diffract | A set of d3 based visualization components built for React | Data Visualization library

I need to do sort of like 'hyperlink' a url to an image/List(). Currently I am making a UI and upon clicking an image it will open a specific url. And also i have made a search list and i want to achieve upon clicking the topic it will open the respective url. For example, clicking imageA will open https://example.com/data1.html and imageD https://example.com/data4.html, topicA will open https://example.com/data1.html and topicD https://example.com/data4.html. Should I use url_launcher or WebView to show the html page? Thank you.

CODE

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?