| import numpy as np | |
| from matplotlib import pyplot as plt # imports plotting library, uses 'plt' as the handle. for help, google "matplotlib" | |
| x = np.linspace(-1, 1, 100) | |
| y = x.**2 + 2.*x + 1 | |
| plt.figure() # open a new figure (sometimes you see ax, fig = plt.figure() if people want to instantiate multiple figures. | |
| plt.plot(x,y) | |
| plt.xlabel("x axis") | |
| plt.ylabel("y axis") |
| save_dir = "images/4D_2/" | |
| QoI_list = [[12],[10], [7,16]] | |
| std = 20 | |
| # image of the scatterplots. | |
| portion = 0.32 # should be a hair less than 1/cols | |
| cols = 3 | |
| rows = 2 | |
| # the following variable is how you want the row*cols images that will be in the figure to be ordered. | |
| order = [ [1 3], [2 3], [1 4], [3 4], [1 2], [2 4] ] # part of the filenames (these are 2D scatterplots in 4D), serpentine column-dominant ordering. (top left to bot right) | |
| for QoI_inds in QoI_list # loop through a list that differentiates the files from one another. each one of these is a different figure saved. |
| from matplotlib import pyplot as plt # the standard python plotting library | |
| # installation instructions here: https://github.com/matplotlib/matplotlib | |
| import numpy as np # math library for data-handling | |
| # DEMO on random matrix to show how filtering based on pixel intensity works. How to make an image. | |
| n = 20 | |
| I = np.random.random((n, n)) | |
| I[I>0.5] = 1 # highpass filter in two lines of code. | |
| I[I<0.5] = 0 |
We'll be using nose for testing purposes.
The basic premise is simple. Write a file that imports solely the modules you want to test.
Write one test for each type-support that you want to be able to check.
From this guide,
we have the following example of ensuring that a user-defined function multiply operates the way it is intended to:
| import scipy.stats as sstats | |
| def ev(*args, **kwds): | |
| return sstats.norm(*args, **kwds) | |
| kw = {'loc':2} | |
| def kwrap(kwds): | |
| return ev(**kwds) | |
| print(kwrap(kw).rvs(3)) |
Note: this procedure requires an .img file that you will be required to create from the .iso file you download.
Download the desired file. Convert the .iso file to .img using the convert option of hdiutil
hdiutil convert /path/to/ubuntu.iso -format UDRW -o /path/to/target.img
Note: OS X tends to put the .dmg ending on the output file automatically.
| # Here we utilize scipy to draw samples from two distributions and plot the result. | |
| import seaborn as sns | |
| import numpy as np | |
| import scipy.stats as sstats | |
| x_dist = sstats.norm(loc=0, scale=2) | |
| y_dist = sstats.uniform(loc=0, scale=1) | |
| N = 10000 | |
| x_data = x_dist.rvs(N) |
| ## | |
| # Create a figure space matrix consisting of 3 columns and 2 rows | |
| # | |
| # Here is a useful template to use for working with subplots. | |
| # | |
| ################################################################## | |
| fig, ax = plt.subplots(figsize=(10,5), ncols=3, nrows=2) | |
| left = 0.125 # the left side of the subplots of the figure | |
| right = 0.9 # the right side of the subplots of the figure |
| from mpl_toolkits.mplot3d import Axes3D | |
| import matplotlib.pyplot as plt | |
| from matplotlib import cm | |
| from matplotlib.ticker import LinearLocator, FormatStrFormatter | |
| import numpy as np | |
| def q1(lam): | |
| L1 = lam[:,0] # local column-vectors. | |
| L2 = lam[:,1] |