def showErrorBars(populations, steps, num_steps, num_trials): """ Plot error bars to represent confidence intervals of the estimates of the average bacteria populations at a sequence of time steps. Args: populations (list of lists or 2D array): populations[i][j] is the number of bacteria present in trial i at time step j steps (list of time step points): steps[i] is a time step point num_steps (int): number of time steps recorded by populations num_trials (int): number of simulation trials recorded by populations For each error bar at time t, it demonstrates a sample mean of populations as well as 95% confidence interval of the estimate regarding the average population. That is, it provides a graphical representation of calc_95_ci. """ pass #TODO #assume that the below populations records simulation data (50 trials and 200 steps on each trial) steps = [i for i in range(25, 200, 25)] showErrorBars(populations,steps, 200, 50) l = [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]] rows, cols = len(l), len(l[0]) print(rows, cols)
Problem 6 Plotting variability and uncertainty
Plot error bars that visualize 95% confidence intervals of the estimates of the average bacteria populations at a sequence of time steps. More specifically, you need to implement the function
showErrorBars
according to the behavior described in the docstring specifications in the below code cell.
Note that the data used to produce the error bars are based on the populations collected in problem 4. The calc_95_ci defined in problem 5 should be used to provide the standard error data to produce the plot.
##########################
# PROBLEM 6
##########################
def showErrorBars(populations, steps, num_steps, num_trials):
"""
Plot error bars to represent confidence intervals of the estimates
of the average bacteria populations at a sequence of time steps.
Args:
populations (list of lists or 2D array): populations[i][j] is the
number of bacteria present in trial i at time step j
steps (list of time step points): steps[i] is a time step point
num_steps (int): number of time steps recorded by populations
num_trials (int): number of simulation trials recorded by populations
For each error bar at time t, it demonstrates a sample mean of populations
as well as 95% confidence interval of the estimate regarding the average
population. That is, it provides a graphical representation of calc_95_ci.
"""
pass #TODO
#assume that the below populations records simulation data (50 trials and 200 steps on each trial)
steps = [i for i in range(25, 200, 25)]
showErrorBars(populations,steps, 200, 50)
l = [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
rows, cols = len(l), len(l[0])
print(rows, cols)
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