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Fix four small issues in Python lectures (#862) #863

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Fix four small issues in Python lectures (#862) #863

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4 changes: 2 additions & 2 deletions lectures/lake_model_intro.md
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Expand Up @@ -93,7 +93,7 @@ We are interested in how $u_t$ and $e_t$ evolve over time.

What long-run unemployment rate and employment rate should we expect?

Do long-run outcomes depend on the initial values $(u_0, e_o)$?
Do long-run outcomes depend on the initial values $(u_0, e_0)$?

### Visualising the long-run outcomes

Expand Down Expand Up @@ -335,7 +335,7 @@ def plot_time_paths(lm, x0=None, T=1000, ax=None):

# Plot time series
for x in x0:
x_ts = lm.simulate_path(x0=x)
x_ts = lm.simulate_path(x0=x, T=T)

ax.scatter(x_ts[0, :], x_ts[1, :], s=4,)

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2 changes: 1 addition & 1 deletion lectures/long_run_growth.md
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Expand Up @@ -141,7 +141,7 @@ We create a variable `color_mapping` to store a map between country codes and co
```{code-cell} ipython3
:tags: [hide-input]

country_names = data['countrycode']
country_names = data['countrycode'].unique()

# Generate a colormap with the number of colors matching the number of countries
colors = cm.tab20(np.linspace(0, 0.95, len(country_names)))
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6 changes: 4 additions & 2 deletions lectures/time_series_with_matrices.md
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Expand Up @@ -467,11 +467,13 @@ Let's begin by generating $N$ time realizations of $y$ plotting them together wi
# Plot mean
N = 100

ys = series_process.sample_y(N, rng=rng)

for i in range(N):
col = cm.viridis(rng.random()) # Choose a random color from viridis
ys = series_process.sample_y(N, rng=rng)
plt.plot(ys[i,:], lw=0.5, color=col)
plt.plot(μ_y, color='red')

plt.plot(μ_y, color='red')

plt.xlabel('t')
plt.ylabel('y')
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