def setUp(self):
"""
Read a sample catalogue containing 8 events after instantiating
the CsvCatalogueParser object.
"""
filename = os.path.join(self.BASE_DATA_PATH, 'test_catalogue.csv')
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
def setUp(self):
"""
Read a sample catalogue containing 8 events after instantiating
the CsvCatalogueParser object.
"""
filename = os.path.join(self.BASE_DATA_PATH, 'test_catalogue.csv')
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
def test_specifying_years(self):
"""
Tests that when the catalogue is parsed with the specified start and
end years that this are recognised as attributes of the catalogue
"""
filename = os.path.join(self.BASE_DATA_PATH, 'test_catalogue.csv')
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file(start_year=1000, end_year=1100)
self.assertEqual(self.cat.start_year, 1000)
self.assertEqual(self.cat.end_year, 1100)
def test_catalogue_writer_no_purging(self):
'''
Tests the writer without any purging
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
self.check_catalogues_are_equal(self.catalogue, cat2)
def test_specifying_years(self):
"""
Tests that when the catalogue is parsed with the specified start and
end years that this are recognised as attributes of the catalogue
"""
filename = os.path.join(self.BASE_DATA_PATH, 'test_catalogue.csv')
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file(start_year=1000, end_year=1100)
self.assertEqual(self.cat.start_year, 1000)
self.assertEqual(self.cat.end_year, 1100)
def test_catalogue_writer_no_purging(self):
'''
Tests the writer without any purging
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
self.check_catalogues_are_equal(self.catalogue, cat2)
def test_without_specifying_years(self):
"""
Tests that when the catalogue is parsed without specifying the start
and end year that the start and end year come from the minimum and
maximum in the catalogue
"""
filename = os.path.join(self.BASE_DATA_PATH, 'test_catalogue.csv')
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.assertEqual(self.cat.start_year, np.min(self.cat.data['year']))
self.assertEqual(self.cat.end_year, np.max(self.cat.data['year']))
def test_without_specifying_years(self):
"""
Tests that when the catalogue is parsed without specifying the start
and end year that the start and end year come from the minimum and
maximum in the catalogue
"""
filename = os.path.join(self.BASE_DATA_PATH, 'test_catalogue.csv')
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.assertEqual(self.cat.start_year, np.min(self.cat.data['year']))
self.assertEqual(self.cat.end_year, np.max(self.cat.data['year']))
def decluster_iscgem_gk74(hmtk_csv):
from hmtk.parsers.catalogue.csv_catalogue_parser import CsvCatalogueParser
from writers import htmk2shp_isc
from os import path
# parse HMTK csv
parser = CsvCatalogueParser(hmtk_csv)
cat = parser.read_file()
# write shapefile
htmk2shp_isc(cat, path.join('shapefiles', 'ISC-GEM_V4_hmtk_full.shp'))
decluster_GK74(cat, hmtk_csv)
def parse_orig_hmtk_cat(hmtk_csv):
print 'parsing HMTK catalogue...'
# parse HMTK csv using modified version of HMTK parser
parser = CsvCatalogueParser(hmtk_csv)
hmtkcat = parser.read_file()
# get number of earthquakes
neq = len(hmtkcat.data['magnitude'])
# reformat HMTK dict to one expected for code below
cat = []
for i in range(0, neq):
# first make datestr
try:
if not isnan(hmtkcat.data['second'][i]):
datestr = str(hmtkcat.data['eventID'][i]) \
+ str('%2.2f' % hmtkcat.data['second'][i])
else:
datestr = str(hmtkcat.data['eventID'][i]) + '00.00'
evdt = datetime.strptime(datestr, '%Y%m%d%H%M%S.%f')
# if ID not date form, do it the hard way!
except:
if hmtkcat.data['day'][i] == 0:
hmtkcat.data['day'][i] = 1
if hmtkcat.data['month'][i] == 0:
hmtkcat.data['month'][i] = 1
datestr = ''.join((str(hmtkcat.data['year'][i]),
str('%02d' % hmtkcat.data['month'][i]),
str('%02d' % hmtkcat.data['day'][i]),
str('%02d' % hmtkcat.data['hour'][i]),
str('%02d' % hmtkcat.data['minute'][i])))
evdt = datetime.strptime(datestr, '%Y%m%d%H%M')
tdict = {'datetime':evdt, 'prefmag':hmtkcat.data['magnitude'][i], \
'lon':hmtkcat.data['longitude'][i], 'lat':hmtkcat.data['latitude'][i], \
'dep':hmtkcat.data['depth'][i], 'year':hmtkcat.data['year'][i], \
'month':hmtkcat.data['month'][i], 'fixdep':0, 'prefmagtype':'MW', \
'auth':hmtkcat.data['Agency'][i]}
cat.append(tdict)
return cat, neq
def test_catalogue_writer_only_flag_purging(self):
'''
Tests the writer only purging according to the flag
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue, flag_vector=self.flag)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
expected_catalogue = Catalogue()
expected_catalogue.data['eventID'] = ['1', '2', '3', '4']
expected_catalogue.data['magnitude'] = np.array([5.6, 5.4, 4.8, 4.3])
expected_catalogue.data['year'] = np.array([1960, 1965, 1970, 1980])
expected_catalogue.data['ErrorStrike'] = np.array(
[np.nan, np.nan, np.nan, np.nan])
self.check_catalogues_are_equal(expected_catalogue, cat2)
def test_catalogue_writer_only_mag_table_purging(self):
'''
Tests the writer only purging according to the magnitude table
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue, magnitude_table=self.magnitude_table)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
expected_catalogue = Catalogue()
expected_catalogue.data['eventID'] = ['1', '3', '5']
expected_catalogue.data['magnitude'] = np.array([5.6, 4.8, 5.0])
expected_catalogue.data['year'] = np.array([1960, 1970, 1990])
expected_catalogue.data['ErrorStrike'] = np.array(
[np.nan, np.nan, np.nan])
self.check_catalogues_are_equal(expected_catalogue, cat2)
def test_catalogue_writer_only_flag_purging(self):
'''
Tests the writer only purging according to the flag
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue, flag_vector=self.flag)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
expected_catalogue = Catalogue()
expected_catalogue.data['eventID'] = np.array([1, 2, 3, 4])
expected_catalogue.data['magnitude'] = np.array([5.6, 5.4, 4.8, 4.3])
expected_catalogue.data['year'] = np.array([1960, 1965, 1970, 1980])
expected_catalogue.data['ErrorStrike'] = np.array([np.nan, np.nan,
np.nan, np.nan])
self.check_catalogues_are_equal(expected_catalogue, cat2)
def test_catalogue_writer_only_mag_table_purging(self):
'''
Tests the writer only purging according to the magnitude table
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue, magnitude_table=self.magnitude_table)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
expected_catalogue = Catalogue()
expected_catalogue.data['eventID'] = np.array([1, 3, 5])
expected_catalogue.data['magnitude'] = np.array([5.6, 4.8, 5.0])
expected_catalogue.data['year'] = np.array([1960, 1970, 1990])
expected_catalogue.data['ErrorStrike'] =np.array([np.nan, np.nan,
np.nan])
self.check_catalogues_are_equal(expected_catalogue, cat2)
def test_catalogue_writer_both_purging(self):
'''
Tests the writer only purging according to the magnitude table and
the flag vector
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue,
flag_vector=self.flag,
magnitude_table=self.magnitude_table)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
expected_catalogue = Catalogue()
expected_catalogue.data['eventID'] = ['1', '3']
expected_catalogue.data['magnitude'] = np.array([5.6, 4.8])
expected_catalogue.data['year'] = np.array([1960, 1970])
expected_catalogue.data['ErrorStrike'] = np.array([np.nan, np.nan])
self.check_catalogues_are_equal(expected_catalogue, cat2)
def test_catalogue_writer_both_purging(self):
'''
Tests the writer only purging according to the magnitude table and
the flag vector
'''
# Write to file
writer = CsvCatalogueWriter(self.output_filename)
writer.write_file(self.catalogue,
flag_vector=self.flag,
magnitude_table=self.magnitude_table)
parser = CsvCatalogueParser(self.output_filename)
cat2 = parser.read_file()
expected_catalogue = Catalogue()
expected_catalogue.data['eventID'] = np.array([1, 3])
expected_catalogue.data['magnitude'] = np.array([5.6, 4.8])
expected_catalogue.data['year'] = np.array([1960, 1970])
expected_catalogue.data['ErrorStrike'] = np.array([np.nan, np.nan])
self.check_catalogues_are_equal(expected_catalogue, cat2)
plt.rcParams['pdf.fonttype'] = 42
mpl.style.use('classic')
##########################################################################################
# parse epicentres
##########################################################################################
# parse HMTK csv
'''
hmtk_csv = '/nas/gemd/ehp/georisk_earthquake/modelling/sandpits/tallen/NSHA2018/catalogue/data/NSHA18CAT_V0.1_hmtk_declustered.csv'
parser = CsvCatalogueParser(hmtk_csv)
declcat = parser.read_file()
'''
hmtk_csv = '/nas/gemd/ehp/georisk_earthquake/modelling/sandpits/tallen/NSHA2018/catalogue/data/NSHA18CAT_V0.1_hmtk_declustered.csv'
parser = CsvCatalogueParser(hmtk_csv)
cat = parser.read_file()
##########################################################################################
#108/152/-44/-8
urcrnrlat = -8.
llcrnrlat = -46.
urcrnrlon = 157.
llcrnrlon = 109.
lon_0 = mean([llcrnrlon, urcrnrlon])
lat_1 = percentile([llcrnrlat, urcrnrlat], 25)
lat_2 = percentile([llcrnrlat, urcrnrlat], 75)
fig = plt.figure(figsize=(20, 12))
ax = fig.add_subplot(121)
plt.tick_params(labelsize=8)
### Catalogue
###
#input_catalogue_file = 'data_input/hmtk_sa3'
input_catalogue_file = 'data_input/hmtk_bsb2013'
###
### Catalogue cache or read/cache
###
try:
catalogue = pickle.load(open(input_catalogue_file + ".pkl", 'rb'))
except:
parser = CsvCatalogueParser(input_catalogue_file + ".csv")
catalogue = parser.read_file()
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
# Sort catalogue chronologically
catalogue.sort_catalogue_chronologically()
print 'Catalogue sorted chronologically!'
valid_magnitudes = np.logical_not(np.isnan(catalogue.data['magnitude']))
catalogue.select_catalogue_events(valid_magnitudes)
valid_magnitudes = catalogue.data['magnitude'] >= 3.0
catalogue.select_catalogue_events(valid_magnitudes)
#print catalogue.data['magnitude']
valid_depths = np.logical_not(np.isnan(catalogue.data['depth']))
catalogue.select_catalogue_events(valid_depths)
def read_catalog(input_catalogue_file, m_min=3.0):
###
### Catalogue cache or read/cache
###
try:
print '--Reading Catalog'
print input_catalogue_file
catalogue = pickle.load(open(input_catalogue_file + ".pkl", 'rb'))
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
except:
print '--Reading Catalog'
parser = CsvCatalogueParser(input_catalogue_file + ".csv")
catalogue = parser.read_file()
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
# Sort catalogue chronologically
catalogue.sort_catalogue_chronologically()
print 'Catalogue sorted chronologically!'
print '--Removing nan magnitudes'
valid_magnitudes = np.logical_not(np.isnan(catalogue.data['magnitude']))
catalogue.select_catalogue_events(valid_magnitudes)
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
print '--Removing magnitudes < %f'%m_min
valid_magnitudes = catalogue.data['magnitude'] >= m_min
catalogue.select_catalogue_events(valid_magnitudes)
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
print '--Removing nan depths'
valid_depths = np.logical_not(np.isnan(catalogue.data['depth']))
catalogue.select_catalogue_events(valid_depths)
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
print '--Removing 0 days'
valid_months = catalogue.data['day'] != 0
catalogue.select_catalogue_events(valid_months)
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
# Cache
# Set-up the file writer
print '--Caching'
output_file_name = input_catalogue_file + '.csv'
#writer = CsvCatalogueWriter(output_file_name)
#writer.write_file(catalogue)
#exit()
#print 'File %s written' % output_file_name
f=open(input_catalogue_file + ".pkl",'wb')
pickle.dump(catalogue, f)
f.close()
return catalogue
kagan_i1 = probs.get_i1()
print "Poisson LLH = %.6f, I0 = %.6f, I1 = %.6f, I' = %.6f" % (
poiss_llh, kagan_i0, kagan_i1, kagan_i0 - kagan_i1)
SARA_COMP_TABLE = np.array([[1992., 4.5], [1974., 5.], [1964., 5.5],
[1954., 5.75], [1949., 6.], [1949., 6.5],
[1930., 7.0]])
SARA_CAT_FILE = "catalogue/sara_all_v07_harm_per123_dist_crustal_clean.csv"
SARA_DECLUST_CAT = "catalogue/sara_cat_shallow_declust.csv"
COMP_FILE = "sam_completeness_zones.hdf5"
if __name__ == "__main__":
# Load in catalogue
parser = CsvCatalogueParser(SARA_DECLUST_CAT)
cat1 = parser.read_file()
idx = cat1.data["magnitude"] >= 3.0
cat1.purge_catalogue(idx)
bbox = [-90.5, -30.0, 0.1, -60.5, 15.5, 0.1, 0., 100.0, 100.0]
config = {
"k": 5,
"r_min": 0.0005 / 25.0,
"bvalue": 1.0,
"mmin": 3.0,
"learning_start": 1930,
"learning_end": 2003,
"target_start": 2004,
"target_end": 2013
}
# Run
run_helmstetter_spatial(cat1,
# -*- coding: utf-8 -*-
import numpy as np
from hmtk.parsers.catalogue.csv_catalogue_parser import CsvCatalogueParser
catalogue_file = "data_input/hmtk_bsb2013_pp_decluster.csv"
#from hmtk.seismicity.catalogue import Catalogue
# catalogue
parser = CsvCatalogueParser(catalogue_file)
catalogue = parser.read_file()
catalogue.sort_catalogue_chronologically()
method = "frankel1995"
method = "woo1996"
#method = "helmstetter2012"
#method = "oq-dourado2014_b2"
filename = "data_output/poe_0.1_smooth_decluster_%s.csv"%(method)
filename = "data_output/poe_0.1_%s.csv"%(method)
#filename = "data_output/poe_0.1_smooth_decluster_%s_cum.csv"%(method)
filename = "data_output/bsb2013_helmstetter2012.csv"
d = np.genfromtxt(fname=filename,
#comments='#',
delimiter=',',
def get_hmtk_catalogue(filename):
catalogue_parser = CsvCatalogueParser(filename)
return catalogue_parser.read_file()
### Catalogue
###
#input_catalogue_file = 'data_input/hmtk_sa3'
input_catalogue_file = 'data_input/hmtk_bsb2013'
###
### Catalogue cache or read/cache
###
try:
catalogue = pickle.load(open(input_catalogue_file + ".pkl", 'rb'))
except:
parser = CsvCatalogueParser(input_catalogue_file + ".csv")
catalogue = parser.read_file()
print 'Input complete: %s events in catalogue' % catalogue.get_number_events(
)
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year,
catalogue.end_year)
# Sort catalogue chronologically
catalogue.sort_catalogue_chronologically()
print 'Catalogue sorted chronologically!'
valid_magnitudes = np.logical_not(np.isnan(catalogue.data['magnitude']))
catalogue.select_catalogue_events(valid_magnitudes)
valid_magnitudes = catalogue.data['magnitude'] >= 3.0
catalogue.select_catalogue_events(valid_magnitudes)
#print catalogue.data['magnitude']
dashes=[2, 2],
color='0.5',
linewidth=0.75)
return m
#############################################################
# parse catalogue & plot
#############################################################
sheef_full = path.join('2010SHEEF', 'SHEEF2010Mw2_hmtk.csv')
sheef_decl = path.join('2010SHEEF', 'SHEEF2010Mw2_hmtk_declustered.csv')
parser = CsvCatalogueParser(sheef_full)
cat_full = parser.read_file()
lonf = cat_full.data['longitude']
latf = cat_full.data['latitude']
magf = cat_full.data['magnitude']
###############################################################
# plt full catalogue
###############################################################
plt.subplot(121)
# map earthquakes that pass completeness
m = make_basemap(cnrs)
# get index of events
idx = where((lonf >= cnrs[0]) & (lonf <= cnrs[1]) \
IsotropicGaussian
BASE_PATH = 'data_input/'
#OUTPUT_FILE = 'data_output/hmtk_bsb2013_decluster_frankel1995.csv'
OUTPUT_FILE = '/Users/pirchiner/dev/pshab/data_output/hmtk_sa3_decluster_frankel1995.csv'
model_name = 'hmtk_bsb2013'
model_name = 'hmtk_sa3'
#TEST_CATALOGUE = 'hmtk_bsb2013_pp_decluster.csv'
TEST_CATALOGUE = 'hmtk_sa3_pp_decluster.csv'
_CATALOGUE = os.path.join(BASE_PATH, TEST_CATALOGUE)
# catalogue
parser = CsvCatalogueParser(_CATALOGUE)
catalogue = parser.read_file()
catalogue.sort_catalogue_chronologically()
#print catalogue.get_number_events()
#res, spc = 0.5, 100
res, spc = 1, 50
#res, spc = 0.2, 250
# model
#[xmin, xmax, spcx, ymin, ymax, spcy, zmin, spcz]
map_config = {
'min_lon': -95.0,
'max_lon': -25.0,
'min_lat': -65.0,
'max_lat': 25.0,
from os import path, walk, system
#from obspy.imaging.beachball import Beach
from hmtk.parsers.catalogue.csv_catalogue_parser import CsvCatalogueParser, CsvCatalogueWriter
from misc_tools import remove_last_cmap_colour
plt.rcParams['pdf.fonttype'] = 42
mpl.style.use('classic')
##########################################################################################
# parse epicentres
##########################################################################################
# parse HMTK csv
hmtk_csv = '/nas/gemd/ehp/georisk_earthquake/modelling/sandpits/tallen/NSHA2018/catalogue/data/merged_NSHA18-ISCGEM_hmtk.csv'
parser = CsvCatalogueParser(hmtk_csv)
fullcat = parser.read_file()
hmtk_csv = '/nas/gemd/ehp/georisk_earthquake/modelling/sandpits/tallen/NSHA2018/catalogue/data/NSHA18CAT_V0.1_hmtk_declustered.csv'
parser = CsvCatalogueParser(hmtk_csv)
declcat = parser.read_file()
##########################################################################################
#108/152/-44/-8
urcrnrlat = -29.5
llcrnrlat = -33.3
urcrnrlon = 118.75
llcrnrlon = 115.25
lon_0 = mean([llcrnrlon, urcrnrlon])
lat_1 = percentile([llcrnrlat, urcrnrlat], 25)
lat_2 = percentile([llcrnrlat, urcrnrlat], 75)
