I'm glad that you like the videos. The course gives additional information and resources: apmonitor.com/che263 Let me know if there are topics that you'd recommend.
I very much appreciate the time you spend to help others. It is a huge benefit to us seeking knowledge. Thank you. Something to consider is re-watching your videos and trying to place yourself in the mind of your intended audience and think about any questions that might come to mind and then re-do or add these clarifications. For example, you create a for loop for the number of rows (for i in np.size...) and columns (for j in ...), but don't explain why or what the iteration is doing. It seems to just be getting the correct number of iterations, but I'm not sure. And you don't explain i and j. Are these special variables somehow? How are they used. And what does i iteration and j iteration do -- like really?
Thanks for the feedback. The i and j are iterators at 19:35, meaning that they are only useful in the loops to keep track of the row and column position. The nested for loop is completes all of the inner (j) loops and then increments the next outer (i) loop. This continues until all of the outer loops (i) are complete, meaning that all of the rows have been visited. It is like typing on a word processor where j are the individual letters in one row of a sentence and then then outer loop i is incremented for every additional row in the document. Additional information on loops is available at apmonitor.com/che263/index.php/Main/PythonLoops
Hi! I didn't understand how did Pyhton compute the inverse of matrix A since its determinant is zero (thus it has no inverse). I tried to calculate the determinant and it gave me something like -9.51619735393e-16 so maybe this is a question of precision? Thanks!
You can also try the pseudo-inverse for those matrices that are nearly singular. Just replace numpy.linalg.inv with numpy.linalg.pinv as shown at 31:15. Alternatively, you can use numpy.linalg.solve if you need to find the solution to a system of equations.
I'm using your lessons to familiarize myself with python and then teach myself CFD. You are the man
I'm glad that you like the videos. The course gives additional information and resources: apmonitor.com/che263 Let me know if there are topics that you'd recommend.
i know I'm kinda off topic but does anyone know of a good site to stream newly released series online?
@Quinn Augustus flixportal :D
@Jaxtyn Arturo thanks, I signed up and it seems like they got a lot of movies there =) I appreciate it!!
@Quinn Augustus No problem xD
I very much appreciate the time you spend to help others. It is a huge benefit to us seeking knowledge. Thank you. Something to consider is re-watching your videos and trying to place yourself in the mind of your intended audience and think about any questions that might come to mind and then re-do or add these clarifications. For example, you create a for loop for the number of rows (for i in np.size...) and columns (for j in ...), but don't explain why or what the iteration is doing. It seems to just be getting the correct number of iterations, but I'm not sure. And you don't explain i and j. Are these special variables somehow? How are they used. And what does i iteration and j iteration do -- like really?
Thanks for the feedback. The i and j are iterators at 19:35, meaning that they are only useful in the loops to keep track of the row and column position. The nested for loop is completes all of the inner (j) loops and then increments the next outer (i) loop. This continues until all of the outer loops (i) are complete, meaning that all of the rows have been visited. It is like typing on a word processor where j are the individual letters in one row of a sentence and then then outer loop i is incremented for every additional row in the document. Additional information on loops is available at apmonitor.com/che263/index.php/Main/PythonLoops
Hi!
I didn't understand how did Pyhton compute the inverse of matrix A since its determinant is zero (thus it has no inverse).
I tried to calculate the determinant and it gave me something like -9.51619735393e-16 so maybe this is a question of precision?
Thanks!
You can also try the pseudo-inverse for those matrices that are nearly singular. Just replace numpy.linalg.inv with numpy.linalg.pinv as shown at 31:15. Alternatively, you can use numpy.linalg.solve if you need to find the solution to a system of equations.
Thank you!