Previously I showed how it was possible to obtain sunrise and sunset times for a whole year at any location on Earth, from a public source. This time I am going to explain how to fetch that data, clean it up and create graphical visualizations like the one below, all using Python. A Jupyter Notebook is available on GitHub. Such data might even be useful in, for example, simulation of solar power generation. ...
jinsngjung / Pixabay Today is 2nd January and the days are getting longer again. I was thinking about sunrise and sunset times and wondering if I should do some data analysis and plotting of these to visualize how they change over the course of a year. Of course, the first step is to get a suitable data set. You can get a data set of sunrise and sunset times from this page on the US Naval Observatory (USNO) website. Sunrise and sunset times depend on your specific location on the globe so you have to specify the location for your data set. Using the “Form B” Locations Worldwide" section on the page it is possible to enter precise coordinates for any location in terms of latitude and longitude. You can also do it for a timezone, but this is very broad and not precise enough for my liking. ...
UPDATE: These days it is recommended to use Python3 instead of Python2 Apparently it is tricky to get Python integration working in the KNIME Analytics Platform. If you read the official guide too quickly you can miss some critical information at the bottom of the page. I was getting an error complaining that the google.protobuf library was missing even though I thought that I had everything installed correctly: Library google.protobuf is missing, required minimum version is 2.5.0 ...
Figure: Building a geometry in the Salome graphical user interface (GUI). How Salome tracks ports When Salome is starting up, it checks for free ports on your system using a few built-in Python scripts. Then when you close Salome those ports should be freed up again for the next one. This has a number of uses, but one reason is to stop multiple instances of Salome trying to use the same port at once. ...
It can be ridiculously easy to parallelize code in Python. Check out the following simple example: import time from joblib import Parallel, delayed # A function that can be called to do work: def work(arg): print "Function receives the arguments as a list:", arg # Split the list to individual variables: i, j = arg # All this work function does is wait 1 second... time.sleep(1) # ... and prints a string containing the inputs: print "%s_%s" % (i, j) return "%s_%s" % (i, j) # List of arguments to pass to work(): arg_instances = [(1, 1), (1, 2), (1, 3), (1, 4)] # Anything returned by work() can be stored: results = Parallel(n_jobs=4, verbose=1, backend="threading")(map(delayed(work), arg_instances)) print results Output: ...
If you have ever been stuck as a user on an out-of-date cluster without root access it can be frustrating to ask the admin guy to install packages for you. Even if they respond, by the time they get round to it you might have moved onto something else. The moment could be gone. Luckily, as far as Python is concerned, the pyenv project allows users to install their own local Python version or even assign different versions to different directories/projects. ...
The problem of how to visualize multivariate data sets is something I often face in my work. When using numerical optimization we might have a single objective function and multiple design variables that can be represented by columnar data in the form {x1, x2, x3, … xn, y} a.k.a. NXY. With design spaces of more than a few dimensions it is difficult to visualize them in order to estimate the relationship between each independent variable and the objective, or perform a sensitivity study. ...
As is known by anyone in the field of data visualization, the “jet” colormap has some flaws: Doesn’t work when printed black & white Doesn’t work well for colourblind people Not linear in colour space, so it’s hard to estimate numerical values from the resulting image The Matlab team recently developed a new colormap called “parula” but since Matlab is commercially-licensed software, it’s use is restricted. The Matplotlib team have therefore developed their own version, based on the principles of colour theory (covered in my own BSc lecture courses on Visualization). The new Matplotlib default colormap is named “viridis” and it will become the new default colour map starting with Matplotlib v2.0. Users of older versions v1.5.1 can still choose viridis manually using cmap=plt.cm.viridis. ...
Salome is an open source software package used to create geometric models and finite element meshes for use in numerical simulations. It is also able to perform its own numerical simulations and has post-processing capabilities built in. Here are my 5 tips for anyone who is interested in using Salome for model and mesh creation. 1. Practice manually first This goes without saying. Although Salome has a powerful Python-based scripting capability, it is worth practicing with manual model generation. By that I mean, clicking with your mouse in the GUI. Manual practice lets you get familiar with the quirks of the Salome workflow, which has a different mentality to many other model generator programs. ...