human
Solve the following python programming problem given the following code:
# %load report_resp_times.py
from typing import TYPE_CHECKING, Sequence, Tuple
import functools as ft
from collections import OrderedDict

import matplotlib.pyplot as plt
from livestats import livestats

if TYPE_CHECKING:
    from serversim import UserGroup

def minibatch_resp_times(time_resolution, grp):
    # type: (float, UserGroup) -> Tuple[Sequence[float], Sequence[float], Sequence[float], Sequence[float], Sequence[float], Sequence[float]]
    quantiles = [0.5, 0.95, 0.99]

    xys = [(int(svc_req.time_dict["submitted"]/time_resolution),
            svc_req.time_dict["completed"] - svc_req.time_dict["submitted"])
           for (_, svc_req) in grp.svc_req_log
           if svc_req.is_completed]

    def ffold(map_, p):
        x, y = p
        if x not in map_:
            map_[x] = livestats.LiveStats(quantiles)
        map_[x].add(y)
        return map_

    xlvs = ft.reduce(ffold, xys, dict())

    xs = xlvs.keys()
    xs.sort()

    counts = [xlvs[x].count for x in xs]
    means = [xlvs[x].average for x in xs]
    q_50 = [xlvs[x].quantiles()[0] for x in xs]
    q_95 = [xlvs[x].quantiles()[1] for x in xs]
    q_99 = [xlvs[x].quantiles()[2] for x in xs]

    return xs, counts, means, q_50, q_95, q_99

def plot_counts_means_q95(quantiles1, quantiles2):

    x = quantiles1[0]  # should be same as quantiles2[0]

    counts1 = quantiles1[1]
    counts2 = quantiles2[1]

    means1 = quantiles1[2]
    means2 = quantiles2[2]

    q1_95 = quantiles1[4]
    q2_95 = quantiles2[4]

    # Plot counts
    plt.plot(x, counts1, color='b', label="Counts 1")
    plt.plot(x, counts2, color='r', label="Counts 2")
    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
    plt.xlabel("Time buckets")
    plt.ylabel("Throughput")
    plt.show()

    # Plot averages and 95th percentiles

    plt.plot(x, means1, color='b', label="Means 1")
    plt.plot(x, q1_95, color='c', label="95th Percentile 1")

    plt.plot(x, means2, color='r', label="Means 2")
    plt.plot(x, q2_95, color='m', label="95th Percentile 2")

 ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Mini-batching, plotting, and comparison of results
The following three functions handle mini-batching, plotting, and comparison of results.

minibatch_resp_times -- This function takes the user group from the results of the deployment_example function, scans the service request log of the user group, and produces mini-batch statistics for every time_resolution time units.  For example, with a simulation of 200 time units and a time_resolution of 5 time units, we end up with 40 mini-batches.  The statistics produced are the x values corresponding to each mini-batch, and the counts, means, medians, 95th percentile, and 99th percentile in each mini-batch.
plot_counts_means_q95 -- Plots superimposed counts, means, and 95th percentiles for two mini-batch sets coming from two simulations.
compare_scenarios -- Combines the above two functions to produce comparison plots from two simulations.