Homework 2 due Monday Sept 14 at 11:59pm
Agenda
Today
p6.py) will be graded by the
autograderImplementation file
Test file
For better or for worse: most of your time will be spent debugging, testing, and troubleshooting your code.
This is especially true now that AI tools have become available.
Debugging is like detective work
It also pays to “think like a scientist”
It pays to “think like a scientist”
count that counts how
many times the loop runs”count = 5 so my loop is iterating
correctly. The problem must lie elsewhere”Useful questions to ask before errors occur:
Useful questions to ask when errors occur:
Practice using 2D arrays and the debugger to parse error messages
import numpy as np
from plot_array import *
# Define array
temperature = np.array([
[20.1, 20.4, 20.8, 21.0],
[19.8, 20.2, 20.6, 20.9],
[19.5, 19.9, 20.3, 20.7]
])
# Plot array
plot_array(temperature, "temps.png")
# -----------------------------------------------------------------
# Array indexing
val_00 = temperature[0,0]
val_23 = temperature[2,3]
# val_32 = temperature[3,2]
col_0 = temperature[:,0]
col_1 = temperature[:,1]
row_1 = temperature[1,:]
col_m1 = temperature[-1,:]
# -----------------------------------------------------------------
# Combing rows and columns?
# combined = col_0 + row_1
combined = col_0 + col_1
# -----------------------------------------------------------------
# Find all grid points hotter than 20.5 degrees
hot_points = temperature > 20.5
plot_array(hot_points, "hot_points.png")
# -----------------------------------------------------------------
# Computing averages across rows and columns
column_means = np.mean(temperature, axis=1)
print("Column means:", column_means)
# -----------------------------------------------------------------
# Compute approximate derivative in x direction
dx = 0.5
dTdx = np.zeros_like(temperature)
for i in range(temperature.shape[0]):
for j in range(1, temperature.shape[1]-1):
dTdx[i, j] = (temperature[i, j + 1] - temperature[i, j - 1]) / (2 * dx)
print("dT/dx:")
print(dTdx)