Question

我想分析四个工具在运行多个程序时的性能。一个子图是所有程序中一个工具的结果。结果应如下所示：

我使用for循环迭代程序列表并每次绘制一个部分如下：

但是这些情节看起来像是一个，我不能使用axis.set_xticks()来区分它们的x刻度。看来这个功能没有效果。

我是否使用正确的函数来设置x刻度？或者我该如何制作这个情节呢？

draw_hist_query()可能是我问题最重要的功能

数据样本：

boolector,ppbv,stp,z3
0.05349588394165039,0.015434503555297852,0.028127193450927734,0.11303281784057617
0.0027561187744140625,0.004331827163696289,0.007134914398193359,0.016040563583374023
0.003190755844116211,0.005587577819824219,0.002897500991821289,0.013916015625
0.009758472442626953,0.02006363868713379,0.0031282901763916016,0.011539697647094727
0.057138681411743164,0.012826681137084961,0.030836820602416992,0.0217435359954834

代码：

index = range(len(solvers))
fig, axes = plt.subplots(nrows=4)
solvers = ['z3', 'stp', 'boolector', 'ppbv']
colors = ['g', 'c', 'b', 'r', 'y', 'orange', 'grey']
ticks = [0.1, 0.5, 1.0, 2.0]
width=0.2

# program entry
def all_time_query(path):
    csv = xxx.csv # the array of data to be analyzed, one csv for one program
    for axis in axes:
        axis.set_xticks(range(len(csv)))
    for c in csv:
        multi_time_query(c) # draw the bar pair for c, which shows the upper image for one program on four tools


def multi_time_query(csv):
    data = pd.read_csv(csv)
    for solver in solvers: # the four tools
        bin = index[solvers.index(solver)]
        hist_t_query(data, solver, ax=axes[bin]) # details to draw the bar pair, uses dataframe.plot.bar



def hist_t_query(data, solver, ax):
    solver_data = pd.DataFrame(data).as_matrix(columns=[solver])
    # draw one bar for demo
    draw_hist_query(pd.DataFrame(solver_data), ax)


# left of bar pair, the right one is similar
def draw_hist_query(df, ax):
    count = []
    for i in range(len(ticks)):
        count.append(df[df < ticks[i]].count())
        color = stat.colors[i]
        if i == 0:
            count[i].plot.bar(ax=ax, color=color, width=width, position=0)
        else:
            (count[i] - count[i - 1]).plot.bar(bottom=count[i - 1],
                                               ax=ax, color=color, width=width, position=0)

Answer 1

一般来说，你几乎没有选择。您可以使用plt.tight_layout()并自动完成所有操作，也可以使用plt.subplot_adjust()并自行指定每个参数。正如您在文档中看到的那样，签名就是这样：

subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

如果你要进入交互式窗口，你可以选择调整那里的参数，你可以看到你的图表随每个参数的变化情况

然后你可以用最适合你的方式调用subplot_adjust。

我希望它有所帮助。

Answer 2

最后我解决了这个问题：

我以前错误地想到的是关于次要情节。我希望在一对已经存在之后在子图中添加另一个条形对。 然而，一个子图应该一起绘制（一次），并且不应该分开。在我的情况下，一个子图的条应该一起出现，只需要四次绘制所有子图。

这是我的新版代码：

def time_query_project(path):
    fig, axis = plt.subplots(nrows=4)
    csv = sio.find_csv(path)
    data = {}

    for solver in solvers:
        for c in csv:
            df = pd.DataFrame(pd.read_csv(c), columns=[solver])
            data.update({get_name(c): df.to_dict()[solver]})
        df = pd.DataFrame.from_dict(data, orient='columns')
        ax = axis[solvers.index(solver)]
        ax.set_ylabel(solver)
        hist_t_query(df, ax)


def hist_t_query(data, solver, ax):
    solver_data = pd.DataFrame(data).as_matrix(columns=[solver])
    # draw one bar for demo
    draw_hist_query(pd.DataFrame(solver_data), ax)


# left of bar pair, the right one is similar
def draw_hist_query(df, ax):
    count = []
    for i in range(len(ticks)):
        count.append(df[df < ticks[i]].count())
        color = stat.colors[i]
        if i == 0:
            count[i].plot.bar(ax=ax, color=color, width=width, position=0)
        else:
            (count[i] - count[i - 1]).plot.bar(bottom=count[i - 1],
                                               ax=ax, color=color, width=width, position=0)

Python：专门为子图中的条形设置x刻度？

2 个答案: