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Enables scientists and researchers to efficiently use one of the most popular programming languages in their day-to-day work
Streamlining Your Research Laboratory with Python covers the Python programming language and its ecosystem of tools applied to tasks encountered by laboratory scientists and technicians working in the life sciences. After opening with the basics of Python, the chapters move through working with and analyzing data, generating reports, and automating the lab environment.
The book includes example processes within chapters and code listings on nearly every page along with schematics and plots that can clearly illustrate Python at work in the lab. The book also explores some real-world examples of Python's application in research settings, demonstrating its potential to streamline processes, improve productivity, and foster innovation.
Streamlining Your Research Laboratory with Python includes information on:
Whether you are a biologist analyzing genetic data, a chemist scouting synthesis routes, an engineer optimizing machine parameters, or a social scientist studying human behavior, Streamlining Your Research Laboratory with Python serves as a logical and practical guide to add Python to your research toolkit.
Mark F. Russo, PhD is currently on the faculty in the Department of Computer Science at The College of New Jersey. Previously, he had a multi-decade professional career in biotech and pharma with a focus on scientific computing, automation, and scientific data.
William Neil is currently at Bristol Myers Squibb and has been working in the pharmaceutical industry since 1995.
Contents
Dedication. 1
Contents. 2
Preface. 10
Chapter 1 : Introduction. 11
Section 1.1 : Python Implementations. 11
Section 1.2 : Installing the Python Toolkit. 12
Section 1.3 : Python 3 vs. Python 2. 13
Section 1.4 : Python Package Index. 13
Section 1.5 : Programming Editors. 14
Section 1.6 : Notebook Editors. 15
Section 1.7 : Using the Jupyter Notebook Interface. 17
Section 1.8 : JupyterLite. 18
Section 1.9 : Things Change. 20
Section 1.10 : Key Takeaways. 20
Chapter 2 : Language Basics. 21
Section 2.1 : Python Interactive Console. 21
Section 2.2 : Data Types. 22
Section 2.3 : Variables and Literals. 24
Section 2.4 : Strings. 26
2.4.1 : Simple Strings. 26
2.4.2 : Multi-line Strings. 26
2.4.3 : Escape Characters in a String. 26
2.4.4 : Raw Strings. 28
2.4.5 : Formatted Strings. 28
2.4.6 : Strings as Objects. 30
2.4.7 : Characters and Encodings. 31
Section 2.5 : Expressions using Operators. 32
2.5.1 : Arithmetic Operators. 33
2.5.2 : Assignment Operators. 35
2.5.3 : Comparison Operators. 36
2.5.4 : Boolean Operators. 37
2.5.5 : Chaining Comparisons. 39
2.5.6 : Comparing Floating Point Numbers. 39
Section 2.6 : Functions and How to Use Them.. 40
2.6.1 : Invoking Functions. 40
2.6.2 : Built-in Functions. 41
2.6.3 : The math Module for Additional Mathematical Functions. 42
2.6.4 : The random Module for Pseudo-random Number Generation. 44
2.6.5 : The time and datetime Modules for Handling Dates and Times. 47
2.6.6 : The sys Module for System Interactions. 50
2.6.7 : Scope and Namespace. 51
Section 2.7 : Your First Python Program.. 52
Section 2.8 : Key Takeaways. 53
Chapter 3 : Data Structures. 55
Section 3.1 : Lists. 55
3.1.1 : Introducing Lists. 55
3.1.2 : Global Functions that Operate on Lists. 55
3.1.3 : Accessing List Elements. 56
3.1.4 : Slicing Lists. 57
3.1.5 : Lists Operators. 59
3.1.6 : Lists as Objects. 61
Section 3.2 : Tuples. 65
3.2.1 : Introducing Tuples. 65
Section 3.3 : Dictionaries. 66
3.3.1 : Introducing Dictionaries. 66
3.3.2 : Global Functions that Operate on Dictionaries. 67
3.3.3 : Accessing Dictionary Items. 67
3.3.4 : Dictionary Operators. 68
3.3.5 : Dictionaries as Objects. 69
Section 3.4 : Sets. 71
3.4.1 : Introducing Sets. 71
3.4.2 : Global Functions that Operate on Sets. 71
3.4.3 : Accessing Set Elements. 72
3.4.4 : Set Operators. 72
3.4.5 : Sets as Objects. 74
Section 3.5 : Destructuring Assignment. 75
Section 3.6 : Key Takeaways. 76
Chapter 4 : Controlling the Flow of a Program.. 77
Section 4.1 : Conditional Execution. 77
4.1.1 : If-statements. 77
4.1.2 : if-else Statements. 78
4.1.3 : If-elif-else Statements. 80
4.1.4 : If-statement Strategies. 82
4.1.5 : Truthy and Falsy Values. 85
4.1.6 : Conditional Expressions. 86
Section 4.2 : Repeated Execution. 88
4.2.1 : While-statements. 88
4.2.2 : For-statements. 93
4.2.3 : For-statements with Range. 95
4.2.4 : Break and Continue. 96
4.2.5 : Comprehensions. 98
Section 4.3 : Key Takeaways. 99
Chapter 5 : Custom Functions and Exceptions. 101
Section 5.1 : Defining Custom Functions. 101
Section 5.2 : Arguments and Parameters. 106
Section 5.3 : Names and Scope. 109
5.3.1 : Local vs. Global 109
5.3.2 : Built-in and Nonlocal Scope. 111
Section 5.4 : Scope vs Namespace. 113
Section 5.5 : Organizing your Code with Modules. 114
Section 5.6 : Decorators. 116
Section 5.7 : How Things Go Wrong. 118
Section 5.8 : Python Exceptions. 119
Section 5.9 : Handling Exceptions. 120
Section 5.10 : Raising Your Own Exceptions. 123
Section 5.11 : Key Takeaways. 126
Chapter 6 : Regular Expressions. 128
Section 6.1 : Matching Literal Text. 128
Section 6.2 : Alternation. 129
Section 6.3 : Defining and Matching Character Classes. 129
Section 6.4 : Metaclasses. 130
Section 6.5 : Pattern Sequences. 130
Section 6.6 : Repeating Patterns with Quantifiers. 130
Section 6.7 : Anchors. 132
Section 6.8 : Capturing Groups. 133
Section 6.9 : Regular Expressions in Python. 135
Section 6.10 : Project - A Formula Mass Calculator. 136
Section 6.11 : Key Takeaways. 143
Chapter 7 : Working with Data. 144
Section 7.1 : A File System Primer. 144
Section 7.2 : Text Files. 145
Section 7.3 : Reading and Writing Text Files. 147
Section 7.4 : Working with Comma-Separated Values (CSV) Files. 153
Section 7.5 : The csv Module. 157
Section 7.6 : Reading and Writing Excel Spreadsheet. 158
7.6.1 : openpyxl Workbook Object. 159
7.6.2 : openpyxl Worksheet Object. 160
7.6.3 : openpyxl Cell Object. 161
Section 7.7 : Project - Generate a Random Sample Layout in a Spreadsheet 161
Section 7.8 : Project - Forecast Monthly Sample Processing. 163
Section 7.9 : Managing the File System.. 168
7.9.1 : The Path Object. 168
7.9.2 : Path Properties. 169
7.9.3 : Path Attributes. 170
7.9.4 : Operating a Path. 170
7.9.5 : Combining Paths. 172
7.9.6 : The shutil Module for High-level File Operations. 172
Section 7.10 : Walking a File System Tree. 173
Section 7.11 : Project - Find Duplicate Files. 174
Section 7.12 : Working with Zip Files. 176
7.12.1 : ZipFile Object. 177
7.12.2 : zipfile.Path Object. 177
7.12.3 : Creating Zip Archives. 178
Section 7.13 : Working with Standard Data Formats. 179
7.13.1 : JSON - JavaScript Object Notation. 179
7.13.2 : json Python Module. 180
7.13.3 : XML - Extensible Markup Language. 182
7.13.4 : Python XML modules. 183
7.13.5 : Other Standard Data Formats. 189
Section 7.14 : Key Takeaways. 189
Chapter 8 : Web Resources. 191
Section 8.1 : TCP/IP Networks - What You Need to Know.. 191
8.1.1 : Internet Protocol (IP). 191
8.1.2 : Transmission Control Protocol (TCP). 192
8.1.3 : Connections and Ports. 192
8.1.4 : Application-layer Protocols. 192
8.1.5 : IPv4 vs. IPv6 Addresses. 193
8.1.6 : Proxy Servers. 193
Section 8.2 : Introduction to Hypertext Transfer Protocol (HTTP). 194
8.2.1 : The Uniform Resource Locator (URL). 195
8.2.2 : Anatomy of an HTTP Request. 196
8.2.3 : Anatomy of an HTTP Response. 198
Section 8.3 : Web Services and the Python Requests Module. 199
8.3.1 : HTTP GET Requests and the Response Object. 200
8.3.2 : HTTP POST Requests. 201
8.3.3 : Binary Responses. 202
8.3.4 : Customizing the Request Object. 205
8.3.5 : Verifying Certificates and Encryption. 205
8.3.6 : Other Request Module Options. 206
Section 8.4 : Project - Print Weather Forecast for a Location. 207
8.4.1 : National Weather Service API Web Service. 207
8.4.2 : Getting Forecast URL from Geolocation. 209
8.4.3 : Loading and Processing Forecast Data. 209
8.4.4 : Completed Program to Generate Temperature Forecast. 210
Section 8.5 : Project - Scraping HTML Page Content. 213
Section 8.6 : Key Takeaways. 218
Chapter 9 : Data Analysis and Visualization. 220
Section 9.1 : JupyterLab. 220
Section 9.2 : Scientific Plotting with Matplotlib. 222
9.2.1 : The pyplot Submodule. 223
9.2.2 : The pyplot.plot() function. 223
9.2.3 : Customizing a Plot. 224
9.2.4 : Multiple Curves on a Single Plot. 225
9.2.5 : Additional Plot Types. 227
9.2.6 : Multiple Axes on a Single Figure. 227
9.2.7 : Other Useful Functions. 229
9.2.8 : Project - Plotting Weather Forecast. 229
9.2.9 : Project - A Custom Microplate Heat Map. 231
9.2.10 : Other Scientific Plotting Libraries. 235
Section 9.3 : NumPy - Numerical Python. 236
9.3.1 : Creating ndarray Objects. 236
9.3.2 : Working with ndarray Objects. 236
9.3.3 : Accessing and Updating ndarray Elements. 237
9.3.4 : Broadcasting. 239
Section 9.4 : pandas DataFrame. 240
9.4.1 : Creating and Inspecting DataFrames. 240
9.4.2 : Filtering DataFrames. 243
9.4.3 : Project - A Screening Experiment. 246
Section 9.5 : SciPy - A Library for Mathematics, Science, and Engineering. 254
9.5.1 : Descriptive Statistics with SciPy. 254
9.5.2 : Hypothesis Testing. 255
9.5.3 : Project - Running Hypothesis Tests on Two Samples. 256
9.5.4 : Project - Comparing Liquid Handler Syringe Performance. 258
9.5.5 : Linear Regression. 260
9.5.6 : Fitting Nonlinear Models to Data. 261
9.5.7 : Project - Four-Parameter Logistic Regression. 264
Section 9.6 : Key Takeaways. 269
Chapter 10 : Report Generation. 271
Section 10.1 : BytesIO Object. 271
Section 10.2 : Generating Reports in Microsoft Word. 272
10.2.1 : Document Object. 273
10.2.2 : Paragraph Object. 274
10.2.3 : Run Object. 275
10.2.4 : Picture and InlineShape Objects. 276
10.2.5 : Table Object. 277
10.2.6 : Project - Generate a Complete Word Report. 279
Section 10.3 : Generating Microsoft PowerPoint Presentations. 282
10.3.1 : Presentation Object. 283
10.3.2 : Slide Objects. 284
10.3.3 : SlideShapes Object. 284
10.3.4 : Length Objects. 285
10.3.5 : Table Object. 287
10.3.6 : Project - Generate a PowerPoint Document with Figures and Tables. 289
Section 10.4 : Generating PDF File Reports. 293
10.4.1 : ReportLab PDF Generation Process. 294
10.4.2 : Creating a Canvas Object. 294
10.4.3 : Setting Canvas Styles. 294
10.4.4 : Managing Text Blocks with PDFTextObjects. 297
10.4.5 : Canvas State Stack. 299
10.4.6 : Drawing Images. 300
10.4.7 : PLATYPUS for Page Layout. 302
10.4.8 : Project - Generate a Complete PDF Report. 303
Section 10.5 : Sending E-mail Programmatically. 306
10.5.1 : Simple Mail Transfer Protocol 306
10.5.2 : SMTP Mail Server. 307
10.5.3 : Send a Simple Email Message. 307
10.5.4 : Sending Email Messages over a Secure Connection. 310
10.5.5 : Building an Email Message with Attachments. 311
Section 10.6 : Serving Results with an HTTP Server. 314
Section 10.7 : Key Takeaways. 315
Chapter 11 : Control and Automation. 317
Section 11.1 : Concurrency in Python. 317
Section 11.2 : Asynchronous Execution. 319
Section 11.3 : Concurrent Programs with AsyncIO.. 320
Section 11.4 : Asynchronous Instrument Control and Coordination. 324
11.4.1 : Project - Integrated Laboratory System Control and Coordination. 324
Section 11.5 : Communicating over a Serial Port. 332
11.5.1 : Reading Barcodes from a Serial Port. 334
11.5.2 : Project - Scanning Sample Tasks into a Running Controller. 339
Section 11.6 : Execute Remote Commands over HTTP. 341
11.6.1 : A Basic HTTP Server with aiohttp. 341
11.6.2 : Routing an HTTP Request to a Custom Python Function. 343
Section 11.7 : Persistent Network Connections using a WebSocket. 347
11.7.1 : A User Interface for an Asynchronous Networked Programs. 348
11.7.2 : WebSocketResponse and FileResponse Objects. 348
11.7.3 : Project - A Browser-Based WebSocket Message Broadcaster. 349
11.7.4 : Project - A Browser UI to Schedule Samples for Analysis. 357
Section 11.8 : Responding to File System Changes. 361
11.8.1 : Watching a Directory for Changes with watchfiles. 361
11.8.2 : File System Monitoring Options. 363
Section 11.9 : Executing Tasks on a Schedule. 365
11.9.1 : sched Module. 365
11.9.2 : Project - Taking and Sending Images on a Schedule. 367
Section 11.10 : Key Takeaways. 371
Postface. 373
References. 374
Appendix A: ASCII American Standard Code for Information Interchange. 377
Index. 378
Python is one of the most popular programming languages, and for good reasons. Among the principles that guide the design of the language, called the Zen of Python, is the principle that Readability Counts. You will discover this repeatedly throughout your learning journey. Unlike source code that you may have encountered in the past written in other programming languages, Python will not appear to the untrained eye as an undiscovered form of hieroglyphics. If written well, Python source code can be relatively easy to read and understand, and it can be equally straightforward to write. Perhaps this is what has driven the popularity of Python.
As a general-purpose programming language, you will find that learning Python provides you with the power to solve virtually any computing problem that you may encounter. This includes data collection and processing, instrument control, scientific computations, publication quality graphing, report generation, and much more. Many of these packages are designed to solve the kinds of scientific problems encountered in a research laboratory. We will cover many of the most popular and widely used scientific Python packages. When combining Python's clean and simple syntax with over 600,000 packages that are freely available in the Python Package Index (PyPI), you will find that you have at your fingertips an incredibly powerful toolkit to solve authentic scientific and research laboratory problems.
Another incredible feature of Python is that it has been selected as one of the two most popular languages used for Data Science. As a research scientist, experimental data is likely the lens through which you learn about the world. While you may never need the full range of advanced numerical, statistical, and modeling features required by a professional data scientist, it is no doubt that you will benefit from the power of Python to operate legitimately in the Data Science field.
Finally, it is worth noting that Python is an open-source language, which means it is free to use and has a large and active community of developers. This community continuously maintains and improves Python's vast array of libraries and other tools, making it a robust platform for scientific research. Python is available for most operating systems, which ensures that you will be able to run it wherever you need it, even on microcontrollers.
Although not formally standardized like other programming languages such as C, C++, and JavaScript, the Python language syntax is defined by The Python Language Reference [1] as well as its reference implementation in C called CPython, which is available for all major operating systems. Both the language reference documentation and CPython implementation are available from Python's official website at https://www.python.org [2].
Python may be implemented by anyone on any platform. Consequently, and due to its significant popularity, you will find Python available on almost every computing platform. In addition to CPython, which is available for all major operating systems, including Windows, MacOS, and Linux, an implementation of Python called IronPython has been implemented for the .Net runtime [3], Jython has been implemented for the Java Virtual Machine (JVM) [4], and at least two Python subsets that run on microcontrollers: MicroPython [5] and CircuitPython [6]. A version of Python has even been implemented in Python itself, appropriately called PyPy [7].
More recently, CPython and many of its most important packages have been compiled to WebAssembly [8]. WebAssembly is a stack-based virtual machine that runs entirely in and is confined by, a web browser. The WebAssembly port of Python is called Pyodide [9]. Pyodide allows us to run Python in a web browser without the need to install it first. Pyodide is an important option for laboratory scientists because laboratory computers are often locked down for security reasons. Typically, the primary purpose of a lab computer is to operate an attached instrument or process data, not to perform general-purpose computing. For security reasons, it is often the case that installing new software is strictly forbidden. Fortunately, because the entire Pyodide Python environment may be loaded into a web browser directly, Pyodide provides a means to access the power of Python without the need to convince your IT team to grant you the elevated privileges required to install software.
No matter which implementation of Python you choose, your knowledge of the Python programming language will be instrumental in helping you streamline your laboratory operations.
To install CPython, visit the official Python home page at https://www.python.org/ and click the Downloads link [2]. An appropriate installer for your operating system will be offered. Download and run the installer. Python will be installed for you on your computer.
To test your installation, open a terminal program and enter the python command. Many terminal programs are available and will change based upon your computer's operating system. On MacOS and Linux, you should find Terminal as one of your program options. On Windows, you may use PowerShell, Command Prompt, or another option. But no matter which terminal program you use, simply enter the python or python3 command into the terminal program and press Enter. This command will run the Python Interactive Console, which you may use to execute Python commands interactively.
python
python3
To exit the Python console, enter the Python command exit(). See Figure 1.1 for an example.
exit()
Figure 1.1 The Python Interactive Console running in Windows PowerShell.
If the installation of Python is successful but you are still having problems starting the Python console, our experience suggests that the problem lies with your operating system's ability to find the Python executable. Investigate where Python was installed and make sure that the path location is included in your PATH environment variable. Also check that you have the necessary permissions to access and execute the python program.
Python 3 was introduced to the community in 2008 as a "breaking change" version of Python. Programs written for the previous Python 2 would not run in Python 3 due to significant changes to syntax and other implementation details. This change was necessary because several of the design decisions made for Python 2 needed an upgrade to make the language more suitable for modern applications. Some changes made to Python 3 were fundamental, including the way binary data is stored and processed.
There was a significant number of existing Python 2 programs in production around the world when Python 3 was announced. It is no surprise that many Python 2 programmers were less than enthusiastic about porting their source code to Python 3. Nevertheless, Python 2 was scheduled to be retired in 2015, but the resistance was so strong that this date had to be delayed. It wasn't until January 1, 2020 that Python 2 was finally and fully retired.
Even though Python 2 has been retired and it no longer receives security patches, you can still install and use it. If you have a version of Python 2 installed, please resist the urge to use it to write new programs and install Python 3 instead. For guidance porting Python 2 code to Python 3, refer to Python's own porting guide [10]. If you need both versions of Python available, you may install Python 3 with Python 2 and use them both simultaneously. With both versions of Python installed, run Python 3 from a terminal using the python3 command in place of the python command. To make sure you have a recent version of Python 3 installed, you can enter the following command into a terminal. In this book we use Python 3 exclusively.
python --version
One of Python's mottos is "batteries included," and for a good reason. A Python distribution comes with a huge library of prewritten modules for you to use and build upon. While it's true that a Python distribution includes quite a few "batteries," it is not possible to include them all.
If a module is not shipped with Python, there is a good chance someone in the Python community has contributed a module that will help you. Additional Python modules are distributed through a Python package repository, with the two most popular being the PyPI [11] and the Package Repository for Anaconda [12]. Anaconda is an exceptional platform that provides high quality Python installations, package distributions, and other open-source resources. Importantly, it also offers paid support plans, which may be critical for businesses that depend upon Python as part of their core operations. In the following, we describe how to use PyPI for installing additional Python packages.
The PyPI provides a way for package authors to post their open-source Python packages, and for package users to find and install Python packages that are not distributed with Python. The PyPI hosts over 600,000 freely available Python packages ready for you to install and use. If you need something specific, there is a good chance that the PyPI has a package for that.
Python packages are...
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