tupper | Tupper 's self-referential '' formula

when you change offset to 120, it causes that to read 1,027,260 blocks from table daily_income.

try and let me know if moving subquery to the join section helps at all , also I removed an extra join with person table:

You can use a webhook to do what you describe. If you already have a webhook link see here on how to use it. This works from external programs too.

If you don't, you can create a webhook with your bot and then send messages to it. In this example we will create a simple webhook and send a message to it.

First we should check if the channel already has a webhook. That way you don't create a new one everytime you use the command. You do that with the fetchWebhooks() function which returns a collection if there is a webhook present. If that is the case we will get the first webhook in the collection and use that. Note: if you have more then one webhook you can of course add additional checks after the initial one.

If there are no webhooks present, we create a new one with createWebhook(). You can pass additional options after the name.

There are many things to address in your code:

• Python doesn't need ; to end a line, python knows when lines end. Python only uses ; to place two lines of code on the same physical line. Like "x=7;print(x)".
• If you don't use a variable in a list comprehension, then it is customary to use an _. For example, [0 for _ in range(10)]. Of course, python has better ways "[0]*10".
• I notice that you import numpy, but you don't use the operations for numpy arrays. This would greatly improve your code (and make it way faster) and make it way easier to read. For example, you can write to and read from an entire row or column all at once. "matrix[0,:]" (1st row), "matrix[:,0]" (1st col).
• There is no need to include the length of your objects in python, since python automatically stores the length of it's objects, which can always be retrieved with the len() builtin.
• Not really important to your code (as you may be doing this for practice), but as you may already know, lu decomposition, already exists, for example scipy.linalg.lu(). In fact, a quick check of linalg.lu(A,True) reveals that you have bugs both in your L and your U.
• It is really weird to see you manually generate the L and U matricies and then proceed to use np.linalg.inv() on the L and U. If you were willing to use the np.linalg.inv() function, then your answer is one line, "np.linalg.inv(A) @ b". Normally, people find L and U to manually solve for X more easily. To find L and U and then use numpy's inverse function, kindof defeats the purpose.
• Although it can be helpful at times, python does not require you to carve out your space in memory before creating objects. Python automatically manages your memory creation and removal (no need to create empty lists of zeros).
• np.dot() is simply the manual way of accessing the "@" operator in python.

Some examples: