Merge pull request #13 from dimitri-yatsenko/main

Harmonize exercises.

Author: dimitri-yatsenko

book/80-examples/000-example-designs.ipynb

@@ -8,105 +8,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## Overview of Examples\n",
-    "\n",
-    "This section presents a variety of schema examples that demonstrate different aspects of DataJoint design principles. Each example showcases specific patterns, relationships, and use cases ranging from classic database tutorials to real-world scientific applications.\n",
-    "\n",
-    "### Classic Database Examples\n",
-    "\n",
-    "These examples are adapted from well-known database tutorials to show how traditional relational designs map to DataJoint's entity-workflow model.\n",
-    "\n",
-    "#### [Classic Sales Database](010-classic-sales.ipynb)\n",
-    "The classic MySQL tutorial sales database featuring customers, orders, products, and employees. This example demonstrates:\n",
-    "- Traditional business database design\n",
-    "- Many-to-many relationships through order details\n",
-    "- Employee management hierarchies\n",
-    "- Product lines and order fulfillment\n",
-    "\n",
-    "#### [University Database](015-university.ipynb)\n",
-    "A university course enrollment system with students, courses, instructors, and enrollments. This example illustrates:\n",
-    "- Academic record keeping\n",
-    "- Course registration workflows\n",
-    "- Student-course many-to-many relationships\n",
-    "- Grade tracking and prerequisites\n",
-    "\n",
-    "#### [Nations Database](030-nations.ipynb)\n",
-    "A geographical database with countries, regions, and continents from the MariaDB tutorial. This example shows:\n",
-    "- Hierarchical geographical relationships\n",
-    "- Lookup tables for standardized data\n",
-    "- Regional groupings and classifications\n",
-    "\n",
-    "### Conceptual Examples\n",
-    "\n",
-    "These examples focus on specific design patterns and conceptual challenges in database modeling.\n",
-    "\n",
-    "#### [Hotel Reservation System](040-hotel.ipynb)\n",
-    "A complete hotel management system with rooms, reservations, check-ins, and check-outs. This design exercise demonstrates:\n",
-    "- Time-series data (room availability and pricing)\n",
-    "- Business rule enforcement (no double-booking, check-in requires reservation)\n",
-    "- Guest management and payment processing\n",
-    "- Temporal queries (who stayed when, room history)\n",
-    "\n",
-    "#### [Social Generations](044-generations.ipynb)\n",
-    "Defines social generations as birth cohorts sharing cultural and historical experiences. This example explores:\n",
-    "- Lookup tables for reference data\n",
-    "- Birth year ranges and generational characteristics\n",
-    "- Cultural archetypes and life principles\n",
-    "- Using DataJoint to model sociological concepts\n",
-    "\n",
-    "#### [Languages and Proficiency](050-languages.ipynb)\n",
-    "A many-to-many relationship design tracking people and their language skills using international standards. This comprehensive example demonstrates:\n",
-    "- **ISO 639-1 language codes**: Standardized two-letter codes for major world languages\n",
-    "- **CEFR proficiency levels**: Common European Framework of Reference for Languages (A1-C2)\n",
-    "- **Association tables**: `Proficiency` as a workflow step linking people, languages, and levels\n",
-    "- **Lookup tables**: Both `Language` and `CEFRLevel` with predefined, standardized content\n",
-    "- **International standards**: Using established frameworks ensures consistency and compatibility\n",
-    "- **Sample queries**: Finding multilingual individuals, proficient speakers, and language statistics\n",
-    "\n",
-    "### Organizational Examples\n",
-    "\n",
-    "These examples model organizational structures and hierarchies.\n",
-    "\n",
-    "#### [Management Hierarchy](060-management.ipynb)\n",
-    "Demonstrates self-referencing tables and organizational relationships. This example shows:\n",
-    "- Employees managing other employees (recursive relationships)\n",
-    "- Department structure with chairs and budgets\n",
-    "- Organizational hierarchies and reporting chains\n",
-    "- One-to-one relationships (department chairs)\n",
-    "- Normalized department data (codes, names, budgets, locations)\n",
-    "\n",
-    "### Scientific Examples\n",
-    "\n",
-    "Real-world scientific applications demonstrating DataJoint in research contexts.\n",
-    "\n",
-    "#### [Allen Common Coordinate Framework](065-allen-ccf.ipynb)\n",
-    "The Allen Brain Atlas coordinate system for mouse brain anatomy. This example illustrates:\n",
-    "- 3D spatial coordinates and brain structure atlases\n",
-    "- Hierarchical anatomical relationships\n",
-    "- Integration with neuroimaging data\n",
-    "- Real scientific data pipeline from the DataJoint Elements project\n",
-    "- Published standards in neuroscience research\n",
-    "\n",
-    "#### [Julia Sets](070-julia.ipynb)\n",
-    "Computational visualization of Julia sets from fractal mathematics. This example demonstrates:\n",
-    "- Computed tables for mathematical functions\n",
-    "- Parameter spaces and computational workflows\n",
-    "- Image generation and visualization\n",
-    "- Using DataJoint for reproducible computational art\n",
-    "- Automatic computation with `populate()`\n",
-    "\n",
-    "---\n",
-    "\n",
-    "Each example is self-contained with:\n",
-    "- Complete schema definitions\n",
-    "- Sample data population\n",
-    "- Example queries demonstrating key concepts\n",
-    "- Visual diagrams showing table relationships\n",
-    "- Code that you can run and modify\n",
-    "\n",
-    "Browse through these examples to see different design patterns in action and learn how to apply DataJoint principles to your own domains.\n"
-   ]
+   "source": "## Overview of Examples\n\nThis section presents a variety of schema examples that demonstrate different aspects of DataJoint design principles. Each example showcases specific patterns, relationships, and use cases ranging from classic database tutorials to real-world scientific applications.\n\n### Classic Database Examples\n\nThese examples are adapted from well-known database tutorials to show how traditional relational designs map to DataJoint's entity-workflow model.\n\n#### [Classic Sales Database](010-classic-sales.ipynb)\nThe classic MySQL tutorial sales database featuring customers, orders, products, and employees. This example demonstrates:\n- Traditional business database design\n- Many-to-many relationships through order details\n- Employee management hierarchies\n- Product lines and order fulfillment\n\n#### [University Database](015-university.ipynb)\nA university course enrollment system with students, courses, instructors, and enrollments. This example illustrates:\n- Academic record keeping\n- Course registration workflows\n- Student-course many-to-many relationships\n- Grade tracking and prerequisites\n\n#### [Nations Database](030-nations.ipynb)\nA geographical database with countries, regions, and continents from the MariaDB tutorial. This example shows:\n- Hierarchical geographical relationships\n- Lookup tables for standardized data\n- Regional groupings and classifications\n\n### Conceptual Examples\n\nThese examples focus on specific design patterns and conceptual challenges in database modeling.\n\n#### [Hotel Reservation System](040-hotel.ipynb)\nA complete hotel management system with rooms, reservations, check-ins, and check-outs. This design exercise demonstrates:\n- Time-series data (room availability and pricing)\n- Business rule enforcement (no double-booking, check-in requires reservation)\n- Guest management and payment processing\n- Temporal queries (who stayed when, room history)\n\n#### [Social Generations](044-generations.ipynb)\nDefines social generations as birth cohorts sharing cultural and historical experiences. This example explores:\n- Lookup tables for reference data\n- Birth year ranges and generational characteristics\n- Cultural archetypes and life principles\n- Using DataJoint to model sociological concepts\n\n#### [Languages and Proficiency](050-languages.ipynb)\nA many-to-many relationship design tracking people and their language skills using international standards. This comprehensive example demonstrates:\n- **ISO 639-1 language codes**: Standardized two-letter codes for major world languages\n- **CEFR proficiency levels**: Common European Framework of Reference for Languages (A1-C2)\n- **Association tables**: `Proficiency` as a workflow step linking people, languages, and levels\n- **Lookup tables**: Both `Language` and `CEFRLevel` with predefined, standardized content\n- **International standards**: Using established frameworks ensures consistency and compatibility\n- **Sample queries**: Finding multilingual individuals, proficient speakers, and language statistics\n\n### Organizational Examples\n\nThese examples model organizational structures and hierarchies.\n\n#### [Management Hierarchy](060-management.ipynb)\nDemonstrates self-referencing tables and organizational relationships. This example shows:\n- Employees managing other employees (recursive relationships)\n- Department structure with chairs and budgets\n- Organizational hierarchies and reporting chains\n- One-to-one relationships (department chairs)\n- Normalized department data (codes, names, budgets, locations)\n\n### Scientific Examples\n\nReal-world scientific applications demonstrating DataJoint in research contexts.\n\n#### [Allen Common Coordinate Framework](065-allen-ccf.ipynb)\nThe Allen Brain Atlas coordinate system for mouse brain anatomy. This example illustrates:\n- 3D spatial coordinates and brain structure atlases\n- Hierarchical anatomical relationships\n- Integration with neuroimaging data\n- Real scientific data pipeline from the DataJoint Elements project\n- Published standards in neuroscience research\n\n#### [Julia Fractals](070-fractals.ipynb)\nComputational visualization of Julia sets from fractal mathematics. This example demonstrates:\n- Computed tables for mathematical functions\n- Parameter spaces and computational workflows\n- Image generation and visualization\n- Using DataJoint for reproducible computational art\n- Automatic computation with `populate()`\n\n#### [Blob Detection Pipeline](075-blob-detection.ipynb)\nAn image analysis pipeline that detects bright blobs in images. This example demonstrates:\n- Master-part table relationships for computational workflows\n- Lookup tables for algorithm parameters\n- Computed tables with automatic population\n- Visualization of detection results\n- Atomic transactions for workflow integrity\n\n---\n\nEach example is self-contained with:\n- Complete schema definitions\n- Sample data population\n- Example queries demonstrating key concepts\n- Visual diagrams showing table relationships\n- Code that you can run and modify\n\nBrowse through these examples to see different design patterns in action and learn how to apply DataJoint principles to your own domains."
   }
  ],
  "metadata": {

book/80-examples/010-classic-sales.ipynb

@@ -31,11 +31,6 @@
     "Because we run against the canonical SQL definitions, you can compare the DataJoint diagrams directly with the diagrams that appear in business-database textbooks. It also illustrates how DataJoint fits into an existing workflow—you can adopt legacy schemas while gaining the benefits of workflow execution, cascading deletes, and DAG-based querying.\n"
    ]
   },
-  {
-   "cell_type": "markdown",
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-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -587,11 +582,6 @@
     "\n",
     "From here you can explore the database using either SQL (`%sql`) or DataJoint operators. Because the schema follows the relational workflow model, joins such as `Order * Payment` or `Product * Order.Details` naturally trace the same business processes that the foreign keys encode. Try recreating some of the analytical queries from the MySQL tutorial using DataJoint’s restriction (`&`), projection (`.proj()`), and aggregation (`.aggr()`) operators to see how workflow-aware queries feel in a classic OLTP dataset.\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

book/80-examples/016-university-queries.ipynb

@@ -1808,11 +1808,6 @@
     "The restriction operators & and - can use other queries as conditions."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -2521,11 +2516,6 @@
     "# Joins"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 22,
@@ -3528,4 +3518,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}