Download A Student's Guide to Python for Physical Modeling by Jesse M. Kinder, Philip Nelson PDF

By Jesse M. Kinder, Philip Nelson

Python is a working laptop or computer programming language that's quickly rising in popularity through the sciences. A Student's consultant to Python for actual Modeling goals that will help you, the scholar, train your self sufficient of the Python programming language to start with actual modeling. you'll tips on how to set up an open-source Python programming surroundings and use it to complete many universal clinical computing projects: uploading, exporting, and visualizing facts; numerical research; and simulation. No previous programming event is assumed.

This instructional specializes in basics and introduces a variety of priceless concepts, including:

  • Basic Python programming and scripting
  • Numerical arrays
  • Two- and 3-dimensional graphics
  • Monte Carlo simulations
  • Numerical equipment, together with fixing usual differential equations
  • Image processing
  • Animation

Numerous code samples and exercises--with solutions--illustrate new principles as they're brought. Web-based assets additionally accompany this consultant and contain code samples, facts units, and more.

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A Student's Guide to Python for Physical Modeling

Python is a working laptop or computer programming language that's quickly rising in popularity in the course of the sciences. A Student's consultant to Python for actual Modeling goals that will help you, the scholar, train your self adequate of the Python programming language to start with actual modeling. you are going to easy methods to set up an open-source Python programming atmosphere and use it to complete many universal medical computing projects: uploading, exporting, and visualizing information; numerical research; and simulation.

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Extra info for A Student's Guide to Python for Physical Modeling

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Figure out how to code this in vectorized notation. b. ) over the integer values n = 0, 1, . . , N . Here, the exclamation point denotes the factorial function. Figure out how to code this in vectorized notation for N = 10 and µ = 2. ) The item-by-item operations work equally well with multidimensional arrays. Again, the arrays to be combined must have the same shape. shape is True or a and b can be “broadcast” to a common shape. (For example, you can always add a number to an array of any shape.

Knowing that all of the elements in an array are numbers of the same type allows NumPy to do efficient calculations on an entire array. Because Python lists can contain any mix of object types, processing their elements is far less efficient. A Python list is not the same as a NumPy array. We will almost always use NumPy arrays. 71], a list containing one numeric object. A list can even contain no objects: L=[]. 4 If you want a tuple with just one element, write (0,). zeros requires one argument that describes the shape of the array—in this case, the integer 4—and creates an array with four elements.

14 13 Python may actually use a more efficient procedure. The module we are using is important: The sqrt function in the math module will not accept an array as an argument. 14 Jump to Contents Jump to Index 34 Chapter 2 Structure and Control 4. Python adds -b to the array from 3 using the same rule as in 2. 5. Python divides the array from 4 by 2*a. This operation involves two arrays and will be discussed in a moment. It is also evaluated item by item. py is a vectorized form of the earlier for loop.

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