In [111]:

```
import random
import math
line_len = 1000
num_darts = 100
line = [0 for x in xrange(line_len)]
print "throwing darts..."
for i in xrange(num_darts):
pos = random.randint(0,line_len-1)
line[pos] += 1
if (i < 10):
print "%d: %d" % (i, pos)
print "The maximum number of darts at one position was %d" % (max(line))
```

In [112]:

```
plt.figure(figsize=(15,4), dpi=100)
plt.bar(range(line_len), line)
plt.show()
```

In [113]:

```
## compute the distances between darts
distances = []
last_dart = -1
left_distance = line_len
for i in xrange(line_len):
if (line[i] > 0):
right_dist = i - last_dart
min_dist = right_dist
if (left_distance < min_dist):
min_distance = left_distance
if (last_dart != -1):
distances.append(min_dist)
if (line[i] > 1):
for dd in xrange(line[i]-1):
distances.append(0)
left_distance = 0
else:
left_distance = right_dist
last_dart = i
distances.append(last_distance)
mean = (sum(distances) + 0.) / len(distances)
expected_mean = float(line_len) / num_darts
print "The observed mean distance was %.02f and the expected mean was %.02f" % (mean, expected_mean)
```

In [114]:

```
sumdiff = 0.0
for d in distances:
diff = (d - mean) ** 2
sumdiff += diff
sumdiff /= len(distances)
stdev = math.sqrt(sumdiff)
print "The range in distances was %d to %d" % (min(distances), max(distances))
print "The average distance between darts was: %.02f +/- %.02f" % (mean, stdev)
print "The expected distances was %.02f" % (float(line_len) / num_darts)
```

In [115]:

```
plt.figure()
plt.hist(distances)
plt.show()
```

In [116]:

```
plt.figure()
plt.hist(distances, bins=range(max(distances)+1), normed=True)
plt.show()
```

So the probability of a given dart not landing at the next position is

$$ 1-p \text{ (90%)} $$And the probability of not having 2 darts in a row is

$$ (1-p)^2 \text{ (90% * 90% = .81%) } $$And not having 3 in a row

$$ (1-p)^3 \text{ (90% * 90% * 90% = 72.9%) } $$So the probability of not having several darts in a row followed by 1 dart is

$$ pdf = (1-p)^{k} p $$In [117]:

```
p = .1
geom_dist = []
for i in xrange(max(distances)+1):
geom_prob = (1-p)**i * p
geom_dist.append(geom_prob)
```

In [118]:

```
plt.figure()
plt.bar(range(len(geom_dist)), geom_dist, color="green")
plt.show()
```

In [119]:

```
plt.figure()
plt.hist(distances, bins=range(max(distances)+1), normed=True, label="Observed")
plt.plot(range(len(geom_dist)), geom_dist, color="green", linewidth=4, label="Geometric")
plt.legend()
plt.show()
```

In [120]:

```
expected_stdev = math.sqrt((1-p)/(p**2))
print "The expected stdev was %.02f and we observed %.02f" % (expected_stdev, stdev)
```

The geometric distribution can be well approximated by an exponential distribution

$$ pdf(x) = p * e^{-p * x} $$In [121]:

```
p = .1
exp_dist = []
for i in xrange(max(distances)+1):
exp_prob = p * math.exp(-p * i)
exp_dist.append(exp_prob)
```

In [122]:

```
plt.figure()
plt.bar(range(len(exp_dist)), exp_dist, color="orange")
plt.show()
```

In [123]:

```
plt.figure()
plt.hist(distances, bins=range(max(distances)+1), normed=True, label="Observed")
plt.plot(range(len(geom_dist)), geom_dist, color="green", linewidth=4, label="Geometric")
plt.plot(range(len(exp_dist)), exp_dist, color="orange", linewidth=4, linestyle="dashed", label="Exponential")
plt.legend()
plt.show()
```

- http://www.matplotlib.org - The project web page for matplotlib.
- http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended!
- http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.
- http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.
- https://docs.python.org/2/tutorial/ Python Tutorial
- http://en.wikipedia.org/wiki/Probability_distribution Probability distributions
- https://schatzlab.cshl.edu/teaching All my teaching materials