NFW | Python routines for computing Navarro | Computer Vision library

Step #1: Set project.ext.supportLibVersion to 26.1.0 or higher

Step #2: Note that you are now getting deprecation warnings on all your new NotificationCompat.Builder() calls

Step #3: Define a NotificationChannel (if you have not defined it on some previous run of the app)

Step #4: Pass the channel ID to the NotificationCompat.Builder constructor

The problem is arising because you have M=[], which makes it a list. That completely overrides the M = m1 + m2 you have done at the top. Then on line 55, you have M * BLAH, where M is NOT a numpy array (it is a list), hence the * operator is interpreted to mean "Repeat the list BLAH times". That's where the problem is.

PS: changing it to np.array(M) * (340*eta/(3*cos(1.)*vrel(y,z,m)**9))**(1/7) fixes this problem but creates others. For instance if you are using python2, then (1/7) = 0, you probably want (1./7.), and so on. You know the masses upfront - so it will probably be faster to use np.arange to create the full M array and then process it.

PS: Since you have astronomy related calculations, do check out the astropy package, it does some of the things you are looking for.

As far as I got this, you want to find the number of red dots that are outside the region defined by a non-linear function you call Escape_Velocity and the red dots have x-positions and y-positions that are stored in the arrays Dist and RedVal, respectively, ordered so that a red dot has a position (x,y)=(Dist[n], RedVel[n]). Also, it looks like (and your definition of UnboundSubhalos suggests it) the dotted lines defined by Escape_Velocity are symmetric around RedVel = 0, i.e. you actually want to find the number of red dots with an absolute value of RedVel larger than Escape_Velocity.

In this case you can simply do the following: Assuming Escape_Velocity is a function you can pass an array to, you find the Escape_Velocity for every element of your array Dist and store it in the array esc_vel = Escape_Velocity(Mass, Dist). Else you will have to calculate these values with a loop over the elements of Dist. From your code I can assume that Escape_Velocity gives a positive value, i.e. the dotted line at positive RedVal values. Then the array outside_dots = np.abs(RedVel) >= esc_vel contains True for every dot outside the region enclosed by the dotted curves and False for every dot inside. Then np.sum(outside_dots) gives you the desired number of dots outside.

The following code works like described: