csep.core.forecasts.GriddedForecast

class csep.core.forecasts.GriddedForecast(start_time=None, end_time=None, *args, **kwargs)

Class to represent grid-based forecasts

__init__(start_time=None, end_time=None, *args, **kwargs)

Constructor for GriddedForecast class

Parameters:

• start_time (datetime.datetime) –

• end_time (datetime.datetime) –

Methods

__init__([start_time, end_time])	Constructor for GriddedForecast class
from_custom(func[, func_args])	Creates MarkedGriddedDataSet class from custom parsing function.
from_dict(adict)
get_index_of(lons, lats)	Returns the index of lons, lats in spatial region
get_latitudes()	Returns the latitude of the lower left node of the spatial grid
get_longitudes()	Returns the lognitude of the lower left node of the spatial grid
get_magnitude_index(mags[, tol])	Returns the indices into the magnitude bins of selected magnitudes
get_magnitudes()	Returns the left edge of the magnitude bins.
get_rates(lons, lats, mags[, data, ret_inds])	Returns the rate associated with a longitude, latitude, and magnitude.
get_valid_midpoints()	Returns the midpoints of the valid testing region
load_ascii(ascii_fname[, start_date, ...])	Reads Forecast file from CSEP1 ascii format.
magnitude_counts()	Returns counts of events in magnitude bins
plot([ax, show, log, extent, set_global, ...])	Plot gridded forecast according to plate-carree projection
scale(val)	Scales forecast by floating point value.
scale_to_test_date(test_datetime)	Scales forecast data by the fraction of the date.
spatial_counts([cartesian])	Integrates over magnitudes to return the spatial version of the forecast.
sum()	Sums over all of the forecast data
target_event_rates(target_catalog[, scale])	Generates data set of target event rates given a target data.
to_dict()

Attributes

data	Contains the spatio-magnitude forecast as 2d numpy.ndarray.
event_count	Returns a sum of the forecast data
log
magnitudes
min_magnitude	Returns the lowest magnitude bin edge
num_mag_bins
num_nodes
polygons