This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC and Tcl. Key features mentioned include rapid development cycle without compiling, automatic memory management, object-oriented programming, and embedding in C. The document also covers Python basics like data types, control flow, functions, modules, and lists/dictionaries. Common uses of Python include shell tools, system administration, rapid prototyping, and graphical user interfaces.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC, Tcl, and Perl. The document outlines Python's philosophy of coherence, power, and rapid development. Key Python features are summarized, including no compiling, dynamic typing, automatic memory management, and support for object-oriented, functional, and procedural programming. Example uses of Python like shell tools, system administration, GUIs, and web development are provided. The document also covers basic Python concepts like modules, statements, control flow, functions, strings, lists, dictionaries, and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also covers Python's basic syntax and data structures like lists, tuples, and dictionaries. It provides examples of control flow, functions, lambda forms, and list/dictionary methods.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability. It has automatic memory management, high-level data types, and support for procedural, object-oriented, and functional programming. Python can be used for tasks like shell scripting, system administration, rapid prototyping, web development, and more.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from teaching languages. Key Python features include rapid development without compiling, automatic memory management, high-level data types, object-oriented programming, and embedding in C. The document also covers Python syntax, basic programming constructs like functions and control flow, data structures like lists and dictionaries, and functional programming tools.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability and rapid development. It has automatic memory management, high-level data types, and built-in interfaces for tasks like GUI development. The document also covers Python programming basics like modules, functions, control flow, and data structures like lists, tuples, and dictionaries.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also gives examples of common Python constructs like functions, control flow, lists, dictionaries, and modules.
This document provides an overview of the Python programming language as presented in an advanced programming course at Columbia University in Spring 2002. It discusses Python's history and philosophy, features such as dynamic typing and memory management, basic syntax and programming constructs, functions, modules, and other language elements. The document is intended to introduce students to Python and provide an overview of its capabilities.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, and features such as rapid development, object orientation, embedding in C, dynamic loading of modules, universal objects, and built-in interfaces to external services. The document also covers Python basics like data types, control flow, functions, modules, and exceptions. It provides examples of Python code and describes how to use Python in areas like shell tools, system administration, GUIs, databases, and distributed programming.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins in 1991 and heritage from languages like ABC, Tcl, and Perl. The document outlines Python's philosophy of coherence, power, and rapid development. Key Python features are summarized, including no compiling, dynamic typing, automatic memory management, and support for object-oriented, functional, and procedural programming. Example uses of Python like shell tools, system administration, GUIs, and web development are provided. The document also covers basic Python concepts like modules, statements, control flow, functions, strings, lists, dictionaries, and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also covers Python's basic syntax and data structures like lists, tuples, and dictionaries. It provides examples of control flow, functions, lambda forms, and list/dictionary methods.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability. It has automatic memory management, high-level data types, and support for procedural, object-oriented, and functional programming. Python can be used for tasks like shell scripting, system administration, rapid prototyping, web development, and more.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from teaching languages. Key Python features include rapid development without compiling, automatic memory management, high-level data types, object-oriented programming, and embedding in C. The document also covers Python syntax, basic programming constructs like functions and control flow, data structures like lists and dictionaries, and functional programming tools.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, features, and uses. Key points include that Python is an interpreted, object-oriented scripting language designed for readability and rapid development. It has automatic memory management, high-level data types, and built-in interfaces for tasks like GUI development. The document also covers Python programming basics like modules, functions, control flow, and data structures like lists, tuples, and dictionaries.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and influences from other languages. Key features of Python mentioned include its rapid development cycle, automatic memory management, object-oriented programming support, and ability to be embedded in C/C++. The document also gives examples of common Python constructs like functions, control flow, lists, dictionaries, and modules.
This document provides an overview of the Python programming language as presented in an advanced programming course at Columbia University in Spring 2002. It discusses Python's history and philosophy, features such as dynamic typing and memory management, basic syntax and programming constructs, functions, modules, and other language elements. The document is intended to introduce students to Python and provide an overview of its capabilities.
This document provides an overview of the Python programming language. It discusses Python's origins, philosophy, and features such as rapid development, object orientation, embedding in C, dynamic loading of modules, universal objects, and built-in interfaces to external services. The document also covers Python basics like data types, control flow, functions, modules, and exceptions. It provides examples of Python code and describes how to use Python in areas like shell tools, system administration, GUIs, databases, and distributed programming.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being coherent, powerful, and easy to read and maintain. Key features of Python mentioned include rapid development, object orientation, embedding in C, dynamic typing, exceptions, and built-in interfaces to external services. The document also outlines some common uses of Python and examples of basic Python code structure, variables, operations, control flow, functions, and data types like lists, tuples, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's history and origins, philosophy of being readable and powerful, features like dynamic typing and automatic memory management, uses for shell tools, prototyping, GUIs and more. It also covers Python syntax, modules, functions, control flow, objects and data types like lists, dictionaries and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being readable, powerful, and allowing for rapid development. Key Python features highlighted include dynamic typing, automatic memory management, object-oriented programming, and extensive standard libraries. The document also provides examples of basic Python syntax like variables, strings, lists, functions, control flow, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from other languages like ABC and Tcl. Key features mentioned include Python being an object-oriented language, its readability, power for both rapid development and large systems, integration capabilities, and elements borrowed from other languages. Various applications of Python like shell tools, extensions, GUI development, and scripting are also listed.
This document provides an overview of the Python programming language. It discusses that Python is a popular, object-oriented scripting language that emphasizes code readability. The document summarizes key Python features such as rapid development, automatic memory management, object-oriented programming, and embedding/extending with C. It also outlines common uses of Python and when it may not be suitable.
Python classes in mumbai
best Python classes in mumbai with job assistance.
our features are:
expert guidance by it industry professionals
lowest fees of 5000
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after course resume writing guidance
An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.
The document describes the AlexNet neural network architecture and its application to classifying images from the Fashion-MNIST dataset. It constructs an AlexNet model, loads and preprocesses the Fashion-MNIST data, and trains the model on this dataset for 5 epochs. Key aspects covered include the convolutional and pooling layers in AlexNet, reading and transforming the Fashion-MNIST data, calculating training and test accuracy, and observing slower progress during training compared to LeNet due to the larger image size.
Compiler Construction | Lecture 12 | Virtual MachinesEelco Visser
The document discusses the architecture of the Java Virtual Machine (JVM). It describes how the JVM uses threads, a stack, heap, and method area. It explains JVM control flow through bytecode instructions like goto, and how the operand stack is used to perform operations and hold method arguments and return values.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
The main challenge of concurrent software verification has always been in achieving modularity, i.e., the ability to divide and conquer the correctness proofs with the goal of scaling the verification effort. Types are a formal method well-known for its ability to modularize programs, and in the case of dependent types, the ability to modularize and scale complex mathematical proofs.
In this talk I will present our recent work towards reconciling dependent types with shared memory concurrency, with the goal of achieving modular proofs for the latter. Applying the type-theoretic paradigm to concurrency has lead us to view separation logic as a type theory of state, and has motivated novel abstractions for expressing concurrency proofs based on the algebraic structure of a resource and on structure-preserving functions (i.e., morphisms) between resources.
This document discusses arrays and structures. It begins by defining an array as a set of index and value pairs where each index has an associated value. Arrays can be implemented using consecutive memory locations. Structures allow grouping of different data types together. Self-referential structures have one or more components that point back to the structure itself. Examples of abstract data types discussed include arrays, polynomials, and sparse matrices. Common operations on these data types like addition, multiplication, and transposition are also described.
R is a software package for data analysis and graphical representation. It provides functions, results of analysis as objects, and a flexible environment for model building. This document provides tutorials on basic R operations including computation, vectors, matrices, and graphics. Key functions introduced are cbind(), rbind(), seq(), rep(), and matrix() for creating and manipulating objects, and plot() for data visualization.
The document discusses Python testing in the Kytos SDN platform. It covers unit testing, integration testing, and system testing. It also discusses mocking, code coverage, linting tools, documentation testing, and continuous integration used for Python testing in Kytos. Testing helps verify code behavior, find bugs, improve code quality, and encourage fast development. Kytos uses tools like unittest, pytest, coverage, pylint, tox and GitHub for testing.
This document provides an overview of programming tools and how to use them. It discusses compilers, linkers, libraries, debugging tools like gdb and strace, profiling tools like top, version control with cvs, and more. It explains what each tool is used for at a high level and provides some basic usage examples.
Parallel R in snow (english after 2nd slide)Cdiscount
This presentation discusses parallelizing computations in R using the snow package. It demonstrates how to:
1. Create a cluster with multiple R sessions using makeCluster()
2. Split data across the sessions using clusterSplit() and export data to each node
3. Write functions to execute in parallel on each node using clusterEvalQ()
4. Collect the results, such as by summing outputs, to obtain the final parallelized computation. As an example, it shows how to parallelize the likelihood calculation for a probit regression model, reducing the computation time.
The document provides an introduction to various MATLAB fundamentals including:
- Modeling the problem of a falling object using differential equations and analytical/numerical solutions.
- Conservation laws that constrain numerical solutions.
- MATLAB commands for defining variables, arrays, matrices, and performing basic operations.
- Plotting the velocity-time solution and customizing graphs.
- Describing algorithms using flowcharts and pseudocode.
- Structured programming in MATLAB using scripts, functions, decisions, and loops.
This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.
This document contains a presentation on self-learning modules in Python. It discusses:
1. Assigning modules to different students for learning.
2. Modules, packages, and libraries as different ways to reuse code in Python. A module is a file with the .py extension, a package is a folder containing modules, and a library is a collection of packages.
3. The Python standard library contains built-in functions and modules that are part of the Python installation. Common modules discussed include math, random, and urllib.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being coherent, powerful, and easy to read and maintain. Key features of Python mentioned include rapid development, object orientation, embedding in C, dynamic typing, exceptions, and built-in interfaces to external services. The document also outlines some common uses of Python and examples of basic Python code structure, variables, operations, control flow, functions, and data types like lists, tuples, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's history and origins, philosophy of being readable and powerful, features like dynamic typing and automatic memory management, uses for shell tools, prototyping, GUIs and more. It also covers Python syntax, modules, functions, control flow, objects and data types like lists, dictionaries and tuples.
This document provides an introduction and overview of the Python programming language. It discusses Python's origins and philosophy of being readable, powerful, and allowing for rapid development. Key Python features highlighted include dynamic typing, automatic memory management, object-oriented programming, and extensive standard libraries. The document also provides examples of basic Python syntax like variables, strings, lists, functions, control flow, and dictionaries.
This document provides an overview of the Python programming language. It discusses Python's origins in 1991 and heritage from other languages like ABC and Tcl. Key features mentioned include Python being an object-oriented language, its readability, power for both rapid development and large systems, integration capabilities, and elements borrowed from other languages. Various applications of Python like shell tools, extensions, GUI development, and scripting are also listed.
This document provides an overview of the Python programming language. It discusses that Python is a popular, object-oriented scripting language that emphasizes code readability. The document summarizes key Python features such as rapid development, automatic memory management, object-oriented programming, and embedding/extending with C. It also outlines common uses of Python and when it may not be suitable.
Python classes in mumbai
best Python classes in mumbai with job assistance.
our features are:
expert guidance by it industry professionals
lowest fees of 5000
practical exposure to handle projects
well equiped lab
after course resume writing guidance
An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.
The document describes the AlexNet neural network architecture and its application to classifying images from the Fashion-MNIST dataset. It constructs an AlexNet model, loads and preprocesses the Fashion-MNIST data, and trains the model on this dataset for 5 epochs. Key aspects covered include the convolutional and pooling layers in AlexNet, reading and transforming the Fashion-MNIST data, calculating training and test accuracy, and observing slower progress during training compared to LeNet due to the larger image size.
Compiler Construction | Lecture 12 | Virtual MachinesEelco Visser
The document discusses the architecture of the Java Virtual Machine (JVM). It describes how the JVM uses threads, a stack, heap, and method area. It explains JVM control flow through bytecode instructions like goto, and how the operand stack is used to perform operations and hold method arguments and return values.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
The main challenge of concurrent software verification has always been in achieving modularity, i.e., the ability to divide and conquer the correctness proofs with the goal of scaling the verification effort. Types are a formal method well-known for its ability to modularize programs, and in the case of dependent types, the ability to modularize and scale complex mathematical proofs.
In this talk I will present our recent work towards reconciling dependent types with shared memory concurrency, with the goal of achieving modular proofs for the latter. Applying the type-theoretic paradigm to concurrency has lead us to view separation logic as a type theory of state, and has motivated novel abstractions for expressing concurrency proofs based on the algebraic structure of a resource and on structure-preserving functions (i.e., morphisms) between resources.
This document discusses arrays and structures. It begins by defining an array as a set of index and value pairs where each index has an associated value. Arrays can be implemented using consecutive memory locations. Structures allow grouping of different data types together. Self-referential structures have one or more components that point back to the structure itself. Examples of abstract data types discussed include arrays, polynomials, and sparse matrices. Common operations on these data types like addition, multiplication, and transposition are also described.
R is a software package for data analysis and graphical representation. It provides functions, results of analysis as objects, and a flexible environment for model building. This document provides tutorials on basic R operations including computation, vectors, matrices, and graphics. Key functions introduced are cbind(), rbind(), seq(), rep(), and matrix() for creating and manipulating objects, and plot() for data visualization.
The document discusses Python testing in the Kytos SDN platform. It covers unit testing, integration testing, and system testing. It also discusses mocking, code coverage, linting tools, documentation testing, and continuous integration used for Python testing in Kytos. Testing helps verify code behavior, find bugs, improve code quality, and encourage fast development. Kytos uses tools like unittest, pytest, coverage, pylint, tox and GitHub for testing.
This document provides an overview of programming tools and how to use them. It discusses compilers, linkers, libraries, debugging tools like gdb and strace, profiling tools like top, version control with cvs, and more. It explains what each tool is used for at a high level and provides some basic usage examples.
Parallel R in snow (english after 2nd slide)Cdiscount
This presentation discusses parallelizing computations in R using the snow package. It demonstrates how to:
1. Create a cluster with multiple R sessions using makeCluster()
2. Split data across the sessions using clusterSplit() and export data to each node
3. Write functions to execute in parallel on each node using clusterEvalQ()
4. Collect the results, such as by summing outputs, to obtain the final parallelized computation. As an example, it shows how to parallelize the likelihood calculation for a probit regression model, reducing the computation time.
The document provides an introduction to various MATLAB fundamentals including:
- Modeling the problem of a falling object using differential equations and analytical/numerical solutions.
- Conservation laws that constrain numerical solutions.
- MATLAB commands for defining variables, arrays, matrices, and performing basic operations.
- Plotting the velocity-time solution and customizing graphs.
- Describing algorithms using flowcharts and pseudocode.
- Structured programming in MATLAB using scripts, functions, decisions, and loops.
This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.
This document contains a presentation on self-learning modules in Python. It discusses:
1. Assigning modules to different students for learning.
2. Modules, packages, and libraries as different ways to reuse code in Python. A module is a file with the .py extension, a package is a folder containing modules, and a library is a collection of packages.
3. The Python standard library contains built-in functions and modules that are part of the Python installation. Common modules discussed include math, random, and urllib.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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Slideshare: http://www.slideshare.net/PECBCERTIFICATION
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Chapter wise All Notes of First year Basic Civil Engineering.pptx
python(1).ppt
1. 24-May-23 Advanced Programming
Spring 2002
Python
Henning Schulzrinne
Department of Computer Science
Columbia University
(based on tutorial by Guido van Rossum)
2. 24-May-23 Advanced Programming
Spring 2002
Introduction
Most recent popular
(scripting/extension) language
although origin ~1991
heritage: teaching language (ABC)
Tcl: shell
perl: string (regex) processing
object-oriented
rather than add-on (OOTcl)
3. 24-May-23 Advanced Programming
Spring 2002
Python philosophy
Coherence
not hard to read, write and maintain
power
scope
rapid development + large systems
objects
integration
hybrid systems
4. 24-May-23 Advanced Programming
Spring 2002
Python features
no compiling or linking rapid development cycle
no type declarations simpler, shorter, more flexible
automatic memory management garbage collection
high-level data types and
operations
fast development
object-oriented programming code structuring and reuse, C++
embedding and extending in C mixed language systems
classes, modules, exceptions "programming-in-the-large"
support
dynamic loading of C modules simplified extensions, smaller
binaries
dynamic reloading of C modules programs can be modified without
stopping
Lutz, Programming Python
5. 24-May-23 Advanced Programming
Spring 2002
Python features
universal "first-class" object model fewer restrictions and rules
run-time program construction handles unforeseen needs, end-
user coding
interactive, dynamic nature incremental development and
testing
access to interpreter information metaprogramming, introspective
objects
wide portability cross-platform programming
without ports
compilation to portable byte-code execution speed, protecting source
code
built-in interfaces to external
services
system tools, GUIs, persistence,
databases, etc.
Lutz, Programming Python
6. 24-May-23 Advanced Programming
Spring 2002
Python
elements from C++, Modula-3
(modules), ABC, Icon (slicing)
same family as Perl, Tcl, Scheme, REXX,
BASIC dialects
7. 24-May-23 Advanced Programming
Spring 2002
Uses of Python
shell tools
system admin tools, command line programs
extension-language work
rapid prototyping and development
language-based modules
instead of special-purpose parsers
graphical user interfaces
database access
distributed programming
Internet scripting
8. 24-May-23 Advanced Programming
Spring 2002
What not to use Python (and
kin) for
most scripting languages share these
not as efficient as C
but sometimes better built-in algorithms
(e.g., hashing and sorting)
delayed error notification
lack of profiling tools
9. 24-May-23 Advanced Programming
Spring 2002
Using python
/usr/local/bin/python
#! /usr/bin/env python
interactive use
Python 1.6 (#1, Sep 24 2000, 20:40:45) [GCC 2.95.1 19990816 (release)] on sunos5
Copyright (c) 1995-2000 Corporation for National Research Initiatives.
All Rights Reserved.
Copyright (c) 1991-1995 Stichting Mathematisch Centrum, Amsterdam.
All Rights Reserved.
>>>
python –c command [arg] ...
python –i script
read script first, then interactive
10. 24-May-23 Advanced Programming
Spring 2002
Python structure
modules: Python source files or C extensions
import, top-level via from, reload
statements
control flow
create objects
indentation matters – instead of {}
objects
everything is an object
automatically reclaimed when no longer needed
11. 24-May-23 Advanced Programming
Spring 2002
First example
#!/usr/local/bin/python
# import systems module
import sys
marker = '::::::'
for name in sys.argv[1:]:
input = open(name, 'r')
print marker + name
print input.read()
12. 24-May-23 Advanced Programming
Spring 2002
Basic operations
Assignment:
size = 40
a = b = c = 3
Numbers
integer, float
complex numbers: 1j+3, abs(z)
Strings
'hello world', 'it's hot'
"bye world"
continuation via or use """ long text """"
13. 24-May-23 Advanced Programming
Spring 2002
String operations
concatenate with + or neighbors
word = 'Help' + x
word = 'Help' 'a'
subscripting of strings
'Hello'[2] 'l'
slice: 'Hello'[1:2] 'el'
word[-1] last character
len(word) 5
immutable: cannot assign to subscript
14. 24-May-23 Advanced Programming
Spring 2002
Lists
lists can be heterogeneous
a = ['spam', 'eggs', 100, 1234, 2*2]
Lists can be indexed and sliced:
a[0] spam
a[:2] ['spam', 'eggs']
Lists can be manipulated
a[2] = a[2] + 23
a[0:2] = [1,12]
a[0:0] = []
len(a) 5
15. 24-May-23 Advanced Programming
Spring 2002
Basic programming
a,b = 0, 1
# non-zero = true
while b < 10:
# formatted output, without n
print b,
# multiple assignment
a,b = b, a+b
16. 24-May-23 Advanced Programming
Spring 2002
Control flow: if
x = int(raw_input("Please enter #:"))
if x < 0:
x = 0
print 'Negative changed to zero'
elif x == 0:
print 'Zero'
elif x == 1:
print 'Single'
else:
print 'More'
no case statement
17. 24-May-23 Advanced Programming
Spring 2002
Control flow: for
a = ['cat', 'window', 'defenestrate']
for x in a:
print x, len(x)
no arithmetic progression, but
range(10) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
for i in range(len(a)):
print i, a[i]
do not modify the sequence being iterated
over
18. 24-May-23 Advanced Programming
Spring 2002
Loops: break, continue, else
break and continue like C
else after loop exhaustion
for n in range(2,10):
for x in range(2,n):
if n % x == 0:
print n, 'equals', x, '*', n/x
break
else:
# loop fell through without finding a factor
print n, 'is prime'
20. 24-May-23 Advanced Programming
Spring 2002
Defining functions
def fib(n):
"""Print a Fibonacci series up to n."""
a, b = 0, 1
while b < n:
print b,
a, b = b, a+b
>>> fib(2000)
First line is docstring
first look for variables in local, then global
need global to assign global variables
21. 24-May-23 Advanced Programming
Spring 2002
Functions: default argument
values
def ask_ok(prompt, retries=4,
complaint='Yes or no, please!'):
while 1:
ok = raw_input(prompt)
if ok in ('y', 'ye', 'yes'): return 1
if ok in ('n', 'no'): return 0
retries = retries - 1
if retries < 0: raise IOError,
'refusenik error'
print complaint
>>> ask_ok('Really?')
22. 24-May-23 Advanced Programming
Spring 2002
Keyword arguments
last arguments can be given as keywords
def parrot(voltage, state='a stiff', action='voom',
type='Norwegian blue'):
print "-- This parrot wouldn't", action,
print "if you put", voltage, "Volts through it."
print "Lovely plumage, the ", type
print "-- It's", state, "!"
parrot(1000)
parrot(action='VOOOM', voltage=100000)
23. 24-May-23 Advanced Programming
Spring 2002
Lambda forms
anonymous functions
may not work in older versions
def make_incrementor(n):
return lambda x: x + n
f = make_incrementor(42)
f(0)
f(1)
24. 24-May-23 Advanced Programming
Spring 2002
List methods
append(x)
extend(L)
append all items in list (like Tcl lappend)
insert(i,x)
remove(x)
pop([i]), pop()
create stack (FIFO), or queue (LIFO) pop(0)
index(x)
return the index for value x
25. 24-May-23 Advanced Programming
Spring 2002
List methods
count(x)
how many times x appears in list
sort()
sort items in place
reverse()
reverse list
26. 24-May-23 Advanced Programming
Spring 2002
Functional programming tools
filter(function, sequence)
def f(x): return x%2 != 0 and x%3 0
filter(f, range(2,25))
map(function, sequence)
call function for each item
return list of return values
reduce(function, sequence)
return a single value
call binary function on the first two items
then on the result and next item
iterate
27. 24-May-23 Advanced Programming
Spring 2002
List comprehensions (2.0)
Create lists without map(),
filter(), lambda
= expression followed by for clause +
zero or more for or of clauses
>>> vec = [2,4,6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [{x: x**2} for x in vec}
[{2: 4}, {4: 16}, {6: 36}]
28. 24-May-23 Advanced Programming
Spring 2002
List comprehensions
cross products:
>>> vec1 = [2,4,6]
>>> vec2 = [4,3,-9]
>>> [x*y for x in vec1 for y in vec2]
[8,6,-18, 16,12,-36, 24,18,-54]
>>> [x+y for x in vec1 and y in vec2]
[6,5,-7,8,7,-5,10,9,-3]
>>> [vec1[i]*vec2[i] for i in
range(len(vec1))]
[8,12,-54]
29. 24-May-23 Advanced Programming
Spring 2002
List comprehensions
can also use if:
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
[]
30. 24-May-23 Advanced Programming
Spring 2002
del – removing list items
remove by index, not value
remove slices from list (rather than by
assigning an empty list)
>>> a = [-1,1,66.6,333,333,1234.5]
>>> del a[0]
>>> a
[1,66.6,333,333,1234.5]
>>> del a[2:4]
>>> a
[1,66.6,1234.5]
31. 24-May-23 Advanced Programming
Spring 2002
Tuples and sequences
lists, strings, tuples: examples of
sequence type
tuple = values separated by commas
>>> t = 123, 543, 'bar'
>>> t[0]
123
>>> t
(123, 543, 'bar')
32. 24-May-23 Advanced Programming
Spring 2002
Tuples
Tuples may be nested
>>> u = t, (1,2)
>>> u
((123, 542, 'bar'), (1,2))
kind of like structs, but no element names:
(x,y) coordinates
database records
like strings, immutable can't assign to
individual items
34. 24-May-23 Advanced Programming
Spring 2002
Tuples
sequence unpacking distribute
elements across variables
>>> t = 123, 543, 'bar'
>>> x, y, z = t
>>> x
123
packing always creates tuple
unpacking works for any sequence
35. 24-May-23 Advanced Programming
Spring 2002
Dictionaries
like Tcl or awk associative arrays
indexed by keys
keys are any immutable type: e.g., tuples
but not lists (mutable!)
uses 'key: value' notation
>>> tel = {'hgs' : 7042, 'lennox': 7018}
>>> tel['cs'] = 7000
>>> tel
36. 24-May-23 Advanced Programming
Spring 2002
Dictionaries
no particular order
delete elements with del
>>> del tel['foo']
keys() method unsorted list of keys
>>> tel.keys()
['cs', 'lennox', 'hgs']
use has_key() to check for existence
>>> tel.has_key('foo')
0
37. 24-May-23 Advanced Programming
Spring 2002
Conditions
can check for sequence membership with is
and is not:
>>> if (4 in vec):
... print '4 is'
chained comparisons: a less than b AND b
equals c:
a < b == c
and and or are short-circuit operators:
evaluated from left to right
stop evaluation as soon as outcome clear
38. 24-May-23 Advanced Programming
Spring 2002
Conditions
Can assign comparison to variable:
>>> s1,s2,s3='', 'foo', 'bar'
>>> non_null = s1 or s2 or s3
>>> non_null
foo
Unlike C, no assignment within
expression
39. 24-May-23 Advanced Programming
Spring 2002
Comparing sequences
unlike C, can compare sequences (lists,
tuples, ...)
lexicographical comparison:
compare first; if different outcome
continue recursively
subsequences are smaller
strings use ASCII comparison
can compare objects of different type, but
by type name (list < string < tuple)
41. 24-May-23 Advanced Programming
Spring 2002
Modules
collection of functions and variables,
typically in scripts
definitions can be imported
file name is module name + .py
e.g., create module fibo.py
def fib(n): # write Fib. series up to n
...
def fib2(n): # return Fib. series up to n
42. 24-May-23 Advanced Programming
Spring 2002
Modules
import module:
import fibo
Use modules via "name space":
>>> fibo.fib(1000)
>>> fibo.__name__
'fibo'
can give it a local name:
>>> fib = fibo.fib
>>> fib(500)
43. 24-May-23 Advanced Programming
Spring 2002
Modules
function definition + executable statements
executed only when module is imported
modules have private symbol tables
avoids name clash for global variables
accessible as module.globalname
can import into name space:
>>> from fibo import fib, fib2
>>> fib(500)
can import all names defined by module:
>>> from fibo import *
44. 24-May-23 Advanced Programming
Spring 2002
Module search path
current directory
list of directories specified in PYTHONPATH
environment variable
uses installation-default if not defined, e.g.,
.:/usr/local/lib/python
uses sys.path
>>> import sys
>>> sys.path
['', 'C:PROGRA~1Python2.2', 'C:Program
FilesPython2.2DLLs', 'C:Program
FilesPython2.2lib', 'C:Program
FilesPython2.2liblib-tk', 'C:Program
FilesPython2.2', 'C:Program FilesPython2.2libsite-
packages']
45. 24-May-23 Advanced Programming
Spring 2002
Compiled Python files
include byte-compiled version of module if
there exists fibo.pyc in same directory as
fibo.py
only if creation time of fibo.pyc matches
fibo.py
automatically write compiled file, if possible
platform independent
doesn't run any faster, but loads faster
can have only .pyc file hide source
46. 24-May-23 Advanced Programming
Spring 2002
Standard modules
system-dependent list
always sys module
>>> import sys
>>> sys.p1
'>>> '
>>> sys.p2
'... '
>>> sys.path.append('/some/directory')
48. 24-May-23 Advanced Programming
Spring 2002
Classes
mixture of C++ and Modula-3
multiple base classes
derived class can override any methods of its
base class(es)
method can call the method of a base class
with the same name
objects have private data
C++ terms:
all class members are public
all member functions are virtual
no constructors or destructors (not needed)
49. 24-May-23 Advanced Programming
Spring 2002
Classes
classes (and data types) are objects
built-in types cannot be used as base
classes by user
arithmetic operators, subscripting can
be redefined for class instances (like
C++, unlike Java)
50. 24-May-23 Advanced Programming
Spring 2002
Class definitions
Class ClassName:
<statement-1>
...
<statement-N>
must be executed
can be executed conditionally (see Tcl)
creates new namespace
51. 24-May-23 Advanced Programming
Spring 2002
Namespaces
mapping from name to object:
built-in names (abs())
global names in module
local names in function invocation
attributes = any following a dot
z.real, z.imag
attributes read-only or writable
module attributes are writeable
52. 24-May-23 Advanced Programming
Spring 2002
Namespaces
scope = textual region of Python program
where a namespace is directly accessible
(without dot)
innermost scope (first) = local names
middle scope = current module's global names
outermost scope (last) = built-in names
assignments always affect innermost scope
don't copy, just create name bindings to objects
global indicates name is in global scope
53. 24-May-23 Advanced Programming
Spring 2002
Class objects
obj.name references (plus module!):
class MyClass:
"A simple example class"
i = 123
def f(self):
return 'hello world'
>>> MyClass.i
123
MyClass.f is method object
54. 24-May-23 Advanced Programming
Spring 2002
Class objects
class instantiation:
>>> x = MyClass()
>>> x.f()
'hello world'
creates new instance of class
note x = MyClass vs. x = MyClass()
___init__() special method for
initialization of object
def __init__(self,realpart,imagpart):
self.r = realpart
self.i = imagpart
55. 24-May-23 Advanced Programming
Spring 2002
Instance objects
attribute references
data attributes (C++/Java data
members)
created dynamically
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print x.counter
del x.counter
56. 24-May-23 Advanced Programming
Spring 2002
Method objects
Called immediately:
x.f()
can be referenced:
xf = x.f
while 1:
print xf()
object is passed as first argument of
function 'self'
x.f() is equivalent to MyClass.f(x)
57. 24-May-23 Advanced Programming
Spring 2002
Notes on classes
Data attributes override method
attributes with the same name
no real hiding not usable to
implement pure abstract data types
clients (users) of an object can add
data attributes
first argument of method usually called
self
'self' has no special meaning (cf. Java)
58. 24-May-23 Advanced Programming
Spring 2002
Another example
bag.py
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self,x):
self.add(x)
self.add(x)
59. 24-May-23 Advanced Programming
Spring 2002
Another example, cont'd.
invoke:
>>> from bag import *
>>> l = Bag()
>>> l.add('first')
>>> l.add('second')
>>> l.data
['first', 'second']
60. 24-May-23 Advanced Programming
Spring 2002
Inheritance
class DerivedClassName(BaseClassName)
<statement-1>
...
<statement-N>
search class attribute, descending chain
of base classes
may override methods in the base class
call directly via BaseClassName.method
61. 24-May-23 Advanced Programming
Spring 2002
Multiple inheritance
class DerivedClass(Base1,Base2,Base3):
<statement>
depth-first, left-to-right
problem: class derived from two classes
with a common base class
62. 24-May-23 Advanced Programming
Spring 2002
Private variables
No real support, but textual
replacement (name mangling)
__var is replaced by
_classname_var
prevents only accidental modification,
not true protection
63. 24-May-23 Advanced Programming
Spring 2002
~ C structs
Empty class definition:
class Employee:
pass
john = Employee()
john.name = 'John Doe'
john.dept = 'CS'
john.salary = 1000
64. 24-May-23 Advanced Programming
Spring 2002
Exceptions
syntax (parsing) errors
while 1 print 'Hello World'
File "<stdin>", line 1
while 1 print 'Hello World'
^
SyntaxError: invalid syntax
exceptions
run-time errors
e.g., ZeroDivisionError,
NameError, TypeError
65. 24-May-23 Advanced Programming
Spring 2002
Handling exceptions
while 1:
try:
x = int(raw_input("Please enter a number: "))
break
except ValueError:
print "Not a valid number"
First, execute try clause
if no exception, skip except clause
if exception, skip rest of try clause and use except
clause
if no matching exception, attempt outer try
statement
66. 24-May-23 Advanced Programming
Spring 2002
Handling exceptions
try.py
import sys
for arg in sys.argv[1:]:
try:
f = open(arg, 'r')
except IOError:
print 'cannot open', arg
else:
print arg, 'lines:', len(f.readlines())
f.close
e.g., as python try.py *.py