Diffraction | Physical Optics Diffraction experiments with Python | Data Visualization library

I am writing some code to identify possible lattice parameters from diffraction peaks. In order to select the correct parameters I wish to determine which combinations of integers are either wholly even or odd. To do this I have used the following function guess_table.insert(5,'FCC_condition',(guess_table[0]%2+guess_table[1]%2+guess_table[2]%2)/3) which gives the output :

Here I want my FCC_condition column to remove any values that arent either 0 or 1 by using guess_table2=guess_table.drop(guess_table[(guess_table['FCC_condition']<1)|(guess_table['FCC_condition']!=0)].index).sort_values('Sums_sqd',ascending=1).reset_index(). However, the problem is that I need to either specify my FCC_condition values as integers or floats, meaning I cant select wholely even or odd combinations of integers in this way. Therefore, is there a way that I can almost dynamically assign a type within a function to avoid this problem?

I am using overleaf online to compile my report and I am experiencing this problem with wrap figure. particularly over a weight of 6cm I experience the problem as shown in figure, image overlaps the text while independent on the size of the image there is some text with a weird formation in the successive page. Can someone help me?

I am looking to fit a log-normal curve to data that roughly follows a lognormal distribution.

The data I have is from a laser diffraction machine which measures particle size distributions of sprays. The ultimate goal of this code is to recreate this method for my data, which uses OriginPro software designed for XRD data curve fitting; a similar problem. I would like to integrate the method into my own analysis for my research, which is being done in Python.

I adapted the code from this post to (ideally) handle log-normal distributions. I have simplified my code to handle only the first log-normal peak in the data, so now it is only trying to fit ONE log-normal distribution. The data I have provided are also simplified to only have one peak to fit. Sample data and code are given at the bottom of this post.

I have some previous experience with model fitting using LMFIT, though I was using a user-defined state-space model for temporal modelling and the LMFIT minimize() function. I am unsure of where to even start debugging the curve-fitting component of this code.

Can anyone help me figure out why I am unable to get a fit to this data? Note that the result I am getting is a trivial one (straight line at y=0).

Working on Windows 7 (laptop) and 10 (desktop)

Running python -V in a CMD window gives:

After many times I've tried installing opticspy as what the instructions written on its documentation page, this pops up Command

I am currently trying to produce a data viz of my experiment I did which involved xy scan files and then plotting them as a function of time. I have managed to get them into a pandas DataFrame that is (1027,281) in shape with the x axis as the index, time as the column labels and the values for the scan as the values in the df. The contour plot looks like this.

What is the dm-script command for projector alignment shift (on JEOL)? Or the "diffraction adjust" (on FEI)? I want to shift the diffraction pattern through command. Thanks,

I want to extract the covers for different journals on the cambridge university press website. The I want to save it as it's online ISSN. The following code works but after one or two journals, it gives me this error:

Calculating daily hours of sunlight (sunset-sunrise) and finding the maximum for each year (generally on the solstice but not always), an interesting pattern emerges. About 5 seconds of sunlight are lost each century.

Is this an error factor within PyEphem? Is this accurate and PyEphem taking into account variations in Earth's orbit? some other reason?

I'm struggling to make a plot that I find useful with my current set of X-ray diffraction data. I have multiple x-ray scans that I'd like to place in a 3d arrangement according to a third variable. [i.e., compare the theta-two-theta (x,y) measured data to a variable in my control-space]

What I'm looking for is either like a waterfall plot or a ribbon plot, but both appear to be limiting. For the waterfall plot, it appears spacing is determined not by a variable, but by the function, similarly to a ribbon-plot, which takes x,y data only, and uses equal spacing and colors along the colormap.

This is similar to stacking XRD plots, but I figured it would be nice to have the spacing between each plot be directly linked to a testing variable in our lab.

Color I'd like to be constant for each line, determined by the nice color routine linspecer.

Are there any plots that control the third dimension by a variable for spacing of the lines? I have each line separate in a data struct right now, but can massage the data to be what is needed for suggested solutions.