Matplotlib 以奇数间隔日期 YearLocator

Question

现在，当我的时间序列在十年之初（即 1990、2000、2010 年等）开始时，我有一些符合我的规范的工作代码，但我不知道如何调整我的代码以具有当我的时间序列从不是偶数的年份（即 1993 年）开始时的正确格式。

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates

def format_xaxis(fig):

     years = dates.YearLocator(10,month=1,day=1)
     years1=dates.YearLocator(2,month=1,day=1)
     dfmt = dates.DateFormatter('%Y')
     dfmt1 = dates.DateFormatter('%y')

     [i.xaxis.set_major_locator(years) for i in fig.axes]
     [i.xaxis.set_minor_locator(years1) for i in fig.axes]
     [i.xaxis.set_major_formatter(dfmt) for i in fig.axes]
     [i.xaxis.set_minor_formatter(dfmt1) for i in fig.axes]
     [i.get_xaxis().set_tick_params(which='major', pad=15) for i in fig.axes]

     for t in fig.axes:
         for tick in t.xaxis.get_major_ticks():
             tick.label1.set_horizontalalignment('center')
         for label in t.get_xmajorticklabels() :
             label.set_rotation(0)
             label.set_weight('bold')
         for label in t.xaxis.get_minorticklabels():
             label.set_fontsize('small')
         for label in t.xaxis.get_minorticklabels()[::5]:
             label.set_visible(False)


df = pd.DataFrame.from_dict({'Y': {0: 0.15,  1: 0.18,  2: 0.23,  3: 0.15,  4: 0.15,  5: 0.15,  6: 0.17,  7: 0.175,  8: 0.212,  9: 0.184,  10: 0.18,  11: 0.18,  12: 0.21,  13: 0.139,  14: 0.15,  15: 0.128,  16: 0.126,  17: 0.1,  18: 0.11,  19: 0.183,  20: 0.14,  21: 0.12,  22: 0.155,  23: 0.245,  24: 0.248,  25: 0.262,  26: 0.17,  27: 0.143,  28: 0.13,  29: 0.102,  30: 0.258,  31: 0.293,  32: 0.196,  33: 0.21,  34: 0.14,  35: 0.17}, 
                             'Date': {0: '1990-06-10 00:00:00',  1: '1991-07-26 00:00:00',  2: '1992-10-15 00:00:00',  3: '1993-10-08 00:00:00',  4: '1994-04-07 00:00:00',  5: '1994-11-20 00:00:00',  6: '1995-04-24 00:00:00',  7: '1996-02-13 00:00:00',  8: '1996-04-15 00:00:00',  9: '1996-09-12 00:00:00',  10: '1997-02-13 00:00:00',  11: '1997-04-20 00:00:00',  12: '1997-08-23 00:00:00',  13: '1997-11-06 00:00:00',  14: '1998-04-15 00:00:00',  15: '1999-05-04 00:00:00',  16: '2000-03-17 00:00:00',  17: '2000-06-01 00:00:00',  18: '2001-10-05 00:00:00',  19: '2002-09-20 00:00:00',  20: '2003-04-25 00:00:00',  21: '2003-09-20 00:00:00',  22: '2005-05-07 00:00:00',  23: '2006-10-07 00:00:00',  24: '2007-10-13 00:00:00',  25: '2008-02-02 00:00:00',  26: '2008-03-28 00:00:00',  27: '2008-10-10 00:00:00',  28: '2009-10-10 00:00:00',  29: '2011-10-05 00:00:00',  30: '2012-10-03 00:00:00',  31: '2013-09-21 00:00:00',  32: '2014-09-23 00:00:00',  33: '2015-09-22 00:00:00',  34: '2016-10-01 00:00:00',  35: '2017-09-29 00:00:00'}})

df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S')

fig, ax = plt.subplots()

df.plot('Date','Y',ax=ax,marker='x',ls='-')
ax.set_xlim(pd.datetime(1990, 1, 1), pd.datetime(2018, 1, 1))

format_xaxis(fig)

这会产生一个如下所示的情节：

我将如何使用从 1993 年开始的时间序列重新创建上述图？我仍然希望每两年设置一次次要刻度标签（即 95,97,99,01,....）。当时间序列图从奇数年开始时，是否可以使用matplotlib.dates.YearLocator格式化日期？

Answer 1

您可以子类化YearLocator以拥有您的自定义OffsetYearLocator.

版本为matplotlib <= 3.0

from matplotlib import dates

class OffsetYearLocator(dates.YearLocator):
    def __init__(self, *args, **kwargs):
        self.offset = kwargs.pop("offset", 0)
        dates.YearLocator.__init__(self,*args, **kwargs)
    def tick_values(self, vmin, vmax):
        ymin = self.base.le(vmin.year)-self.offset
        ymax = self.base.ge(vmax.year)+(self.base._base-self.offset)
        ticks = [vmin.replace(year=ymin, **self.replaced)]
        while True:
            dt = ticks[-1]
            if dt.year >= ymax:
                return dates.date2num(ticks)
            year = dt.year + self.base.get_base()
            ticks.append(dt.replace(year=year, **self.replaced))

matplotlib版本>= 3.1

from matplotlib import dates

class OffsetYearLocator(dates.YearLocator):
    def __init__(self, *args, **kwargs):
        self.offset = kwargs.pop("offset", 0)
        dates.YearLocator.__init__(self,*args, **kwargs)
    def tick_values(self, vmin, vmax):
        ymin = self.base.le(vmin.year) * self.base.step - self.offset
        ymax = self.base.ge(vmax.year) * self.base.step + (self.base.step-self.offset)
        ticks = [vmin.replace(year=ymin, **self.replaced)]
        while True:
            dt = ticks[-1]
            if dt.year >= ymax:
                return dates.date2num(ticks)
            year = dt.year + self.base.step
            ticks.append(dt.replace(year=year, **self.replaced))

这可以处理offset从年份中减去的额外参数。在这种情况下，将保留baseas 2（每两年一次），但使用偏移量1.

years1 = OffsetYearLocator(2, month=1, day=1, offset=1)

完整示例（使用 matplotlib >= 3.1 的版本）：

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates

class OffsetYearLocator(dates.YearLocator):
    def __init__(self, *args, **kwargs):
        self.offset = kwargs.pop("offset", 0)
        dates.YearLocator.__init__(self,*args, **kwargs)
    def tick_values(self, vmin, vmax):
        ymin = self.base.le(vmin.year) * self.base.step - self.offset
        ymax = self.base.ge(vmax.year) * self.base.step + (self.base.step-self.offset)
        ticks = [vmin.replace(year=ymin, **self.replaced)]
        while True:
            dt = ticks[-1]
            if dt.year >= ymax:
                return dates.date2num(ticks)
            year = dt.year + self.base.step
            ticks.append(dt.replace(year=year, **self.replaced))

def format_xaxis(ax):

    years = dates.YearLocator(10,month=1,day=1)
    years1=OffsetYearLocator(2,month=1,day=1, offset=1)
    dfmt = dates.DateFormatter('%Y')
    dfmt1 = dates.DateFormatter('%y')

    ax.xaxis.set_major_locator(years)
    ax.xaxis.set_minor_locator(years1)
    ax.xaxis.set_major_formatter(dfmt)
    ax.xaxis.set_minor_formatter(dfmt1)
    ax.get_xaxis().set_tick_params(which='major', pad=15)

    plt.setp(ax.get_xmajorticklabels(), rotation=0, weight="bold", ha="center")


df = pd.DataFrame.from_dict({'YData': {0: 0.15,  1: 0.18,  2: 0.23,  3: 0.15,  4: 0.15,  5: 0.15,  6: 0.17,  7: 0.175,  8: 0.212,  9: 0.184,  10: 0.18,  11: 0.18,  12: 0.21,  13: 0.139,  14: 0.15,  15: 0.128,  16: 0.126,  17: 0.1,  18: 0.11,  19: 0.183,  20: 0.14,  21: 0.12,  22: 0.155,  23: 0.245,  24: 0.248,  25: 0.262,  26: 0.17,  27: 0.143,  28: 0.13,  29: 0.102,  30: 0.258,  31: 0.293,  32: 0.196,  33: 0.21,  34: 0.14,  35: 0.17}, 
                             'Date': {0: '1990-06-10 00:00:00',  1: '1991-07-26 00:00:00',  2: '1992-10-15 00:00:00',  3: '1993-10-08 00:00:00',  4: '1994-04-07 00:00:00',  5: '1994-11-20 00:00:00',  6: '1995-04-24 00:00:00',  7: '1996-02-13 00:00:00',  8: '1996-04-15 00:00:00',  9: '1996-09-12 00:00:00',  10: '1997-02-13 00:00:00',  11: '1997-04-20 00:00:00',  12: '1997-08-23 00:00:00',  13: '1997-11-06 00:00:00',  14: '1998-04-15 00:00:00',  15: '1999-05-04 00:00:00',  16: '2000-03-17 00:00:00',  17: '2000-06-01 00:00:00',  18: '2001-10-05 00:00:00',  19: '2002-09-20 00:00:00',  20: '2003-04-25 00:00:00',  21: '2003-09-20 00:00:00',  22: '2005-05-07 00:00:00',  23: '2006-10-07 00:00:00',  24: '2007-10-13 00:00:00',  25: '2008-02-02 00:00:00',  26: '2008-03-28 00:00:00',  27: '2008-10-10 00:00:00',  28: '2009-10-10 00:00:00',  29: '2011-10-05 00:00:00',  30: '2012-10-03 00:00:00',  31: '2013-09-21 00:00:00',  32: '2014-09-23 00:00:00',  33: '2015-09-22 00:00:00',  34: '2016-10-01 00:00:00',  35: '2017-09-29 00:00:00'}})

df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S')

fig, ax = plt.subplots()

ax.plot('Date','YData', data=df, marker='x',ls='-')
ax.set_xlim(pd.datetime(1990,1,1), pd.datetime(2018,1,1))

format_xaxis(ax)

plt.show()