Python matplotlib.dates.YearLocator() Examples

def _plot_drawdown(self, stats, ax=None, **kwargs):
        """
        Plots the underwater curve
        """
        def format_perc(x, pos):
            return '%.0f%%' % x

        drawdown = stats['drawdowns']

        if ax is None:
            ax = plt.gca()

        y_axis_formatter = FuncFormatter(format_perc)
        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
        ax.yaxis.grid(linestyle=':')
        ax.xaxis.set_tick_params(reset=True)
        ax.xaxis.set_major_locator(mdates.YearLocator(1))
        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
        ax.xaxis.grid(linestyle=':')

        underwater = -100 * drawdown
        underwater.plot(ax=ax, lw=2, kind='area', color='red', alpha=0.3, **kwargs)
        ax.set_ylabel('')
        ax.set_xlabel('')
        plt.setp(ax.get_xticklabels(), visible=True, rotation=0, ha='center')
        ax.set_title('Drawdown (%)', fontweight='bold')
        return ax 

def set_x_axis_locator(self, x_from, x_to):
        x_axis_range = x_to - x_from
        years = mdates.YearLocator()
        if x_axis_range < 200:
            self.ax.xaxis.set_major_locator(mdates.MonthLocator())
            self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))
            # self.ax.xaxis.set_major_locator(mdates.AutoDateLocator())
        elif x_axis_range < 7*365:
            self.ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
            self.ax.xaxis.set_major_locator(years)
            self.ax.xaxis.set_minor_locator(mdates.MonthLocator())
        else:
            self.ax.xaxis.set_major_locator(years)
            self.ax.xaxis.set_major_locator(mdates.AutoDateLocator()) 

def _plot_drawdown(self, stats, ax=None, **kwargs):
        """
        Plots the underwater curve
        """
        def format_perc(x, pos):
            return '%.0f%%' % x

        drawdown = stats['drawdowns']

        if ax is None:
            ax = plt.gca()

        y_axis_formatter = FuncFormatter(format_perc)
        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
        ax.yaxis.grid(linestyle=':')
        ax.xaxis.set_tick_params(reset=True)
        ax.xaxis.set_major_locator(mdates.YearLocator(1))
        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
        ax.xaxis.grid(linestyle=':')

        underwater = -100 * drawdown
        underwater.plot(ax=ax, lw=2, kind='area', color='red', alpha=0.3, **kwargs)
        ax.set_ylabel('')
        ax.set_xlabel('')
        plt.setp(ax.get_xticklabels(), visible=True, rotation=0, ha='center')
        ax.set_title('Drawdown (%)', fontweight='bold')
        return ax 

def _plot_rolling_sharpe(self, stats, ax=None, **kwargs):
        """
        Plots the curve of rolling Sharpe ratio.
        """
        def format_two_dec(x, pos):
            return '%.2f' % x

        sharpe = stats['rolling_sharpe']

        if ax is None:
            ax = plt.gca()

        y_axis_formatter = FuncFormatter(format_two_dec)
        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
        ax.xaxis.set_tick_params(reset=True)
        ax.yaxis.grid(linestyle=':')
        ax.xaxis.set_major_locator(mdates.YearLocator(1))
        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
        ax.xaxis.grid(linestyle=':')

        if self.benchmark is not None:
            benchmark = stats['rolling_sharpe_b']
            benchmark.plot(
                lw=2, color='gray', label=self.benchmark, alpha=0.60,
                ax=ax, **kwargs
            )

        sharpe.plot(lw=2, color='green', alpha=0.6, x_compat=False,
                    label='Backtest', ax=ax, **kwargs)

        ax.axvline(sharpe.index[252], linestyle="dashed", c="gray", lw=2)
        ax.set_ylabel('Rolling Annualised Sharpe')
        ax.legend(loc='best')
        ax.set_xlabel('')
        plt.setp(ax.get_xticklabels(), visible=True, rotation=0, ha='center')

        return ax 

def _plot_equity(self, stats, ax=None, **kwargs):
        """
        Plots cumulative rolling returns versus some benchmark.
        """
        def format_two_dec(x, pos):
            return '%.2f' % x

        equity = stats['cum_returns']

        if ax is None:
            ax = plt.gca()

        y_axis_formatter = FuncFormatter(format_two_dec)
        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
        ax.xaxis.set_tick_params(reset=True)
        ax.yaxis.grid(linestyle=':')
        ax.xaxis.set_major_locator(mdates.YearLocator(1))
        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
        ax.xaxis.grid(linestyle=':')

        if self.benchmark is not None:
            benchmark = stats['cum_returns_b']
            benchmark.plot(
                lw=2, color='gray', label=self.benchmark, alpha=0.60,
                ax=ax, **kwargs
            )

        equity.plot(lw=2, color='green', alpha=0.6, x_compat=False,
                    label='Backtest', ax=ax, **kwargs)

        ax.axhline(1.0, linestyle='--', color='black', lw=1)
        ax.set_ylabel('Cumulative returns')
        ax.legend(loc='best')
        ax.set_xlabel('')
        plt.setp(ax.get_xticklabels(), visible=True, rotation=0, ha='center')

        if self.log_scale:
            ax.set_yscale('log')

        return ax 

def drawData(ax, _data):
    """
    使用柱状图表示股市数据
    """
    candlestick_ochl(ax,
        _data[["date2num", "open_price", "close_price", "high_price", "low_price"]].values,
        colorup="r", colordown="g", width=0.5)
    ax.xaxis.set_major_locator(YearLocator())
    ax.xaxis.set_major_formatter(DateFormatter('%Y'))
    return ax 

def main(args):
    _, fetch = load_dataset(SP500, shuffle=False)
    dates, returns = fetch()
    init_rng_key, sample_rng_key = random.split(random.PRNGKey(args.rng_seed))
    model_info = initialize_model(init_rng_key, model, model_args=(returns,))
    init_kernel, sample_kernel = hmc(model_info.potential_fn, algo='NUTS')
    hmc_state = init_kernel(model_info.param_info, args.num_warmup, rng_key=sample_rng_key)
    hmc_states = fori_collect(args.num_warmup, args.num_warmup + args.num_samples, sample_kernel, hmc_state,
                              transform=lambda hmc_state: model_info.postprocess_fn(hmc_state.z),
                              progbar=False if "NUMPYRO_SPHINXBUILD" in os.environ else True)
    print_results(hmc_states, dates)

    fig, ax = plt.subplots(1, 1)
    dates = mdates.num2date(mdates.datestr2num(dates))
    ax.plot(dates, returns, lw=0.5)
    # format the ticks
    ax.xaxis.set_major_locator(mdates.YearLocator())
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
    ax.xaxis.set_minor_locator(mdates.MonthLocator())

    ax.plot(dates, jnp.exp(hmc_states['s'].T), 'r', alpha=0.01)
    legend = ax.legend(['returns', 'volatility'], loc='upper right')
    legend.legendHandles[1].set_alpha(0.6)
    ax.set(xlabel='time', ylabel='returns', title='Volatility of S&P500 over time')

    plt.savefig("stochastic_volatility_plot.pdf")
    plt.tight_layout() 

def PlotScalerDateAdjust(minDate:datetime, maxDate:datetime, ax):
	if type(minDate)==str:
		daysInGraph = DateDiffDays(minDate,maxDate)
	else:
		daysInGraph = (maxDate-minDate).days
	if daysInGraph >= 365*3:
		majorlocator =  mdates.YearLocator()
		minorLocator = mdates.MonthLocator()
		majorFormatter = mdates.DateFormatter('%m/%d/%Y')
	elif daysInGraph >= 365:
		majorlocator =  mdates.MonthLocator()
		minorLocator = mdates.WeekdayLocator()
		majorFormatter = mdates.DateFormatter('%m/%d/%Y')
	elif daysInGraph < 90:
		majorlocator =  mdates.DayLocator()
		minorLocator = mdates.DayLocator()
		majorFormatter =  mdates.DateFormatter('%m/%d/%Y')
	else:
		majorlocator =  mdates.WeekdayLocator()
		minorLocator = mdates.DayLocator()
		majorFormatter =  mdates.DateFormatter('%m/%d/%Y')
	ax.xaxis.set_major_locator(majorlocator)
	ax.xaxis.set_major_formatter(majorFormatter)
	ax.xaxis.set_minor_locator(minorLocator)
	#ax.xaxis.set_minor_formatter(daysFmt)
	ax.set_xlim(minDate, maxDate) 

def _plot_equity(self, strat_stats, bench_stats=None, ax=None, **kwargs):
        """
        Plots cumulative rolling returns versus some benchmark.
        """
        def format_two_dec(x, pos):
            return '%.2f' % x

        equity = strat_stats['cum_returns']

        if ax is None:
            ax = plt.gca()

        y_axis_formatter = FuncFormatter(format_two_dec)
        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
        ax.xaxis.set_tick_params(reset=True)
        ax.yaxis.grid(linestyle=':')
        ax.xaxis.set_major_locator(mdates.YearLocator(1))
        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
        ax.xaxis.grid(linestyle=':')

        equity.plot(lw=2, color='green', alpha=0.6, x_compat=False,
                    label='Strategy', ax=ax, **kwargs)
        if bench_stats is not None:
            bench_stats['cum_returns'].plot(
                lw=2, color='gray', alpha=0.6, x_compat=False,
                label='Benchmark', ax=ax, **kwargs
            )

        ax.axhline(1.0, linestyle='--', color='black', lw=1)
        ax.set_ylabel('Cumulative returns')
        ax.legend(loc='best')
        ax.set_xlabel('')
        plt.setp(ax.get_xticklabels(), visible=True, rotation=0, ha='center')
        return ax 

def plot_results(features, dates, water, clouds, save_directory=None, ground_truth_file=None):
    fig, ax = plt.subplots()
    water_line = ax.plot(dates, water, linestyle='-', color='b', linewidth=1,
                         label='Landsat-Generated Surface Area')
    ax.plot(dates, water, 'gs', ms=3)
    # ax.bar(dates, water, color='b', width=15, linewidth=0)
    # ax.bar(dates, clouds, bottom=water, color='r', width=15, linewidth=0)
    ax.xaxis.set_major_locator(mdates.YearLocator())
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
    ax.xaxis.set_minor_locator(mdates.MonthLocator())
    ax.set_xlabel('Time')
    ax.format_xdata = mdates.DateFormatter('%m/%d/%Y')

    if ground_truth_file is not None:
        (ground_truth_dates, ground_truth_levels) = load_ground_truth(ground_truth_file)
        ax2 = ax.twinx()
        ground_truth_line = ax2.plot(ground_truth_dates, ground_truth_levels, linestyle='--', color='r', linewidth=2, label='Measured Elevation')
        ax2.set_ylabel('Lake Elevation (ft)')
        ax2.format_ydata = (lambda x: '%g ft' % (x))
        ax2.set_ylim([6372, 6385.5])

    def onpick(event):
        thisline = event.artist
        xdata = thisline.get_xdata()
        ydata = thisline.get_ydata()
        ind = event.ind
        print 'onpick points:', zip(xdata[ind], ydata[ind])
    fig.canvas.mpl_connect('pick_event', onpick)

    ax.format_ydata = (lambda x: '%g km^2' % (x))
    ax.set_ylabel('Lake Surface Area (km^2)')
    fig.suptitle(features['name'] + ' Surface Area from Landsat')
    lns = water_line# + ground_truth_line
    labs = [l.get_label() for l in lns]
    ax.legend(lns, labs, loc=4)

    ax.grid(True)
    fig.autofmt_xdate()

    if save_directory is not None:
        fig.savefig(os.path.join(save_directory, features['name'] + '.pdf')) 

def plot_results(features, dates, water, clouds, save_directory=None, ground_truth_file=None):
    fig, ax = plt.subplots()
    water_line = ax.plot(dates, water, linestyle='-', color='b', linewidth=1,
                         label='Landsat Surface Area')
    ax.plot(dates, water, 'gs', ms=3)
    # ax.bar(dates, water, color='b', width=15, linewidth=0)
    # ax.bar(dates, clouds, bottom=water, color='r', width=15, linewidth=0)
    ax.xaxis.set_major_locator(mdates.YearLocator(2))
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
    ax.xaxis.set_minor_locator(mdates.MonthLocator([1, 4, 7, 10]))
    ax.set_xlabel('Time')
    ax.format_xdata = mdates.DateFormatter('%m/%d/%Y')
    ax.set_xlim([datetime.date(1984, 1, 1), datetime.date(1984, 12, 31)])
    ax.set_ylim([150, 190])

    lns = water_line
    if ground_truth_file is not None:
        (ground_truth_dates, ground_truth_levels) = load_ground_truth(ground_truth_file)
        ax2 = ax.twinx()
        ground_truth_line = ax2.plot(ground_truth_dates, ground_truth_levels, linestyle='--', color='r', linewidth=2, label='Measured Elevation')
        ax2.set_ylabel('Lake Elevation (ft)')
        ax2.format_ydata = (lambda x: '%g ft' % (x))
        ax2.set_ylim([6372.0, 6385.5])
        lns = lns + ground_truth_line
        ax2.set_xlim([datetime.date(1984, 6, 1), datetime.date(2015, 10, 1)])

    ax.format_ydata = (lambda x: '%g km^2' % (x))
    ax.set_ylabel('Lake Surface Area (km^2)')
    fig.suptitle(features['name'] + ' Surface Area from Landsat')
    labs = [l.get_label() for l in lns]
    ax.legend(lns, labs, loc=4)

    ax.grid(True)
    fig.autofmt_xdate()

    if save_directory is not None:
        fig.savefig(os.path.join(save_directory, features['name'] + '.pdf')) 

def back_test_plot(self):
        import matplotlib.pyplot as plt
        import matplotlib.dates as mdates
        fig = plt.figure()
        all_lines = []
        ax = fig.add_subplot(111)
        ax.set_ylabel('PnL')
        has_right_ax = False
        if 'quant_index' in self.used_vars or \
            'quant_index1' in self.used_vars or \
            'quant_index2' in self.used_vars or \
            'quant_index3' in self.used_vars:
            has_right_ax = True
        dates = [ x[0] for x in self.pnls['portfolio'] ]
        for v in self.used_vars:
            if 'portfolio' in v:
                all_lines += ax.plot(dates, [x[1] for x in self.pnls[v]],label=v,linewidth=1)

        if has_right_ax:
            right_ax = ax.twinx()
            for v in self.used_vars:
                if 'index' in v:
                    all_lines += right_ax.plot(dates, self.quant_indices[v],label=v,linewidth=1,ls='dotted')
            
            right_ax.set_ylabel('quant_index')

        # format the ticks
        years = mdates.YearLocator()   # every year
        months = mdates.MonthLocator()  # every month
        yearsFmt = mdates.DateFormatter('%Y')

        ax.xaxis.set_major_locator(years)
        ax.xaxis.set_major_formatter(yearsFmt)
        ax.xaxis.set_minor_locator(months)
        datemin = min(dates)
        datemax = max(dates)
        ax.set_xlim(datemin, datemax)
        ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
        ax.grid(True)


        # rotates and right aligns the x labels, and moves the bottom of the
        # axes up to make room for them
        fig.autofmt_xdate()
        fig.tight_layout()
        plt.legend(all_lines,[l.get_label() for l in all_lines],loc='best')
        plt.show() 

def createLinePlot(self):
        nseries = len(self.meta())
        res = self.results()
        meta = self.meta()

        timeSeries = [datetime.fromtimestamp(m[0]["time"] / 1000) for m in res]

        means = [[np.nan] * len(res) for n in range(0, nseries)]

        plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None
        if plotSeries is None:
            plotSeries = "mean"

        for n in range(0, len(res)):
            timeSlot = res[n]
            for seriesValues in timeSlot:
                means[seriesValues['ds']][n] = seriesValues[plotSeries]

        x = timeSeries

        fig, axMain = plt.subplots()
        fig.set_size_inches(11.0, 8.5)
        fig.autofmt_xdate()

        title = ', '.join(set([m['title'] for m in meta]))
        sources = ', '.join(set([m['source'] for m in meta]))
        dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y'))

        axMain.set_title("%s\n%s\n%s" % (title, sources, dateRange))
        axMain.set_xlabel('Date')
        axMain.grid(True)
        axMain.xaxis.set_major_locator(mdates.YearLocator())
        axMain.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
        axMain.xaxis.set_minor_locator(mdates.MonthLocator())
        axMain.format_xdata = mdates.DateFormatter('%Y-%m-%d')

        plots = []

        for n in range(0, nseries):
            if n == 0:
                ax = axMain
            else:
                ax = ax.twinx()

            plots += ax.plot(x, means[n], color=self.__SERIES_COLORS[n], zorder=10, linewidth=3, label=meta[n]['title'])
            ax.set_ylabel(meta[n]['units'])

        labs = [l.get_label() for l in plots]
        axMain.legend(plots, labs, loc=0)

        sio = StringIO()
        plt.savefig(sio, format='png')
        return sio.getvalue() 

def createLinePlot(self):
        nseries = len(self.meta())
        res = self.results()
        meta = self.meta()

        timeSeries = [datetime.fromtimestamp(m[0]["time"] / 1000) for m in res]

        means = [[np.nan] * len(res) for n in range(0, nseries)]

        plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None
        if plotSeries is None:
            plotSeries = "mean"

        for n in range(0, len(res)):
            timeSlot = res[n]
            for seriesValues in timeSlot:
                means[seriesValues['ds']][n] = seriesValues[plotSeries]

        x = timeSeries

        fig, axMain = plt.subplots()
        fig.set_size_inches(11.0, 8.5)
        fig.autofmt_xdate()

        title = ', '.join(set([m['title'] for m in meta]))
        sources = ', '.join(set([m['source'] for m in meta]))
        dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y'))

        axMain.set_title("%s\n%s\n%s" % (title, sources, dateRange))
        axMain.set_xlabel('Date')
        axMain.grid(True)
        axMain.xaxis.set_major_locator(mdates.YearLocator())
        axMain.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
        axMain.xaxis.set_minor_locator(mdates.MonthLocator())
        axMain.format_xdata = mdates.DateFormatter('%Y-%m-%d')

        plots = []

        for n in range(0, nseries):
            if n == 0:
                ax = axMain
            else:
                ax = ax.twinx()

            plots += ax.plot(x, means[n], color=self.__SERIES_COLORS[n], zorder=10, linewidth=3, label=meta[n]['title'])
            ax.set_ylabel(meta[n]['units'])

        labs = [l.get_label() for l in plots]
        axMain.legend(plots, labs, loc=0)

        sio = StringIO()
        plt.savefig(sio, format='png')
        return sio.getvalue() 

def createTimeSeries(res, meta, nseries=1):
    # maxSeries = [m[0]['maxFiltered'] for m in res]
    # minSeries = [m[0]['minFiltered'] for m in res]
    # mean = [m[0]["meanFiltered"] for m in res]
    # mean1 = [m[1]["meanFiltered"] for m in res]
    # stdSeries = [m[0]['std'] for m in res]

    timeSeries = [datetime.datetime.fromtimestamp(m[0]["time"] / 1000) for m in res]

    means = [[np.nan] * len(res) for n in range(0, nseries)]

    for n in range(0, len(res)):
        timeSlot = res[n]
        for seriesValues in timeSlot:
            means[seriesValues['ds']][n] = seriesValues['mean']

    x = timeSeries

    fig, axMain = plt.subplots()
    fig.set_size_inches(11.0, 8.5)
    fig.autofmt_xdate()

    title = ', '.join(set([m['title'] for m in meta]))
    sources = ', '.join(set([m['source'] for m in meta]))
    dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y'))

    axMain.set_title("%s\n%s\n%s" % (title, sources, dateRange))
    axMain.set_xlabel('Date')
    axMain.grid(True)
    axMain.xaxis.set_major_locator(mdates.YearLocator())
    axMain.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
    axMain.xaxis.set_minor_locator(mdates.MonthLocator())
    axMain.format_xdata = mdates.DateFormatter('%Y-%m-%d')

    plots = []

    for n in range(0, nseries):
        if n == 0:
            ax = axMain
        else:
            ax = ax.twinx()

        plots += ax.plot(x, means[n], color=SERIES_COLORS[n], zorder=10, linewidth=3, label=meta[n]['title'])
        ax.set_ylabel(meta[n]['units'])

    labs = [l.get_label() for l in plots]
    axMain.legend(plots, labs, loc=0)

    sio = StringIO()
    plt.savefig(sio, format='png')
    return sio.getvalue() 

def createLinePlot(self):
        nseries = len(self.meta())
        res = self.results()
        meta = self.meta()

        timeSeries = [datetime.fromtimestamp(m[0]["time"] / 1000) for m in res]

        means = [[np.nan] * len(res) for n in range(0, nseries)]

        plotSeries = self.computeOptions().get_plot_series() if self.computeOptions is not None else None
        if plotSeries is None:
            plotSeries = "mean"

        for n in range(0, len(res)):
            timeSlot = res[n]
            for seriesValues in timeSlot:
                means[seriesValues['ds']][n] = seriesValues[plotSeries]

        x = timeSeries

        fig, axMain = plt.subplots()
        fig.set_size_inches(11.0, 8.5)
        fig.autofmt_xdate()

        title = ', '.join(set([m['title'] for m in meta]))
        sources = ', '.join(set([m['source'] for m in meta]))
        dateRange = "%s - %s" % (timeSeries[0].strftime('%b %Y'), timeSeries[-1].strftime('%b %Y'))

        axMain.set_title("%s\n%s\n%s" % (title, sources, dateRange))
        axMain.set_xlabel('Date')
        axMain.grid(True)
        axMain.xaxis.set_major_locator(mdates.YearLocator())
        axMain.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
        axMain.xaxis.set_minor_locator(mdates.MonthLocator())
        axMain.format_xdata = mdates.DateFormatter('%Y-%m-%d')

        plots = []

        for n in range(0, nseries):
            if n == 0:
                ax = axMain
            else:
                ax = ax.twinx()

            plots += ax.plot(x, means[n], color=self.__SERIES_COLORS[n], zorder=10, linewidth=3,
                             label=meta[n]['title'])
            ax.set_ylabel(meta[n]['units'])

        labs = [l.get_label() for l in plots]
        axMain.legend(plots, labs, loc=0)

        sio = StringIO()
        plt.savefig(sio, format='png')
        return sio.getvalue() 

def timeline_plot():
    df_ori = pd.read_csv('articles.csv', sep=';', header=None)

    # 取第一列并分割日期与标题
    df = df_ori.iloc[:, 0]
    df = df.str.split(';', expand=True)

    # 格式化日期，设置column，并将日期设置为index
    df.columns = ['date', 'title']
    df.date = pd.to_datetime(df.date)
    df = df.set_index('date')

    # 按月统计文章数"MS"为月初
    cacu = df.resample("MS").count()

    # 画图
    fig, ax = plt.subplots(figsize=[18, 5])

    # 线条
    from pandas.plotting import register_matplotlib_converters
    register_matplotlib_converters()
    ax.plot(cacu, 'o-')

    # fig.autofmt_xdate()

    # 通过设置中文字体方式解决中文展示问题
    font = FontProperties(fname='../common/font/PingFang.ttc', size=18)
    ax.set_title("新世相文章统计", fontproperties=font)
    ax.set_xlabel("日期", fontproperties=font)
    ax.set_ylabel("文章数", fontproperties=font)

    # 设置时间轴
    formater = mdate.DateFormatter('%Y-%m')
    ax.xaxis.set_major_formatter(formater)
    ax.xaxis.set_minor_locator(mdate.MonthLocator())
    ax.xaxis.set_minor_formatter(mdate.DateFormatter('%m'))
    ax.xaxis.set_major_locator(mdate.YearLocator())
    ax.xaxis.set_major_formatter(mdate.DateFormatter('\n\n%Y'))
    # 显示网格
    # ax.xaxis.grid(True, which="minor")
    # ax.yaxis.grid()
    # 显示数值
    # 显示全部数值
    # for a,b in zip(cacu.index, cacu.values):
    #     ax.text(a, b, b[0])
    # 显示最大值
    x = cacu['title'].idxmax()
    y = cacu['title'].max()
    ax.text(x, y, y, verticalalignment='bottom', horizontalalignment='center', fontsize='large')
    # plt.annotate(y, xy=(x,y))
    # 保存图片
    plt.savefig('timeline_analysis.png')
    # 显示图片
    plt.show()