Pubbl/distr/stampa	[New York], : Elsevier
Descrizione fisica	1 online resource
Disciplina	519.4/05
Soggetto topico	Mathematics Mathematics - Data processing Numerical analysis Numerical analysis - Data processing Mathématiques - Périodiques Mathématiques - Informatique - Périodiques Analyse numérique - Périodiques Analyse numérique - Informatique - Périodiques
Soggetto genere / forma	Periodicals.
ISSN	1879-1778
Formato	Materiale a stampa
Livello bibliografico	Periodico
Lingua di pubblicazione	eng
Record Nr.	UNINA-9910146579903321

Pubbl/distr/stampa	Taipei City, : Tingmao Publish Co., 2003-
Descrizione fisica	1 online resource
Soggetto topico	Science - Data processing Numerical analysis - Data processing Análisis numérico - Proceso de datos
Soggetto genere / forma	Revistas Periodicals
ISSN	1680-743X
Formato	Materiale a stampa
Livello bibliografico	Periodico
Lingua di pubblicazione	eng
Altri titoli varianti	JDS
Record Nr.	UNINA-9910142880703321

Pubbl/distr/stampa	London, : Academic Press
Disciplina	510
Soggetto topico	Mathematics - Data processing Numerical analysis - Data processing Automatic programming (Computer science) Mathématiques - Informatique Analyse numérique - Informatique Programmation automatique
Soggetto genere / forma	Periodicals.
ISSN	1095-855X
Formato	Materiale a stampa
Livello bibliografico	Periodico
Lingua di pubblicazione	eng
Record Nr.	UNISA-996205859003316

Autore	Idris Ivan
Edizione	[1st edition]
Pubbl/distr/stampa	Birmingham, England : , : Packt Publishing, , 2014
Descrizione fisica	1 online resource (164 p.)
Disciplina	005.133
Collana	Community experience distilled
Soggetto topico	Python (Computer program language) Numerical analysis - Data processing
Soggetto genere / forma	Electronic books.
ISBN	1-78398-391-4
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with NumPy; Python; Installing NumPy, Matplotlib, SciPy, and IPython on Windows; Installing NumPy, Matplotlib, SciPy, and IPython on Linux; Installing NumPy, Matplotlib, and SciPy on Mac OS X; Building from source; NumPy arrays; Adding arrays; Online resources and help; Summary; Chapter 2: NumPy Basics; The NumPy array object; The advantages of using NumPy arrays; Creating a multidimensional array; Selecting array elements; NumPy numerical types Data type objectsCharacter codes; dtype constructors; dtype attributes; Creating a record data type; One-dimensional slicing and indexing; Manipulating array shapes; Stacking arrays; Splitting arrays; Array attributes; Converting arrays; Creating views and copies; Fancy indexing; Indexing with a list of locations; Indexing arrays with Booleans; Stride tricks for Sudoku; Broadcasting arrays; Summary; Chapter 3: Basic Data Analysis with NumPy; Introducing the dataset; Determining the daily temperature range; Looking for evidence of global warming; Comparing solar radiation versus temperature Analyzing wind directionAnalyzing wind speed; Analyzing precipitation and sunshine duration; Analyzing monthly precipitation in De Bilt; Analyzing atmospheric pressure in De Bilt; Analyzing atmospheric humidity in De Bilt; Summary; Chapter 4: Simple Predictive Analytics with NumPy; Examining autocorrelation of average temperature with pandas; Describing data with pandas DataFrames; Correlating weather and stocks with pandas; Predicting temperature; Autoregressive model with lag 1; Autoregressive model with lag 2; Analysing intra-year daily average temperatures Introducing the day-of-the-year temperature modelModeling temperature with the SciPy leastsq function; Day-of-year temperature take two; Moving-average temperature model with lag 1; The Autoregressive Moving Average temperature model; The time-dependent temperature mean adjusted autoregressive model; Outliers analysis of average De Bilt temperature; Using more robust statistics; Summary; Chapter 5: Signal Processing Techniques; Introducing the Sunspot data; Sifting continued; Moving averages; Smoothing functions; Forecasting with an ARMA model; Filtering a signal; Designing the filter Demonstrating cointegrationSummary; Chapter 6: Profiling, Debugging, and Testing; Assert functions; The assert_almost_equal function; Approximately equal arrays; The assert_array_almost_equal function; Profiling a program with IPython; Debugging with IPython; Performing Unit tests; Nose tests decorators; Summary; Chapter 7: The Scientific Python Ecosystem; Numerical integration; Interpolation; Using Cython with NumPy; Clustering stocks with scikit-learn; Detecting corners; Comparing NumPy to Blaze; Summary; Index
Record Nr.	UNINA-9910458552603321

Autore	Rojas Sergio J.G
Edizione	[Second edition.]
Pubbl/distr/stampa	Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2015
Descrizione fisica	1 online resource (188 p.)
Disciplina	519.4
Collana	Community Experience Distilled
Soggetto topico	Numerical analysis - Data processing Python (Computer program language)
Soggetto genere / forma	Electronic books.
ISBN	1-78398-771-5
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to SciPy; What is SciPy?; Installing SciPy; Installing SciPy on Mac OS X; Installing SciPy on Unix/Linux; Installing SciPy on Windows; Testing SciPy installation; SciPy organization; How to find documentation; Scientific visualization; How to open IPython Notebooks; Summary; Chapter 2: Working with the NumPy Array As a First Step to SciPy; Object essentials; Using datatype; Indexing and slicing arrays; The array object; Array conversions Shape selection/manipulationsObject calculations; Array routines; Routines to create arrays; Routines for the combination of two or more arrays; Routines for array manipulation; Routines to extract information from arrays; Summary; Chapter 3: SciPy for Linear Algebra; Vector creation; Vector operations; Addition/subtraction; Scalar/Dot product; Cross / Vector product - on three-dimensional space vectors; Creating a matrix; Matrix methods; Operations between matrices; Functions on matrices; Eigenvalue problems and matrix decompositions; Image compression via the singular value decomposition SolversSummary; Chapter 4: SciPy for Numerical Analysis; Evaluation of special functions; Convenience and test functions; Univariate polynomials; The gamma function; The Riemann zeta function; Airy and Bairy functions; The Bessel and Struve functions; Other special functions; Interpolation; Regression; Optimization; Minimization; Roots; Integration; Exponential/logarithm integrals; Trigonometric and hyperbolic trigonometric integrals; Elliptic integrals; Gamma and beta integrals ; Numerical integration; Ordinary differential equations; Lorenz attractors; Summary Chapter 5: SciPy for Signal ProcessingDiscrete Fourier Transforms; Signal construction; Filters; LTI system theory; Filter design; Window functions; Image interpolation; Morphology; Summary; Chapter 6: SciPy for Data Mining; Descriptive statistics; Distributions; Interval estimation, correlation measures, and statistical tests; Distribution fitting; Distances; Clustering; Vector quantization and k-means; Hierarchical clustering; Clustering mammals by their dentition; Summary; Chapter 7: SciPy for Computational Geometry; Structural model of oxides A finite element solver for Laplace's equationSummary; Chapter 8: Interaction with Other Languages; Interaction with Fortran; Interaction with C/C++; Interaction with MATLAB/Octave; Summary; Index
Record Nr.	UNINA-9910464178903321