Introduction to Enthought Open Source Technologies

1. Introduction to Enthought Open Source Technologies Travis E. Oliphant oliphant@enthought.com Enthought, Inc. www.enthought.com Sage Days 11

2. Enthought Location

3. Enthought�s Business Enthought provides products, training, and consulting services for scientific software solutions. These clients primarily represent the scientific contracting that we�ve done. Several unlisted clients made up half our revenues up through 2004.These clients primarily represent the scientific contracting that we�ve done. Several unlisted clients made up half our revenues up through 2004.

4. Software Application Layers

5. IPython An enhanced interactive Python shell

6. IPython command prompt Available at http://ipython.scipy.org/ Fernando Perez, Brian Granger, and others Provides a nice environment for scientific computing with Python

7. Starting PyLab

8. PyLab: Interactive Python Environment

9. IPython

10. Directory Navigation

11. Running Scripts

12. Function Info -- Magic Commands

13. Function Info -- Magic Commands

14. NumPy and SciPy

15. Helpful Sites

16. Getting Started >>> from numpy import * >>> __version__ 1.0.2.dev3487 or >>> from numpy import array, ...

17. Array Operations

18. Introducing NumPy Arrays

19. Introducing NumPy Arrays

20. Setting Array Elements

21. Arrays from ASCII Data

22. Slicing

23. Multi-Dimensional Arrays

24. Array Slicing

25. Slices Are References Good 1. More efficient because it doesn�t force array copies for every operation. 2. It is often nice to rename the view of an array for manipulation. (A view of the odd and even arrays) Bad 1. Can cause unexpected side-effects that are hard to track down. 2. When you would rather have a copy, it requires some ugliness. Good 1. More efficient because it doesn�t force array copies for every operation. 2. It is often nice to rename the view of an array for manipulation. (A view of the odd and even arrays) Bad 1. Can cause unexpected side-effects that are hard to track down. 2. When you would rather have a copy, it requires some ugliness.

26. Fancy Indexing

27. Fancy Indexing in 2-D

28. �Incomplete� Indexing

29. 3-D Example

30. Array Data Structure

31. Array Data Structure

32. Indexing with None

33. NumPy dtypes

34. Summary of (most) array attributes/methods These are definitely summaries � I have not included all the arguments for functions in many cases. These are definitely summaries � I have not included all the arguments for functions in many cases.

35. Summary of (most) array attributes/methods

36. Summary of (most) array attributes/methods

37. Summary of (most) array attributes/methods

38. Matrix Objects

39. Vectorizing Functions

40. Array Broadcasting

41. Broadcasting Rules

42. Broadcasting in Action

43. Broadcasting Indices

44. Controlling Output Format

45. Controlling Output Formats

46. Controlling Error Handling

47. Controlling Error Handling

48. �Structured� Arrays Good 1. More efficient because it doesn�t force array copies for every operation. 2. It is often nice to rename the view of an array for manipulation. (A view of the odd and even arrays) Bad 1. Can cause unexpected side-effects that are hard to track down. 2. When you would rather have a copy, it requires some ugliness. Good 1. More efficient because it doesn�t force array copies for every operation. 2. It is often nice to rename the view of an array for manipulation. (A view of the odd and even arrays) Bad 1. Can cause unexpected side-effects that are hard to track down. 2. When you would rather have a copy, it requires some ugliness.

49. Structured Arrays

50. �Structured� Arrays Good 1. More efficient because it doesn�t force array copies for every operation. 2. It is often nice to rename the view of an array for manipulation. (A view of the odd and even arrays) Bad 1. Can cause unexpected side-effects that are hard to track down. 2. When you would rather have a copy, it requires some ugliness. Good 1. More efficient because it doesn�t force array copies for every operation. 2. It is often nice to rename the view of an array for manipulation. (A view of the odd and even arrays) Bad 1. Can cause unexpected side-effects that are hard to track down. 2. When you would rather have a copy, it requires some ugliness.

51. Overview

52. Polynomials

53. Polynomials

54. Special Functions

55. Special Functions

56. Interpolation

57. 1D Spline Interpolation

58. 1D Spline Interpolation

59. 2D Spline Interpolation

60. 2D Spline Interpolation

61. Statistics

62. Statistics

63. Using stats objects

64. Using stats objects

65. Statistics

66. Linear Algebra

67. Linear Algebra

68. Matrix Objects

69. Optimization

70. Optimization: 1D Minimization

71. Optimization: Solving Nonlinear Equations

72. Optimization: Using Derivatives

73. Optimization: Data Fitting

74. Fitting Polynomials (NumPy)

75. Signal Processing

76. FFT Functions

77. Filter Design

78. LTI Systems

79. Image Processing

80. Image Processing

81. Integration

82. Integration # compute short-time FFT # compute short-time FFT # compute short-time FFT # compute short-time FFT # compute short-time FFT # compute short-time FFT

83. Integration # compute short-time FFT # compute short-time FFT # compute short-time FFT # compute short-time FFT # compute short-time FFT # compute short-time FFT

84. Ordinary Differential Equation

85. MATLAB FILES

86. GA and Clustering

87. Enthought Tool Suite

88. What are traits? Notification Visualization Others Validation Initialization Delegation

89. Defining Simple Traits

90. Traits for Basic Python Types

91. Derived properties

92. Traits UI � Default Views

93. Trait Listeners

94. Static Trait Notification

95. Dynamic Trait Notification

96. @on_trait_change decorator

97. UI Demos Table Demo

98. Chaco: Interactive Graphics

100. Contexts with Events

101. Three Classes of Users

102. Envisage Application Framework Extensible Scriptable �Humane� Interface Tools built-in Easy Deployment

103. Multiple Plug-ins. One Application This begins the technical overview�this section of our presentation was interspersed with several live demos which I�ll not try to duplicate here.This begins the technical overview�this section of our presentation was interspersed with several live demos which I�ll not try to duplicate here.

105. 3D Visualization

106. One example # create arrays x, y = mgrid[-5:5:100j,-5:5:100j] r = x**2 + y**2 z = sin(r)/r

107. Plotting commands 0D data mlab.points3d(x, y, z)

108. Figure decorations mlab.title('A title') mlab.axes() mlab.colorbar() mlab.clf() mlab.figure() mlab.gcf()

109. Graphical User Interface mlab.show_pipeline()?

110. Examples and Demos mlab.test_points3d() mlab.test_plot3d() mlab.test_surf() mlab.test_contour3d() mlab.test_quiver3d() mlab.test_molecule() mlab.test_flow() mlab.test_mesh()

111. PDE packages PDE approaches Sfepy (Robert Cimrman) FiPy (NIST) PyADH (US Army) PetSC (Argonne) Trilinios (Sandia)