def setUp(self):
cat1 = Catalogue()
cat1.end_year = 2000
cat1.start_year = 1900
cat1.data['eventID'] = [1.0, 2.0, 3.0]
cat1.data['magnitude'] = np.array([1.0, 2.0, 3.0])
cat2 = Catalogue()
cat2.end_year = 1990
cat2.start_year = 1910
cat2.data['eventID'] = [1.0, 2.0, 3.0]
cat2.data['magnitude'] = np.array([1.0, 2.0, 3.0])
self.cat1 = cat1
self.cat2 = cat2
def get_catalogue_from_ses(fname, duration):
"""
Converts a set of ruptures into an instance of
:class:`openquake.hmtk.seismicity.catalogue.Catalogue`.
:param fname:
Name of the .csv file
:param float duration:
Duration [in years] of the SES
:returns:
A :class:`openquake.hmtk.seismicity.catalogue.Catalogue` instance
"""
# Read the set of ruptures
ses = pd.read_csv(fname, sep='\t', skiprows=1)
if len(ses.columns) < 2:
ses = pd.read_csv(fname, sep=',', skiprows=1)
# Create an empty catalogue
cat = Catalogue()
# Set catalogue data
cnt = 0
year = []
eventids = []
mags = []
lons = []
lats = []
deps = []
print(ses['rup_id'])
print('Columns:', ses.columns)
for i in range(len(ses)):
nevents = ses['multiplicity'][i]
for j in range(nevents):
eventids.append(':d'.format(cnt))
mags.append(ses['mag'].values[i])
lons.append(ses['centroid_lon'].values[i])
lats.append(ses['centroid_lat'].values[i])
deps.append(ses['centroid_depth'].values[i])
cnt += 1
year.append(numpy.random.random_integers(1, duration, 1))
data = {}
year = numpy.array(year, dtype=int)
data['year'] = year
data['month'] = numpy.ones_like(year, dtype=int)
data['day'] = numpy.ones_like(year, dtype=int)
data['hour'] = numpy.zeros_like(year, dtype=int)
data['minute'] = numpy.zeros_like(year, dtype=int)
data['second'] = numpy.zeros_like(year)
data['magnitude'] = numpy.array(mags)
data['longitude'] = numpy.array(lons)
data['latitude'] = numpy.array(lats)
data['depth'] = numpy.array(deps)
data['eventID'] = eventids
cat.data = data
cat.end_year = duration
cat.start_year = 0
cat.data['dtime'] = cat.get_decimal_time()
return cat
def from_df(df, end_year=None):
cat = Catalogue()
for column in df:
if (column in Catalogue.FLOAT_ATTRIBUTE_LIST
or column in Catalogue.INT_ATTRIBUTE_LIST):
cat.data[column] = df[column].to_numpy()
else:
cat.data[column] = df[column]
cat.end_year = np.max(df.year) if end_year is None else end_year
return cat
def from_df(df, end_year=None):
"""
:param df:
A :class:`pd.DataFrame` instance with the catalogue
"""
cat = Catalogue()
for column in df:
if (column in Catalogue.FLOAT_ATTRIBUTE_LIST
or column in Catalogue.INT_ATTRIBUTE_LIST):
cat.data[column] = df[column].to_numpy()
else:
cat.data[column] = df[column]
cat.end_year = np.max(df.year) if end_year is None else end_year
return cat
def from_df(df, end_year=None):
"""
Converts a dataframe into a
:class:`openquake.hmtk.seismicity.catalogue.Catalogue` instance
:param df:
The dataframe with the catalogue
:returns:
The catalogue instance
"""
cat = Catalogue()
for column in df:
if (column in Catalogue.FLOAT_ATTRIBUTE_LIST
or column in Catalogue.INT_ATTRIBUTE_LIST):
cat.data[column] = df[column].to_numpy()
else:
cat.data[column] = df[column]
cat.end_year = np.max(df.year) if end_year is None else end_year
return cat