basin | A lightweight AST interpreter built in javascript | Parser library

You can do it with the $event parameter. Make sure to compare your values safely.

If your value is not in the right type or has spaces or unwanted chars, this c.state == val might not work.

You can use the trim function to compare your value safely: c.state.trim() == val.trim()

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The Unet simulator supports multiple levels of motion models depending on the required complexity in the simulation.

The NodeInfo agent in each simulated node implements a basic dynamics model. The model is turned on using the mobility flag which can be set in a simulation script or directly on the NodeInfo agent. When mobility is enabled, the agent automatically updates location based on motion parameters such as speed and heading using the simple dynamics model. This can be handy to simulate the motion of a node, for example, an AUV swimming away from an underwater modem.

We can easily do this by manually updating the parameters of one of the nodes in a simulation. Use the 2-node-network example from Unet IDE. The "Map" view of the IDE is handy to visualize the motion of nodes. Connect to the WebShell of Node A and set the mobility, heading, and speed parameters. As the simulation continues the location of Node A will continue to be updated based on the speed and heading.

The generic split operation divides the data into several sets of data. See help(split).

The method split.ppp in the spatstat package divides a point pattern into a list of point patterns. For example if you had a point pattern X representing the locations of trees in a forest, with marks indicating the species of each tree, then split(X) would produce a list of point patterns, each containing the locations of trees of one species.

It seems that you did not want to split your point pattern as you had asked in the previous three questions. Rather, it seems that you probably wanted to produce a single point pattern with factor-valued marks, where the mark value identifies the grouping/classification. You've already done this in the previous question, by doing marks(X) <- factor(.....), so just plot that point pattern X.

With spatstat loaded and the ppp object saved as sf_owin (very counterintuitive name for a ppp object) you can simply overwrite the marks as:

This question is about the spatstat package.

The error message indicates that the split command is dispatched to split.ppp, so you could look up the help for split.ppp to figure out the problem.

The help file would tell you that the argument which.marks is ignored by split.ppp. So you can remove that argument.

To split the point pattern, you need a factor (a vector of categorical values which specify the grouping). The marks in the point pattern are not factors. (This is what the error message is saying.) According to your printed output, the column STATION_NA is character valued. If you want to split the pattern according to these values, you need to convert them to factor values. For example

The question can be answered the R way with a double lapply loop. As long as both arguments, the precipitation array and the masks are in list objects. That's what the preliminary code does, to create two lists.

Two things I noticed after a quick look where you can definitely save some time. Both require some more thinking before, but afterwards you will be rewarded with an optimised and cleaner code.

1. Try to avoid using append. append is highly inefficient. You start with an empty array, and afterwards need to allocate more memory each time. This leads to inefficient memory handling, as you copy your array each time you append a number. The longer the array gets, the more inefficient append becomes.

Alternative: Use np.zeros((m,n)) to initialise the array, with m and n being the size it will end up with. Then you need a counter that puts the new values in the corresponding places. If the size of the array is not defined before your calculation, you can initialise it as a big number, and afterwards cut it.

2. Try to avoid using for loops. They are generally very slow, especially when iterating through large matrices, as you need to index each entry individually.

Alternative: Matrix operations are generally much faster. For example, instead of r = np.sqrt(sum((matposg[j,:]-matposg[k,:])**2)) inside two for loops, you could first define two matrices A and B that correspond to matposg[j,:] and matposg[k,:] (should be possible without the use of loops!) and then simply use r = np.linalg.norm(A-B)

Alright, there are 2 problems that I spotted. Problem 1 is that in your draw_group() ggproto method, you convert the radii from nautical miles to meters (I think), but you write this to the coords variable. However, you use the data variable to do the geosphere::destPoint calculation.

Here is a version of that method that I think should work:

The issue was the environment variables. The reason it worked on my local machine was because of the env file, but when I deploy to Heroku, the env file is gitignored. So I had to set up the environment variables again in Heroku by going to setting > config vars, and configuring the variables the same way they are on my local machine. This fixed the problem instantly.