# Exercise_1 
# Import numpy as np
import numpy as np

# Set the seed
np.random.seed(123)

# Generate and print random float
print(np.random.rand())

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# Exercise_2 
# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Use randint() to simulate a dice
print(np.random.randint(1,7))

# Use randint() again
print(np.random.randint(1,7))

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# Exercise_3 
# Numpy is imported, seed is set

# Starting step
step = 50

# Roll the dice
dice = np.random.randint(1,7)

# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice >2 and dice <6:
    step = step + 1
else :
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
print(step)

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# Exercise_4 
# Numpy is imported, seed is set

# Initialize random_walk

random_walk = [0]

# Complete the ___
for x in range(100) :
    # Set step: last element in random_walk
    step = random_walk[-1]

    # Roll the dice
    dice = np.random.randint(1,7)

    # Determine next step
    if dice <= 2:
        step = step - 1
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    # append next_step to random_walk
    random_walk.append(step)

# Print random_walk
print(random_walk)

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# Exercise_5 
# Numpy is imported, seed is set

# Initialize random_walk
random_walk = [0]

for x in range(100) :
    step = random_walk[-1]
    dice = np.random.randint(1,7)

    if dice <= 2:
        # Replace below: use max to make sure step can't go below 0
        step = max(0,step - 1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    random_walk.append(step)

print(random_walk)

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# Exercise_6 
# Numpy is imported, seed is set

# Initialization
random_walk = [0]

for x in range(100) :
    step = random_walk[-1]
    dice = np.random.randint(1,7)

    if dice <= 2:
        step = max(0, step - 1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    random_walk.append(step)

# Import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

# Plot random_walk
plt.plot(random_walk)

# Show the plot
plt.show()

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# Exercise_7 
# Numpy is imported; seed is set

# Initialize all_walks (don't change this line)
all_walks = []

# Simulate random walk 10 times
for i in range(10) :

    # Code from before
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)

        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        random_walk.append(step)

    # Append random_walk to all_walks
    all_walks.append(random_walk)

# Print all_walks
print(all_walks)

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# Exercise_8 
# numpy and matplotlib imported, seed set.

# initialize and populate all_walks
all_walks = []
for i in range(10) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        random_walk.append(step)
    all_walks.append(random_walk)

# Convert all_walks to Numpy array: np_aw

np_aw = np.array(all_walks)

# Plot np_aw and show
plt.plot(np_aw)
plt.show()

# Clear the figure
plt.clf()

# Transpose np_aw: np_aw_t
np_aw_t = np.transpose(np_aw)

# Plot np_aw_t and show
plt.plot(np_aw_t)
plt.show()


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# Exercise_9 
# numpy and matplotlib imported, seed set

# Simulate random walk 250 times
all_walks = []
for i in range(250) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)

        # Implement clumsiness
        if np.random.rand() <=0.001 :
            step = 0

        random_walk.append(step)
    all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))
plt.plot(np_aw_t)
plt.show()

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# Exercise_10 
# numpy and matplotlib imported, seed set

# Simulate random walk 500 times
all_walks = []
for i in range(500) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        if np.random.rand() <= 0.001 :
            step = 0
        random_walk.append(step)
    all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1,:]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show()

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