def length_stats_for_intervals(t_start,
t_end,
dt,
D,
b_s,
chi2=5.0,
stations=5):
"""
"""
t_edges, duration = time_edges(t_start, t_end, dt.total_seconds())
t_ref, t_edges_seconds = seconds_since_start_of_day(t_start, t_edges)
n_frames = len(t_edges) - 1
max_alt, d_alt = 20.0, 0.5
alt_bins = np.arange(0.0, max_alt + d_alt, d_alt)
results_aggregator = StatResults(alt_bins, basedate=t_ref)
all_frame_targets = []
for n in range(n_frames):
t0, t1 = t_edges_seconds[n:n + 2]
statframer = results_aggregator.timeframe_results_rcvr()
this_frame = in_time_range(t0, t1, statframer)
all_frame_targets.append(this_frame)
brancher = Branchpoint(all_frame_targets)
ev_fl_rcvr = length_for_these_flashes(D,
b_s,
alt_bins,
chi2=chi2,
stations=stations,
target=brancher.broadcast())
return ev_fl_rcvr, all_frame_targets, results_aggregator
def flash_size_stats_for_intervals(t_start,
t_end,
dt,
lat_bounds=None,
lon_bounds=None,
outdir=None):
""" Create a pipeline to process an arbitrary number of flashes which are
accumulated in dt-length intervals between t_start and t_end.
t_start, t_end: datetime.datetime instances
dt: datetime.timedelta
Returns:
printer_of_stats:
all_frame_targets: a list of targets that receive (events, flashes) from the flash file reader
all_frame_targets can be passed as other_analysis_targets to lmatools.flash_stats.plot_spectra_for_files.
When all data have been read (i.e., plot_spectra_for_files has returned), the accumulated statistics are
retrieved by closing the targets returned by this function, i.e.
for target in timeframe_targets:
target.close()
printer_of_stats.close()
plot_spectra_for_files itself could be refactored to make use of these same time windows natively, but
that's a project for later. The dependency here on stormdrain is another issue; it may make sense to have
lmatools depend on stormdrain.
"""
t_edges, duration = time_edges(t_start, t_end, dt.total_seconds())
t_ref, t_edges_seconds = seconds_since_start_of_day(t_start, t_edges)
n_frames = len(t_edges) - 1
results = StatResults(basedate=t_ref, outdir=outdir)
printer_of_stats = results.stats_printer()
all_frame_targets = []
for n in range(n_frames):
# New BoundsFilter object for filtering flashes and flash_stats.stats_for_parameter that sits behind the bounds filter
t0, t1 = t_edges_seconds[n:n + 2]
b = Bounds()
if lat_bounds is not None:
b.ctr_lat = lat_bounds
if lon_bounds is not None:
b.ctr_lon = lon_bounds
b.start = t0, t1
statframer = results.stats_for_frame(t_edges[n],
t_edges[n + 1],
target=printer_of_stats)
momentizer = raw_moments_for_parameter('area',
preprocess=np.sqrt,
output_target=statframer)
bound_filt = BoundsFilter(bounds=b, target=momentizer)
ev_fl_rcvr = events_flashes_receiver(target=bound_filt.filter())
all_frame_targets.append(ev_fl_rcvr)
return printer_of_stats, all_frame_targets
def __init__(self, t_start, t_end, dt, base_date=None):
self.lma = None
if base_date is None:
self.base_date = datetime(t_start.year, t_start.month, t_start.day)
else:
self.base_date = base_date
t_edges, duration = time_edges(t_start, t_end, dt.total_seconds())
self.t_edges = t_edges
self.basedate, t_edges_seconds = seconds_since_start_of_day(t_start, t_edges)
self.t_edges_seconds = np.asarray(t_edges_seconds)
self.n_frames = len(t_edges)-1
self.t_chopper = ArrayChopper(t_edges_seconds)
def flash_size_stats_for_intervals(t_start, t_end, dt, lat_bounds=None, lon_bounds=None, outdir=None):
""" Create a pipeline to process an arbitrary number of flashes which are
accumulated in dt-length intervals between t_start and t_end.
t_start, t_end: datetime.datetime instances
dt: datetime.timedelta
Returns:
printer_of_stats:
all_frame_targets: a list of targets that receive (events, flashes) from the flash file reader
all_frame_targets can be passed as other_analysis_targets to lmatools.flash_stats.plot_spectra_for_files.
When all data have been read (i.e., plot_spectra_for_files has returned), the accumulated statistics are
retrieved by closing the targets returned by this function, i.e.
for target in timeframe_targets:
target.close()
printer_of_stats.close()
plot_spectra_for_files itself could be refactored to make use of these same time windows natively, but
that's a project for later. The dependency here on stormdrain is another issue; it may make sense to have
lmatools depend on stormdrain.
"""
t_edges, duration = time_edges(t_start, t_end, dt.total_seconds())
t_ref, t_edges_seconds = seconds_since_start_of_day(t_start, t_edges)
n_frames = len(t_edges)-1
results = StatResults(basedate=t_ref, outdir=outdir)
printer_of_stats = results.stats_printer()
all_frame_targets = []
for n in range(n_frames):
# New BoundsFilter object for filtering flashes and flash_stats.stats_for_parameter that sits behind the bounds filter
t0, t1 = t_edges_seconds[n:n+2]
b = Bounds()
if lat_bounds is not None:
b.ctr_lat = lat_bounds
if lon_bounds is not None:
b.ctr_lon = lon_bounds
b.start = t0, t1
statframer = results.stats_for_frame(t_edges[n], t_edges[n+1], target=printer_of_stats)
momentizer = raw_moments_for_parameter('area', preprocess=np.sqrt, output_target=statframer)
bound_filt = BoundsFilter(bounds=b, target=momentizer)
ev_fl_rcvr = events_flashes_receiver(target=bound_filt.filter())
all_frame_targets.append(ev_fl_rcvr)
return printer_of_stats, all_frame_targets
def length_stats_for_intervals(t_start, t_end, dt, D, b_s, chi2=5.0, stations=5):
"""
"""
t_edges, duration = time_edges(t_start, t_end, dt.total_seconds())
t_ref, t_edges_seconds = seconds_since_start_of_day(t_start, t_edges)
n_frames = len(t_edges)-1
max_alt, d_alt = 20.0, 0.5
alt_bins = np.arange(0.0,max_alt+d_alt, d_alt)
results_aggregator = StatResults(alt_bins, basedate=t_ref)
all_frame_targets = []
for n in range(n_frames):
t0, t1 = t_edges_seconds[n:n+2]
statframer = results_aggregator.timeframe_results_rcvr()
this_frame = in_time_range(t0, t1, statframer)
all_frame_targets.append(this_frame)
brancher = Branchpoint(all_frame_targets)
ev_fl_rcvr = length_for_these_flashes(D, b_s, alt_bins,
chi2=chi2, stations=stations, target=brancher.broadcast())
return ev_fl_rcvr, all_frame_targets, results_aggregator