# 9.5. Phase Change¶

Now let’s test whether a random array contains a percolating cluster:

```
def test_perc(perc):
num_wet = perc.num_wet()
while True:
perc.step()
if perc.bottom_row_wet():
return True
new_num_wet = perc.num_wet()
if new_num_wet == num_wet:
return False
num_wet = new_num_wet
```

`test_perc`

takes a `Percolation`

object as a parameter. Each time through the loop, it advances the CA one time step. It checks the bottom row to see if any cells are wet; if so, it returns `True`

, to indicate that there is a percolating cluster.

During each time step, it also computes the number of wet cells and checks whether the number increased since the last step. If not, we have reached a fixed point without finding a percolating cluster, so `test_perc`

returns `False`

.

To estimate the probability of a percolating cluster, we generate many random arrays and test them:

```
def estimate_prob_percolating(n=100, q=0.5, iters=100):
t = [test_perc(Percolation(n, q)) for i in range(iters)]
return np.mean(t)
```

`estimate_prob_percolating`

makes 100 Percolation objects with the given values of `n`

and `q`

and calls `test_perc`

to see how many of them have a percolating cluster. The return value is the fraction of those that have a percolating cluster.

When `p=0.55`

, the probability of a percolating cluster is near 0. At `p=0.60`

, it is about 70%, and at `p=0.65`

it is near 1. This rapid transition suggests that there is a critical value of `p`

near 0.6.

We can estimate the critical value more precisely using a **random walk**. Starting from an initial value of `q`

, we construct a Percolation object and check whether it has a percolating cluster. If so, `q`

is probably too high, so we decrease it. If not, `q`

is probably too low, so we increase it.

Here’s the code:

```
def find_critical(n=100, q=0.6, iters=100):
qs = [q]
for i in range(iters):
perc = Percolation(n, q)
if test_perc(perc):
q -= 0.005
else:
q += 0.005
qs.append(q)
return qs
```

The result is a list of values for `q`

. We can estimate the critical value, `q_crit`

, by computing the mean of this list. With `n=100`

the mean of `qs`

is about `0.59`

; this value does not seem to depend on `n`

.

The rapid change in behavior near the critical value is called a **phase change** by analogy with phase changes in physical systems, like the way water changes from liquid to solid at its freezing point.

A wide variety of systems display a common set of behaviors and characteristics when they are at or near a critical point. These behaviors are known collectively as **critical phenomena**. In the next section, we explore one of them: fractal geometry.

Q-1: If you wanted to estimate the critical value more precisely you would want to use a .