Description
Lab Objective
ENGSCI 233
Often we’ll store data or the output of a computer model across a series of different files, e.g., for different days, or different simulations. These might also be contained in different directories. Ideally, these files will be organised according to some sen- sible directory hierarchy or naming convention. Therefore, it is useful to be able to navigate directory structures and access files from within a computer program.
In this lab, your overall goal is to read data from a series of files and directories containing information about the NZ electricity network. This information will then be passed to a plotting function that will display a map of the network.
In order to complete this objective, you have been provided with the following files:
- an example dataset in example file.txt
- partially completed classes and methods in datalab functions.py
- an example network structure in network.txt
- data summarising the NZ electricity network in nz network.zip
- some practice exercises in datalab practice.py
- an implementation file to generate the NZ electricity network in datalab NZnetwork.py
Your main tasks will be to complete the methods add_node, join_nodes, and read_network defined as part of the Network class. Although these methods initially appear in the non-assessed practice exercises, they must eventually be completed as part of the assessed exercise. You will also be completing the read_network method defined as part of the NZNetwork class. These methods can be located in datalab functions.py.
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Practice exercises
In your IDE, open datalab practice.py and read the commented instructions. Complete the exercises before moving onto the assessed task in datalab NZnetwork.py.
Reading data quickly using the genfromtxt command
In Section 1 of the data.ipynb notebook, we saw how a text file could be opened,
read, and its contents interpreted in terms of data and metadata.
RESEARCH how to use the function np.genfromtxt to read data from
example file.txt, returning vectors xs and Ts. You may want to investigate fur- ther the role of the delimiter and skip_header arguments passed to np.genfromtxt.
Networks
We can think of a network as a collection of nodes linked together by arcs:
- Each arc has associated with it a weight, which could be, say, the capacity of that network connection. An arc points from one node to another, i.e., these arcs are singly directed.
- Each node has associated with it a name that identifies it, a value, a list of arcs entering the node, and a list of arcs leaving the node.
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Representing networks in a computer program
We want to express the concepts above – nodes, arcs, weights – using computational structures like objects, attributes and methods. To do this, you have been provided pre-prepared scaffolding – a set of partially completed classes and methods – in the file datalab functions.py.
Open datalab functions.py and inspect the method add_node defined in the Network class. Complete the add_node method.
This will require you to CREATE an empty node object and then ASSIGN values to its attributes. There is a space to write pseudocode for these steps if you wish, or simply complete the relevant commands. Note the hints provided if you are having difficulty.
When you have completed add_node, test its functionality by running
datalab practice.py and checking that you pass the two Part 1 assert commands. If your code fails the asserts, use the debugger to investigate the error.
Complete the join_nodes method.
The code your write here will be similar to the previous method. You will need to CREATE an empty arc object and ASSIGN values to its attributes. In addition, you will need to ensure that the arc object is SAVED to the Network object (i.e. self). You will also need to modify the input node arguments so that they are ASSOCIATED with the arc you have created.
It may help to draw a picture to understand how and with which attribute each item relates to the others. The Network object OWNS the arcs and the nodes. Arcs KNOW about the nodes they link (to and from). Nodes KNOW about the arcs that link with them (into and out of).
When you have completed join_nodes, test its functionality by checking that you pass the Part 2 assert commands. If your code fails the asserts, use the debugger to investigate the error.
Complete the read_network method for the Network class.
Considerably more of this method has been written for you, including some pseu-
docode and a while loop. You will need to:
• Split individual strings, corresponding to each line of the network file, to get
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their information.
- Add new nodes (this one already done).
- Get a node (an object) from a node name (a string).
- Join nodes to create arcs.
- Get the next line in the network file.
If you’re unfamiliar, then it can be difficult to know how a particular command will work. It is often useful to have some test environment in which to experiment.
In VS Code, click on the TERMINAL tab, just to the right of DEBUG CONSOLE. This is like a Windows Command Prompt, and we can open a Python Interpreter by running the command ipython. A Python interpreter works in a similar way to the MATLAB workspace. You can execute individual commands and inspect variables.
Try running the commands
test = 'A,B;2,C;4' test.split(';') test.split(',') test.split(',')[0]To exit the interpreter, type exit.
Once completed, you can test your implementation of the read_network method by running the Part 3 commands. In particular, the network.display() command should print the screen output below
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network has 6 nodes: A, B, C, D, E, F, A --> B with weight 2 A --> C with weight 4 B --> C with weight 1
B --> D with weight 4 C --> D with weight 2 C --> E with weight 1 D --> E with weight 2 D --> F with weight 2 E --> F with weight 3
Assessed Exercise
For this task, you will need to read data from a series of files and directories con- taining information about the NZ electricity network. The nodes of the network represent locations in NZ where electricity is generated or consumed. The arcs are high-voltage power lines that link the nodes, with the arc weights representing the amount of current usually carried by that line.
You will be working on the read_network method of the NZNetwork object. This object is a derived class of the Network object, which means it keeps all the same methods of Network (including add_node and join_nodes that you defined earlier) EXCEPT for the ones that you choose to overwrite. In this case, you will be overwriting or “overloading” the read_network method.
Because add_node and join_nodes will be required when implementing read_network, all three methods form part of the assessment.
Extract the contents of nz network.zip and inspect the files and directory structure. Make a plan (pseudocode) for how you are going to read this information into the NZNetwork object. Finally, write code to implement your read_network method.
If you’re getting stuck, check the suggestions at the bottom of datalab NZnetwork.py. You will know that your method is working correctly if you are able to generate the file datalab network.png looking the same as the plot below.
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Submission Instructions and Rubric
For this lab, you should upload the following files to Canvas:
• datalab functions.py
• datalab network.png
DO NOT modify the name of these files. DO NOT submit any other files (e.g. you do not need to submit datalab NZnetwork.py or datalab practice.py). DO NOT put your submission in a zip archive.
Lab assignments are due by the time advertised on Canvas.
The marking rubric is available on the Canvas assignment page.
Finally, all submissions are compared against each other and those from previous years for similarity. Copying someone else’s code and changing the variable names constitutes academic misconduct by both parties.
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