def to_timestamp(self, freq=None, how='<STR_LIT:start>'):
from pandas.core.arrays import DatetimeArray<EOL>how = libperiod._validate_end_alias(how)<EOL>end = how == '<STR_LIT:E>'<EOL>if end:<EOL><INDENT>if freq == '<STR_LIT:B>':<EOL><INDENT>adjust = Timedelta(<NUM_LIT:1>, '<STR_LIT:D>') - Timedelta(<NUM_LIT:1>, '<STR_LIT>')<EOL>return self.to_timestamp(how='<STR_LIT:start>') + adjust<EOL><DEDENT>else:<EOL><INDENT>adjust = Timedelta(<NUM_LIT:1>, '<STR_LIT>')<EOL>return (self + self.freq).to_timestamp(how='<STR_LIT:start>') - adjust<EOL><DEDENT><DEDENT>if freq is None:<EOL><INDENT>base, mult = libfrequencies.get_freq_code(self.freq)<EOL>freq = libfrequencies.get_to_timestamp_base(base)<EOL><DEDENT>else:<EOL><INDENT>freq = Period._maybe_convert_freq(freq)<EOL><DEDENT>base, mult = libfrequencies.get_freq_code(freq)<EOL>new_data = self.asfreq(freq, how=how)<EOL>new_data = libperiod.periodarr_to_dt64arr(new_data.asi8, base)<EOL>return DatetimeArray._from_sequence(new_data, freq='<STR_LIT>')<EOL>
Cast to DatetimeArray/Index.

Parameters
----------
freq : string or DateOffset, optional
    Target frequency. The default is 'D' for week or longer,
    'S' otherwise
how : {'s', 'e', 'start', 'end'}

Returns
-------
DatetimeArray/Index