def to_event_time(self, jtimes):
j_start = wr.j2000(date=self.t_start)
j_stop = wr.j2000(date=self.t_stop)
assert j_start < j_stop
ev_time_start = pd.Timestamp(self.t_start).value
ev_time_stop = pd.Timestamp(self.t_stop).value
assert ev_time_start < ev_time_stop
jfrac = (jtimes - j_start) / (j_stop - j_start)
return jfrac * (ev_time_stop - ev_time_start) + ev_time_start
def __init__(self, *args, wimp_kwargs=None, **kwargs):
# Compute the energy spectrum in a given time range
# Times used by wimprates are J2000 timestamps
assert self.n_time_bins >= 1, "Need >= 1 time bin"
if hasattr(self, 'n_in'):
raise RuntimeError(
"n_in is gone! Use n_time_bins to control accuracy, or set "
"pretend_wimps_dont_modulate to use a time-averaged spectrum.")
times = np.linspace(wr.j2000(self.t_start.value),
wr.j2000(self.t_stop.value), self.n_time_bins + 1)
time_centers = self.bin_centers(times)
if wimp_kwargs is None:
# No arguments given at all;
# use default mass, xsec and energy range
wimp_kwargs = dict(mw=self.mw,
sigma_nucleon=self.sigma_nucleon,
es=self.es)
else:
assert 'mw' in wimp_kwargs and 'sigma_nucleon' in wimp_kwargs, \
"Pass at least 'mw' and 'sigma_nucleon' in wimp_kwargs"
if 'es' not in wimp_kwargs:
# Energies not given, use default energy bin edges
wimp_kwargs['es'] = self.es
es = wimp_kwargs['es']
es_centers = self.bin_centers(es)
del wimp_kwargs['es'] # To avoid confusion centers / edges
# Transform wimp_kwargs to arguments that can be passed to wimprates
# which means transforming es from edges to centers
spectra = np.array([
wr.rate_wimp_std(t=t, es=es_centers, **wimp_kwargs) * np.diff(es)
for t in time_centers
])
assert spectra.shape == (len(time_centers), len(es_centers))
self.energy_hist = Histdd.from_histogram(spectra,
bin_edges=(times, es))
if self.pretend_wimps_dont_modulate:
self.energy_hist.histogram = (
np.ones_like(self.energy_hist.histogram) *
self.energy_hist.sum(axis=0).histogram.reshape(1, -1) /
self.n_time_bins)
# Initialize the rest of the source, needs to be after energy_hist is
# computed because of _populate_tensor_cache
super().__init__(*args, **kwargs)
def add_extra_columns(self, d):
super().add_extra_columns(d)
# Add J2000 timestamps to data for use with wimprates
if 't' not in d:
d['t'] = [
wr.j2000(date=t) for t in pd.to_datetime(d['event_time'])
]
def test_j2000_conversion():
j2000_times = pd.np.linspace(0., 10000., 100)
# convert j2000 -> event_time -> j2000
test_times = wr.j2000(fd.j2000_to_event_time(j2000_times))
pd.np.testing.assert_array_almost_equal(j2000_times,
test_times,
decimal=6)
def __init__(self, *args, wimp_kwargs=None, **kwargs):
# Compute the energy spectrum in a given time range
# Times used by wimprates are J2000 timestamps
assert self.n_in > 1, \
f"Number of time bin edges needs to be at least 2"
times = np.linspace(wr.j2000(date=self.t_start),
wr.j2000(date=self.t_stop), self.n_in)
time_centers = self.bin_centers(times)
if wimp_kwargs is None:
# No arguments given at all;
# use default mass, xsec and energy range
wimp_kwargs = dict(mw=self.mw,
sigma_nucleon=self.sigma_nucleon,
es=self.es)
else:
assert 'mw' in wimp_kwargs and 'sigma_nucleon' in wimp_kwargs, \
"Pass at least 'mw' and 'sigma_nucleon' in wimp_kwargs"
if 'es' not in wimp_kwargs:
# Energies not given, use default energy bin edges
wimp_kwargs['es'] = self.es
es = wimp_kwargs['es']
es_centers = self.bin_centers(es)
del wimp_kwargs['es'] # To avoid confusion centers / edges
# Transform wimp_kwargs to arguments that can be passed to wimprates
# which means transforming es from edges to centers
spectra = np.array([
wr.rate_wimp_std(t=t, es=es_centers, **wimp_kwargs) * np.diff(es)
for t in time_centers
])
assert spectra.shape == (len(time_centers), len(es_centers))
self.energy_hist = Histdd.from_histogram(spectra,
bin_edges=(times, es))
# Initialize the rest of the source, needs to be after energy_hist is
# computed because of _populate_tensor_cache
super().__init__(*args, **kwargs)
def test_j2000_ns_array():
# Generate some test cases and compare the two implementations
years = np.arange(2008, 2020)
months = np.arange(1, 12 + 1)
days = np.arange(1, 12 + 1)
hours = np.arange(1, 12 + 1)
dates = np.zeros(12)
j2000_ymd = np.zeros(12)
for i in range(len(j2000_ymd)):
print(years[i], months[i], days[i], hours[i])
date = pd.datetime(year=years[i],
month=months[i],
day=days[i],
hour=hours[i])
dates[i] = pd.to_datetime(date).value
# Compute according to j2000_from_ymd
j2000_ymd[i] = j2000_from_ymd(years[i], months[i],
days[i] + hours[i] / 24)
np.testing.assert_array_almost_equal(j2000(dates), j2000_ymd, decimal=6)
def test_j2000_datetime():
date = pd.datetime(year=2009, month=1, day=31, hour=18)
assert j2000(date=date) == 3318.25
def test_j2000():
assert j2000(2009, 1, 31.75) == 3318.25
def add_extra_columns(self, d):
super().add_extra_columns(d)
# Add J2000 timestamps to data for use with wimprates
d['t_j2000'] = wr.j2000(d['event_time'])
def test_j2000_ns_int():
date = pd.datetime(year=2009, month=1, day=31, hour=18)
value = pd.to_datetime(date).value
assert isinstance(value, int)
assert j2000(value) == 3318.25