def test_get_detail_data_frame():
datadir = get_datadir()
tape_file = os.path.join(datadir, 'vcr_detail_frame.yaml')
with vcr.use_cassette(tape_file):
events = search(starttime=datetime(1994, 6, 1),
endtime=datetime(1994, 10, 6),
minmagnitude=8.0, maxmagnitude=9.0)
all_mags = get_detail_data_frame(
events, get_all_magnitudes=True, verbose=True)
assert all_mags.iloc[0]['magnitude'] == 8.2
def test_get_detail_data_frame():
cassettes, datadir = get_datadir()
tape_file = os.path.join(cassettes, 'dataframes_detailed.yaml')
with vcr.use_cassette(tape_file):
events = search(starttime=datetime(1994, 6, 1),
endtime=datetime(1994, 10, 6),
minmagnitude=8.0,
maxmagnitude=9.0)
all_mags = get_detail_data_frame(events, get_all_magnitudes=True)
assert all_mags.iloc[0]['magnitude'] == 8.2
def test_get_detail_data_frame():
cassettes, datadir = get_datadir()
tape_file = os.path.join(cassettes, "dataframes_detailed.yaml")
with vcr.use_cassette(tape_file, record_mode="new_episodes"):
events = search.search(
starttime=datetime(1994, 6, 1),
endtime=datetime(1994, 10, 6),
minmagnitude=8.0,
maxmagnitude=9.0,
)
all_mags = get_detail_data_frame(events, get_all_magnitudes=True)
assert all_mags.iloc[0]["magnitude"] == 8.2
from datetime import datetime
from libcomcat.dataframes import get_detail_data_frame
from libcomcat.search import search, get_event_by_id
summary_events = search(starttime=datetime(2010, 1, 1, 00, 00),
endtime=datetime(2019, 12, 31, 23, 59),
minlatitude=45,
maxlatitude=72,
minlongitude=-180,
maxlongitude=-125,
minmagnitude=4)
detail_df = get_detail_data_frame(summary_events,
get_tensors='all',
get_focals='all',
get_moment_supplement=True)
detail_df.to_pickle('usgs_alaska_2010.p')
def main():
parser = get_parser()
args = parser.parse_args()
# make sure we don't have -e option AND --numdays option
if args.endTime is not None and args.numdays is not None:
msg = ('You must specify end time or number of days since '
'start time, not both. Exiting.')
print(msg)
sys.exit(1)
if not args.endTime and args.numdays:
args.endTime = args.startTime + timedelta(args.numdays)
setup_logger(args.logfile, args.loglevel)
tsum = (args.bounds is not None) + \
(args.radius is not None) + (args.country is not None)
if tsum != 1:
logging.error(
'Please specify a bounding box, radius, or country code.')
sys.exit(1)
latitude = None
longitude = None
radiuskm = None
lonmin = latmin = lonmax = latmax = None
bounds = None
if args.radius:
latitude = args.radius[0]
longitude = args.radius[1]
radiuskm = args.radius[2]
if args.bounds:
lonmin, lonmax, latmin, latmax = args.bounds
# fix longitude bounds when crossing dateline
if lonmin > lonmax and lonmax >= -180:
lonmin -= 360
else:
lonmin, lonmax, latmin, latmax = None, None, None, None
bounds = (lonmin, lonmax, latmin, latmax)
if args.country:
ccode = args.country
if not check_ccode(ccode):
curl = 'https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2'
fmt = ('%s is not a valid ISO 3166 country code. '
'See %s for the list.')
tpl = (ccode, curl)
logging.error(fmt % tpl)
sys.exit(1)
bounds = get_country_bounds(ccode, args.buffer) # this returns a list
minmag = 0.0
maxmag = 9.9
if args.magRange:
minmag = args.magRange[0]
maxmag = args.magRange[1]
minsig = 0
maxsig = 5000
if args.sigRange:
minsig = args.sigRange[0]
maxsig = args.sigRange[1]
if args.getCount:
if isinstance(bounds, tuple) or bounds is None:
nevents = count(starttime=args.startTime,
endtime=args.endTime,
updatedafter=args.after,
minlatitude=latmin,
maxlatitude=latmax,
minlongitude=lonmin,
maxlongitude=lonmax,
latitude=latitude,
longitude=longitude,
maxradiuskm=radiuskm,
catalog=args.catalog,
contributor=args.contributor,
maxmagnitude=maxmag,
minmagnitude=minmag,
minsig=minsig,
maxsig=maxsig,
producttype=args.limitByProductType)
else:
for lonmin, lonmax, latmin, latmax in bounds:
nevents = 0
nevents += count(starttime=args.startTime,
endtime=args.endTime,
updatedafter=args.after,
minlatitude=latmin,
maxlatitude=latmax,
minlongitude=lonmin,
maxlongitude=lonmax,
latitude=latitude,
longitude=longitude,
maxradiuskm=radiuskm,
catalog=args.catalog,
contributor=args.contributor,
minsig=minsig,
maxsig=maxsig,
maxmagnitude=maxmag,
minmagnitude=minmag,
producttype=args.limitByProductType)
print('There are %i events matching input criteria.' % nevents)
sys.exit(0)
if isinstance(bounds, tuple) or bounds is None:
events = search(starttime=args.startTime,
endtime=args.endTime,
updatedafter=args.after,
minlatitude=latmin,
maxlatitude=latmax,
minlongitude=lonmin,
maxlongitude=lonmax,
latitude=latitude,
longitude=longitude,
maxradiuskm=radiuskm,
catalog=args.catalog,
contributor=args.contributor,
maxmagnitude=maxmag,
minmagnitude=minmag,
minsig=minsig,
maxsig=maxsig,
producttype=args.limitByProductType,
host=args.host,
eventtype=args.event_type,
alertlevel=args.alert_level)
else:
events = []
for i, tbounds in enumerate(bounds):
lonmin, lonmax, latmin, latmax = tbounds
fmt = 'Checking bounds %i of %i for %s...\n'
tpl = (i + 1, len(bounds), ccode)
logging.debug(fmt % tpl)
tevents = search(starttime=args.startTime,
endtime=args.endTime,
updatedafter=args.after,
minlatitude=latmin,
maxlatitude=latmax,
minlongitude=lonmin,
maxlongitude=lonmax,
latitude=latitude,
longitude=longitude,
maxradiuskm=radiuskm,
catalog=args.catalog,
contributor=args.contributor,
maxmagnitude=maxmag,
minmagnitude=minmag,
minsig=minsig,
maxsig=maxsig,
producttype=args.limitByProductType,
host=args.host,
eventtype=args.event_type,
alertlevel=args.alert_level)
events += tevents
if not len(events):
logging.info('No events found matching your search criteria. Exiting.')
sys.exit(0)
if (args.getAngles != 'none' or
args.getAllMags or
args.getComponents != 'none'):
logging.info(
'Fetched %i events...creating table.\n' % (len(events)))
supp = args.getMomentSupplement
df = get_detail_data_frame(events, get_all_magnitudes=args.getAllMags,
get_tensors=args.getComponents,
get_focals=args.getAngles,
get_moment_supplement=supp)
else:
logging.info(
'Fetched %i events...creating summary table.\n' % (len(events)))
df = get_summary_data_frame(events)
# order the columns so that at least the initial parameters come the way
# we want them...
first_columns = list(events[0].toDict().keys())
col_list = list(df.columns)
for column in first_columns:
try:
col_list.remove(column)
except Exception as e:
x = 1
df = df[first_columns + col_list]
if args.country:
df = filter_by_country(df, ccode, buffer_km=args.buffer)
logging.info('Created table...saving %i records to %s.\n' %
(len(df), args.filename))
if args.format == 'excel':
df.to_excel(args.filename, index=False)
elif args.format == 'tab':
df.to_csv(args.filename, sep='\t', index=False)
else:
df.to_csv(args.filename, index=False, chunksize=1000)
logging.info('%i records saved to %s.' % (len(df), args.filename))
sys.exit(0)
remember to load correct environment in order to use the libcomcat library
@author: talongi
"""
from libcomcat.dataframes import get_summary_data_frame, get_detail_data_frame
from libcomcat.search import search, DetailEvent
from datetime import datetime
events = search(starttime=datetime(2006, 10, 30),
endtime=datetime(2006, 11, 15),
minlatitude=40.4,
maxlatitude=40.6,
minlongitude=-125,
maxlongitude=-123)
detail_events = get_detail_data_frame(events)
#detail_events.to_csv('/auto/home/talongi/Cascadia/Data_tables/Events/ANSS_1980-2019_detailed.csv')
#%%
#find nans
import numpy as np
import pandas as pd
index_match = []
for i, x in enumerate(detail_events.nc_np1_strike):
if type(x) == str:
index_match.append(i)
foc_mech_cat = detail_events.iloc[index_match]
def main(args):
intervalno = args.intervalno
directory = args.directory
if intervalno > 11:
if args.previous is not None:
previous = args.previous
else:
print('previous file required at input if interval no > 11.')
print('enter previous file with flag -p previousFile at input')
print('Exiting ...')
exit()
os.system('mkdir %s' % directory)
if intervalno == 1:
start = datetime(1900, 1, 1)
finish = datetime(1970, 1, 1)
if intervalno == 2:
start = datetime(1970, 1, 1)
finish = datetime(1980, 1, 1)
if intervalno == 3:
start = datetime(1980, 1, 1)
finish = datetime(1990, 1, 1)
if intervalno == 4:
start = datetime(1990, 1, 1)
finish = datetime(1995, 1, 1)
if intervalno == 5:
start = datetime(1995, 1, 1)
finish = datetime(2000, 1, 1)
if intervalno == 6:
start = datetime(2000, 1, 1)
finish = datetime(2005, 1, 1)
if intervalno == 7:
start = datetime(2005, 1, 1)
finish = datetime(2010, 1, 1)
if intervalno == 8:
start = datetime(2010, 1, 1)
finish = datetime(2012, 1, 1)
if intervalno == 9:
start = datetime(2012, 1, 1)
finish = datetime(2014, 1, 1)
if intervalno == 10:
start = datetime(2014, 1, 1)
finish = datetime(2016, 1, 1)
if intervalno == 11:
start = datetime(2016, 1, 1)
finish = datetime.utcnow()
if intervalno > 11:
predata = pd.read_csv(previous)
predata['time'] = pd.to_datetime(predata['time'])
start = predata['time'].max()
finish = datetime.utcnow()
print(start)
print(finish)
#define magnitude range of earthquakes to search over for shallow earthquakes
#(these are whatever magnitude range is defined in the catalogues)
min_mag = 3.0
max_mag = 9.9
magrange = (min_mag, max_mag)
#define depth to search over
min_sh = 0
max_sh = 900
depthrange_sh = (min_sh, max_sh)
#define grid size and extent of search (representative of lower left corner)
if intervalno > 11:
grid = 50.0
else:
grid = 10.0
lonmin, lonmax = -180, 180
latmin, latmax = -75, 75
#define grid of searches (representative of lower left corner)
xall = np.arange(lonmin, lonmax, grid)
yall = np.arange(latmin, latmax, grid)
lons1, lats1 = np.meshgrid(xall, yall)
#flatten into list of lower left corners
lllons = lons1.flatten()
lllats = lats1.flatten()
#define lists representing upper right corners
urlons = lllons + grid
urlats = lllats + grid
#combine into one array (lonmin,lonmax,latmin,latmax)
bounds = np.zeros((len(lllons), 4))
bounds[:, 0] = lllons
bounds[:, 1] = urlons
bounds[:, 2] = lllats
bounds[:, 3] = urlats
iterations = len(bounds)
lllatfail = []
lllonfail = []
urlatfail = []
urlonfail = []
totdf = pd.DataFrame()
num = 0
for i, line in enumerate(bounds):
bounds = line
#to follow along with the progress of data querying
#since there are a lot of iterations, only print every so often
k = 100
if i % k == 0:
print('Now querying grid %s of %s' % (i, iterations))
searchlist = search(starttime=start,
endtime=finish,
minlatitude=bounds[2],
maxlatitude=bounds[3],
minlongitude=bounds[0],
maxlongitude=bounds[1],
minmagnitude=3.0)
if len(searchlist) > 0:
detaildf = get_detail_data_frame(searchlist,
get_tensors='preferred',
get_moment_supplement=True)
totdf = pd.concat([totdf, detaildf], sort=True)
print(bounds, len(detaildf), len(totdf))
if len(totdf) > 5000:
totdf.to_csv('%s/%s_%i.%i.csv' %
(directory, directory, intervalno, num),
header=True,
index=False,
na_rep=np.nan)
num += 1
totdf = pd.DataFrame()
totdf.to_csv('%s/%s_%i.csv' % (directory, directory, intervalno),
header=True,
index=False,
na_rep=np.nan)