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| python for astronomers: Numerical Python in Astronomy and Astrophysics Wolfram Schmidt, Marcel Völschow, 2021-07-15 This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler’s laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks. |
| python for astronomers: Numerical Python in Astronomy and Astrophysics Wolfram Schmidt, Marcel Völschow, 2021-07-14 This book provides a solid foundation in the Python programming language, numerical methods, and data analysis, all embedded within the context of astronomy and astrophysics. It not only enables students to learn programming with the aid of examples from these fields but also provides ample motivation for engagement in independent research. The book opens by outlining the importance of computational methods and programming algorithms in contemporary astronomical and astrophysical research, showing why programming in Python is a good choice for beginners. The performance of basic calculations with Python is then explained with reference to, for example, Kepler’s laws of planetary motion and gravitational and tidal forces. Here, essential background knowledge is provided as necessary. Subsequent chapters are designed to teach the reader to define and use important functions in Python and to utilize numerical methods to solve differential equations and landmark dynamical problems in astrophysics. Finally, the analysis of astronomical data is discussed, with various hands-on examples as well as guidance on astronomical image analysis and applications of artificial neural networks. |
| python for astronomers: A Practical Guide to Observational Astronomy M. Shane Burns, 2021-09-16 A Practical Guide to Observational Astronomy provides a practical and accessible introduction to the ideas and concepts that are essential to making and analyzing astronomical observations. A key emphasis of the book is on how modern astronomy would be impossible without the extensive use of computers, both for the control of astronomical instruments and the subsequent data analysis. Astronomers now need to use software to access and assess the data they produce, so understanding how to use computers to control equipment and analyze data is as crucial to modern astronomers as a telescope. Therefore, this book contains an array of practical problems for readers to test their knowledge, in addition to a wealth of examples and tutorials using Python on the author’s website, where readers can download and create image processing scripts. This is an excellent study guide or textbook for an observational astronomy course for advanced undergraduate and graduate astronomy and physics students familiar with writing and running simple Python scripts. Key Features Contains the latest developments and technologies from astronomical observatories and telescope facilities on the ground and in space Accompanied by a companion website with examples, tutorials, Python scripts, and resources Authored by an observational astronomer with over thirty years of observing and teaching experience About the Author M. Shane Burns earned his BA in physics at UC San Diego in 1979. He began graduate work at UC Berkeley in 1979, where he worked on an automated search for nearby supernovae. After being awarded a PhD in 1985, Professor Burns became a postdoctoral researcher at the University of Wyoming. He spent the summer of 1988 as a visiting scientist at Lawrence Berkeley National Lab, where he helped found the Supernova Cosmology Project (SCP). He continued to work as a member of the SCP group while a faculty member at Harvey Mudd College, the US Air Force Academy, and Colorado College. The 2011 Nobel Prize in Physics was awarded to the leader of the SCP for the group’s discovery of the accelerating expansion of the Universe through observations of distant supernovae. During his career, Professor Burns has observed using essentially all of the world’s great observatories, including the Keck Observatory and the Hubble Space Telescope. Companion website for the book: https://mshaneburns.github.io/ObsAstro/ |
| python for astronomers: Python for Scientists John M. Stewart, 2017-07-20 Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively. |
| python for astronomers: Data Analysis in Astronomy V. di Gesù, L. Scarsi, P. Crane, J.H. Friedman, S. Levialdi, 2012-12-06 The international Workshop on Data Analysis in Astronomy was in tended to give a presentation of experiences that have been acqui red in data analysis and image processing, developments and appli cations that are steadly growing up in Astronomy. The quality and the quantity of ground and satellite observations require more so phisticated data analysis methods and better computational tools. The Workshop has reviewed the present state of the art, explored new methods and discussed a wide range of applications. The topics which have been selected have covered the main fields of interest for data analysis in Astronomy. The Workshop has been focused on the methods used and their significant applications. Results which gave a major contribution to the physical interpre tation of the data have been stressed in the presentations. Atten tion has been devoted to the description of operational system for data analysis in astronomy. The success of the meeting has been the results of the coordinated effort of several people from the organizers to those who presen ted a contribution and/or took part in the discussion. We wish to thank the members of the Workshop scientific committee Prof. M. Ca paccioli, Prof. G. De Biase, Prof. G. Sedmak, Prof. A. Zichichi and of the local organizing committee Dr. R. Buccheri and Dr. M.C. Macca rone together with Miss P. Savalli and Dr. A. Gabriele of the E. Majo rana Center for their support and the unvaluable part in arranging the Workshop. |
| python for astronomers: Bayesian Models for Astrophysical Data Joseph M. Hilbe, Rafael S. de Souza, Emille E. O. Ishida, 2017-04-27 This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally. |
| python for astronomers: Essential Radio Astronomy James J. Condon, Scott M. Ransom, 2016-04-05 The ideal text for a one-semester course in radio astronomy Essential Radio Astronomy is the only textbook on the subject specifically designed for a one-semester introductory course for advanced undergraduates or graduate students in astronomy and astrophysics. It starts from first principles in order to fill gaps in students' backgrounds, make teaching easier for professors who are not expert radio astronomers, and provide a useful reference to the essential equations used by practitioners. This unique textbook reflects the fact that students of multiwavelength astronomy typically can afford to spend only one semester studying the observational techniques particular to each wavelength band. Essential Radio Astronomy presents only the most crucial concepts—succinctly and accessibly. It covers the general principles behind radio telescopes, receivers, and digital backends without getting bogged down in engineering details. Emphasizing the physical processes in radio sources, the book's approach is shaped by the view that radio astrophysics owes more to thermodynamics than electromagnetism. Proven in the classroom and generously illustrated throughout, Essential Radio Astronomy is an invaluable resource for students and researchers alike. The only textbook specifically designed for a one-semester course in radio astronomy Starts from first principles Makes teaching easier for astronomy professors who are not expert radio astronomers Emphasizes the physical processes in radio sources Covers the principles behind radio telescopes and receivers Provides the essential equations and fundamental constants used by practitioners Supplementary website includes lecture notes, problem sets, exams, and links to interactive demonstrations An online illustration package is available to professors |
| python for astronomers: Astrophysical Recipes Simon Portegies Zwart, Stephen McMillan, 2018-12-21 Astrophysical Recipes: The art of AMUSE delves into the ways in which computational science and astrophysics are connected and how the bridge between observation and theory are understood. This book provides a unique outline of the basic principles of performing simulations for astrophysical phenomena, in order to better increase and understand these observations and theories. |
| python for astronomers: Celestial Calculations J. L. Lawrence, 2019-05-14 A step-by-step guide to predicting and calculating the positions of stars, planets, the sun, the moon, and satellites using a personal computer and high school mathematics—for amateur astronomers Our knowledge of the universe is expanding rapidly, as space probes launched decades ago begin to send information back to earth. There has never been a better time to learn about how planets, stars, and satellites move through the heavens. This book is for amateur astronomers who want to move beyond pictures of constellations in star guides and solve the mysteries of a starry night. It is a book for readers who have wondered where Saturn will appear in the night sky, when the sun will rise and set—or how long the space station will be over their location. In Celestial Calculations, J. L. Lawrence shows readers how to find the answers to these and other astronomy questions with only a personal computer and high school math. Using an easy-to-follow step-by-step approach, Lawrence explains what calculations are required, why they are needed, and how they all fit together. Lawrence begins with basic principles: unit of measure conversions, time conversions, and coordinate systems. He combines these concepts into a computer program that can calculate the location of a star and uses the same methods for predicting the locations of the sun, moon, and planets. He then shows how to use these methods for locating the many satellites we have sent into orbit. Finally, he describes a variety of resources and tools available to the amateur astronomer, including star charts and astronomical tables. Diagrams illustrate the major concepts, and computer programs that implement the algorithms are included. Photographs of actual celestial objects accompany the text, and interesting astronomical facts are interspersed throughout. Source code (in Python 3, JAVA, and Visual Basic) and executables for all the programs and examples presented in the book are available for download at https://CelestialCalculations.github.io. |
| python for astronomers: Observational Astronomy D. Scott Birney, Guillermo Gonzalez, David Oesper, 2006-06-29 New and updated edition of advanced undergraduate or beginning graduate textbook on observational astronomy. |
| python for astronomers: Astronomy Hacks Robert Bruce Thompson, Barbara Fritchman Thompson, 2005-06-17 Astronomy Hacks begins the space exploration by getting you set up with the right equipment for observing and admiring the stars in an urban setting. Along for the trip are first rate tips for making most of observations. The hacks show you how to: Dark-Adapt Your Notebook Computer. Choose the Best Binocular. Clean Your Eyepieces and Lenses Safely. Upgrade Your Optical Finder. Photograph the Stars with Basic Equipment. |
| python for astronomers: Eclipsing Binary Stars Josef Kallrath, Eugene F. Milone, 2013-11-11 Have you ever stopped at a construction project on the way to your office and the day's astrophysics? Remember the other onlookers-folks just en joying the spectade, as we all do in following developments away from our areas of active work? We are excited and thrilled when the Hubble Space Telescope discovers an Einstein Cross, when the marvelous pulsars enter our lives, and when computer scientists put a little box on our desk that out-performs yesterday's giant machines. We are free to make use of such achievements and we respect the imagination and discipline needed to bring them about, just as onlookers respect the abilities and planning needed to create a building they may later use. After all, each of us contributes in our own areas as best we can. In addition to the serious onlookers there will be passers-by who take only a casual look at the site. They may use the building later, but have little or no interest in its construction, and give no thought to the resources needed to bring it to completion. Upon arriving at work, those persons write astronomy and astrophysics books at various levels, in which they must say something about dose binary stars. Usually a page or two will do, and the emphasis is on the MLR (mass, luminosity, radius) data obtained only from binaries. |
| python for astronomers: Observational Astrophysics Pierre Lena, 2013-03-09 For the last twenty years astronomy has been developing dramatically. Until the nineteen-fifties, telescopes, spectrometers, and photographic plates consti tuted a relatively simple set of tools which had been refined to a high degree of perfection by the joint efforts of physicists and astronomers. Indeed these tools helped at the birth of modern astrophysics: the discovery of the expan sion of the Universe. Then came radioastronomy and the advent of electronics; the last thirty years have seen the application to astrophysics of a wealth of new experimental techniques, based on the most advanced fields of physics, and a constant interchange of ideas between physicists and astronomers. Last, but not least, modern computers have sharply reduced the burden of dealing with the information painfully extracted from the skies, whether from ever scarce photons, or from the gigantic data flows provided by satellites and large telescopes. The aim of this book is not to give an extensive overview of all the tech niques currently in use in astronomy, nor to provide detailed instructions for preparing or carrying out an astronomical project. Its purpose is methodologi cal: photons are still the main carriers of information between celestial sources and the observer. How we are to collect, sample, measure, and store this infor mation is the unifying theme of the book. Rather than the diversity of tech niques appropriate for each wavelength range, we emphasize the physical and mathematical bases which are common to all wavelength regimes. |
| python for astronomers: Fundamentals of Astrophysics Stan Owocki, 2021-06-03 This concise textbook, designed specifically for a one-semester course in astrophysics, introduces astrophysical concepts to undergraduate science and engineering students with a background in college-level, calculus-based physics. The text is organized into five parts covering: stellar properties; stellar structure and evolution; the interstellar medium and star/planet formation; the Milky Way and other galaxies; and cosmology. Structured around short easily digestible chapters, instructors have flexibility to adjust their course's emphasis as it suits them. Exposition drawn from the author's decade of teaching his course guides students toward a basic but quantitative understanding, with 'quick questions' to spur practice in basic computations, together with more challenging multi-part exercises at the end of each chapter. Advanced concepts like the quantum nature of energy and radiation are developed as needed. The text's approach and level bridge the wide gap between introductory astronomy texts for non-science majors and advanced undergraduate texts for astrophysics majors. |
| python for astronomers: 3D Scientific Visualization with Blender Brian R. Kent, 2014-04-01 This is the first book written on using Blender (an open-source visualization suite widely used in the entertainment and gaming industries) for scientific visualization. It is a practical and interesting introduction to Blender for understanding key parts of 3D rendering that pertain to the sciences via step-by-step guided tutorials. Any time you see an awesome science animation in the news, you will now know how to develop exciting visualizations and animations with your own data. 3D Scientific Visualization with Blender takes you through an understanding of 3D graphics and modeling for different visualization scenarios in the physical sciences. This includes guides and tutorials for: understanding and manipulating the interface; generating 3D models; understanding lighting, animation, and camera control; and scripting data import with the Python API. The agility of Blender and its well organized Python API make it an exciting and unique visualization suite every modern scientific/engineering workbench should include. Blender provides multiple scientific visualizations including: solid models/surfaces/rigid body simulations; data cubes/transparent/translucent rendering; 3D catalogs; N-body simulations; soft body simulations; surface/terrain maps; and phenomenological models. The possibilities for generating visualizations are considerable via this ever growing software package replete with a vast community of users providing support and ideas. |
| python for astronomers: Learning Python Mark Lutz, 2013-06-12 Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing |
| python for astronomers: Galaxy Morphology B. W. Holwerda, 2021 Galaxy morphology is a long-standing subfield of astronomy, moving from visual qualifications to quantitative morphometrics. This book covers the descriptions developed by astronomers to describe the appearance of galaxies, primarily in optical, ultraviolet and near-infrared wavelengths. |
| python for astronomers: Fundamentals of Astrometry Jean Kovalevsky, P. Kenneth Seidelmann, 2011-12-18 This text details the fundamentals of astrometry at milli- and micro-arcsecond accuracies. |
| python for astronomers: A Practical Guide to Lightcurve Photometry and Analysis Brian D. Warner, 2003-01-01 A beginning to intermediate guide to obtaining and analyzing the lightcurves of asteroids and variable stars. |
| python for astronomers: The Astronomers' Magic Envelope Prasenjit Saha, Paul A. Taylor, 2018 Working physicists, and especially astrophysicists, value a good back-of-the-envelope' calculation, meaning a short, elegant computation or argument that starts from general principles and leads to an interesting result. This book guides students on how to understand astrophysics using general principles and concise calculations -- endeavouring to be elegant where possible and using short computer programs where necessary. The material proceeds in approximate historical order. The book begins with the Enlightenment-era insight that the orbits of the planets is easy, but the orbit of the Moon is a real headache, and continues to deterministic chaos. This is followed by a chapter on spacetime and black holes. Four chapters reveal how microphysics, especially quantum mechanics, allow us to understand how stars work. The last two chapters are about cosmology, bringing us to 21st-century developments on the microwave background and gravitational waves. |
| python for astronomers: Computer Modeling J. M. A. Danby, 1997 |
| python for astronomers: Calculating the Cosmos Ian Stewart, 2016-10-25 A prize-winning popular science writer uses mathematical modeling to explain the cosmos. In Calculating the Cosmos, Ian Stewart presents an exhilarating guide to the cosmos, from our solar system to the entire universe. He describes the architecture of space and time, dark matter and dark energy, how galaxies form, why stars implode, how everything began, and how it's all going to end. He considers parallel universes, the fine-tuning of the cosmos for life, what forms extraterrestrial life might take, and the likelihood of life on Earth being snuffed out by an asteroid. Beginning with the Babylonian integration of mathematics into the study of astronomy and cosmology, Stewart traces the evolution of our understanding of the cosmos: How Kepler's laws of planetary motion led Newton to formulate his theory of gravity. How, two centuries later, tiny irregularities in the motion of Mars inspired Einstein to devise his general theory of relativity. How, eighty years ago, the discovery that the universe is expanding led to the development of the Big Bang theory of its origins. How single-point origin and expansion led cosmologists to theorize new components of the universe, such as inflation, dark matter, and dark energy. But does inflation explain the structure of today's universe? Does dark matter actually exist? Could a scientific revolution that will challenge the long-held scientific orthodoxy and once again transform our understanding of the universe be on the way? In an exciting and engaging style, Calculating the Cosmos is a mathematical quest through the intricate realms of astronomy and cosmology. |
| python for astronomers: A Student's Guide to the Schrödinger Equation Daniel A. Fleisch, 2020-02-20 A clear guide to the key concepts and mathematical techniques underlying the Schrödinger equation, including homework problems and fully worked solutions. |
| python for astronomers: Modern Statistical Methods for Astronomy Eric D. Feigelson, 2012 Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Yet most astronomers still use a narrow suite of traditional statistical methods. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public-domain R statistical software environment.-- |
| python for astronomers: Fundamentals of Radio Astronomy Jonathan M. Marr, Ronald L. Snell, Stanley E. Kurtz, 2015-11-30 As evidenced by five Nobel Prizes in physics, radio astronomy in its 80-year history has contributed greatly to our understanding of the universe. Yet for too long, there has been no suitable textbook on radio astronomy for undergraduate students.Fundamentals of Radio Astronomy: Observational Methods is the first undergraduate-level textbook exclus |
| python for astronomers: Python and Matplotlib Essentials for Scientists and Engineers M A Wood, 2015-06-23 |
| python for astronomers: Practical Machine Learning for Data Analysis Using Python Abdulhamit Subasi, 2020-06-07 Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. |
| python for astronomers: Problems in Astrophysics Agnes Mary Clerke, 2022-10-27 This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. |
| python for astronomers: The Manga Guide to the Universe Kenji Ishikawa, Kiyoshi Kawabata, Yutaka Hiiragi, Verte Corp Verte, 2011-07-15 Join Kanna, Kanta, Yamane, and Gloria in The Manga Guide to the Universe as they explore our solar system, the Milky Way, and faraway galaxies in search of the universe’s greatest mysteries: dark matter, cosmic expansion, and the Big Bang itself. As you rocket across the night sky, you’ll become acquainted with modern astronomy and astrophysics, as well as the classical discoveries and theories on which they’re built. You’ll even learn why some scientists believe finding extraterrestrial life is inevitable! You’ll also learn about: –Discoveries made by Copernicus, Galileo, Kepler, Hubble, and other seminal astronomers –Theories of the universe’s origins, evolution, and geometry –The ways you can measure and observe heavenly bodies with different telescopes, and how astronomers calculate distances in space –Stellar classifications and how the temperature, size, and magnitude of a star are related –Cosmic background radiation, what the WMAP satellite discovered, and scientists’ predictions for the future of the universe So dust off your flight suit and take a fantastic voyage through the cosmos in The Manga Guide to the Universe. |
| python for astronomers: Astronomical Data Analysis Software and Systems IX Nadine Manset, Christian Veillet, Dennis Richard Crabtree, 2000 |
| python for astronomers: Astrophysics James Binney, 2016 Astrophysics is said to have been born when Isaac Newton saw an apple drop in his orchard and had the electrifying insight that the Moon falls just like that apple. James Binney shows how the application of physical laws derived on Earth allows us to understand objects that exist on the far side of the Universe. |
| python for astronomers: An Introduction to Modern Astrophysics Bradley W. Carroll, Dale A. Ostlie, 1996 This exciting text opens the entire field of modern astrophysics to the reader by using only the basic tools of physics. Designed for the junior- level astrophysics course, each topic is approached in the context of the major unresolved questions in astrophysics. The core chapters have been designed for a course in stellar structure and evolution, while the extended chapters provide additional coverage of the solar system, galactic structure, dynamics, evolution, and cosmology. |
| python for astronomers: Knowledge Discovery in Big Data from Astronomy and Earth Observation Petr Skoda, Fathalrahman Adam, 2020-04-10 Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. - Addresses both astronomy and geosciences in parallel, from a big data perspective - Includes introductory information, key principles, applications and the latest techniques - Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields |
| python for astronomers: The Glass Universe Dava Sobel, 2017-10-31 From #1 New York Times bestselling author Dava Sobel, the inspiring (People), little-known true story of women's landmark contributions to astronomy A New York Times Book Review Notable Book Named one of the best books of the year by NPR, The Economist, Smithsonian, Nature, and NPR's Science Friday Nominated for the PEN/E.O. Wilson Literary Science Writing Award A joy to read.” —The Wall Street Journal In the mid-nineteenth century, the Harvard College Observatory began employing women as calculators, or “human computers,” to interpret the observations their male counterparts made via telescope each night. At the outset this group included the wives, sisters, and daughters of the resident astronomers, but soon the female corps included graduates of the new women's colleges—Vassar, Wellesley, and Smith. As photography transformed the practice of astronomy, the ladies turned from computation to studying the stars captured nightly on glass photographic plates. The “glass universe” of half a million plates that Harvard amassed over the ensuing decades—through the generous support of Mrs. Anna Palmer Draper, the widow of a pioneer in stellar photography—enabled the women to make extraordinary discoveries that attracted worldwide acclaim. They helped discern what stars were made of, divided the stars into meaningful categories for further research, and found a way to measure distances across space by starlight. Their ranks included Williamina Fleming, a Scottish woman originally hired as a maid who went on to identify ten novae and more than three hundred variable stars; Annie Jump Cannon, who designed a stellar classification system that was adopted by astronomers the world over and is still in use; and Dr. Cecilia Helena Payne, who in 1956 became the first ever woman professor of astronomy at Harvard—and Harvard’s first female department chair. Elegantly written and enriched by excerpts from letters, diaries, and memoirs, The Glass Universe is the hidden history of the women whose contributions to the burgeoning field of astronomy forever changed our understanding of the stars and our place in the universe. |
| python for astronomers: The Theory of Information and Coding R. J. McEliece, 2004-07-15 Student edition of the classic text in information and coding theory |
| python for astronomers: Programming Python Mark Lutz, 1996 This handbook describes how to use Python, an increasingly popular object-oriented scripting language freely available over the Net. Python is an interpreted language, useful for quick prototyping and simple programs for which C++ is too complex and unwieldy. The Python interpreter is available on most popular UNIX platforms, including Linux, as well as Windows and the Mac. |
| python for astronomers: A First Course in Coding Theory Raymond Hill, 1986 Algebraic coding theory is a new and rapidly developing subject, popular for its many practical applications and for its fascinatingly rich mathematical structure. This book provides an elementary yet rigorous introduction to the theory of error-correcting codes. Based on courses given by the author over several years to advanced undergraduates and first-year graduated students, this guide includes a large number of exercises, all with solutions, making the book highly suitable for individual study. |
| python for astronomers: The Observation and Analysis of Stellar Photospheres David F. Gray, 2021-12-16 This textbook describes the equipment, observational techniques, and analysis used in the investigation of stellar photospheres. Now in its fourth edition, the text has been thoroughly updated and revised to be more accessible to students. New figures have been added to illustrate key concepts, while diagrams have been redrawn and refreshed throughout. The book starts by developing the tools of analysis, and then demonstrates how they can be applied. Topics covered include radiation transfer, models of stellar photospheres, spectroscopic equipment, how to observe stellar spectra, and techniques for measuring stellar temperatures, radii, surface gravities, chemical composition, velocity fields, and rotation rates. Up-to-date results for real stars are included. Written for starting graduate students or advanced undergraduates, this textbook also includes a wealth of reference material useful to researchers. eBook formats include color imagery while print formats are greyscale only; a wide selection of the color images are available online. |
| python for astronomers: Python Programs for Astronomical Solutions Manohar Narayan Purohit, 2020-11-26 This book gives ready-made scripts of Python coding for the solution to all practical problems in Astronomy such as finding Planetary positions at any instant of time on any date, Detailed calculation of lunar and solar eclipses, past or future, with a production of visual simulations like videos, pictures and maps. It gives insight into the technics of Python-programming and in-depth knowledge of Astronomical calculations. It is a must for every astronomical enthusiast and students of computer programming. |
| python for astronomers: The ASTRONET Infrastructure Roadmap Michael F. Bode, Maria J. Cruz, Frank J. Molster, 2008 |
Is there a "not equal" operator in Python? - Stack Overflow
Jun 16, 2012 · Python is dynamically, but strongly typed, and other statically typed languages would complain about comparing different types. There's also the else clause: # This will always print …
What does colon equal (:=) in Python mean? - Stack Overflow
In Python this is simply =. To translate this pseudocode into Python you would need to know the data structures being referenced, and a bit more of the algorithm implementation. Some notes …
What is Python's equivalent of && (logical-and) in an if-statement?
Sep 13, 2023 · There is no bitwise negation in Python (just the bitwise inverse operator ~ - but that is not equivalent to not). See also 6.6. Unary arithmetic and bitwise/binary operations and 6.7. …
What does the "at" (@) symbol do in Python? - Stack Overflow
Jun 17, 2011 · Functions, in Python, are first class objects - which means you can pass a function as an argument to another function, and return functions. Decorators do both of these things. If we …
python - What is the purpose of the -m switch? - Stack Overflow
You must run python my_script.py from the directory where the file is located. Alternatively - python path/to/my_script.py. However, you can run python -m my_script (ie refer to the script by module …
What does [:-1] mean/do in python? - Stack Overflow
Mar 20, 2013 · Working on a python assignment and was curious as to what [:-1] means in the context of the following code: instructions = f.readline()[:-1] Have searched on here on S.O. and …
python - Errno 13 Permission denied - Stack Overflow
Jul 16, 2020 · The problem here is your user doesn't have proper rights/permissions to open the file this means that you'd need to grant some administrative privileges to your python ide before you …
python - Iterating over dictionaries using 'for' loops - Stack Overflow
Jul 21, 2010 · In Python 3.x, iteritems() was replaced with simply items(), which returns a set-like view backed by the dict, like iteritems() but even better. This is also available in 2.7 as …
python - What exactly do "u" and "r" string prefixes do, and what …
There are two types of string in Python 2: the traditional str type and the newer unicode type. If you type a string literal without the u in front you get the old str type which stores 8-bit characters, …
python - How do I execute a program or call a system command?
Note on Python version: If you are still using Python 2, subprocess.call works in a similar way. ProTip: shlex.split can help you to parse the command for run, call, and other subprocess …
Is there a "not equal" operator in Python? - Stack Overflow
Jun 16, 2012 · Python is dynamically, but strongly typed, and other statically typed languages would complain about comparing different types. There's also the else clause: # This will …
What does colon equal (:=) in Python mean? - Stack Overflow
In Python this is simply =. To translate this pseudocode into Python you would need to know the data structures being referenced, and a bit more of the algorithm implementation. Some notes …
What is Python's equivalent of && (logical-and) in an if-statement?
Sep 13, 2023 · There is no bitwise negation in Python (just the bitwise inverse operator ~ - but that is not equivalent to not). See also 6.6. Unary arithmetic and bitwise/binary operations and …
What does the "at" (@) symbol do in Python? - Stack Overflow
Jun 17, 2011 · Functions, in Python, are first class objects - which means you can pass a function as an argument to another function, and return functions. Decorators do both of these things. If …
python - What is the purpose of the -m switch? - Stack Overflow
You must run python my_script.py from the directory where the file is located. Alternatively - python path/to/my_script.py. However, you can run python -m my_script (ie refer to the script …
What does [:-1] mean/do in python? - Stack Overflow
Mar 20, 2013 · Working on a python assignment and was curious as to what [:-1] means in the context of the following code: instructions = f.readline()[:-1] Have searched on here on S.O. …
python - Errno 13 Permission denied - Stack Overflow
Jul 16, 2020 · The problem here is your user doesn't have proper rights/permissions to open the file this means that you'd need to grant some administrative privileges to your python ide …
python - Iterating over dictionaries using 'for' loops - Stack Overflow
Jul 21, 2010 · In Python 3.x, iteritems() was replaced with simply items(), which returns a set-like view backed by the dict, like iteritems() but even better. This is also available in 2.7 as …
python - What exactly do "u" and "r" string prefixes do, and what …
There are two types of string in Python 2: the traditional str type and the newer unicode type. If you type a string literal without the u in front you get the old str type which stores 8-bit …
python - How do I execute a program or call a system command?
Note on Python version: If you are still using Python 2, subprocess.call works in a similar way. ProTip: shlex.split can help you to parse the command for run, call, and other subprocess …
