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kandi X-RAY | python-course Summary
Problem sets for Python Lunchtime Programming Sessions at Arup. There is a problem set for each session and a hacker-edition problem set for those who are eager for a challenge.
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QUESTION
I found the following piece of code here.
...ANSWER
Answered 2021-Dec-18 at 12:44If you want to draw a line, you need to register the coordinates of where the mouse moves and create line based on that using create_line
method because it connects those points. So append positions of mouse to a list and draw a line and use the coordinates from that list. When you start drawing (click mouse), append the starting coordinates. Then on movement append the coordinates, delete the previous line (basically updating) and draw a new one. When mouse button is released clear the list of points and set the current line id to None to indicate that currently no line is drawn.
QUESTION
I have defined a graph by using from pyvis.network import Network
and defining a network as net = Network()
.
I added to the net edges and nodes using the functions net.add_nodes(nodes)
and net.add_edge(x, y)
. There is any chance to deal with this graph not only using a visualization? So for e.g. to find all paths between two specific nodes? Because searching in the documentation there is only the possibility to plot the graph. I saw other options (e.g. class) to deal with graphs (example) but is very annoying to define the graph with a dict of a dict.
ANSWER
Answered 2021-Dec-15 at 09:57From the pyvis
documentation:
The pyvis library is meant for quick generation of visual network graphs with minimal python code. It is designed as a wrapper around the popular Javascript visJS library found at this link.
You probably want a library such as networkx
, which you probably have tagged by accident. It's description states
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
This includes many operations, see the tutorial or to take your example: shortest path
QUESTION
I have generate a spec-list below with conda list --explicit > spec-list.txt
ANSWER
Answered 2021-Nov-08 at 23:10Generally a bad idea to do this with Conda's base,1 however, to install additional packages with a specification list, one uses the conda update
command, e.g.
QUESTION
When a divide-and-conquer recursive function doesn't yield runtimes low enough, which other improvements could be done?
Let's say, for example, this power
function taken from here:
ANSWER
Answered 2021-Jun-15 at 17:36The primary optimization you should use here is common subexpression elimination. Consider your first piece of code:
QUESTION
This is a very basic question relating to setting attributes that I don't find a definitive answer to. I have a python class as follows
...ANSWER
Answered 2021-Mar-20 at 22:51The second option is confusing, as you said, because you would need to go to the method definition to understand there is an assignment to a new variable in the class instance. The first option is somewhat clearer in that sense, but I would still like to know what is the necessary input arguments to that method.
The third option however is wrong for two reasons. One, you pass the radius but you still access self.radius, so the argument is redundant. Second, even if you use the argument, it's supposed to be a static method decorated with @staticmethod, because you don't use the class instance (self).
Therefore, I would go with a static method that utilizes the proper input variables it needs. Something like:
QUESTION
I have a Networkx graph like the following image (image source)
I perform edge attacks and observe the change in values at the node of the resulting subgraph.
Example, If I attack edge (a,2): edge (a, 2) and (2, 1) will be removed. To explain a bit, when edge (a, 2) is attacked the node 2 will have a degree < 2. So the edge that's connected to node 2 is also removed.
The above attack results in a subgraph
Each time an edge is attacked, the value of the terminal node labelled e
observed over time changes. Let's say I perform 5 (attack = 5) attacks, I have a time x attack
matrix (time=25, attack=5) that stores the time-series data of node e
.
I would like to ask for suggestions on how to visualize the effect of these attacks on the value of node e
changing over time.
EDIT:
What information do you want to be able to see or identify from your visualizations?
I want to see the attack on which edge has the maximum effect on the time course value observed at e
. We could imagine this to be a transportation network and the values at node reflect the amount of a product that has reached the location/node. From the source node b
, the goods are transported to target node e
. The observation made is the change in node values after an edge is attacked and no observation of the edge value is available.
Please find the code that is used to attack edges
...ANSWER
Answered 2020-Sep-08 at 14:37Prior to deleting the node add arrows to the edges pointing towards node e
, node and edges to be removed in green, then red, and repeat. Alphas can also be incorporated to represent min-max distances and how they change as the graph is modified.
NetworkX directed graph example: https://networkx.github.io/documentation/stable/auto_examples/drawing/plot_directed.html
NetworkX draw_networkx_edges arguments (includes arrow, color and alpha): https://networkx.github.io/documentation/stable/reference/generated/networkx.drawing.nx_pylab.draw_networkx_edges.html
QUESTION
Is it possible to overload arbitrary operators in Python? Or is one restricted to the list of operators which have associated magic methods as listed here: https://www.python-course.eu/python3_magic_methods.php ?
I'm asking because I noticed that Numpy uses the @ operator to perform matrix multiplication e.g. C=A@B where A,B are Numpy arrays, and I was wondering how they did it.
Edit: The @ operator is not in the list I linked to.
Could someone point me to the Numpy source where this is done?
...ANSWER
Answered 2020-Jul-10 at 10:08In Python, you cannot create new operators, no. By defining those "magic" functions, you can affect what happens when objects of your own definition are operated upon using the standard operators.
However, the list you linked to is not complete. In Python 3.5, they added special methods for the @
operator. Here's the rather terse listing in the Python operator module docs and here are the docs on operator overloading.
operator.matmul(a, b)
operator.__matmul__(a, b)
Return
a @ b
.New in version 3.5.
I hadn't seen that operator personally, so I did a little more research. It's intended specifically for matrix multiplication. But, I was able to use it for other purposes, though I would argue against doing so as a matter of style:
QUESTION
MAIN TROUBLE
As an example, say I would like to decorate split()
method of str
class
(the example is representative of what I try to do, except I would like to decorate agg
method of pandas DataFrame
class).
At least, I get the same error.
I prefer not to use @
nor to create a child of str
class to keep things simple (and maybe it is why it is not working...)
I encounter following trouble.
...ANSWER
Answered 2020-May-09 at 14:52Ok, just found out thanks to this explanation: https://www.bogotobogo.com/python/python_differences_between_static_method_and_class_method_instance_method.php
Another way of calling is by going through the class name as shown below:
Here is the working code.
QUESTION
In this example below, the method m
on class A
is called just once.
I understand this is a feature, this is the Pythonic way to solve the issue where A
's m
method would be called twice (if it was implemented in the naive way) in this diamond-like inheritance scenario.
This is all described here:
https://www.python-course.eu/python3_multiple_inheritance.php
(1) But under the hood... how did they achieve this behavior i.e. that class A
's m
method is called ONLY once?!
Asked simplistically: which line is being "skipped" during execution - is it line #1
or line # 2
?
Could someone shed more light on this?
I have never used multiple inheritance seriously because I mostly program in Java. So I am really curious about this scenario here and more specifically to the inner-workings behind it.
Note: I just want to get the general idea of how this works in Python, not really understand every tiny detail here.
(2) What if I want (in this same scenario and for some reason) A
's m
method to be called twice (or N
times depending on how many base classes of D
we have), while still going through using super()
. Is this possible? Does super()
support such mode of operation?
(3) Is this just some tree or DAG visiting algorithm where they keep track which class's m
method has already been visited and just don't visit it (call it) twice? If so then simplistically speaking I guess '# 2' is the line which is skipped.
ANSWER
Answered 2020-Apr-28 at 18:35This has to do with the Method Resolution Order, which the article you linked already provided some insight (and more information from this other article as well):
The question arises how the super functions makes its decision. How does it decide which class has to be used? As we have already mentioned, it uses the so-called method resolution order(MRO). It is based on the C3 superclass linearisation algorithm. This is called a linearisation, because the tree structure is broken down into a linear order. The mro method can be used to create this list:
QUESTION
First post on Overstack.
I'm new to Python and coding in general. I'm now learning about functions and presently looking at some examples that describe global variables on Python-Course.eu:
...ANSWER
Answered 2020-Apr-24 at 08:22It's reasonably straightforward, as per the Python FAQ (with my emphasis):
In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a value anywhere within the function’s body, it’s assumed to be a local unless explicitly declared as global.
So, if you assign to a variable anywhere within a function, that variables is considered local everywhere in the function, leading to issues such as:
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