Download Learning SciPy for Numerical and Scientific Computing (2nd by Francisco J. Blanco-Silva, Sergio J. Rojas G., Erik A. PDF

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By Francisco J. Blanco-Silva, Sergio J. Rojas G., Erik A. Christensen

Quickly ideas to advanced numerical difficulties in physics, utilized arithmetic, and technology with SciPy

About This Book

Use assorted modules and workouts from the SciPy library speedy and efficiently
Create vectors and matrices and methods to practice average mathematical operations among them or at the respective array in a practical form
A step by step educational that might support clients resolve research-based difficulties from a variety of components of technological know-how utilizing Scipy
Who This booklet Is For

This booklet objectives programmers and scientists who've simple Python wisdom and who're willing to accomplish medical and numerical computations with SciPy.

In Detail

SciPy is an open resource Python library used to accomplish medical computing. The SciPy (Scientific Python) package deal extends the performance of NumPy with a considerable choice of valuable algorithms.

The publication starts off with a short description of the SciPy libraries, by way of a bankruptcy that may be a enjoyable and fast paced primer on array production, manipulation, and problem-solving. additionally, you will how one can use SciPy in linear algebra, such as subject matters corresponding to computation of eigenvalues and eigenvectors. moreover, the e-book is predicated on attention-grabbing matters equivalent to definition and manipulation of features, computation of derivatives, integration, interpolation, and regression. additionally, you will how one can use SciPy in sign processing and the way purposes of SciPy can be utilized to assemble, set up, examine, and interpret data.

By the tip of the e-book, you might have quickly, exact, and easy-to-code options for numerical and medical computing purposes.

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Extra info for Learning SciPy for Numerical and Scientific Computing (2nd Edition)

Sample text

Notice the behavior with non-vectorized Python functions. Let's define such a function for computing, for each value of x, the maximum between x and 100 without using any routine from the NumPy libraries: # function max100 >>> def max100(x): return(x) If we try to apply this function to the preceding array, the system raises an error, as follows: >>> max100(a) The output is an error which is shown as: ValueError: The truth value of an array with more than one element is ambiguous. all() We need to explicitly indicate to the system when we desire to apply one of our functions to arrays, as well as scalars.

If we desire to capture a subarray of A with indices in the Cartesian product of two sets of indices, respecting the row and column choice and creating a new array with the dimensions of the Cartesian product, we use the ix_ command. ix_( [0,1], [0,3] )]) The output is shown as follows: [[1 4] [2 8]] The array object At this point, we are ready for a thorough study of all interesting attributes of ndarray for scientific computing purposes. We have already covered a few, such as dtype, shape, and size.

Where() command helps us gather those entries more efficiently. putmask() command instead, and better manage the memory at the same time. =0, B**2+1) >>> print (B) The output is shown as follows: [[ 0 2 4 82] [ 2 2 2 10] [ 4 2 4 [82 10 6] 6 82]] Note how the putmask procedure updates the values of B, without the explicit need to make a new assignment. There are three additional commands that create arrays in the form of meshes. The arange and linspace commands create uniformly spaced values between two numbers.

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