Getting usable dates from Axes.get_xlim() in a pandas time series plot

Question

I'm trying to get the xlimits of a plot as a python datetime object from a time series plot created with pandas. Using ax.get_xlim() returns the axis limits as a numpy.float64, and I can't figure out how to convert the numbers to a usable datetime.

import pandas 
from matplotlib import dates
import matplotlib.pyplot as plt
from datetime import datetime
from numpy.random import randn

ts = pandas.Series(randn(10000), index=pandas.date_range('1/1/2000',
    periods=10000, freq='H')) 
ts.plot()
ax = plt.gca()

ax.set_xlim(datetime(2000,1,1))
d1, d2 = ax.get_xlim()
print "%s(%s) to %s(%s)" % (d1, type(d1), d2, type(d2))

print "Using matplotlib: %s" % dates.num2date(d1)
print "Using datetime: %s" % datetime.fromtimestamp(d1)

which returns:

262968.0 (<type 'numpy.float64'>) to 272967.0 (<type 'numpy.float64'>)
Using matplotlib: 0720-12-25 00:00:00+00:00
Using datetime: 1970-01-03 19:02:48

According to the pandas timeseries docs, pandas uses the numpy.datetime64 dtype. I'm using pandas version '0.9.0'.

I am using get_xlim() instead directly accessing the pandas series because I am using the xlim_changed callback to do other things when the user moves around in the plot area.

Hack to get usable values

For the above example, the limits are returned in hours since the Epoch. So I can convert to seconds since the Epoch and use time.gmtime() to get somewhere usable, but this still doesn't feel right.

In [66]: d1, d2 = ax.get_xlim()

In [67]: time.gmtime(d1*60*60)
Out[67]: time.struct_time(tm_year=2000, tm_mon=1, tm_mday=1, tm_hour=0, tm_min=0, tm_sec=0, tm_wday=5, tm_yday=1, tm_isdst=0)  

Answer 1

The current behavior of matplotlib.dates:

datetime objects are converted to floating point numbers which represent time in days since 0001-01-01 UTC, plus 1. For example, 0001-01-01, 06:00 is 1.25, not 0.25. The helper functions date2num(), num2date() and drange() are used to facilitate easy conversion to and from datetime and numeric ranges.

pandas.tseries.converter.PandasAutoDateFormatter() seems to build on this, so:

x = pandas.date_range(start='01/01/2000', end='01/02/2000')
plt.plot(x, x)
matplotlib.dates.num2date(plt.gca().get_xlim()[0])

gives:

datetime.datetime(2000, 1, 1, 0, 0, tzinfo=<matplotlib.dates._UTC object at 0x7ff73a60f290>)

Answer 2

# First convert to pandas Period
period = pandas.tseries.period.Period(ordinal=int(d1), freq=ax.freq)
# Then convert to pandas timestamp
ts = period.to_timestamp()
# Then convert to date object
dt = ts.to_datetime()