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kandi X-RAY | Spectra Summary
kandi X-RAY | Spectra Summary
The code here is strictly an example (Currently, it is the legacy code). Running this code on your own is not supported; I will not be providing the resources necessary to self-host.
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Trending Discussions on Spectra
QUESTION
I'm using spectra indices code for hyperspectral data. https://github.com/serbinsh/R-FieldSpectra this is the link for the program I am using. I get to step 2, in line 19 I receive an error for object 'dims' not found. This is the line of code where I receive the error.
for (i in 1:dims[1]){ print(paste("File: ",spectra$Spectra[i],sep="")) }
ANSWER
Answered 2021-May-03 at 17:06That's because there is no object dims
.
Try changing it to for (i in 1:dim(spectra)[1])
, which will loop over the rows (1st dimension) of the data frame spectra
.
QUESTION
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:
...ANSWER
Answered 2021-Apr-13 at 13:12Given that all your files have the same structure, I think it would be better to use readtable()
.
QUESTION
ANSWER
Answered 2021-Apr-12 at 08:32numpy.std()
For excluding the peak, your going to have to define what you want to consider a peak to be - otherwise you are going to be making a solution for only the curve you present.
If you know: (i) your data oscillates around 0, (ii) and that there are no massive troughs (i.e. very negative mins), (iii) and that it should roughly balance around 0 then you could use that to define a peak as greater than 2x the absolute of the min
QUESTION
I am currently processing experimental data for my thesis and am running into a problem with scipy curve_fit.
BackgroundThis 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),
...ANSWER
Answered 2021-Mar-28 at 21:19Apparently the trouble is due to a non convenient criteria of fitting.
LMSE (Least Mean Square Error) in probably implemented in your software. This is not a good choice of criteria of fitting in case of data extended on several decades.
LMSRE (Least Mean Square Relative Error) is recommended in the case of your data.
See below the comparison of results.
NOTE: The expected value of about a=0.0005 is absurd compared to the range of the data from 1. to 240. This would be with no effect, just like a=0. May be a muddle in scales or units ?
QUESTION
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!
...ANSWER
Answered 2021-Feb-09 at 16:57I will give you some tips, but can be not a solution:
You are doing the rigth thing to run over the matrix, but check if can maximize the cache transposing your matrix (when you have a very tall and narrow case)
Have your heard about of sparse-matrix or matrix compressing techniques?
do the stuff that you need to do when i<1 outside of the for loop - you will save 1 comparison if you take out that
can you use parallel computation? like Omp for python?
QUESTION
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:
...ANSWER
Answered 2021-Jan-26 at 21:10If you were to run the spawned program manually, you should be able to see that when you reply to the y/n
question you only need to type y
and the answer is taken immediately without the need for a carriage return.
So you need to send a single character, and not use sendline()
which adds a newline to the sent string. Replace
QUESTION
For example, I have a data table with several columns:
...ANSWER
Answered 2021-Jan-04 at 01:42You can use separate
which uses non-letters and separates the string into columns defined in into
QUESTION
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.
...ANSWER
Answered 2020-Dec-02 at 16:26I don't recognize a conceptual error. What do you get when you integrate the spectral direct irradiance? I'd expect to recover the sw_dr broadband value. Something to check. The line of code that calculates uvi looks odd. sw_dr from total_irrad is a Series, sw_dr(λ[280:400]) indicates that sw_dr is a function.
QUESTION
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:
...ANSWER
Answered 2020-Nov-22 at 11:17At the end I settled on using a median filter from scipy (medfilt) and with a high enough kernel it removed the spikes completely.
The problem on my specutils implementation was that I needed to exclude the peaks in the spectrum window by doing this, althought I found this too hard to implement on 100-120 data measurement I had:
QUESTION
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:
...ANSWER
Answered 2020-Nov-08 at 18:12First, transpose your dataframe, so that you have the datapoints as rows as is the standard. It should look like this:
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