SPEctRA | A Scalable Pipeline for RNA‬‬‬-seq Ana‬‬lysis | Genomics library

I built a script (from different scripts of my work group) to read out data from a folder. The problem is at the end I get a data array with 1x49 cell. I need the data in a matrix array for a waterfall plot with matlab.

The final matrix I need has in the first column the variable 'wave' and from column 2 to i ( the number of cells in my data array) the data from my data array.

I don't know how to get into the single array fields.

This is what I wrote to get the data in the array:

This is the spectra I want to analyze. How do I measure the standard deviation excluding the channel where the peak is present? Lets say the peak is present between 30,000m/s and 90,000m/s.

I am currently processing experimental data for my thesis and am running into a problem with scipy curve_fit.

This is a study of LED emission with the following model depicting the absorption spectra for a specific LED composition/wavelength.

The model is this:

The basic idea is, we got experimental data and we want to fit this equation to give us a best guess of a vertical shift in the data that is a result of the equipment used in the experiment. And to get that vertical shift, the function to be used in the curve_fit would take the form of a + c * E * np.sqrt(E-bandE) * np.exp(-E*b). bandE/Eg refers to the bandgap energy of the material which will be provided in the code section. E refers to the photon energy.

The values I am using in a pandas dataframe that I kept as a list for you to copy and paste (if you want it),

I want to create an animation from roughly 250 individual frames, showing data plotted as 2D images in a figure with 4 x 11 subpanels. The data represent power spectra of velocity as a function of temporal frequency and latitude. However, each frame takes about 4 seconds to create and save, including run-time computation of the data. In the non-interactive plotting mode, I use 'agg' as the backend to avoid time spent for interactivity plotting features.

The speed bottleneck here is not the computation of the data to plot, but saving the plots to disk. Example run-times for random data (see code below) and only 5 frames without saving the plots are sth. like 5 seconds, with saving the plots 17-19 seconds. For the actual data I use, there are some more plot artists to be drawn (text on panels, an additional line plot etc.), but the script execution time is quite similar. For the about 250 frames in total, this indicates roughly 900 seconds, thus 15 minutes to compute the data and then save the plots. However, since I likely want to generate similar frames several times or with slightly different data, it would be good to decrease this script execution time.

A (hopefully) reproducible code, using random data, but with data sizes equal to the actual data I use, is given below. An example frame (the first one generated by the code) can also be found below. In the code, the function create_fig() generates a figure with subpanels containing dummy data and in the for-loop over the different frames, only the data in the subpanels is replaced.

Is there a way to speed-up saving the plots into the png files? Any help is much appreciated!

I am using pexpect to automate running a C program in a zsh terminal on Ubuntu 20.04. The program in question is a spectrum convertor: http://www.np.ph.bham.ac.uk/research_resources/programs/spec_conv/spec_conv.c

I have this installed and in my path. I can not run 'spec_conv' in my terminal and the program runs correctly.

When the program starts there is an initial set of options (0-9). I need to choose 5. The second option I click 'Y'. The program then asks for a file name. I have a file called 'file_list' which I type into the terminal and the spectrum is processed as expected.

I am trying to automate this with python. My code so far is:

For example, I have a data table with several columns:

I am using pvlib to calculate diffuse and direct incoming solar radiation at the surface and this works well. I account for clouds, albedo, sea-ice etc. when calculating the total incoming shortwave radiation at the surface.

it's my first time processing spectra from a black body radiation experiment, I'm using Python and having some troubles... I have this spectra with 2 peaks and uneven background noise which I want to normalize, I tried using specutils 1.1 "continuum-fitting" (documentation here: https://specutils.readthedocs.io/en/stable/fitting.html#continuum-fitting )

My implementation is this:

I have been looking at clustering infrared spectroscopy data with the sklearn clustering methods. I am having trouble getting the clustering to work with the data, since I'm new to this I don't know if the way I'm coding it is wrong or my approach is wrong.

My data, in Pandas DataFrame format, looks like this: