MANE 3351
Lecture 3, August 30
Classroom Management
Agenda
- Attendance
- Textbook
Resources
Handouts
- Lecture 3 Slides
- Lecture 3 Marked Slides
Calendar
| Lecture | Date | Content |
|---|---|---|
| 1 | August 23 | Welcome to Class, Syllabus |
| 2 | August 25 | Error Analysis |
| 3 | August 30 | Introduction to Jupyter Notebook |
Assignments
- Set up PC to run Jupyter Notebook
Git
GitHub
- Source
- Classroom lecture materials will be available at http://github.com/douglastimmer/MANE3351_Fall2021
- Easiest approach is download on PC or Mac using gitHub Desktop
- For Raspberry Pi, git must be used (command line)
- Laboratories will be handled slightly differently (more later)
GitHub Desktop
- Source
- Graphical interface that makes using GitHub very easy to use
- Downloading lecture materials
- First use: clone repository
- Subsequent uses: pull repository (will update local material)
- Please do not push
Python with Jupyter Notebook
GitHub Demonstration
Standard Normal, Case 1
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as sct
import math
a=0.5
x=np.linspace(-4,4,500)
y=sct.norm.pdf(x,0,1)
y2=0.0*x
maske =(x<a)
plt.plot(x,y,'b')
plt.fill_between(x,y,color='#666666',where=maske)
plt.plot(x,y2,'b')
plt.show()
answer=sct.norm.cdf(a,0,1)
print ("%8.6f" % (answer))
First 4 Lines
- Imports allow external packages to be used
- Most standard packages are included in the Anaconda installation
- Matplotlib "is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits"[^1]
- NumPy "is the fundamental package for scientific computering with Python. It contains amont other things: 1). a powerfull N-dimensional array object, 2). sophisticated (broadcasting) functions, 3. tools for integrating C/C++ and Fortran code, and 4). usefull linear algebra, Fourier transform, and random number capabilities."[^2]
- SciPy "is a Python-based ecosystem of open-source software for mathematics, science, and engineering. In particular, these are some of the core packages: NumPy, SciPy library, Matplotlib, IPython, Sympy, and pandas."[^3]
- Math "provides access to the mathematical functions defined by the C standard.
Python Libraries
Numpy Linspace
-
[Source]((https://numpy.org/doc/stable/reference/generated/numpy.linspace.html)
-
Experiment with endpoint
scipy.stats.norm
Matplotlib
