In our daily life, we use mathematics knowingly and unknowingly. Many of us are unaware that forensic experts use mathematics to solve crime mysteries. In this workshop, we will explore how Sherlock Holmes, the famous fictional detective character created by Sir Arthur Conan Doyle uses Mathematics and Python programming language to solve crime mysteries.

Mathematics is a science and one of the most important discoveries of the human race on earth. Math is everywhere and around us. It is in nature, music, sports, economics, engineering, and so on. In our daily life, we use mathematics knowingly and unknowingly. Many of us are unaware that forensic experts use mathematics to solve crime mysteries. In this workshop, we will explore how Sherlock Holmes, the famous fictional detective character created by Sir Arthur Conan Doyle uses Mathematics and Python programming language to solve crime mysteries. In short, the workshop begins with an introduction to forensic mathematics and covers basic principles thereby setting the stage. Then, we will solve simple crime puzzles using mathematics and simple python scripts. Finally, we will solve a few complex hypothetical crime mysteries using advanced python concepts. The participants will learn how to use the concepts of mathematics such as statistics, probability, trigonometry, and graph theory, and python and its packages such as SciPy, NumPy, and Matplotlib to solve the crime puzzles.

Outline
1. Introduction to Forensic Mathematics and overview of basic concepts (15 Minutes)
- Numbers and their representation
- Units of Measurements
- Basic chemical calculations 
- Functions, Formulae and equations 
- Pythagoras Theorem 
- Trigonometric methods 
- Graphs 
- Statistics and probability

2. Problems
- Estimate the pressure of a shoe print on a soft ground (05 Minutes) 
- Calculate the uncertainty given the measurement of bullet diameter (05 Minutes) 
- Calculate the mean molar mass (10 Minutes)
- Calculate the percentage of concentrations (10 Minutes)

------BREAK -------- (05 Minutes)
- Compute bloodstain thickness (05 Minutes)
- Calculate terminal velocity for a fine blood droplet (05 Minutes)
- Calculate the persistence of gunshot residue particles in air (05 Minutes)
- Calculate the impact speed and estimate the drop height of blood droplet (05 Minutes)
- Post-mortem body cooling (05 minutes)
- Ricochet analysis and aspects of ballistics (10 Minutes)
- Suicide, Accident or murder? (05 Minutes)
- Blood stain pattern analysis (10 minutes)
- Persistence of hair, fibres, and flints on clothing (05 minutes)
----------BREAK----------- (05 Minutes)
- Determine the time since death (05 Minutes)
- Determine the age from bone or tooth material (05 Minutes)
- Matching of hair evidence (05 Minutes)
- Matching bite marks (05 Minutes)

3. Digital Forensics with Python (30 minutes) 

4. Advanced Problems
- Bicycle Problem (10 Minutes) 
- Detect the location of a serial killer (10 Minutes)

Gajendra Deshpande

Three Musketeers: Sherlock Holmes, Mathematics and Python

[PyVista](https://github.com/pyvista/pyvista)は、1400以上のオープンソースプロジェクトで使用されている汎用3D可視化ライブラリで、[コンピュータ支援エンジニアリングや地球物理学から火山やデジタルアートワークまで](https://pyvista.github.io/pyvista-docs-dev-ja/getting-started/external_examples.html)のあらゆるものを可視化することができます。

PyVistaは、[Visualization Toolkit (VTK)](http://www.vtk.org) のPythonic APIを公開し、VTKの予備知識がなくてもすぐに使えるツールを提供しています。

このチュートリアルでは、PyVistaの最新機能を紹介し、幅広いユーザーにPythonによる3Dビジュアライゼーションの最前線をお届けします。

- PyVistaを使って、一般的なフォーマットの様々なデータセットから3Dビジュアライゼーションを作成する。
- PyVistaのクラスとデータ構造を実例を交えて概説します。
- PyVistaのさまざまなフィルタと機能を理解する。
- PyVistaで使用され、使用できるPythonライブラリ（meshio、trimeshなど）を知っておく。

このチュートリアルは、Pythonの基本的なユーザーから上級のパワーユーザーまで、あらゆる分野のデータを可視化したいと考えるすべての人を対象にしています。

Tetsuo Koyama

PyVistaによる3Dビジュアライゼーション

Through the use of NetworkX's API, tutorial participants will learn about the basics of graph theory and its use in applied network science. Starting with a computationally-oriented definition of a graph and its associated methods, we will build out into progressively more advanced concepts (path and structure finding). We will also discuss new advances to speed up NetworkX Code with dispatching.

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial will cover the basic of network analysis, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks. We will also cover recent changes in NetworkX which enables users to dispatch their code to more efficient backends like GraphBLAS to speed up their code.

By the end of the tutorial, participants will have learned how to use network thinking to better understand relationship problems while analyzing data. They will also be comfortable using the NetworkX API to model their data.

Part 1: Introduction

- Networks of all kinds: biological, transportation, web.
- Representation of networks, NetworkX data structures
- Introduction to NetworkX API for modelling and graph operations.

Part 2: Hubs and Paths

- Finding important nodes; applications
- Pathfinding algorithms and their applications
- Hands-on: implementing path-finding algorithms
- Visualize degree and betweenness centrality distributions.

Part 3: Cliques, Triangles & Structures

- Definition of cliques
- Triangles as the simplest complex clique, applications
- Using path-finding algorithms to find structures in a graph.
- Open triangles as recommender systems.

Part 4: Speed up your code with NetworkX dispatching

- Quick introduction to GraphBLAS
- Moving between GraphBLAS and NetworkX.
- Speed up your NetworkX code by changing one line of code!

Tutorial

PyCon APAC 2023 チュートリアル