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DinoSoft Documentation

DinoSoft is a Python library for paleontological data analysis, specializing in dinosaur fossil processing and ecological modeling.

Documentation Types

Tutorials

Learn DinoSoft step-by-step

Start Learning

How-To Guides

Task-focused guides for specific goals

View Guides

Reference

Complete API and configuration documentation

Read Reference

Roadmap

Understand roadmap and support options

Learn More

Key Features

Fossil Analysis

Process and analyze fossil specimens using our advanced ML pipeline

Get Started

Genetic Studies

DNA preservation analysis and ancient genome reconstruction

Learn More

Morphology

Complete morphological analysis and comparison tools

Explore

Statistics

Statistical analysis and visualization tools

View Details

Install DinoSoft today

Getting Started with DinoSoft

This tutorial will guide you through your first fossil analysis using DinoSoft.

import dinosoft as ds

# Load a fossil specimen
specimen = ds.load_specimen('KU45690')
analysis = ds.analyze_specimen(specimen)
ds.visualize(analysis)

Next Steps

Once you've completed your first analysis, learn about:

Core Features Reference

DinoSoft offers three primary data structures:

Species

Represents a single dinosaur species with complete taxonomic and ecological data

tyrannosaurus = ds.Species({
    "name": "Tyrannosaurus rex",
    "period": "Late Cretaceous",
    "diet": "Carnivore",
    "mass_kg": 7000
})

Population

Models population dynamics and ecological interactions

population = ds.Population(tyrannosaurus, 
                         territory_km2=1000, 
                         prey_density=0.5)

Period

Creates a period using standardized geological time periods

custom_period = ds.Period(
    "Late Triassic",  # Start of period
    "Late Cretaceous",
    freq="1Ma"  # 1 million year intervals
)

Configuration Reference

Create dinosoft.yml in your project root:

analysis:
  settings:
    precision: high     # Analysis precision
    units: metric      # Measurement units
  visualization:
    style: journal     # Publication style
    colormap: viridis  # Default colormap

Keyboard Shortcuts

Action Windows/Linux MacOS
Reset Analysis Ctrl+R Cmd+R
Export Results Ctrl+E Cmd+E
Quick Save Ctrl+S Cmd+S

Installing DinoSoft

This guide walks you through installing DinoSoft for your needs.

System Requirements

Prerequisites:

  • Python 3.8 or higher
  • 4GB RAM minimum
  • CUDA-compatible GPU recommended

Installation Methods

Set up a virtual environment, or if done simply run pip. 

python -m venv dinoenv
source dinoenv/bin/activate  # Linux/Mac
pip install dinosoft

Available extras: 

pip install dinosoft[viz]     # Visualization tools
pip install dinosoft[ml]      # Machine learning features
pip install dinosoft[full]    # All optional dependencies

conda

conda create -n dinoenv python=3.9
conda activate dinoenv
conda install -c paleochannel dinosoft

Development

git clone https://github.com/dinosoft/dinosoft.git
cd dinosoft
pip install -e ".[dev]"

Common Issues:

  • ImportError: Check Python version compatibility
  • GPU not detected: Verify CUDA installation
  • Memory Error: Increase available RAM

Using DinoSoft

This guide shows you how to perform common tasks with DinoSoft.

Basic Usage

import dinosoft as ds

def analyze_fossil_record(specimen):
    """Process paleontological data"""
    extinction_dates = ds.timeline.mass_extinctions
    diet_analysis = ds.classify_diet(specimen)
    territory = ds.calculate_territory(specimen.size)

    return ds.Species(
        specimen.name,
        diet_analysis,
        territory,
        extinction_dates.nearest(specimen.period)
    )

Advanced Examples

Species Analysis

# Load and analyze a specimen
specimen = ds.load_specimen(
    "USNM 555000",  # Smithsonian specimen ID
    precision="high"  # Use high-precision analysis
)

Population Modeling

# Create a population model
pop = ds.Population(
    species=specimen,
    territory_km2=1000,
    prey_density=0.5
)

Visualization

import matplotlib.pyplot as plt

# Create publication-ready plots
fig, ax = plt.subplots()
ds.plot_territory_range(
    specimen, 
    ax=ax, 
    style='journal'
)

Error Handling

try:
    result = analyze_fossil_record(specimen)
except ds.errors.InvalidPeriodError as e:
    # Handle invalid geological periods
    logger.error(f"Invalid time period: {e}")
except ds.errors.SpecimenError as e:
    # Handle specimen-related errors
    logger.error(f"Specimen error: {e}")

Roadmap and Support

API Status

  • DONE Core Analysis Engine
  • DONE Data Import/Export
  • WIP Neural Networks1
  • WIP Web Interface

Version History:

  • 1.2.0: Added machine learning models
  • 1.1.0: Improved analysis speed
  • 1.0.0: Initial release

Support

Need Support?

  1. Coming in version 2.0 ↩