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Python for Water and Environment

InnovationsinSustainableTechnologies andComputing

SeriesEditors

JagdishChandBansal, DepartmentofMathematics,SouthAsianUniversity, NewDelhi,India

JoongHoonKim, SchoolofCivil,EnvironmentalandArchitecturalEngineering, KoreaUniversity,Seoul,Korea(Republicof)

AtulyaK.Nagar, LiverpoolHopeUniversity,Liverpool,UK

Thebookseriesaimstopublishresearchontheanalysisanddevelopmentoftechnologicalinnovationsthatconsidernaturalresourcesandnurtureeconomicandsocial development.Innovativesustainabletechnologiesareexpectedtodevelopsustainableproductionplanningandtools,whichreducesenvironmentalandecological risksdrastically.

Theseriescoversresearchrelatedtoinnovativesolutionsinthefieldofsustainable technology,computing,andcommunication.Computationalmethodologiesinthe fieldofcomputerscienceandengineering,cybersecurity,datascience,information systemsandsoftwareengineering,algorithmsforcommunication,smarttransport system,smartcityplanning,e-wastemanagementsystem,andothersuchsustainable technologicalsolutionswithinthescopeofthisseries.

Theserieswillpublishmonographs,editedvolumes,textbooksandproceedingsof importantconferences,symposiaandmeetingsinthefieldofsustainabletechnology andcomputing.

AnilKumar ·

PythonforWater andEnvironment

AnilKumar DepartmentofCivilEngineering IndianInstituteofTechnologyDelhi NewDelhi,Delhi,India

ManabendraSaharia DepartmentofCivilEngineering IndianInstituteofTechnologyDelhi NewDelhi,Delhi,India

ISSN2731-880X

ISSN2731-8818(electronic)

InnovationsinSustainableTechnologiesandComputing

ISBN978-981-99-9407-6

ISBN978-981-99-9408-3(eBook) https://doi.org/10.1007/978-981-99-9408-3

©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SingaporePteLtd.2024

Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped.

Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse.

Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations.

ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSingaporePteLtd. Theregisteredcompanyaddressis:152BeachRoad,#21-01/04GatewayEast,Singapore189721, Singapore

Paperinthisproductisrecyclable.

Tomyfamily, LateNilakhiSaharia,Prof.Shrutidhara Sarma,RameshChandraSaharia,Dhiraj Saharia,GunindraNathSarma,Pranita Devi,andothers.

—ManabendraSaharia

Tomyfamily, RadhamaniDevi,ShukramPuran,Sunil Kumar,SuneetaKumari,AnitaKumari, SnehaMishra

—AnilKumar

ForewordbyDr.MartynClark

DatasciencehasemergedasapowerfultooltounderstandchangesintheEarth’s climate.Theuseoflargeamountsofdatatoproducetransformativeinsightsrequires anewsetoftoolsandskills.Thistextbook,“PythonforWaterandEnvironment”,fills acriticalgapbetweentheory,computation,andapplicationsinthefield,byenabling readerstoimplementtheoreticalconceptsanddevelopanin-depthunderstandingof waterandenvironmentproblems.

Theobjectiveofthebookisnotonlytoserveasaprogrammingguidebutalsoto exposethereadertotheuniquechallengesprevalentinthewaterandenvironment sciences.ThebookusestheversatilePythonprogramminglanguage,whichprovides astraightforwardimplementationofmodelsandrapidtestingofalgorithms.Starting fromthebasics,thebookgraduallytakesthereadersfromabasictoanadvanced levelofprogrammingthatisrelevanttohydrologicandenvironmentalmodeling.The bookdealswithawiderangeoftopicssuchasexploratorydataanalysis,statistical datamodeling,andnumericalmodeling,allorganizedintowell-definedchapters.

Icongratulatetheauthorsforwritingthisbookwhichcanserveasanimportant resourceforresearchersandwaterprofessionalswhowishtoincludePythonintheir day-to-daywork.

Dr.MartynClark

FormerEditor-in-Chief(WaterResourcesResearch) ProfessorofHydrologyandSchulichResearchChairinEnvironmental Prediction DepartmentofCivilEngineering SchulichSchoolofEngineering UniversityofCalgary Calgary,Alberta,Canada

ForewordbyDr.RanganBanerjee

WaterisacriticalandscarceresourceinIndiaandtheworld.Thechallengesof environmentalmanagementandsustainabledevelopmentneednewandinnovative approachesandnewtoolsandtechniques.Ihavegreatpleasureinwritingtheforewordforthetextbook“PythonforWaterandEnvironment”writtenbymycolleague ManabendraSaharia.

Pythonhasemergedasalanguageofchoiceforprogrammersandresearchers. ThisbookdemonstrateshowPythonprovidesanefficientplatformformodeling, analyzingandinterpretingdata,dataanalysis,andcreatingpredictivemodels.This bookprovidesaneasytounderstandintroductiontotheuseofPythonforenvironmentalproblems.Theauthordelvesintohisrichexperienceindevelopingmodels forwaterandenvironmentproblemsandpresentswell-annotatedcodethatcanhelp beginners,practitioners,andexperts.

Ihopethistextbookhelpspropelthenextgenerationofresearchersandfuture professionalstodevelopsoftwareandanalyticaltoolsandtechniquesforsolvingthe problemsofwaterandtheenvironment.

WeatIITDelhihopethatthistextbookwillserveasabridgebetweentheoryand practiceandservetopropelnewinterdisciplinaryresearchinthisimportantarea.The authorwillbehappytogetyourfeedbackandsuggestionsonfurtherenhancements inthisbookandthedomain.

IndianInstituteofTechnologyDelhi ForbesMarshallChairProfessorintheDepartmentofEnergyScience andEngineering(IITBombay) FellowoftheIndianNationalAcademyofEngineering

Preface

WhileteachinggraduatestudentsofCivilEngineeringatIITDelhi,wefeltthe needforatextbookthatfocusesmoreonthepracticalimplementationsideofWater ResourcesEngineeringusingamodernprogramminglanguage,whichcouldsupplementtheexcellenttheoreticaltextbooksthatalreadyexist.Inanerawheredata scienceandmachinelearninghaverevolutionizedallfields,studentscontinueto strugglewithbreakingintospecializeddomainsthatrequireincreasinglyadvanced computationalskills.Thus,thisbookfocusesoncodeexamplesthatreaderscan directlybenefitfrom.Byprovidingconcreteexamples,thebookequipsreaderswith theskillsneededtoaddressthecomplexchallengesfacedbywaterandenvironmental professionalsintoday’srapidlychangingworld.

“PythonforWaterandEnvironment”isconceivedasapracticalguideforprofessionals,researchers,andstudentswhoareworkinginsectorsofwaterandenvironment.ThisprefaceoutlinesourjourneythroughtherealmofPythonprogramming, whereweventureintothescienceofwaterresourcesandenvironmentalmanagement. Theessenceofthisbookliesintheseamlessintegrationoftheoreticalprincipleswith computationalprowess,harnessingthepowerofPythontomodel,analyze,andsolve real-worldproblems.

OuraimistoilluminatethepotentialofPythonasarobustandversatiletoolfor dealingwithcomplexchallengesinthedomainofwaterandenvironment.Weaimto breakdownthebarrierstoentrythathavetraditionallyexistedfornon-programmers. Webelievethatbeinganopen-sourcelanguagePythonalignswellwiththeshared globalresponsibilityofwaterandenvironmentalmanagement.Thisbook,therefore, goesbeyondbeingamereguidetoPythonprogramming.Whetheryouareaseasoned

professionalorapassionatebeginnerinthisdomain,“PythonforWaterandEnvironment”isdesignedtobeyourcompanioninthisexcitingjourneyofdiscoveryand problem-solving.

July2023

Dr.ManabendraSaharia

IndianInstituteofTechnologyDelhi NewDelhi,India

Dr.AnilKumar PrincipalProjectScientist

IndianInstituteofTechnologyDelhi NewDelhi,India

Acknowledgements

WeextendourheartfeltgratitudetotheIndianInstituteofTechnologyDelhicommunity,forprovidinguswithanintellectuallychallengingacademicenvironment. TheDepartmentofCivilEngineeringdeservesaspecialmentionforitscontinual supportandencouragement,enablingustoexploreandexpandthehorizonsofour professionalexpertise.

ManabendraSahariadedicatesthisbooktohismother,LateMrs.NilakhiSaharia, whoselifeofhardworkandkindnessheaspirestoliveupto.Hewouldalsolike toacknowledgethesupportofhislovingwife(Prof.ShrutidharaSarma),father (RameshChandraSaharia),brother(DhirajSaharia),father-in-law(GunindraNath Sarma),andmother-in-law(PranitaDevi).Heacknowledgestheextraordinarydebt heowestoallhiswell-wishersovertheyears:Prof.RajibBhattacharjya,Prof.Sharad K.Jain,Prof.ParthajitRoy,Prof.ParthasarathiChoudhury,Prof.G.V.Ramana,Prof. SumedhaChakma,Prof.D.R.Kaushal,Prof.B.R.Chahar,Prof.PierreKirstetter, Dr.JonathanJ.Gourley,Prof.YangHong,Dr.SujayKumar,Dr.AugustoGetirana, Dr.AndyWood,Dr.AndyNewman,Prof.MartynClark,andmanymore.Healso acknowledgesthefriendshipsthathavesustainedhimovertheyears.

AnilKumarwouldliketodedicatethisbooktohismother(Mrs.Radhamani Devi),father(ShukramPuran),brother(SunilKumar),andsisters(SuneetaDhungia andAnitaKumari).Heisgratefultohiswell-wishers:Prof.KumarHemantSingh, Prof.MohanYellishetty,Prof.TrilokNathSingh,andProf.StuartD.C.Walsh.He acknowledgeshisfriendshipwithDr.SnehaMishraandDr.RohitKumarShrivastava fortheirconstantsupport.

WespeciallyacknowledgetheencouragementandleadershipoftheDirectorof theinstitute,Prof.RanganBanerjee,andtheHeadoftheDepartmentofCivilEngineering.Prof.ArvindK.Nema.Withouttheirsteadfastsupport,thisbookwouldn’t seethelightofday.Finally,wewouldalsoliketoexpressoursincerethankstothe manyreviewers(Dr.AatishAnshuman,Prof.B.R.Chahar,Ms.Reetumoni,etc.) whotookthetimetometiculouslyscrutinizeourwork.Theirinvaluableinsights

xivAcknowledgements andconstructivefeedbackplayedaninstrumentalroleinshapingthisbook.They challengedustorefineourideas,improveourmethodology,andensurestandardsof qualityandaccuracyinwriting.

AbouttheAuthors

Dr.AnilKumar isaseniorprojectscientistintheDepartmentofCivilEngineering attheIndianInstituteofTechnologyDelhi.HereceivedhisPh.D.inComputational GeosciencesjointlyfromMonashUniversity(Australia)andtheIndianInstituteof TechnologyBombay(India).HereceivedaB.Tech.inGeophysicalTechnologyfrom theIndianInstituteofTechnologyRoorkee.Hehasbeenworkingasaresearcher inthefieldofmachinelearningandnumericalmodelingandhashelpeddevelop innovativesolutionsfortheoil,gas,andminingindustry.

Dr.ManabendraSaharia isanassistantprofessorintheDepartmentofCivilEngineeringandanassociatefacultymemberoftheYardiSchoolofArtificialIntelligenceattheIndianInstituteofTechnologyDelhi.PriortojoiningIITDelhi,he heldpositionsinthehydrologylabsoftheNASAGoddardSpaceFlightCenterand theNationalCenterforAtmosphericResearch(NCAR).HereceivedhisPh.D.in WaterResourcesEngineeringfromtheUniversityofOklahoma.AtIITDelhi,his HydroSenseresearchlabfocusesonutilizingphysicsanddata-driventechniques tomonitorandmitigatenaturalhazardssuchasfloodsandlandslides.Hehasbeen recognizedforhisscientificcontributions,havingreceivedYoungScientistawards fromboththeNationalAcademyofSciences,India(NASI),andtheInternational SocietyforEnergy,EnvironmentandSustainability(ISEES).

Part I

Practical Python for a Water and Environment Professional

Chapter 1 Data Analysis in the Water and Environment

1.1 Introduction

Water security is increasingly in jeopardy throughout the world. Too much water causing floods, too little water causing droughts, or poor water quality affecting health can endanger life, economy, and ecosystems. In order to detect, monitor, and mitigate these diverse problems in water and environment, we require actionable intelligence based on data. Data analysis is an important part of understanding the complex and multidimensional relationships between water systems and their surrounding environments. We investigate these relationships through a combination of science, engineering, and technology, which will help in discovering new information relevant to the impact of water and the environment on human life. The efficacy of our strategies in managing water resources, preserving aquatic life, combating pollution, and preparing for climate change rests on our ability to monitor, model, and mitigate various types of water hazards.

Water sustains life and is one of our planet’s most precious resources. It acts as an integral link in the vast chain of ecosystems that allows life to prosper. Various environmental factors such as geological formations and anthropogenic activities impact the quality, availability, and distribution of water. These exchanges result in a perpetually evolving ecosystem, making it necessary to constantly monitor this water-environment interface.

Data has been dubbed as the new oil. And just like crude oil, raw data has to be refined and analyzed to extract valuable and meaningful insights. Understanding patterns, trends, and relationships in data can help us in making inferences about the state and performance of water and environment systems, their interactions, and the potential effects of changes in one system on the other. Data analysis helps us assess the impacts of an oil spill on coastal waters, study seasonal variations in river flow, or model future scenarios of sea-level rise due to global warming.

Data analysis in the water and environment sector involves a combination of methodologies and technologies, ranging from traditional statistical techniques to advanced machine learning algorithms. It relies on data acquired from a variety © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 A. Kumar and M. Saharia, Python for Water and Environment, Innovations in Sustainable Technologies and Computing, https:// doi.org/ 10.1007/ 978- 981- 99- 9408- 3_1

of sources, such as satellite images, weather stations, sensor networks, and socioeconomic databases, to explore and interpret complex phenomena related to water and the environment. By leveraging computational power and algorithms, vast amounts of data can be processed and analyzed, resulting in actionable insights for scientists, policymakers, and stakeholders. Data analysis helps us understand the impact of human activities such as industrial pollution, deforestation, overfishing, and uncontrolled urbanization on water and the environment. Such insights help in the formulation of better policies and effective strategies for sustainable development, water management, and environmental conservation. Data analysis helps in the optimization of resources and predictive modeling for future scenarios. Data analysis thus enables us to anticipate and mitigate risks, harness opportunities for sustainable growth, and create a better balance between human needs and environmental preservation.

However, data analysis in the water and environment sector is not without its challenges. The quality and integrity of data, the complexity of environmental systems, the inherent uncertainty in many types of environmental data, and the need for multidisciplinary approaches are among the many issues that analysts must grapple with. These challenges necessitate a continuous refinement of methods and techniques, emphasizing the field’s dynamic and evolving nature.

Data analysis in the water and environment sector integrates scientific exploration, use of technology, and consideration of natural phenomena. The process involves diving into challenging datasets, and uncovering details of environmental processes. It is a journey of exploration, problem-solving, and creating impact, which is crucial in an era marked by environmental shifts, climate change, depleting resources, and rising human needs. In this chapter, we shall investigate the techniques, significance, and function of data analysis in the water and environment domain.

1.2 Types of Data

Hydrologists study the distribution, movement, and storage of water in the environment, which requires large amounts of data across diverse scientific and engineering disciplines. The data collected in hydrology is generally of three types: spatial, temporal, and attribute data.

Spatial data consists of the geographic and physical properties of an area of interest. In hydrology, examples of spatial data are soil and rock type, topography, the physical features of water bodies, and vegetation. These attributes influence of movement of water within the water cycle. For example, how water moves across the land surface is influenced by topographical attributes such as elevation, slope, and aspect. Geographic Information System (GIS) tools are widely used for managing and analyzing spatial data, which can be collected using different means such as satellite remote sensing, LiDAR, or ground observations.

When numerical data is tracked over time, it is called temporal data. Extracting valuable insights about the trends, patterns, and shifts in temporal attributes is known

as temporal data mining. In hydrology, examples of temporal data include temperature, snowpack thickness, groundwater levels, station precipitation, and evapotranspiration. Ground-based measurements, weather stations, and satellites can collect these hydrological measurements. Time series data are crucial for identifying annual and seasonal patterns, understanding the impact of climate change, and predicting hydrological scenarios in the future.

Attribute data, on the other hand, is any qualitative description that provides additional context regarding the spatial and temporal data. For example, it may involve data on population density and socioeconomic indicators in the study area, which can provide insights into human impacts on the hydrological system. Typically, attribute data is collected using field measurements, laboratory analysis, public databases, and direct surveys.

These categories of data often overlap and lead to rich and multilayered datasets which can be explored from different perspectives. For instance, water quality parameters (attribute data) may be measured over time (temporal data), and across a variety of locations (spatial data) within a watershed. Such a dataset can be explored to understand how water quality in an area changes spatially as well as temporally. Increasingly, in hydrology, the problems have become complex enough that it requires harnessing big data and machine learning to develop deeper insights into large, complex, and multidimensional datasets. By integrating data from diverse sources, hydrologists can unravel the complex dynamics of water in the environment, and aid in the formulation of effective water management strategies and sustainable practices.

In the later chapters, we delve into hydrological data modeling using Python. We explore time series analysis and numerical modeling of water variables such as streamflow, water level, and atmospheric pressure. We will show how Python’s powerful libraries can be leveraged for stochastic and deterministic modeling and trend detection. We will also demonstrate the use of Python for solving partial differential equations in the context of groundwater flow, and solute transport modeling. These steps and techniques will equip the reader with the tools necessary for the analysis of complex water systems.

Chapter 2

Python Environment and Basics

2.1 Integrated Development Environment (IDE)

Integrated Development Environment (IDE) and virtual environments are two crucial components of Python programming. They are key tools in a Python developer’s toolkit, aiding in streamlining the development process, enhancing productivity, and ensuring code reliability and reproducibility.

An IDE is a software application that provides comprehensive facilities to programmers for software development. For Python developers, using an IDE like PyCharm, Jupyter Notebook, or Visual Studio Code has numerous benefits. One of the key advantages is that it combines several tools and features needed for coding into a single interface. This includes text editors for writing and editing code, debuggers for finding and fixing errors, syntax highlighting for better readability, and auto-completion features that save time by suggesting completions for names of functions, keywords, and variables.

Furthermore, many IDEs provide built-in support for version control systems like Git, allowing developers to track changes, revert to previous versions of code, and efficiently collaborate with other developers. IDEs are built with functions for code refactoring, testing, and profiling, which are essential for writing clean, error-free, and efficient code.

2.2 Why Virtual Environments?

Virtual Environments are isolated environments where python packages can be installed without interfering with other projects and installations. This allows the developer to create isolated environments for different projects, which can be communicated to other developers who can recreate the exact development environment for those projects. Without virtual environments, installing different versions of the

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

A. Kumar and M. Saharia, Python for Water and Environment, Innovations in Sustainable Technologies and Computing, https:// doi.org/ 10.1007/ 978- 981- 99- 9408- 3_2

same package for different projects can lead to conflicts and inconsistencies, making it difficult to share or deploy code. Virtual environments solve this problem by creating isolated spaces for each project, where the necessary dependencies can be installed without the risk of interference.

Virtual environments also contribute to the reproducibility of Python code. Maintaining a record of the exact versions of the packages used in a project, allows other programmers and systems to recreate the environment and execute the code under identical conditions. This is particularly important in scientific computation and data analytics, where package dependency is a basic requirement.

Both the Integrated Development Environments (IDEs) and virtual environments play crucial roles in Python programming. While IDEs increase efficiency and code quality by integrating utilities and features into a unified interface, virtual environments ensure the reliability and replicability of Python code by ensuring project dependencies and isolation. Together, they simplify the coding process by providing a versatile framework for Python programming and making it more enjoyable.

2.3 The Anaconda Package Manager

The anaconda package manager (https:// www.anaconda.com/ ) provides an opensource distribution of the Python and R programming languages for scientific computing, designed for simplified package management and deployment. It is loaded with a collection of over 1,500 packages for computation, data analysis, visualization, and machine learning, and incorporates fundamental libraries such as NumPy, Pandas, and Matplotlib. In addition, it is equipped with a package manager, Conda, that assists in managing environments, making the installation and maintenance of different versions of software packages hassle-free.

macOS Installation: To install Anaconda on your operating system, start by downloading the Anaconda installer for your operating system from the official Anaconda website. Python 3.8 version or later is the preferred choice unless a specific earlier version is required. Upon downloading, double-click the downloaded file to launch the installer. This program guides the user through the installation process, possibly suggesting the installation of the Anaconda3 version in the home user directory. After completing the steps, the installation is verified by opening a new terminal window and typing the command: “conda list”. If Anaconda is installed correctly, a list of installed packages appears. Anaconda’s popularity is due to the convenience it offers. Operating systems, like MacOS and Windows Subsystem for Linux, come preinstalled with Python, which might not be suitable for scientific computing due to its potential incompatibility with certain modules. Thus, having a separate, controlled environment like Anaconda ensures compatibility between Python and other packages.

Linux Installation: The installation process for Anaconda on a Linux system begins with downloading the installer script from the Anaconda website. After

downloading, the user needs to verify the data integrity of the installer with cryptographic hash verification through the SHA-256 checksum. A terminal is opened and the bash command is run on the downloaded file, like this: “bash Anaconda32020.02-Linux-x86_64.sh”. “2020.02” would be the version downloaded. Read the license agreement by scrolling with the help of the “Enter” key and accept it by typing “yes”. Next, choose an installation location or accept the default location. Once the installation process is complete, the terminal is closed and a new one is opened to ensure the changes take effect.

Anaconda’s ability to handle dependencies and environments on Linux systems provides a significant advantage as Linux distributions comprise many interdependent packages. Anaconda offers a reliable and easy-to-use interface to manage the complexity of these packages.

Windows Installation: On Windows, the installation process begins by downloading the Anaconda installer .exe file from the official Anaconda website. Once the installer is downloaded, it is run and the instructions are followed. During installation, the user will be asked if they want to add Anaconda to the PATH environment variable—it is recommended not to check this option, as it can interfere with other Python installations or software. Rather, one can use Anaconda software by launching Anaconda Navigator or the Anaconda Command Prompt from the Start Menu.

Windows lacks preinstalled Python, so installing Anaconda is the simplest way to get started with Python, especially for beginners. It can seamlessly handle complex Windows environments, making it effortless to manage and distribute a large number of third-party libraries which frequently pose difficulties during installation and maintenance in the Windows ecosystem.

2.4 The Jupyter Notebook

The Jupyter Notebook is an open-source web-based application that has emerged as a preferred tool among data scientists, academicians, and researchers worldwide. Its simple but powerful user interface allows users to produce and share documents enriched with live code, mathematical equations, data visualizations, and explanatory text.

One of the key features of a Jupyter Notebook is its flexibility in accommodating more than 40 programming languages, including Python, R, Julia, and Scala making it highly adaptable to diverse computational requirements and projects. Users can toggle between these languages inside the notebook, making it an exceptional medium for polyglot data analysis.

Jupyter Notebooks can embed multimedia elements like images, videos, LaTeX scripts, and JavaScript widgets, making it easy to write narrative documents interspersed with code and results. This feature is also suitable for crafting presentations,

dashboards, and interactive tutorials. It also allows connections to multiple data sources, allowing users to pull data from databases, APIs, or cloud storage.

For those already proficient in Python or other compatible programming languages, gaining expertise in Jupyter Notebook is simple. The user interface is quite beginner-friendly, helping users swiftly learn how to execute cells, tweak code, and visualize data. A wide array of tutorials and resources is available online to assist newcomers.

A notable feature of a Jupyter Notebook is that it allows executing code in “cells". This enables users to efficiently segment their work into manageable units, beneficial for code debugging and revision. Each cell operates independently, allowing for iterative code development, testing, and validation of algorithms and functions.

The coding style in Jupyter Notebook fosters an investigative and iterative coding methodology. As it encourages the concept of “literate programming”, users can include with their code, notes ,and comments, leading to improved legibility and sustainability. This style is apt for structured and lucid data analysis workflows.

Jupyter Notebook’s compatibility with Git and GitHub also extends its accessibility. Users can set version control for their notebooks, share them among peers, and even publish them right away as web pages via GitHub.

To summarize, the Jupyter Notebook’s versatility, user-centric design, and support for collaborative and investigative programming make it an invaluable tool for a diverse user base, from researchers involved in complex data analyses to educators instructing in programming or data science disciplines.

2.5 Installing External Packages

Installing external packages in an Anaconda virtual environment is a process that involves several steps, ensuring that the necessary software tools are correctly added to the user’s workspace. The Conda package and environment manager included in Anaconda are used to handle these installations.

1. Creation of a new Conda environment: Before installing external packages, it is good practice to create a new Conda environment. This isolated environment provides a separate space where packages can be installed without interfering with the system’s base Python installation or other Conda environments. This can be executed using the command:

A new name of the environment can be provided in place of “myenv". As per requirement, a Python version may be specified by appending “python=x.x" (where x.x is the version number) to the command.

2. Activating the new environment: Prior to installing any packages, the environment must be activated. This can be done with the command:

3. Installing packages: Depending upon the source, the packages can be installed after activating the environment, either by using the “conda” or “pip” command. It is typical to use Conda when the package exists in the Anaconda distribution, given its effectiveness in handling dependencies. Issuing the following command initiates the installation:

The name of the required package can replace the “package-name”. The pip command can be used in the case when the package is not available in the Anaconda distribution. Pip can access packages uploaded to the Python Package Index (PyPi). To use the following command, do: “pip install package-name", again, replacing “package-name” with the name of the needed package.

4. Verifying the installation: It is a good idea to verify the installed packages. Issuing the “conda list” or “pip list” commands displays the installed packages in the active environment:

The aforementioned steps permit users to install external packages in a Conda environment, which are ready to be stored and shared, making it easy to duplicate the development configuration across varied and distributed groups.

Chapter 3 Python Essentials

3.1 Getting Started with Python

Python is the most popular programming language in the data analysis world and the growing significance of data has made mastering Python an important skill to acquire. Due to its simplicity and code readability, Python is a popular choice among beginners and professionals alike. A wide set of libraries makes it a perfect tool for tasks spanning from data analysis and machine learning to visualization and web development.

Getting started with Python requires setting up an appropriate environment. Beginners may prefer Anaconda, a platform that conveniently bundles Python with many data science libraries. Online resources, such as Python’s official documentation, offer a wealth of knowledge for self-learners. For a structured learning experience, several online courses are available on platforms like Coursera and edX. Practical coding exercises on websites like HackerRank and Codecademy can also be useful. Python’s readability and extensive community support make the learning journey manageable and rewarding. It goes without saying that persistent practice along with practical projects is crucial to mastering Python.

3.2 Setting Up Python Environment

Preparing a Python environment calls for installing Python, selecting an integrated development environment (IDE), and overseeing package management. Python can be installed via the Anaconda manager or from its official website. PyCharm or Jupyter Notebook are feature-rich IDEs that offer robust functionalities for software development. Package management utilities, such as pip and conda, assist in managing dependencies across libraries. Python virtual environments provide project-level

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

A. Kumar and M. Saharia, Python for Water and Environment, Innovations in Sustainable Technologies and Computing, https:// doi.org/ 10.1007/ 978- 981- 99- 9408- 3_3

isolation to maintain specific dependencies, ensuring error-free operation. The reader is referred to Sects. 2.3, 2.4, and 2.5 for setting up the Python environment.

3.3 My First Python Script

Python is a powerful and flexible programming language which is appealing for beginners due to its readability and straightforwardness. Here’s an example of a simple Python code that asks for the name of the user and then welcomes them:

1 # This is a simple Python script

2

3 # Ask the user for their name

4 name = input("What’s your name? ")

5

6 # Print a greeting to the user

7 print(f"Hello, {name}! Welcome to Python programming.")

This script employs the “input()” function for in-taking user input, to store in the variable termed, “name”. The “print()” function then displays a salutation that incorporates the user’s name.

The “f” before the string enables string formatting, which allows the embedding of variable’s value directly within the string object. The “{}” brackets denote insertion point where the variable’s value will be inserted when the “print()” statement is called.

Python annotations start with the “# ” character. Python does not execute subsequent content after “# ” thus serving as a convenient way to add comments or explain the functioning of the code.

This script shows some of the basic aspects of Python such as inclusion of variables, mechanisms for user input, print statements, and string formatting schemes. This is an introductory code for beginners, paving way to explore more elaborate concepts such as loops, conditional statements, function definitions, and data structures.

3.4 Python Fundamentals

3.4.1

Basic Syntax

This sample code demonstrates “single” and “multi” line annotations. It also illustrates how to define a function and display the returned result.

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Louis XV, of France, 140

Louis, Saint, Psalter of, described, 128–131; death of, 129

Lounsbury, Professor, of Yale, 84

Lowell, James Russell, 178

Luther, Martin, 215, 224

Mabie, Hamilton W., 177, 187

Macaulay, Thomas B., quoted on Baskerville editions, 245

Machiavelli, Niccolo, 181, 284

Macmillan Company, the, 26

Magliabecchi, Antonio, 292–295

Magliabecchian Library, the, 284; George Eliot in, 291–298

Malmantile, Lippi’s, 296

Man and Superman, Shaw’s, the making of, 67

Mantegna, Andrea, 95

Manuscripts, methods of reproducing, 9

Manuscripts, illuminated, romance of, 114; not the playthings of the common people, 115

Manutius, Aldus, see Aldus Manutius

Marie, Madame, of France, 130

Marinelli, 280

Marmion, Scott’s, 66

Martelli Chapel, the, in the Church of San Lorenzo, Florence, 275

Mary, Queen, of England, Psalter of, described, 131–134; referred to, 132

Mary, Queen of Scots, 156–157

Matthews, Brander, 177

Mayence, printing at, 198, 204, 216

Mazarin, Cardinal, 198

Médard, Saint, the Golden Gospels of, 127–128

Medicean Library, the, see Laurenziana Library, the Medici, the, 147; Savonarola’s diatribes against, 274; and the Laurenziana Library, 280

Medici, Alessandro de’, 274

Medici archives, the, 14

Medici, Catherine de’, 136

Medici, the Chapel of the, in Florence, 284

Medici, Cosimo I de’, see Cosimo il Vecchio

Medici, Cosimo II de’, see Cosimo II

Medici, Giovanni de’ (later Pope Leo X), 275; and the Laurenziana Library, 282, 283

Medici, Giulio de’ (later Pope Clement VII), 276; commissions Michelangelo to erect building for the Laurenziana Library, 283

Medici, Lorenzo de’, Book of Hours made by d’Antonio for, 111, 146–149, 290; tomb of, 275; and the Laurenziana Library, 281–283; his personality, 281–282; referred to, 111, 112, 148

Medici, Piero de’, 275

Memling, Hans, 142

Menelik, King, of Abyssinia, 104

Mentelin, types of, 19

Menticulture, Horace Fletcher’s, 75

Messina, Antonello di, 142

Metternich, Prince, 289

Michelangelo, letters of, 182, 290; his plan for the façade of S. Lorenzo, 273; Varchi’s tribute to, 275; his tomb for Lorenzo de’ Medici, 175; his work in the Laurenziana Library, 276; his letter to Vasari, 278; manuscript poems of, 287; referred to, 14

Michelangelo archives, the, 14, 182

Migliore, 296

Milan, early printing at, 286

Millar, Eric George, quoted, 122, 123

Miller, Mr., London publisher, 66

Minium, 112, 118

Minuscule characters, described, 123; introduced, 126

“Mirror” title, the, 94

Mochenicho, Doge Pietro, 202

“Modern” type, the introduction of, 251

Molds, early type, 201

Monaco, Lorenzo, 149, 290

Monks, the Irish, 120, 125

Monnier, Philippe, 160

Montanus, Arias, 227

Morelli, librarian of the San Marco Library, Venice, 143

Moretus, inherits the Plantin office, 234

Morgan Library, the, see Pierpont Morgan Library, the Morris chair, the, 258

Morris end papers, the, 258

Morris wall papers, the, 258, 260

Morris, William, demonstrates possibilities of printing as an art, 14; Golden type of, 18; his other type designs, 18–20; placed printing back among the fine arts, 55, 258; Bernard Shaw’s enthusiasm for, 69–70; his dislike of the Bodoni type, 80; his title pages, 96; early experiments of, 258; the Kelmscott Chaucer, 259–268; declines the poet-laureateship of England, 260; death of, 262; estimate of his work, 263; his definition of the type ideal, 268; referred to, 6, 96, 258

Munich, library of, 196

Murray, the House of, 65

Murray II, John, and Walter Scott, 66; letter to Scott from, 66

Murray III, John, burns manuscript of Byron’s memoirs, 65

Murray IV, John, 26, 27, 65

Musée Condé, the, at Chantilly, 116

Naples, early printing at, 286

Nazionale Centrale, the Biblioteca, in Florence, see Magliabecchian Library, the Nemours, the Duc de, 139

Néobar, Royal printer to François I of France, 216

Netherlandish illumination, see Illumination, Flemish Netherlands, the, typographical supremacy of, 194, 223–244; commercial supremacy of, 223; devastated by war, 239

New Forest, the, in England, 158

New York Public Library, the, 196

Norton, Charles Eliot, 26; autograph letter of, 31; his association with the design of the Humanistic type, 32, 180–181; recollections and reflections on, 178–183; his meeting with Guido Biagi, 182–183

Ode to Cervantes, Dobson’s, 164

“Old-style” type, the passing of, 251; revived by Pickering, 251

Orcutt, Reginald Wilson, 165

Orcutt, William Dana, first visit to Italy, 14; meeting with Guido Biagi, 14, 277; his work designing the Humanistic type, 17–33; in the Ambrosiana Library, 24–25; experiences with Willard Fiske, 26, 27; apprenticeship at old University Press, 38; experience with Eugene Field, 38–41; experiences with Mrs. Mary Baker Eddy, 52; becomes head of University Press, 55; his ambition to emulate methods of early printers, 55; experiences with Bernard Shaw, 67–71; returns to Italy in 1903, 75; his interest in the Bodoni and Didot types, 78; his acquaintance with Horace Fletcher, 75, 82, 84, 86; his acquaintance with Henry James, 86; visit to Lamb House, 89; experiences with William James, 90–92; experiences with Cobden-Sanderson, 96–101; experiences with Theodore Roosevelt, 101–106; becomes interested in illumination, 111; meeting with Maurice Hewlett, 155–162; experiences with Austin Dobson, 162–169; experiences with Mark Twain, 170–177; experiences with Charles Eliot Norton, 178–183; experiences with William Dean Howells, 183–188; experiences in the Laurenziana Library, 273–300;

last visit with Guido Biagi, 299–300

Orcutt, Mrs. William Dana, 165, 171

Oriental gold, 112

Orthographia dictionum e Græcia tractarum, Tortelli’s, 286

Oxford, Edward Harley, 2d Earl of, 136

Palatina, the Biblioteca, at Florence, 293

Pan and the Young Shepherd, Hewlett’s, 159

Paper, poorer quality introduced, 238; Italian handmade, 238; French handmade, 238, 257; Baskerville the first to introduce glossy, 250

Parchment, English, 29; Florentine, 28; Roman, 28; virgin, 113

Paris, 227

Paris Exposition of 1801, the, 258

Passerini, Luigi, 297

Patmore, Coventry, 89

Patrons, Italian, attitude toward printed book of, 11; their conception of a book, 11; their real reasons for opposing the art of printing, 12, 151

Peignot foundry, the, in Paris, 80

Persia, 118

Perugia, 291

Petrarca, Francesco, the father of humanism, 15; Italic type said to be based upon handwriting of, 17, 210; portrait of, 287, 289; quoted, 290

Petrarch, see Petrarca, Francesco

Petrarch, the Humanistic, the type design, 17–26; the copy, 26, 27; the illustrations, 28; the parchment, 28; the ink, 29, 30; the composition, 30; Norton’s estimate of, 32

Philip, of Burgundy, 135

Philip II, of Spain, 227; his interest in Plantin’s Biblia Polyglotta, 227–228, 233; makes Plantin prototypographe, 233

Pickering, the London publisher, revives the old-style type, 251

Piedmont, early printing at, 286

Pierpont Morgan Library, the, New York, 99, 196

Pisa, 291

Pius XI, Pope, see Ratti, Achille

Plantin, Christophe, financially embarrassed by his Biblia Polyglotta, 56, 238; his Greek types, 221; leaves France, 223; conception and making of his Biblia Polyglotta, 227–233; his types, 228; his printer’s mark, 228, 236; made prototypographe by Philip II, 233; the value of his work estimated, 233; misfortunes endured by, 233; in Leyden, 239; made printer to University of Leyden, 239; referred to, 79, 237

Plantin-Moretus Museum, the, at Antwerp, 235

Pliny, Elzevir’s, 240

Plutei, in the Laurenziana Library, designed by Michelangelo, 14, 276, 286, 300

Politian, 181.

See also Poliziano, Angelo

Poliziano, Angelo, 215.

See also Politian

Pollard, Alfred W., 27

Polyglot Bible, Plantin’s, see Biblia Polyglotta

Portland, the Duchess of, 136

Pragmatism, William James’, 90

Printed book, the, attitude of Italian patrons toward, 11–12; competed against the written book, 199; Aldus changes format of, 207; Elzevirs change format of, 240; important part played in XVI century by, 215

Printer, the, responsibilities of, in early days, 208

Printing, as an art, opposed by the Italian patrons, 11–13; its possibilities demonstrated by William Morris, 14; brief supremacy of Germany in, 194–201; supremacy of Italy in, 201–215; supremacy of France in, 215–223; supremacy of the Netherlands in, 223–244; lapses into a trade, 238; supremacy of England in, 244–250; second supremacy of France in, 251–258; second supremacy of England in, 258–263

Printing, invention of, made books common, 151

Priorista, Chiari’s, 297

Proofreading, in 1891, 47

Psalter of Saint Louis, the, described, 128–131

Publishers, relations between authors and, 51, 63

Quattrocento, Le, Monnier’s, 160

Queen Mary’s Psalter, described, 131–134, 146

Queen’s Quair, The, Hewlett’s, 155–156

Racine, Pierre Didot’s, 251; described, 252–258

Raphael, 113

Raphelengius, 239

Rastrelli, 297

Ratti, Achille, 25, 117

Ravenna, 128

Reformation, the, 215

Renaissance, the, humanistic movement the forerunner and essence of, 15, 160;

Tours becomes center of, in France, 139

Repplier, Agnes, 177

Riccardi Library, the, 14, 149, 182, 290

Richardson, Samuel, 163

Riverside Press, the, 42

Road in Tuscany, the, Hewlett’s, 159

Robertet, François, 140

Roman Calendar, the, 117

Romanesque illumination, see Illumination, Romanesque

Romans, the, 7

Rome, the rich humanities of, 15; referred to, 126

Romola, George Eliot’s, 292–299; volumes consulted in writing, 296–298

Roosevelt, Theodore, deeply interested in physical side of books, 102; his interest in illustration, 105

Royal Greeks, the, of Étienne, 219–222

Rubens, Peter Paul, 95, 113

Ruskin, John, 178, 182

Russia, the Emperor of, 103

Rutland, the Earl of, 131

Sacristy, the New, in the Church of San Lorenzo, Florence, 275

Sacristy, the Old, in the Church of San Lorenzo, Florence, 275

Saga Library, the, 260

Saint John, the Feast of, 297

St. Louis Exposition, the, 182, 299

Saint Peter, the Holy Sepulchre of, 288

Sala di Michelangiolo, the, in the Laurenziana Library, Florence, 14; described, 276

Sale, Madonna Laura de Noves de, portrait of, 287, 289; Petrarch’s verses to, 290

Salisbury, the Marquis of, 165

San Lorenzo, the Church of, in Florence, 274, 283

San Marco, the Convent of, in Florence, 281, 282, 288

San Marco, the Library of, Venice, 119, 141, 143, 145

San Vitale, the Church of, at Ravenna, 128

Saracens, the, 7

Savonarola, 274, 284

Savoy, the Duke of, 233

Schoeffer, types of, 19; referred to, 198 Science and Health, 52

Scott, Gen. Hugh Lennox, 82

Scott, Walter, and John Murray II, 66; letter from Murray to, 66

Scribes, the humanistic, base their lettering on the Caroline minuscule, 126; referred to, 16, 21, 24

Scribes, the monastic, in XV century, 9

Scribes, the secular, in XV century, 10

Scriptorium, the, 9

Second Book of Verse, Eugene Field’s, 38

Semi-uncial characters, described, 123

Sforza Book of Hours, the, 145

“Shady Hill,” in Cambridge, Mass., home of Charles Eliot Norton, 180, 181

Shakespeare first folio, a, value of, 196

Shaw, G. Bernard, his interest in printing, 67–71; the making of his Man and Superman, 67; his enthusiasm for William Morris, 69; letters from, 68–71

Shelley, Percy Bysshe, 158

Sherry’s, in New York City, 187

Siena, 291

Silas Marner, George Eliot’s, 296

Sinibaldi, Antonio, the Virgil of, 16; the Book of Hours of, 112

Sixtus IV, Pope, 142

Smith, Baldwin, 132

Smith, F. Hopkinson, 177

Somerset, John, 135

Sotheby’s, in London, 140

Spain, the Netherlands under the domination of, 227; referred to, 112, 118

Spanish siege, the, of Leyden, 239

Spanish War, the, 179

Spell, The, Orcutt’s, 90, 184

Spires, the town of, 286

Steele, Sir Richard, 163

Subiaco, early printing at, 285

Sweynheim and Pannartz, ruined by experiments in Greek, 56, 238; engraved blocks of, 285

Switzerland, 238

Syriac Gospels, the, 287

Taft, President William H., 188

Tapestries, the Hall of, in the Laurenziana Library, Florence, 285

Tennyson, Alfred, Lord, 260

Terence, Elzevir’s, 240; described, 241–243

Ther Hoernen, Arnold, 94

Thesaurus, the, printed by Henri Étienne, 56, 238

Thompson, Henry Yates, 140

Thomson, Hugh, 166

Title, the engraved, 95

Title, the “mirror,” 94

Title page, the, Bernard Shaw’s ideas concerning, 67; William James’ ideas concerning, 92; “the door to the house,” 92; evolution of, 92–96.

See also Title, the engraved; Title, the “mirror”

Togo, Admiral, 103

Torresani, Andrea, 214

Torresani, Federico, 214

Torresani, Francesco, friendship of Jean Grolier with, 214; letter from Jean Grolier to, 215 Tortelli, 286

Tours, becomes center of Renaissance in France, 139

Tours, the School of, 126

Très Riches Heures, the, of the Duc de Berry, 116

Tribuna, the, in the Laurenziana Library, Florence, 285

Trionfi, Petrarch’s, 26, 28, 181

Triumphs, Petrarch’s, see Trionfi, Petrarch’s

Trophies of Heredia, 102

Troy type, the, designed by William Morris, 20

Turin, early printing at, 286

Twain, Mark, and the Jumping Frog, 61; recollections and reflections on, 170–177; the Harvey birthday dinner, 176; referred to, 188

Type design, difficulties of, 17

Types, early designs of, 17; Aldus’ designs of, 17; Jenson’s designs of, 18; William Morris’ designs of, 18; William Morris’ definition of, the ideal, 268.

See also Humanistic type, Jenson Roman type, Jenson Gothic type, Golden type, Doves type

Typesetting, in 1891, 44

University Press, the old, Cambridge, Mass., 5, 38, 41, 42, 46, 47, 49, 51, 102

Upsala, Sweden, 119

Urbino, the Duke of, 12

Vacca, the Palazzo dei della, 284

Vallombrosa, 171, 291

Van-der-Meire, Gerard, 142

Varchi, his tribute to Michelangelo, 275

Vasari, his work in the Laurenziana Library, 278; Michelangelo’s letter to, 278

Vatican Library, the, at Rome, 117, 196

Vellum, 115, 257.

See also, Parchment

Venetian Days, Howells’, 186

Venetian Republic, the, 142; encourages the art of printing, 202

Venice, early printing in, 94, 204, 206, 214, 286; Howells’ love for, 186; becomes the Mecca of printers, 202; John of Spires in, 286

Vergetios, Angelos, 219

Verrocchio, 275

Victoire, Pierre, quoted, 220

Victoria, Queen, of England, 140

Vienna, library of, 196

Villa di Quarto, the, in Florence, Mark Twain at, 171

Villa Medici, the, in Rome, 282

Villari, Pasquale, 284, 291, 298

Vinci, da, archives, the, 14, 182, 290

Vinci, Leonardo da, sketches of, 290; referred to, 14, 182

Virgil, Baskerville’s, 244; described, 246–250

Virgil, illuminated by Sinibaldi, 16

Virgil, the Medicean, 287; the story of, 288–289

Virgil, the Vatican, 117

Vita di G. Savonarola, La, Villari’s, 298

Vittoria, Alessandro, 143

Wages, in 1891, 58

Walker, Emery, designs the Doves type, 18, 19; engraves plates for Humanistic Petrarch, 28; at the Doves Press, 263; referred to, 71

Walpole, Horace, 163

Warner, Sir George, 140

Widener, Joseph E., library of, 196

Wiggin, Rev. James Henry, 52

Wiggin, Kate Douglas, 177

Wilhelm, Kaiser, 103, 104

William of Orange, founds the University of Leyden, 239

William the Conqueror, 158

Wilson, Francis, 38

Wilson, John, 5, 6, 38, 40, 42, 46, 52, 53, 55

Windsor Castle, 140

Wood, Gen. Leonard, 82

Wood cuts, 106

Wordsworth, William, quoted, 20

World War, the, 103

Worsley, Sir Robert, 136

Writing, see Hand lettering

Written book, the printed book had to compete against, 199

Yale University Library, the, 196

Zainer, Gunther, types of, 20

THIS VOLUME is composed in Poliphilus type, reproduced by the Lanston Monotype Corporation, London, from the Roman face designed in 1499 by Francesco Griffo, of Bologna, for Aldus Manutius, and originally used in the Hypnerotomachia Poliphili. The Italic is based upon that designed for Antonio Blado, Printer to the Holy See from 1515 to 1567.

The cover, a modern adaptation of the Grolier design used on Capella: L’Anthropologia, is designed by Enrico Monetti.

The illustrations, many now appearing in book form for the first time, were secured chiefly through the courtesy of the librarians of the British Museum, London; the Bibliothèque Nationale, Paris; the Laurenziana Library, Florence; the Ambrosiana Library, Milan; the Marciana Library, Venice; the Vatican Library, Rome; and from private collectors.

The plates of the illustrations were made by the Walker Engraving Company, New York City, and are printed on DeJonge’s Art Mat. The text paper is Warren’s Olde Style.

The typography, presswork, and binding are by the Plimpton Press, Norwood, Massachusetts, under the personal supervision of William Dana Orcutt.

Transcriber’s Notes

Illustrations have been positioned near the relevant text.

Silently corrected typographical errors in the List of Illustrations and the Index.

The page numbers in the List of Illustrations, the Index, and internal cross references reflect the locations in original text, but the hyperlinks point to the current locations.

The ends of each chapter and some block quotes had line lengths that tapered smaller and smaller in a decorative way and did not end with a period Because of the variable nature of electronic texts, the author’s intention sadly cannot be reproduced as intended

Illustration captions on numbered pages were in italic while captions on unnumbered pages were not Italics removed to make illustration captions consistent

In some captions, measurements were in parenthesis and others were in brackets. Converted all to parenthesis for consistency.

Page 44: Corrected “Typsetting” to “Typesetting”.

Page 170: Added a thought break as the topic changes from Dobson to Twain.

Page 178: Added a thought break as the topic changes from Twain to Norton.

Page 269:The word “hopefulness” is not hyphenated in the original as part of the decorative tapered lines. The word has been rejoined.

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