUtils at runtime.

The DBManager is ideal for scenarios where spatial data needs to be read or processed securely from enterprise databases, complementing local DataManager workflows.

For more advanced usage patterns and custom queries, see the source or documentation site (coming soon).

👨‍💻 Developed by

Ather Ashraf Geospatial Data Scientist and AI Specialist

• 📧 Email: [e-mail]
• 🌐 LinkedIn: https://sa.linkedin.com/in/ather-ashraf
• 📜 Google Scholar: View Profile
• 📱 WhatsApp: [phone] | [phone]

---