The algorithm for moving is as follows

```{prf:algorithm} Jump Chain Algorithm

```{prf:algorithm} Move algorithm

:label: move_algo

1. Draw a random location in $S$

plot_args = {'markersize': 8, 'alpha': 0.8}

ax.set_facecolor('azure')

ax.plot(x_values_0, y_values_0,

'o', markerfacecolor='orange', **plot_args)

'o', markerfacecolor='orange', label='Type 0', **plot_args)

ax.plot(x_values_1, y_values_1,

'o', markerfacecolor='green', **plot_args)

'o', markerfacecolor='green', label='Type 1', **plot_args)

ax.set_title(f'Cycle {cycle_num-1}')

ax.legend()

plt.show()

```

And here's some pseudocode for the main loop, where we cycle through the

agents until no one wishes to move.

Here's the main loop, where we cycle through the agents until no one wishes

to move.

The pseudocode is

```{prf:algorithm} Main loop

:label: schelling_main_loop

1. plot the distribution

1. while agents are still moving

1. for agent in agents

1. give agent the opportunity to move

1. plot the distribution

```{code-block} none

plot the distribution

while agents are still moving

for agent in agents

give agent the opportunity to move

plot the distribution

```

The real code is below

# Loop until no agent wishes to move

while count < max_iter:

print('Entering loop ', count)

count += 1

no_one_moved = True

for agent in agents:

'o',

markersize=8,

markerfacecolor=color,

alpha=0.8)

alpha=0.8,

label=f'Type {agent_type}')

ax.set_title(title)

ax.legend()

plt.show()

def sim_random_select(max_iter=100_000, flip_prob=0.01, test_freq=10_000):

print(f"Terminating at iteration {current_iter}")

```

```{solution-end}

```

+++

When we run this we again find that mixed neighborhoods break down and segregation emerges.

Here's a sample run.

sim_random_select(max_iter=50_000, flip_prob=0.01, test_freq=10_000)

```

```{code-cell} ipython3

```{solution-end}

```