import numpy as np

# Step 1: Generate 6 random values
jitter = np.random.rand(6)  # Generates 6 random numbers between 0 and 1

# Step 2: Adjust the values to have the mean of 2.5
mean_target = 2.5  # The target mean for our data
# Subtract the current mean from each value, then add the target mean
# This shifts all values so the new mean is the target mean
jitter_mean_adjusted = jitter - np.mean(jitter) + mean_target

# Step 3: Scale the values to have the desired standard deviation
std_dev_target = 0.5  # The target standard deviation for our data
# Calculate the scaling factor needed to achieve the target standard deviation
scaling_factor = std_dev_target / np.std(jitter_mean_adjusted)
print("Scaling Factor:", scaling_factor)  # Print the scaling factor for reference
# Apply the scaling factor to each value
final_values = jitter_mean_adjusted * scaling_factor

# Ensure the mean is exactly 2.5 again after scaling
# This corrects any minor deviations in the mean due to the scaling operation
final_values += mean_target - np.mean(final_values)

# Print the final adjusted values, their mean, and standard deviation to verify they meet the target
print("Final values:", final_values)
print("Mean:", np.mean(final_values))  # Should be very close to 2.5
print("Standard Deviation:", np.std(final_values))  # Should be very close to 0.5