# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2023 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import io
import ast
import html
import os.path
import numbers
import operator
import functools
import itertools
import collections
import logging
import numpy
import pandas
from openquake.baselib.general import (
humansize, countby, AccumDict, CallableDict,
get_array, group_array, fast_agg)
from openquake.baselib.hdf5 import FLOAT, INT, get_shape_descr, vstr
from openquake.baselib.performance import performance_view, Monitor
from openquake.baselib.python3compat import encode, decode
from openquake.hazardlib import logictree, calc, source, geo
from openquake.hazardlib.shakemap.parsers import download_rupture_dict
from openquake.hazardlib.contexts import (
KNOWN_DISTANCES, ContextMaker, Collapser)
from openquake.commonlib import util
from openquake.risklib import riskmodels
from openquake.risklib.scientific import (
losses_by_period, return_periods, LOSSID, LOSSTYPE)
from openquake.baselib.writers import build_header, scientificformat
from openquake.calculators.classical import get_pmaps_gb
from openquake.calculators.getters import get_ebrupture
from openquake.calculators.extract import extract
F32 = numpy.float32
F64 = numpy.float64
U32 = numpy.uint32
U8 = numpy.uint8
# a dictionary of views datastore -> array
view = CallableDict(keyfunc=lambda s: s.split(':', 1)[0])
code2cls = source.rupture.code2cls
# ########################## utility functions ############################## #
[docs]def dt(names):
"""
:param names: list or a string with space-separated names
:returns: a numpy structured dtype
"""
if isinstance(names, str):
names = names.split()
return numpy.dtype([(name, object) for name in names])
[docs]class HtmlTable(object):
"""
Convert a sequence header+body into a HTML table.
"""
css = """\
tr.evenRow { background-color: lightgreen }
tr.oddRow { }
th { background-color: lightblue }
"""
maxrows = 5000
border = "1"
summary = ""
def __init__(self, header_plus_body, name='noname',
empty_table='Empty table'):
header, body = header_plus_body[0], header_plus_body[1:]
self.name = name
self.empty_table = empty_table
rows = [] # rows is a finite sequence of tuples
for i, row in enumerate(body):
if i == self.maxrows:
rows.append(
["Table truncated because too big: more than %s rows" % i])
break
rows.append(row)
self.rows = rows
self.header = tuple(header) # horizontal header
[docs] def render(self, dummy_ctxt=None):
out = "\n%s\n" % "".join(list(self._gen_table()))
if not self.rows:
out += '<em>%s</em>' % html.escape(self.empty_table, quote=True)
return out
def _gen_table(self):
yield '<table id="%s" border="%s" summary="%s" class="tablesorter">\n'\
% (self.name, self.border, self.summary)
yield '<thead>\n'
yield '<tr>%s</tr>\n' % ''.join(
'<th>%s</th>\n' % h for h in self.header)
yield '</thead>\n'
yield '<tbody\n>'
for r, row in enumerate(self.rows):
yield '<tr class="%s">\n' % ["even", "odd"][r % 2]
for col in row:
yield '<td>%s</td>\n' % col
yield '</tr>\n'
yield '</tbody>\n'
yield '</table>\n'
[docs]def text_table(data, header=None, fmt=None, ext='rst'):
"""
Build a .rst (or .org) table from a matrix or a DataFrame
>>> tbl = [['a', 1], ['b', 2]]
>>> print(text_table(tbl, header=['Name', 'Value']))
+------+-------+
| Name | Value |
+------+-------+
| a | 1 |
+------+-------+
| b | 2 |
+------+-------+
"""
assert ext in 'csv rst org html', ext
if isinstance(data, pandas.DataFrame):
if data.index.name:
data = data.reset_index()
header = header or list(data.columns)
data = zip(*[data[col].to_numpy() for col in data.columns])
if header is None and hasattr(data, '_fields'):
header = data._fields
try:
# see if data is a composite numpy array
data.dtype.fields
except AttributeError:
# not a composite array
header = header or ()
else:
if not header:
header = [col.split(':')[0] for col in build_header(data.dtype)]
if header:
col_sizes = [len(col) for col in header]
else:
col_sizes = [len(str(col)) for col in data[0]]
body = []
fmt = functools.partial(scientificformat, fmt=fmt) if fmt else form
for row in data:
tup = tuple(fmt(c) for c in row)
for (i, col) in enumerate(tup):
col_sizes[i] = max(col_sizes[i], len(col))
if len(tup) != len(col_sizes):
raise ValueError('The header has %d fields but the row %d fields!'
% (len(col_sizes), len(tup)))
body.append(tup)
if ext == 'html':
return HtmlTable([header] + body).render()
wrap = '+-%s-+' if ext == 'rst' else '|-%s-|'
sepline = wrap % '-+-'.join('-' * size for size in col_sizes)
sep = ',' if ext == 'csv' else ' | '
templ = sep.join('%-{}s'.format(size) for size in col_sizes)
if ext != 'csv':
templ = '| %s |' % templ
if header and ext == 'rst':
lines = [sepline, templ % tuple(header), sepline]
elif header and ext == 'org':
lines = [templ % tuple(header), sepline]
elif header and ext == 'csv':
lines = [','.join(header)]
else:
lines = [sepline]
for row in body:
lines.append(templ % row)
if ext == 'rst':
lines.append(sepline)
return '\n'.join(lines)
[docs]@view.add('worst_sources')
def view_worst_sources(token, dstore):
"""
Returns the sources with worst weights
"""
if ':' in token:
step = int(token.split(':')[1])
else:
step = 1
data = dstore.read_df('source_data', 'src_id')
del data['impact']
ser = data.groupby('taskno').ctimes.sum().sort_values().tail(1)
[[taskno, maxtime]] = ser.to_dict().items()
data = data[data.taskno == taskno]
print('Sources in the slowest task (%d seconds, weight=%d, taskno=%d)'
% (maxtime, data['weight'].sum(), taskno))
data['slow_rate'] = data.ctimes / data.weight
del data['taskno']
df = data.sort_values('ctimes', ascending=False)
return df[slice(None, None, step)]
[docs]@view.add('slow_sources')
def view_slow_sources(token, dstore, maxrows=20):
"""
Returns the slowest sources
"""
info = dstore['source_info']['source_id', 'code',
'calc_time', 'num_sites', 'num_ruptures']
info.sort(order='calc_time')
data = numpy.zeros(len(info), dt(info.dtype.names))
for name in info.dtype.names:
data[name] = info[name]
return data[::-1][:maxrows]
[docs]@view.add('rup_info')
def view_rup_info(token, dstore, maxrows=25):
"""
Show the slowest ruptures
"""
if not code2cls:
code2cls.update(source.rupture.BaseRupture.init())
fields = ['code', 'n_occ', 'nsites', 'mag']
rups = dstore.read_df('ruptures', 'id')[fields]
info = dstore.read_df('gmf_data/rup_info', 'rup_id')
df = rups.join(info).sort_values('time', ascending=False)
df['surface'] = [code2cls[code][1].__name__ for code in df.code]
del df['task_no']
del df['code']
return df[:maxrows]
[docs]@view.add('contents')
def view_contents(token, dstore):
"""
Returns the size of the contents of the datastore and its total size
"""
tot = (dstore.filename, humansize(os.path.getsize(dstore.filename)))
data = sorted((dstore.getsize(key), key) for key in dstore)
rows = [(key, humansize(nbytes)) for nbytes, key in data] + [tot]
return numpy.array(rows, dt('dataset size'))
[docs]def short_repr(lst):
if len(lst) <= 10:
return ' '.join(map(str, lst))
return '[%d rlzs]' % len(lst)
[docs]@view.add('full_lt')
def view_full_lt(token, dstore):
full_lt = dstore['full_lt'].init()
num_paths = full_lt.get_num_potential_paths()
if not full_lt.num_samples and num_paths > 15000:
return '<%d realizations>' % num_paths
full_lt.get_trt_rlzs(dstore['trt_smrs'][:]) # set _rlzs_by
header = ['trt_smr', 'gsim', 'rlzs']
rows = []
for trt_smr, rbg in full_lt._rlzs_by.items():
for gsim, rlzs in rbg.items():
rows.append((trt_smr, repr(str(gsim)), short_repr(rlzs)))
return numpy.array(rows, dt(header))
[docs]@view.add('weight_by_src')
def view_eff_ruptures(token, dstore):
info = dstore.read_df('source_info', 'source_id')
df = info.groupby('code').sum()
df['slow_factor'] = df.calc_time / df.weight
del df['grp_id'], df['trti'], df['mutex_weight']
return df
[docs]@view.add('short_source_info')
def view_short_source_info(token, dstore, maxrows=20):
return dstore['source_info'][:maxrows]
[docs]@view.add('params')
def view_params(token, dstore):
oq = dstore['oqparam']
params = ['calculation_mode', 'number_of_logic_tree_samples',
'maximum_distance', 'investigation_time',
'ses_per_logic_tree_path', 'truncation_level',
'rupture_mesh_spacing', 'complex_fault_mesh_spacing',
'width_of_mfd_bin', 'area_source_discretization',
'pointsource_distance',
'ground_motion_correlation_model', 'minimum_intensity',
'random_seed', 'master_seed', 'ses_seed']
if 'risk' in oq.calculation_mode:
params.append('avg_losses')
return numpy.array([(param, repr(getattr(oq, param, None)))
for param in params], dt('parameter value'))
[docs]def rst_links(*fnames):
links = []
for fname in fnames:
bname = os.path.basename(fname)
links.append("`%s <%s>`_" % (bname, bname))
return ' '.join(links)
[docs]def build_links(items):
out = []
for key, fname in items:
if isinstance(fname, dict):
for k, v in fname.items():
b = os.path.basename(v)
out.append(('reqv:' + k, "`%s <%s>`_" % (b, b)))
elif isinstance(fname, list):
out.append((key, rst_links(*fname)))
else:
out.append((key, rst_links(fname)))
return sorted(out)
def _humansize(literal):
dic = ast.literal_eval(decode(literal))
if isinstance(dic, dict):
items = sorted(dic.items(), key=operator.itemgetter(1), reverse=True)
lst = ['%s %s' % (k, humansize(v)) for k, v in items]
return ', '.join(lst)
else:
return str(dic)
[docs]@view.add('job_info')
def view_job_info(token, dstore):
"""
Determine the amount of data transferred from the controller node
to the workers and back in a classical calculation.
"""
data = []
task_info = dstore['task_info'][()]
task_sent = ast.literal_eval(decode(dstore['task_sent'][()]))
for task, dic in task_sent.items():
sent = sorted(dic.items(), key=operator.itemgetter(1), reverse=True)
sent = ['%s=%s' % (k, humansize(v)) for k, v in sent[:3]]
recv = get_array(task_info, taskname=encode(task))['received']
data.append((task, ' '.join(sent),
humansize(recv.sum()), humansize(recv.mean())))
return numpy.array(data, dt('task sent received mean_recv'))
[docs]@view.add('avglosses_data_transfer')
def avglosses_data_transfer(token, dstore):
"""
Determine the amount of average losses transferred from the workers to the
controller node in a risk calculation.
"""
oq = dstore['oqparam']
N = len(dstore['assetcol'])
R = dstore['full_lt'].get_num_paths()
L = len(dstore.get_attr('crm', 'loss_types'))
ct = oq.concurrent_tasks
size_bytes = N * R * L * 8 * ct # 8 byte floats
return (
'%d asset(s) x %d realization(s) x %d loss type(s) losses x '
'8 bytes x %d tasks = %s' % (N, R, L, ct, humansize(size_bytes)))
# for scenario_risk and classical_risk
[docs]@view.add('totlosses')
def view_totlosses(token, dstore):
"""
This is a debugging view. You can use it to check that the total
losses, i.e. the losses obtained by summing the average losses on
all assets are indeed equal to the aggregate losses. This is a
sanity check for the correctness of the implementation.
"""
oq = dstore['oqparam']
tot_losses = 0
for ltype in oq.loss_types:
name = 'avg_losses-stats/' + ltype
try:
tot = dstore[name][()].sum(axis=0)
except KeyError:
name = 'avg_losses-rlzs/' + ltype
tot = dstore[name][()].sum(axis=0)
tot_losses += tot
return text_table(tot_losses.view(oq.loss_dt(F32)), fmt='%.6E')
[docs]def alt_to_many_columns(alt, loss_types):
# convert an risk_by_event in the format
# (event_id, agg_id, loss_id, loss) =>
# (event_id, agg_id, structural, nonstructural, ...)
dic = dict(event_id=[])
for ln in loss_types:
dic[ln] = []
for (eid, kid), df in alt.groupby(['event_id', 'agg_id']):
dic['event_id'].append(eid)
for ln in loss_types:
arr = df[df.loss_id == LOSSID[ln]].loss.to_numpy()
loss = 0 if len(arr) == 0 else arr[0] # arr has size 0 or 1
dic[ln].append(loss)
return pandas.DataFrame(dic)
def _portfolio_loss(dstore):
oq = dstore['oqparam']
R = dstore['full_lt'].get_num_paths()
K = dstore['risk_by_event'].attrs.get('K', 0)
alt = dstore.read_df('risk_by_event', 'agg_id', dict(agg_id=K))
df = alt_to_many_columns(alt, oq.loss_types)
eids = df.pop('event_id').to_numpy()
loss = df.to_numpy()
rlzs = dstore['events']['rlz_id'][eids]
L = loss.shape[1]
data = numpy.zeros((R, L))
for row, rlz in zip(loss, rlzs):
data[rlz] += row
return data
[docs]@view.add('portfolio_losses')
def view_portfolio_losses(token, dstore):
"""
The losses for the full portfolio, for each realization and loss type,
extracted from the event loss table.
"""
oq = dstore['oqparam']
loss_dt = oq.loss_dt()
data = _portfolio_loss(dstore).view(loss_dt)[:, 0] # shape R
rlzids = [str(r) for r in range(len(data))]
array = util.compose_arrays(numpy.array(rlzids), data, 'rlz_id')
# this is very sensitive to rounding errors, so I am using a low precision
return text_table(array, fmt='%.5E')
[docs]@view.add('portfolio_loss')
def view_portfolio_loss(token, dstore):
"""
The mean portfolio loss for each loss type,
extracted from the event loss table.
"""
oq = dstore['oqparam']
K = dstore['risk_by_event'].attrs.get('K', 0)
alt_df = dstore.read_df('risk_by_event', 'agg_id', dict(agg_id=K))
weights = dstore['weights'][:]
rlzs = dstore['events']['rlz_id']
E = len(rlzs)
R = len(weights)
ws = weights[rlzs]
avgs = []
attrs = dstore['gmf_data'].attrs
itime = attrs['investigation_time']
etime = attrs['effective_time']
if itime:
freq = (oq.risk_investigation_time or itime) * E / etime
else:
freq = 1 / R
for ln in oq.loss_types:
df = alt_df[alt_df.loss_id == LOSSID[ln]]
eids = df.pop('event_id').to_numpy()
if (eids >= E).any(): # reduced events
assert len(set(ws)) == 1, 'Weights must be all equal'
weights = ws[:len(eids)]
else:
weights = ws[eids]
avgs.append(weights @ df.loss.to_numpy() / ws.sum() * freq)
return text_table([['avg'] + avgs], ['loss'] + oq.loss_types)
[docs]@view.add('portfolio_dmgdist')
def portfolio_dmgdist(token, dstore):
"""
The portfolio damages extracted from the first realization of damages-rlzs
"""
oq = dstore['oqparam']
dstates = ['no_damage'] + oq.limit_states
D = len(dstates)
arr = dstore['damages-rlzs'][:, 0, :, :D].sum(axis=0) # shape (L, D)
tbl = numpy.zeros(len(arr), dt(['loss_type', 'total'] + dstates))
tbl['loss_type'] = oq.loss_types
tbl['total'] = arr.sum(axis=1)
for dsi, ds in enumerate(dstates):
tbl[ds] = arr[:, dsi]
return tbl
[docs]@view.add('portfolio_damage')
def view_portfolio_damage(token, dstore):
"""
The mean full portfolio damage for each loss type,
extracted from the average damages
"""
if 'aggcurves' in dstore: # event_based_damage
K = dstore.get_attr('risk_by_event', 'K')
df = dstore.read_df('aggcurves', sel=dict(agg_id=K, return_period=0))
lnames = numpy.array(dstore['oqparam'].loss_types)
df['loss_type'] = lnames[df.loss_id.to_numpy()]
del df['loss_id']
del df['agg_id']
del df['return_period']
return df.set_index('loss_type')
# dimensions assets, stat, loss_types, dmg_state
if 'damages-stats' in dstore:
attrs = get_shape_descr(dstore['damages-stats'].attrs['json'])
arr = dstore.sel('damages-stats', stat='mean').sum(axis=(0, 1))
else:
attrs = get_shape_descr(dstore['damages-rlzs'].attrs['json'])
arr = dstore.sel('damages-rlzs', rlz=0).sum(axis=(0, 1))
rows = [(lt,) + tuple(row) for lt, row in zip(attrs['loss_type'], arr)]
return numpy.array(rows, dt(['loss_type'] + list(attrs['dmg_state'])))
[docs]def sum_table(records):
"""
Used to compute summaries. The records are assumed to have numeric
fields, except the first field which is ignored, since it typically
contains a label. Here is an example:
>>> sum_table([('a', 1), ('b', 2)])
['total', 3]
"""
size = len(records[0])
result = [None] * size
firstrec = records[0]
for i in range(size):
if isinstance(firstrec[i], (numbers.Number, numpy.ndarray)):
result[i] = sum(rec[i] for rec in records)
else:
result[i] = 'total'
return result
[docs]@view.add('exposure_info')
def view_exposure_info(token, dstore):
"""
Display info about the exposure model
"""
assetcol = dstore['assetcol/array'][:]
taxonomies = sorted(set(dstore['assetcol'].taxonomies))
data = [('#assets', len(assetcol)),
('#taxonomies', len(taxonomies))]
return text_table(data) + '\n\n' + view_assets_by_site(token, dstore)
[docs]@view.add('ruptures_events')
def view_ruptures_events(token, dstore):
num_ruptures = len(dstore['ruptures'])
num_events = len(dstore['events'])
events_by_rlz = countby(dstore['events'][()], 'rlz_id')
mult = round(num_events / num_ruptures, 3)
lst = [('Total number of ruptures', num_ruptures),
('Total number of events', num_events),
('Rupture multiplicity', mult),
('Events by rlz', events_by_rlz.values())]
return text_table(lst)
[docs]@view.add('fullreport')
def view_fullreport(token, dstore):
"""
Display an .rst report about the computation
"""
# avoid circular imports
from openquake.calculators.reportwriter import ReportWriter
return ReportWriter(dstore).make_report(show_inputs=False)
[docs]def stats(name, array, *extras):
"""
Returns statistics from an array of numbers.
:param name: a descriptive string
:returns: (name, mean, rel_std, min, max, len) + extras
"""
avg = numpy.mean(array)
std = 'nan' if len(array) == 1 else '%02.0f%%' % (
numpy.std(array) / avg * 100)
max_ = numpy.max(array)
return (name, len(array), avg, std, numpy.min(array), max_) + extras
[docs]@view.add('num_units')
def view_num_units(token, dstore):
"""
Display the number of units by taxonomy
"""
taxo = dstore['assetcol/tagcol/taxonomy'][()]
counts = collections.Counter()
for asset in dstore['assetcol']:
counts[taxo[asset['taxonomy']]] += asset['value-number']
data = sorted(counts.items())
data.append(('*ALL*', sum(d[1] for d in data)))
return numpy.array(data, dt('taxonomy num_units'))
[docs]@view.add('assets_by_site')
def view_assets_by_site(token, dstore):
"""
Display statistical information about the distribution of the assets
"""
taxonomies = dstore['assetcol/tagcol/taxonomy'][()]
assets_by = group_array(dstore['assetcol'].array, 'site_id')
data = ['taxonomy num_sites mean stddev min max num_assets'.split()]
num_assets = AccumDict()
for assets in assets_by.values():
num_assets += {k: [len(v)] for k, v in group_array(
assets, 'taxonomy').items()}
for taxo in sorted(num_assets):
val = numpy.array(num_assets[taxo])
data.append(stats(taxonomies[taxo], val, val.sum()))
if len(num_assets) > 1: # more than one taxonomy, add a summary
n_assets = numpy.array([len(assets) for assets in assets_by.values()])
data.append(stats('*ALL*', n_assets, n_assets.sum()))
return text_table(data)
[docs]@view.add('required_params_per_trt')
def view_required_params_per_trt(token, dstore):
"""
Display the parameters needed by each tectonic region type
"""
full_lt = dstore['full_lt']
tbl = []
for trt in full_lt.trts:
gsims = full_lt.gsim_lt.values[trt]
# adding fake mags to the ContextMaker, needed for table-based GMPEs
cmaker = ContextMaker(trt, gsims, {'imtls': {}, 'mags': ['7.00']})
req = set()
for gsim in cmaker.gsims:
req.update(gsim.requires())
req_params = sorted(req - {'mag'})
gsim_str = ' '.join(map(repr, gsims)).replace('\n', '\\n')
if len(gsim_str) > 160:
gsim_str = ', '.join(repr(gsim).split('\n')[0] for gsim in gsims)
tbl.append((trt, gsim_str, req_params))
return text_table(tbl, header='trt gsims req_params'.split(),
fmt=scientificformat)
[docs]@view.add('task_info')
def view_task_info(token, dstore):
"""
Display statistical information about the tasks performance.
It is possible to get full information about a specific task
with a command like this one, for a classical calculation::
$ oq show task_info:classical
"""
task_info = dstore['task_info']
task_info.refresh()
args = token.split(':')[1:] # called as task_info:task_name
if args:
[task] = args
array = get_array(task_info[()], taskname=task.encode('utf8'))
rduration = array['duration'] / array['weight']
data = util.compose_arrays(rduration, array, 'rduration')
data.sort(order='duration')
return data
data = []
for task, arr in group_array(task_info[()], 'taskname').items():
val = arr['duration']
if len(val):
data.append(stats(task, val, val.max() / val.mean()))
if not data:
return 'Not available'
return numpy.array(
data, dt('operation-duration counts mean stddev min max slowfac'))
[docs]def reduce_srcids(srcids):
s = set()
for srcid in srcids:
s.add(srcid.split(':')[0])
return ' '.join(sorted(s))
[docs]@view.add('task_durations')
def view_task_durations(token, dstore):
"""
Display the raw task durations. Here is an example of usage::
$ oq show task_durations
"""
df = dstore.read_df('source_data')
out = []
for taskno, rows in df.groupby('taskno'):
srcids = reduce_srcids(rows.src_id.to_numpy())
out.append((taskno, rows.ctimes.sum(), rows.weight.sum(), srcids))
arr = numpy.array(out, dt('taskno duration weight srcids'))
arr.sort(order='duration')
return arr
[docs]@view.add('task')
def view_task_hazard(token, dstore):
"""
Display info about a given task. Here are a few examples of usage::
$ oq show task:classical:0 # the fastest task
$ oq show task:classical:-1 # the slowest task
"""
_, name, index = token.split(':')
if 'source_data' not in dstore:
return 'Missing source_data'
data = get_array(dstore['task_info'][()], taskname=encode(name))
if len(data) == 0:
raise RuntimeError('No task_info for %s' % name)
data.sort(order='duration')
rec = data[int(index)]
taskno = rec['task_no']
sdata = dstore.read_df('source_data', 'taskno').loc[taskno]
num_ruptures = sdata.nrupts.sum()
eff_sites = sdata.nsites.sum()
msg = ('taskno={:_d}, fragments={:_d}, num_ruptures={:_d}, '
'eff_sites={:_d}, weight={:.1f}, duration={:.1f}s').format(
taskno, len(sdata), num_ruptures, eff_sites,
rec['weight'], rec['duration'])
return msg
[docs]@view.add('source_data')
def view_source_data(token, dstore):
"""
Display info about a given task. Here is an example::
$ oq show source_data:42
"""
if ':' not in token:
return dstore.read_df(token, 'src_id')
_, taskno = token.split(':')
taskno = int(taskno)
if 'source_data' not in dstore:
return 'Missing source_data'
df = dstore.read_df('source_data', 'src_id', sel={'taskno': taskno})
del df['taskno']
df['slowrate'] = df['ctimes'] / df['weight']
return df.sort_values('ctimes')
[docs]@view.add('task_ebrisk')
def view_task_ebrisk(token, dstore):
"""
Display info about ebrisk tasks:
$ oq show task_ebrisk:-1 # the slowest task
"""
idx = int(token.split(':')[1])
task_info = get_array(dstore['task_info'][()], taskname=b'ebrisk')
task_info.sort(order='duration')
info = task_info[idx]
times = get_array(dstore['gmf_info'][()], task_no=info['task_no'])
extra = times[['nsites', 'gmfbytes', 'dt']]
ds = dstore.parent if dstore.parent else dstore
rups = ds['ruptures']['id', 'code', 'n_occ', 'mag'][times['rup_id']]
codeset = set('code_%d' % code for code in numpy.unique(rups['code']))
tbl = text_table(util.compose_arrays(rups, extra))
codes = ['%s: %s' % it for it in ds.getitem('ruptures').attrs.items()
if it[0] in codeset]
msg = '%s\n%s\nHazard time for task %d: %d of %d s, ' % (
tbl, '\n'.join(codes), info['task_no'], extra['dt'].sum(),
info['duration'])
msg += 'gmfbytes=%s, w=%d' % (
humansize(extra['gmfbytes'].sum()),
(rups['n_occ'] * extra['nsites']).sum())
return msg
[docs]@view.add('global_hazard')
def view_global_hazard(token, dstore):
"""
Display the global hazard for the calculation. This is used for
debugging purposes when comparing the results of two
calculations.
"""
imtls = dstore['oqparam'].imtls
arr = dstore.sel('hcurves-stats', stat='mean') # shape N, S, M, L
res = tuple(arr.mean(axis=(0, 1, 3))) # length M
return numpy.array([res], dt(imtls))
[docs]@view.add('global_hmaps')
def view_global_hmaps(token, dstore):
"""
Display the global hazard maps for the calculation. They are
used for debugging purposes when comparing the results of two
calculations. They are the mean over the sites of the mean hazard
maps.
"""
oq = dstore['oqparam']
dt = numpy.dtype([('%s-%s' % (imt, poe), F32)
for imt in oq.imtls for poe in oq.poes])
hmaps = dstore.sel('hmaps-stats', stat='mean')
means = hmaps.mean(axis=(0, 1)) # shape M, P
return numpy.array([tuple(means.flatten())], dt)
[docs]@view.add('global_gmfs')
def view_global_gmfs(token, dstore):
"""
Display GMFs on the first IMT averaged on everything for debugging purposes
"""
imtls = dstore['oqparam'].imtls
row = [dstore[f'gmf_data/gmv_{m}'][:].mean(axis=0)
for m in range(len(imtls))]
return text_table([row], header=imtls)
[docs]@view.add('gmf')
def view_gmf(token, dstore):
"""
Display a mean gmf for debugging purposes
"""
df = dstore.read_df('gmf_data', 'sid')
gmf = df.groupby(df.index).mean()
return str(gmf)
[docs]def binning_error(values, eids, nbins=10):
"""
:param values: E values
:param eids: E integer event indices
:returns: std/mean for the sums of the values
Group the values in nbins depending on the eids and returns the
variability of the sums relative to the mean.
"""
df = pandas.DataFrame({'val': values}, eids)
res = df.groupby(eids % nbins).val.sum()
return res.std() / res.mean()
[docs]@view.add('extreme_gmvs')
def view_extreme_gmvs(token, dstore):
"""
Display table of extreme GMVs with fields (eid, gmv_0, sid, rlz. rup)
"""
if ':' in token:
maxgmv = float(token.split(':')[1])
else:
maxgmv = 5 # PGA=5g is default value defining extreme GMVs
imt0 = list(dstore['oqparam'].imtls)[0]
eids = dstore['gmf_data/eid'][:]
gmvs = dstore['gmf_data/gmv_0'][:]
sids = dstore['gmf_data/sid'][:]
msg = ''
err = binning_error(gmvs, eids)
if err > .05:
msg += ('Your results are expected to have a large dependency '
'from the rupture seed: %d%%' % (err * 100))
if imt0 == 'PGA':
rups = dstore['ruptures'][:]
rupdict = dict(zip(rups['id'], rups))
gmpe = GmpeExtractor(dstore)
df = pandas.DataFrame({'gmv_0': gmvs, 'sid': sids}, eids)
extreme_df = df[df.gmv_0 > maxgmv].rename(columns={'gmv_0': imt0})
if len(extreme_df) == 0:
return 'No PGAs over %s g found' % maxgmv
ev = dstore['events'][()][extreme_df.index]
# extreme_df['rlz'] = ev['rlz_id']
extreme_df['rup'] = ev['rup_id']
extreme_df['mag'] = [rupdict[rupid]['mag'] for rupid in ev['rup_id']]
hypos = numpy.array([rupdict[rupid]['hypo'] for rupid in ev['rup_id']])
# extreme_df['lon'] = numpy.round(hypos[:, 0])
# extreme_df['lat'] = numpy.round(hypos[:, 1])
extreme_df['dep'] = numpy.round(hypos[:, 2])
trt_smrs = [rupdict[rupid]['trt_smr'] for rupid in ev['rup_id']]
extreme_df['gmpe'] = gmpe.extract(trt_smrs, ev['rlz_id'])
exdf = extreme_df.sort_values(imt0).groupby('sid').head(1)
if len(exdf):
msg += ('\nThere are extreme GMVs, run `oq show extreme_gmvs:%s`'
'to see them' % maxgmv)
if ':' in token:
msg = str(exdf.set_index('rup'))
return msg
return msg + '\nCould not extract extreme GMVs for ' + imt0
[docs]@view.add('mean_rates')
def view_mean_rates(token, dstore):
"""
Display mean hazard rates for the first site
"""
oq = dstore['oqparam']
assert oq.use_rates
poes = dstore.sel('hcurves-stats', site_id=0, stat='mean')[0, 0] # NRML1
rates = numpy.zeros(poes.shape[1], dt(oq.imtls))
for m, imt in enumerate(oq.imtls):
rates[imt] = calc.disagg.to_rates(poes[m])
return rates
[docs]@view.add('mean_disagg')
def view_mean_disagg(token, dstore):
"""
Display mean quantities for the disaggregation. Useful for checking
differences between two calculations.
"""
N, M, P = dstore['hmap3'].shape
tbl = []
kd = {key: dset[:] for key, dset in sorted(dstore['disagg-rlzs'].items())}
oq = dstore['oqparam']
for s in range(N):
for m, imt in enumerate(oq.imtls):
for p in range(P):
row = ['%s-sid-%d-poe-%s' % (imt, s, p)]
for k, d in kd.items():
row.append(d[s, ..., m, p, :].mean())
tbl.append(tuple(row))
return numpy.array(sorted(tbl), dt(['key'] + list(kd)))
[docs]@view.add('disagg')
def view_disagg(token, dstore):
"""
Example: $ oq show disagg:Mag
Returns a table poe, imt, mag, contribution for the first site
"""
kind = token.split(':')[1]
assert kind in ('Mag', 'Dist', 'TRT'), kind
site_id = 0
if 'disagg-stats' in dstore:
data = dstore['disagg-stats/' + kind][site_id, ..., 0] # (:, M, P)
else:
data = dstore['disagg-rlzs/' + kind][site_id, ..., 0] # (:, M, P)
Ma, M, P = data.shape
oq = dstore['oqparam']
imts = list(oq.imtls)
dtlist = [('poe', float), ('imt', (numpy.bytes_, 10)),
(kind.lower() + 'bin', int), ('prob', float)]
lst = []
for p, m, ma in itertools.product(range(P), range(M), range(Ma)):
lst.append((oq.poes[p], imts[m], ma, data[ma, m, p]))
return numpy.array(lst, dtlist)
[docs]@view.add('bad_ruptures')
def view_bad_ruptures(token, dstore):
"""
Display the ruptures degenerating to a point
"""
data = dstore['ruptures']['id', 'code', 'mag',
'minlon', 'maxlon', 'minlat', 'maxlat']
bad = data[numpy.logical_and(data['minlon'] == data['maxlon'],
data['minlat'] == data['maxlat'])]
return bad
Source = collections.namedtuple(
'Source', 'source_id code num_ruptures checksum')
[docs]@view.add('gmvs_to_hazard')
def view_gmvs_to_hazard(token, dstore):
"""
Show the number of GMFs over the highest IML
"""
args = token.split(':')[1:] # called as view_gmvs_to_hazard:sid
if not args:
sid = 0
elif len(args) == 1: # only sid specified
sid = int(args[0])
assert sid in dstore['sitecol'].sids
oq = dstore['oqparam']
num_ses = oq.ses_per_logic_tree_path
data = dstore.read_df('gmf_data', 'sid').loc[sid]
tbl = []
for imti, (imt, imls) in enumerate(oq.imtls.items()):
gmv = data['gmv_%d' % imti].to_numpy()
for iml in imls:
# same algorithm as in _gmvs_to_haz_curve
exceeding = numpy.sum(gmv >= iml)
poe = 1 - numpy.exp(- exceeding / num_ses)
tbl.append((sid, imt, iml, exceeding, poe))
return numpy.array(tbl, dt('sid imt iml num_exceeding poe'))
[docs]@view.add('gmvs')
def view_gmvs(token, dstore):
"""
Show the GMVs on a given site ID
"""
sid = int(token.split(':')[1]) # called as view_gmvs:sid
assert sid in dstore['sitecol'].sids
data = dstore.read_df('gmf_data', 'sid')
return data.loc[sid]
[docs]@view.add('events_by_mag')
def view_events_by_mag(token, dstore):
"""
Show how many events there are for each magnitude
"""
rups = dstore['ruptures'][()]
num_evs = fast_agg(dstore['events']['rup_id'])
counts = {}
for mag, grp in group_array(rups, 'mag').items():
counts[mag] = sum(num_evs[rup_id] for rup_id in grp['id'])
return numpy.array(list(counts.items()), dt('mag num_events'))
[docs]@view.add('ebrups_by_mag')
def view_ebrups_by_mag(token, dstore):
"""
Show how many event based ruptures there are for each magnitude
"""
mags = dstore['ruptures']['mag']
uniq, counts = numpy.unique(mags, return_counts=True)
return text_table(zip(uniq, counts), ['mag', 'num_ruptures'])
[docs]@view.add('maximum_intensity')
def view_maximum_intensity(token, dstore):
"""
Show intensities at minimum and maximum distance for the highest magnitude
"""
effect = extract(dstore, 'effect')
data = zip(dstore['full_lt'].trts, effect[-1, -1], effect[-1, 0])
return text_table(data, ['trt', 'intensity1', 'intensity2'])
[docs]@view.add('extreme_sites')
def view_extreme(token, dstore):
"""
Show sites where the mean hazard map reaches maximum values
"""
mean = dstore.sel('hmaps-stats', stat='mean')[:, 0, 0, -1] # shape N1MP
site_ids, = numpy.where(mean == mean.max())
return dstore['sitecol'][site_ids]
[docs]@view.add('zero_losses')
def view_zero_losses(token, dstore):
"""
Sanity check on avg_losses and avg_gmf
"""
R = len(dstore['weights'])
oq = dstore['oqparam']
avg_gmf = dstore['avg_gmf'][0]
asset_df = dstore.read_df('assetcol/array', 'site_id')
for col in asset_df.columns:
if not col.startswith('value-'):
del asset_df[col]
values_df = asset_df.groupby(asset_df.index).sum()
avglosses = dstore['avg_losses-rlzs'][:].sum(axis=1) / R # shape (A, L)
dic = dict(site_id=dstore['assetcol']['site_id'])
for lti, lname in enumerate(oq.loss_types):
dic[lname] = avglosses[:, lti]
losses_df = pandas.DataFrame(dic).groupby('site_id').sum()
sids = losses_df.index.to_numpy()
avg_gmf = avg_gmf[sids]
nonzero_gmf = (avg_gmf > oq.min_iml).any(axis=1)
nonzero_losses = (losses_df > 0).to_numpy().any(axis=1)
bad, = numpy.where(nonzero_gmf != nonzero_losses)
# this happens in scenario_risk/case_shakemap and case_3
msg = 'Site #%d is suspicious:\navg_gmf=%s\navg_loss=%s\nvalues=%s'
for idx in bad:
sid = sids[idx]
logging.warning(msg, sid, dict(zip(oq.all_imts(), avg_gmf[sid])),
_get(losses_df, sid), _get(values_df, sid))
return bad
def _get(df, sid):
return df.loc[sid].to_dict()
[docs]@view.add('gsim_for_event')
def view_gsim_for_event(token, dstore):
"""
Display the GSIM used when computing the GMF for the given event:
$ oq show gsim_for_event:123 -1
[BooreAtkinson2008]
"""
eid = int(token.split(':')[1])
full_lt = dstore['full_lt']
rup_id, rlz_id = dstore['events'][eid][['rup_id', 'rlz_id']]
trt_smr = dict(dstore['ruptures'][:][['id', 'trt_smr']])
trti = trt_smr[rup_id] // 2**24
gsim = full_lt.get_realizations()[rlz_id].gsim_rlz.value[trti]
return gsim
[docs]@view.add('calc_risk')
def view_calc_risk(token, dstore):
"""
Compute the risk_by_event table starting from GMFs
"""
# avoid a mysterious circular import only on macos
from openquake.calculators import event_based_risk as ebr
_, event_id = token.split(':')
oq = dstore['oqparam']
assetcol = dstore['assetcol']
ebr.set_oqparam(oq, assetcol, dstore)
crmodel = riskmodels.CompositeRiskModel.read(dstore, oq)
gmf_df = dstore.read_df('gmf_data')
gmf_df = gmf_df[gmf_df.eid == int(event_id)]
ws = dstore['weights']
rlz_id = dstore['events']['rlz_id']
aggids, _ = assetcol.build_aggids(
oq.aggregate_by, oq.max_aggregations)
agg_keys = numpy.concatenate(
[dstore['agg_keys'][:], numpy.array([b'total'])])
ARK = (oq.A, len(ws), oq.K)
if oq.ignore_master_seed or oq.ignore_covs:
rng = None
else:
rng = ebr.MultiEventRNG(oq.master_seed, gmf_df.eid.unique(),
int(oq.asset_correlation))
mon = Monitor()
outs = [] # ebr.gen_outputs(gmf_df, crmodel, rng, mon)
sids = gmf_df.sid.to_numpy()
assets = assetcol.to_dframe()
for taxo in assets.taxonomy.unique():
adf = assets[assets.taxonomy == taxo]
df = gmf_df[numpy.isin(sids, adf.site_id.unique())]
if len(df) == 0: # common enough
continue
out = crmodel.get_output(adf, df, oq._sec_losses, rng)
outs.append(out)
avg, alt = ebr.aggreg(outs, crmodel, ARK, aggids, rlz_id, oq.ideduc, mon)
del alt['event_id']
del alt['variance']
alt['type'] = LOSSTYPE[alt.loss_id]
del alt['loss_id']
alt['agg_keys'] = decode(agg_keys[alt.agg_id])
del alt['agg_id']
return alt
[docs]@view.add('event_loss_table')
def view_event_loss_table(token, dstore):
"""
Display the top 20 losses of the event loss table for the first loss type
$ oq show event_loss_table
"""
K = dstore['risk_by_event'].attrs.get('K', 0)
df = dstore.read_df('risk_by_event', 'event_id',
dict(agg_id=K, loss_id=0))
df['std'] = numpy.sqrt(df.variance)
df.sort_values('loss', ascending=False, inplace=True)
del df['agg_id']
del df['loss_id']
del df['variance']
return df[:20]
[docs]@view.add('risk_by_event')
def view_risk_by_event(token, dstore):
"""There are two possibilities:
$ oq show risk_by_event:<loss_type>
$ oq show risk_by_event:<event_id>
In both cases displays the top 30 losses of the aggregate loss
table as a TSV, for all events or only the given event.
"""
_, ltype = token.split(':')
try:
loss_id = LOSSID[ltype]
df = dstore.read_df('risk_by_event', sel=dict(loss_id=loss_id))
del df['loss_id']
except KeyError:
event_id = int(ltype)
df = dstore.read_df('risk_by_event', sel=dict(event_id=event_id))
df['ltype'] = LOSSTYPE[df.loss_id.to_numpy()]
del df['loss_id']
del df['event_id']
del df['variance']
df = df[df.agg_id == df.agg_id.max()].sort_values('loss', ascending=False)
del df['agg_id']
out = io.StringIO()
df[:30].to_csv(out, sep='\t', index=False, float_format='%.1f',
lineterminator='\r\n')
return out.getvalue()
[docs]@view.add('risk_by_rup')
def view_risk_by_rup(token, dstore):
"""
Display the top 30 aggregate losses by rupture ID. Usage:
$ oq show risk_by_rup
"""
rbr = dstore.read_df('loss_by_rupture', 'rup_id')
info = dstore.read_df('gmf_data/rup_info', 'rup_id')
rdf = dstore.read_df('ruptures', 'id')
df = rbr.join(rdf).join(info)[
['loss', 'mag', 'n_occ', 'hypo_0', 'hypo_1', 'hypo_2', 'rrup']]
for field in df.columns:
if field not in ('mag', 'n_occ'):
df[field] = numpy.round(F64(df[field]), 1)
return df[:30]
[docs]@view.add('loss_ids')
def view_loss_ids(token, dstore):
"""
Displays the loss IDs corresponding to nonzero losses
"""
K = dstore['risk_by_event'].attrs.get('K', 0)
df = dstore.read_df('risk_by_event', 'event_id', dict(agg_id=K))
loss_ids = df.loss_id.unique()
arr = numpy.zeros(len(loss_ids), dt('loss_type loss_id'))
for i, loss_id in enumerate(loss_ids):
arr[i]['loss_type'] = LOSSTYPE[loss_id]
arr[i]['loss_id'] = loss_id
return arr
[docs]@view.add('delta_loss')
def view_delta_loss(token, dstore):
"""
Estimate the stocastic error on the loss curve by splitting the events
in odd and even. Example:
$ oq show delta_loss # default structural
"""
if ':' in token:
_, li = token.split(':')
li = int(li)
else:
li = 2 # structural
oq = dstore['oqparam']
loss_type = LOSSTYPE[li]
K = dstore['risk_by_event'].attrs.get('K', 0)
df = dstore.read_df('risk_by_event', 'event_id',
dict(agg_id=K, loss_id=li))
if len(df) == 0: # for instance no fatalities
return {'delta': numpy.zeros(1),
'loss_types': view_loss_ids(token, dstore),
'error': f"There are no relevant events for {loss_type=}"}
if oq.calculation_mode == 'scenario_risk':
if oq.number_of_ground_motion_fields == 1:
return {'delta': numpy.zeros(1),
'error': 'number_of_ground_motion_fields=1'}
R = len(dstore['weights'])
df['rlz_id'] = dstore['events']['rlz_id'][df.index]
c0, c1 = numpy.zeros(R), numpy.zeros(R)
for r in range(R):
loss = df.loss[df.rlz_id == r]
c0[r] = loss[::2].mean() # even
c1[r] = loss[1::2].mean() # odd
dic = dict(even=c0, odd=c1, delta=numpy.abs(c0 - c1) / (c0 + c1))
return pandas.DataFrame(dic)
num_events = len(dstore['events'])
efftime = oq.investigation_time * oq.ses_per_logic_tree_path * len(
dstore['weights'])
periods = return_periods(efftime, num_events)[1:-1]
losses0 = df.loss[df.index % 2 == 0]
losses1 = df.loss.loc[df.index % 2 == 1]
c0 = losses_by_period(losses1, periods, len(losses1), efftime / 2)['curve']
c1 = losses_by_period(losses0, periods, len(losses0), efftime / 2)['curve']
ok = (c0 != 0) & (c1 != 0)
c0 = c0[ok]
c1 = c1[ok]
res = losses_by_period(df['loss'], periods, num_events, efftime)
losses = res['curve'][ok]
dic = dict(loss=losses, even=c0, odd=c1,
delta=numpy.abs(c0 - c1) / (c0 + c1))
return pandas.DataFrame(dic, periods[ok])
[docs]def to_str(arr):
return ' '.join(map(str, numpy.unique(arr)))
[docs]@view.add('composite_source_model')
def view_composite_source_model(token, dstore):
"""
Show the structure of the CompositeSourceModel in terms of grp_id
"""
lst = []
full_lt = dstore['full_lt'].init()
for grp_id, df in dstore.read_df('source_info').groupby('grp_id'):
lst.append((str(grp_id), full_lt.trts[df.trti.unique()[0]], len(df)))
return numpy.array(lst, dt('grp_id trt num_sources'))
[docs]@view.add('branches')
def view_branches(token, dstore):
"""
Show info about the branches in the logic tree
"""
full_lt = dstore['full_lt']
smlt = full_lt.source_model_lt
gslt = full_lt.gsim_lt
tbl = []
for k, v in full_lt.source_model_lt.shortener.items():
tbl.append((k, v, smlt.branches[k].value))
gsims = sum(gslt.values.values(), [])
if len(gslt.shortener) < len(gsims): # possible for engine < 3.13
raise ValueError(
'There were duplicated branch IDs in the gsim logic tree %s'
% gslt.filename)
for g, (k, v) in enumerate(gslt.shortener.items()):
tbl.append((k, v, str(gsims[g]).replace('\n', r'\n')))
return numpy.array(tbl, dt('branch_id abbrev uvalue'))
[docs]@view.add('rlz')
def view_rlz(token, dstore):
"""
Show info about a given realization in the logic tree
Example:
$ oq show rlz:0 -1
"""
_, rlz_id = token.split(':')
full_lt = dstore['full_lt']
rlz = full_lt.get_realizations()[int(rlz_id)]
smlt = full_lt.source_model_lt
gslt = full_lt.gsim_lt
tbl = []
for bset, brid in zip(smlt.branchsets, rlz.sm_lt_path):
tbl.append((bset.uncertainty_type, smlt.branches[brid].value))
for trt, value in zip(sorted(gslt.bsetdict), rlz.gsim_rlz.value):
tbl.append((trt, value))
return numpy.array(tbl, dt('uncertainty_type uvalue'))
[docs]@view.add('branchsets')
def view_branchsets(token, dstore):
"""
Show the branchsets in the logic tree
"""
flt = dstore['full_lt']
clt = logictree.compose(flt.source_model_lt, flt.gsim_lt)
return text_table(enumerate(map(repr, clt.branchsets)),
header=['bsno', 'bset'], ext='org')
[docs]@view.add('rupture')
def view_rupture(token, dstore):
"""
Show a rupture with its geometry
"""
rup_id = int(token.split(':')[1])
return get_ebrupture(dstore, rup_id)
[docs]@view.add('event_rates')
def view_event_rates(token, dstore):
"""
Show the number of events per realization multiplied by risk_time/eff_time
"""
oq = dstore['oqparam']
R = dstore['full_lt'].get_num_paths()
if oq.calculation_mode != 'event_based_damage':
return numpy.ones(R)
time_ratio = (oq.risk_investigation_time or oq.investigation_time) / (
oq.ses_per_logic_tree_path * oq.investigation_time)
if oq.collect_rlzs:
return numpy.array([len(dstore['events']) * time_ratio / R])
else:
rlzs = dstore['events']['rlz_id']
return numpy.bincount(rlzs, minlength=R) * time_ratio
[docs]def tup2str(tups):
return ['_'.join(map(str, t)) for t in tups]
[docs]@view.add('sum')
def view_sum(token, dstore):
"""
Show the sum of an array of shape (A, R, L, ...) on the first axis
"""
_, arrayname = token.split(':') # called as sum:damages-rlzs
dset = dstore[arrayname]
A, R, L, *D = dset.shape
cols = ['RL'] + tup2str(itertools.product(*[range(d) for d in D]))
arr = dset[:].sum(axis=0) # shape R, L, *D
z = numpy.zeros(R * L, dt(cols))
for r, ar in enumerate(arr):
for li, a in enumerate(ar):
a = a.flatten()
for c, col in enumerate(cols):
z[r * L + li][col] = a[c-1] if c > 0 else (r, li)
return z
[docs]@view.add('agg_id')
def view_agg_id(token, dstore):
"""
Show the available aggregations
"""
[aggby] = dstore['oqparam'].aggregate_by
keys = [key.decode('utf8').split(',') for key in dstore['agg_keys'][:]]
keys = numpy.array(keys) # shape (N, A)
dic = {aggkey: keys[:, a] for a, aggkey in enumerate(aggby)}
df = pandas.DataFrame(dic)
df.index.name = 'agg_id'
return df
[docs]@view.add('mean_perils')
def view_mean_perils(token, dstore):
"""
For instance `oq show mean_perils`
"""
oq = dstore['oqparam']
pdcols = dstore.get_attr('gmf_data', '__pdcolumns__').split()
perils = [col for col in pdcols[2:] if not col.startswith('gmv_')]
N = len(dstore['sitecol/sids'])
sid = dstore['gmf_data/sid'][:]
out = numpy.zeros(N, [(per, float) for per in perils])
if oq.number_of_logic_tree_samples:
E = len(dstore['events'])
for peril in perils:
out[peril] = fast_agg(sid, dstore['gmf_data/' + peril][:]) / E
else:
rlz_weights = dstore['weights'][:]
ev_weights = rlz_weights[dstore['events']['rlz_id']]
totw = ev_weights.sum() # num_gmfs
for peril in perils:
data = dstore['gmf_data/' + peril][:]
weights = ev_weights[dstore['gmf_data/eid'][:]]
out[peril] = fast_agg(sid, data * weights) / totw
return out
[docs]@view.add('pmaps_size')
def view_pmaps_size(token, dstore):
return humansize(get_pmaps_gb(dstore)[0])
[docs]@view.add('rup_stats')
def view_rup_stats(token, dstore):
"""
Show the statistics of event based ruptures
"""
rups = dstore['ruptures'][:]
out = [stats(f, rups[f]) for f in 'mag n_occ'.split()]
return numpy.array(out, dt('kind counts mean stddev min max'))
[docs]@view.add('rup')
def view_rup(token, dstore):
"""
Show the ruptures (contexts) generated by a given source
"""
try:
source_id = token.split(':', 1)[1]
except IndexError:
print('Use the syntax oq show rup:<source_id>')
return []
source_ids = decode(dstore['source_info']['source_id'])
src_id = source_ids.index(source_id)
df = dstore.read_df('rup', sel=dict(src_id=src_id))
df = df.sort_values(['rup_id', 'sids']).set_index('rup_id')
return df
[docs]@view.add('usgs_rupture')
def view_usgs_rupture(token, dstore):
"""
Show the parameters of a rupture downloaded from the USGS site.
$ oq show usgs_rupture:us70006sj8
{'lon': 74.628, 'lat': 35.5909, 'dep': 13.8, 'mag': 5.6, 'rake': 0.0}
"""
try:
usgs_id = token.split(':', 1)[1]
except IndexError:
return 'Example: oq show usgs_rupture:us70006sj8'
return download_rupture_dict(usgs_id)
[docs]@view.add('collapsible')
def view_collapsible(token, dstore):
"""
Show how much the ruptures are collapsed for each site
"""
if ':' in token:
collapse_level = int(token.split(':')[1])
else:
collapse_level = 0
dist_types = [dt for dt in dstore['rup'] if dt in KNOWN_DISTANCES]
vs30 = dstore['sitecol'].vs30
ctx_df = dstore.read_df('rup')
ctx_df['vs30'] = vs30[ctx_df.sids]
has_vs30 = len(numpy.unique(vs30)) > 1
c = Collapser(collapse_level, dist_types, has_vs30)
ctx_df['mdbin'] = c.calc_mdbin(ctx_df)
print('cfactor = %d/%d' % (len(ctx_df), len(ctx_df['mdbin'].unique())))
out = []
for sid, df in ctx_df.groupby('sids'):
n, u = len(df), len(df.mdbin.unique())
out.append((sid, u, n, n / u))
return numpy.array(out, dt('site_id eff_rups num_rups cfactor'))
# tested in oq-risk-tests etna
[docs]@view.add('event_based_mfd')
def view_event_based_mfd(token, dstore):
"""
Compare n_occ/eff_time with occurrence_rate
"""
dic = extract(dstore, 'event_based_mfd?').to_dict()
del dic['extra']
return pandas.DataFrame(dic).set_index('mag')
# used in the AELO project
[docs]@view.add('relevant_sources')
def view_relevant_sources(token, dstore):
"""
Returns a table with the sources contributing more than 10%
of the highest source.
"""
imt = token.split(':')[1]
kw = dstore['oqparam'].postproc_args
[iml] = kw['imls_by_sid']['0']
aw = extract(dstore, f'mean_rates_by_src?imt={imt}&iml={iml}')
rates = aw.array['rate'] # for each source in decreasing order
return aw.array[rates > .1 * rates[0]]
[docs]def shorten(lst):
"""
Shorten a list of strings
"""
if len(lst) <= 7:
return lst
return lst[:3] + ['...'] + lst[-3:]
[docs]@view.add('sources_branches')
def view_sources_branches(token, dstore):
"""
Returns a table with the sources in the logic tree by branches
"""
sd = dstore['full_lt/source_data'][:]
acc = AccumDict(accum=[])
for src, trt in numpy.unique(sd[['source', 'trt']]):
brs = b' '.join(sorted(sd[sd['source'] == src]['branch']))
acc[brs, trt].append(src.decode('utf8'))
out = [(t, ' '.join(shorten(s)), b)
for ((b, t), s) in sorted(acc.items())]
return numpy.array(sorted(out), dt('trt sources branches'))
[docs]@view.add('MPL')
def view_MPL(token, dstore):
"""
Maximum Probable Loss at a given return period
"""
rp = int(token.split(':')[1])
K = dstore['risk_by_event'].attrs['K']
ltypes = list(dstore['agg_curves-stats'])
out = numpy.zeros(1, [(lt, float) for lt in ltypes])
for ltype in ltypes:
# shape (K+1, S, P)
arr = dstore.sel(f'agg_curves-stats/{ltype}',
stat='mean', agg_id=K, return_period=rp)
out[ltype] = arr
return out
def _drate(df, imt, src):
return df[(df.imt == imt) & (df.source_id == src)].value.sum()
def _irate(df, imt, src, iml, imls):
subdf = df[(df.imt == imt) & (df.src_id == src)]
interp = numpy.interp(numpy.log(iml), numpy.log(imls[subdf.lvl]),
numpy.log(subdf.value))
return numpy.exp(interp)
# used only in AELO calculations
# NB: in presence of !-sources the comparison makes no sense
[docs]@view.add('compare_disagg_rates')
def compare_disagg_rates(token, dstore):
oq = dstore['oqparam']
aw = dstore['mean_disagg_by_src']
mrs = dstore['mean_rates_by_src']
mean_rates_df = mrs.to_dframe()
iml_disagg = dict(zip(aw.imt, aw.iml))
mean_disagg_df = aw.to_dframe()
imts_out, srcs_out, drates, irates = [], [], [], []
for imt, iml in iml_disagg.items():
imls = oq.imtls[imt]
srcs = mean_disagg_df[mean_disagg_df.imt == imt].source_id
for src in set(srcs):
imts_out.append(imt)
srcs_out.append(src)
drates.append(_drate(mean_disagg_df, imt, src))
irates.append(_irate(mean_rates_df, imt, src, iml, imls))
return pandas.DataFrame({'imt': imts_out, 'src': srcs_out,
'disagg_rate': drates,
'interp_rate': irates}
).sort_values(['imt', 'src'])
[docs]@view.add('gh3')
def view_gh3(token, dstore):
sitecol = dstore['sitecol']
gh3 = geo.utils.geohash3(sitecol.lons, sitecol.lats)
uni, cnt = numpy.unique(gh3, return_counts=True)
# print(sorted(cnt))
return numpy.array([stats('gh3', cnt)],
dt('kind counts mean stddev min max'))
[docs]@view.add('sites_by_country')
def view_sites_by_country(token, dstore):
"""
Returns a table with the number of sites per country. The countries
are defined as in the file geoBoundariesCGAZ_ADM0.shp
"""
return dstore['sitecol'].by_country()
[docs]@view.add('aggrisk')
def view_aggrisk(token, dstore):
"""
Returns a table with the aggregate risk by realization and loss type
"""
gsim_lt = dstore['full_lt/gsim_lt']
df = dstore.read_df('aggrisk', sel={'agg_id': 0})
dt = [('gsim', vstr), ('weight', float)] + [
(lt, float) for lt in LOSSTYPE[df.loss_id.unique()]]
rlzs = list(gsim_lt)
AVG = len(rlzs)
arr = numpy.zeros(AVG + 1, dt)
for r, rlz in enumerate(rlzs):
arr[r]['gsim'] = repr(repr(rlz.value[0]))
arr[r]['weight'] = rlz.weight
for r, loss_id, loss in zip(df.rlz_id, df.loss_id, df.loss):
rlz = rlzs[r]
lt = LOSSTYPE[loss_id]
arr[r][lt] = loss
arr[AVG][lt] += loss * rlz.weight
arr[AVG]['gsim'] = 'Average'
arr[AVG]['weight'] = 1
return arr
