| # 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) |
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.
| #!/bin/bash | |
| CHECKGITLOG=$(git log | head -3 | tail -1 | awk '{print $4;}') | |
| TODAY=$(/bin/date | awk '{print $3}') | |
| if [ $CHECKGITLOG -ne $TODAY ] # if you didn't do it today, act accordingly: | |
| then | |
| echo "HEY! YOU! DO YOUR WRITING!!! (repo synced more than a day ago)" | |
| # Do whatever else. I like send_message.py (from my messager repo): | |
| python messager/send_message.py 917xxxxxxx att "HEY BUM. GET WRITING. NOW." | |
| fi |
| 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)) |
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:
Go download it from https://www.anaconda.com/download/ for your Linux/Windows/OSX
I like to use
wget http://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh
| 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 |
My machine (basically this build: https://www.thingiverse.com/thing:1514145) has GRBL 0.9 : https://github.com/grbl/grbl/wiki/Configuring-Grbl-v0.9 The documents for newer version are up there too, obviously. It would definitely be good to get v1.1 working in order to learn the process of getting it on a fresh Arduino board. I'll outline the process of how to do that in the section below this one and fill in the details as I get around to figuring them out. Here's what I do know for sure though...
You can use any number of GUIs but the premise is always the same.
Once the firmware is on your board, connect it via USB, power it on, and search in your /dev/ directory for your Arduino (type that into Terminal and start typing "tty" and then hit Tab until something shows up). If you have the Arduino software, use the drop down menus to identify an address like /dev/ttyusb#### or /dev/wchusbserial####
The numbers will be u
| 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. |
| 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") |