Bonus exercises: Temperature anomalies

Bonus exercises: Temperature anomalies#

# EXECUTE BUT DO NOT CHANGE
import numpy as np

def load_anomalies():
    # Load the full anomaly data set
    data = np.loadtxt('temperature.csv', skiprows=1, delimiter=',')
    years = [int(y) for y in data[:, 0]]  # unpack the table and cast years to int
    temperature_anomalies = list(data[:, 1])  # unpack the table
    return years, temperature_anomalies

years, temperature_anomalies = load_anomalies()
print(years[:10])
print(temperature_anomalies[:10])

[1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889]
[np.float64(-0.1), np.float64(-0.17), np.float64(-0.11), np.float64(-0.17), np.float64(-0.28), np.float64(-0.26), np.float64(-0.27), np.float64(-0.22), np.float64(-0.09), np.float64(-0.23)]

Exercise 1: Smooth the anomaly values#

Smooth using a moving average window spanning +/-4 of the neighbouring values.

To smooth with a span of 4, you replace each data point with the average value of 9 values:

  • the datapoint itself,

  • the 4 preceeding, and

  • the 4 following values.

Label and format the plot, and plot the raw values as dots and the smoothed values as a line, as in the figure below.

Roughly like so:

# Your solution here

Exercise 2: Make your code more readable by using functions#

Write function that smooths its inputs.

# Your solution here

Exercise 3: Smooth the anomaly values with different spans#

Use different spans of the moving average window (1, 2, 4, 8, 16). Overlay smoothed values for the different spans. Make a function for smoothing to simplify your code.

The plot should look roughly like so:

# your solution

Exercise 4: Change in temperature anomalies#

Plot the year-over-year change in anomaly values.

  1. Smooth the change using a moving average window spanning +/-2 neighbouring values.

  2. Compute the difference between the smoothed and the raw values.

Plot the raw change as dots and the smoothed change as lines.

Roughly like so:

# your solution
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