boltzmann | python script computing the entropy of Bitcoin transactions | Cryptocurrency library

I have a list of positive and negative values and a single temperature. I am trying to plot the Maxwell-Boltzmann Distribution using the equation for particles moving in only one direction.

I have a code that solves a hydrostatic solar atmosphere.

I am trying to predict melting temperature (Tm) values for the following sample data using the R DRC library function as shown below to mimic the calculation in GraphPad PRISM using Boltzmann sigmoid function. I selected G.4() function in the DRC package which was the closest self-starting model that was providing Tm values very close to the Boltzmann sigmoid function in PRISM. I am sure it is not the most appropriate choice. I need advice on the correct R library function.

The Boltzmann sigmoid equation used by GraphPad PRISM software:

Y=Y2+((Y1−Y2)/(1+exp((V50−X)/slope))

Four-parameter Gompertz model used in G.4() function in R DRC library:

f(x)=Y2+(Y1−Y2)exp(−exp(b(log(X)−e))).

I want to play around with waLBerla (website and gitlab), which is a C++ library that can be used to simulate certain physical behavior, like fluid dynamics using the Lattice Boltzmann method, but having major difficulties on how to properly set up my CMakeLists.txt, as I don't have much experience with that.

I followed the instructions to install the library, using some of the dependencies like OpenMP, CUDA and OpenMESH, which went fine.

But when trying to compile for example one of the tutorials, I just can't get it to work. So far I have the following Cmake file, where I manually list all the static libraries that have been build when compiling waLBerla:

I'm currently working on a lab report for Brownian Motion using this PDF equation with the intent of evaluating D: Brownian PDF equation

And I am trying to curve_fit it to a histogram. However, whenever I plot my curve_fits, it's a line and does not appear correctly on the histogram. Example Histogram with bad curve_fit

And here is my code:

I have come across a very strange problem where i do a lot of math and the result is inf or nan when my input is of type , but i get the correct (checked analytically) results when my input is of type . The only library functions i use are np.math.factorial(), np.sum() and np.array(). I also use a generator object to sum over series and the Boltzmann constant from scipy.constants.

My question is essentially this: Are their any known cases where np.int64 objects will behave very differently from int objects?

When i run with np.int64 input, i get the RuntimeWarnings: overflow encountered in long_scalars, divide by zero encountered in double_scalars and invalid value encountered in double_scalars. However, the largest number i plug into the factorial function is 36, and i don't get these warnings when i use int input.

Below is a code that reproduces the behaviour. I was unable to find out more exactly where it comes from.

I need to import some functions from several files into a Jupyter Notebook, when I try to do this I get the module not found error despite all necessary files being present.

The original import code looks like this:

I have an admittedly very basic problem: I need to compare two numbers of type double for >=. For some reason, however, my code evaluates to true for values I know to be less than the threshold.

EDIT: My code (the error occurs in the countTrig() method of the Antenna class):

In the article that I am interested in, it states that the data is well represented with a Maxwellian distribution and it also provides a Mean speed (307 km/s) and 1 sigma uncertainty (47 km/s) for the distribution.

Using the provided values, I have attempted to re-generate the data and then fit it with the Maxwellian distribution using the python scipy.stats.

As it described in here, maxwell function in scipy takes two input, 1) "loc" which shifts the x variable and 2) "a" parameter which corresponds to the parameter "a" in the maxwell-Boltzmann equation.

In my case, I have neither of these parameters, so using the Mean and variance (sigma^2) description in wiki page, I have attempted to calculate the "a" and "loc" parameter. Both mean and sigma parameters are only dependent on "a" parameter.

The first problem I have encountered was the "a" parameter that I get from Mean (a = 192.4) and sigma (a = 69.8) are different from each other. The second problem is that I don't know how can I obtain the exact loc (shift) value from Mean and sigma.

Based on the shape of the distribution (where mean speed values fall in the graph, check figure 2), I tried to guess the "loc" value and together with the "a" value obtained from sigma (a = 69.8), I have generated and fitted the data. Approximately it seems correct, but I don't know the answer to the questions I mentioned above and I need some expert's guidance on this. I appreciate any help.