tricks | An entirely unidiomatic approach to working with maps | Map library

I'm very new in MATLAB (this is my first script). I wonder how may I speed up this loop, I don't know any toolbox or 'tricks' as I'm a newbie on it. I tried to code it with instinct, it works, but it is really long.

All are variables get with fread or integer manually entered, so this is basically simple math, but I have no clue on why is it so long (maybe nested loops ?) and how to improve, as I am more familiar with Python and for example multiprocess.

Thanks a lot

I have a concave hull (not convex) that I have the points for eg: A,B,C,D,E. I've gotten the pairs of points that make up the outer edges. [A,B],[A,E],[C,D],[B,C],[E,D]. (This is a very simplified version)

I want to get the connected points in order (CW or CCW doesn't matter) so I can use them as a contour.

But the pairs are not ordered, you can see A goes to B, then A goes to E, etc. The only solution I had was searching for each point and its next pair sequentially in a loop

Is there a way to solve this using numpy only in a vectorized manner so that its fast for a large array of edges? I know shapely exists but I have trouble installing it and I'd prefer no external dependancies

this is my code:

I have a div with contenteditable="true" and resize: both attributes which has centered text via flexbox

For the past days I've been trying to plot circular data with python, by constructing a circular histogram ranging from 0 to 2pi and fitting a Von Mises Distribution. What I really want to achieve is this:

1. Directional data with fitted Von-Mises Distribution. This plot was constructed with Matplotlib, Scipy and Numpy and can be found at: http://jpktd.blogspot.com/2012/11/polar-histogram.html

1. This plot was produced using R, but gives the idea of what I want to plot. It can be found here: https://www.zeileis.org/news/circtree/

WHAT I HAVE DONE SO FAR:

I am still quite new to Pyomo and optimization in general, but have recently come across a problem, and so I am hoping that someone can push me in the right direction. I have developed a supply chain optimisation model that had production sites with various capacities and these sites deliver various products to various customers. I have recently upgraded the model to account for penalty costs for sites that produce fewer items than their minimum "contractual" amounts. Example if a site has a minimum production threshold of 50, it can produce 40 but then it needs to pay a penalty ((50-40)*$1). I did this by adding a binary variable, multiplied by cost per unit shortfall, multiplied by the shortfall variable in the objective function. The problem is I get an error saying this is now a quadratic function

This is the error>>RuntimeError: Selected solver is unable to handle objective functions with quadratic terms. Objective at issue: objective.

I have done quite a bit or research but have not found a way to get around preventing converting this into a quadratic function by adding the penalty into the mix (by multiplying a variable with another variable). So my question is, are there some tricks or methodologies someone can point me to. I can use a different solver, its just I assume by converting this into a quadratic this will require a lot more computational power (slowing the run time down). Thanks very much!

I have a dataframe that contains 5 columns and I am using pandas and numpy to edit and work with the data.

*see edits below

I have a dataframe that contains 6 columns and I am using pandas and numpy to edit and work with the data.

I came across this taken from here:

I am using some conda envs in my workflow which contain useful assets (data, models, etc) that are not in the environment bin/ path, e.g. specific files found under share/ , opt/ or other.
Is there a standard way to access these paths from within the workflow? Maybe a variable containing the path to the environment? Otherwise, I'm finding myself using all sorts of Linux tricks to locate these files. Things like: