In order to effectively do your job of vibration analysis, you may be more interested in some practical information; but it is important to understand a bit of the theory behind FFTs, PSDs and spectrograms. I'll provide an overview of the math behind the FFT, PSD and spectrogram (for more detail, check out our blog on Fourier Transforms); but I'll use plots to make my point instead of only equations and text.
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In many applications the vibration frequency will change with time and you can run into trouble if you only look at the FFT. Let's zoom out of the area where the car engine is running at a relatively fixed rate, and compute an FFT of the entire signal. In this test the engine sat off for a period of time, idled, then the engine was revved before letting it idle again and finally turning it off. The vibration frequency changed pretty dramatically throughout the test; but the FFT doesn't capture that. We know from the previous plot that when it was idling there was a fairly significant dominate vibration frequency of 30 Hz; but this peak gets muted when you try and look at the FFT of a changing vibration environment.
In this example, and others where the vibration frequency changes with time, we need a spectrogram. A spectrogram works by breaking the time domain data into a series of chunks and taking the FFT of these time periods. These series of FFTs are then overlapped on one another to visualize how both the amplitude and frequency of the vibration signal changes with time. Turn this three dimensional surface plot of FFTs on its side, add a color scale to represent the amplitude (often works best when you look at the color/amplitude on a logarithmic scale) and you're left with a spectrogram!
A spectrogram doesn't have to be viewed in two dimensions. In the below example I kept the surface plot in the three dimensional view. This is from data taken by an enDAQ sensor on the outside of an aircraft as it climbed from 23,000 ft to 40,000 feet. The temperature also dropped from 14C to -31C (58F to -24F) during the test.
In our tutorial series on matplotlib, you have learned how create many different plots and how to customize their design. So far, you have looked at the resulting plot by calling .show(). But what if you need to share the plot? For example, if you need to send it in an email or use it in a presentation?
All image-based file formats, such as PNG or JPG, will come with some quality loss. You can increase (or decrease) the quality of the plot by setting the dpi. For example, if you want to create a higher quality PNG export:
If you take a closer look, this will make the plot a lot nicer. However, keep in mind that it also tripled the file size! If you need to export plots to an image format, there is a trade-off between quality and file size.
Exporting to PNG does have one advantage over JPG. PNG allows transparent colors. By default, matplotlib creates plots on a white background and exports them as such. But you can make the background transparent by passing transparent=true to the savefig() method:
If you want to see the effect of the different data formats, zoom up close onto the line plot. With the PDF and SVG format, it will still be a smooth, straight line (left). However, with PNG or JPG, it'll be clunky (right).
Plots are shown in the Atom PlotPane when possible, either when returned to the console or to an inline code block. At any time, the plot can be opened in a standalone window using the gui() command. The PlotPane can be disabled in Juno's settings.
Supported file formats can be written to an IO stream via, for example, png(myplot, pipebuffer::IO), so the image file can be passed via a PipeBuffer to other functions, eg. Cairo.read_from_png(pipebuffer::IO).
You will want to download the .nc file that contains CTD data. Simply click on the file, and if you are accessing the datafile through the THREDDS server, click on HTTPServer link to download the data file directly to your machine.
Note that when you request glider data, you will often receive GPS data files as part of the response. However, you most likely will not need them, since latitude and longitude data should already be incorporated into the data file. Also, if you are downloading data for use on your local machine, you can ignore .ncml files, which will not work when downloaded. NcML files can provide an easy way to point to aggregated data, when a large data request results in multiple data files, but they only contain internal pointers to other data files, which is why they do not work offline. 2ff7e9595c
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