openquake.commonlib package#

openquake.commonlib.datastore module#

class openquake.commonlib.datastore.DataStore(path, ppath=None, mode=None)[source]#

Bases: MutableMapping

DataStore class to store the inputs/outputs of a calculation on the filesystem.

Here is a minimal example of usage:

>>> dstore, log = build_dstore_log()
>>> with dstore, log:
...     dstore['example'] = 42
...     print(dstore['example'][()])
42

When reading the items, the DataStore will return a generator. The items will be ordered lexicographically according to their name.

There is a serialization protocol to store objects in the datastore. An object is serializable if it has a method __toh5__ returning an array and a dictionary, and a method __fromh5__ taking an array and a dictionary and populating the object. For an example of use see openquake.hazardlib.site.SiteCollection.

build_fname(prefix, postfix, fmt, export_dir=None)[source]#

Build a file name from a realization, by using prefix and extension.

Parameters:

prefix – the prefix to use
postfix – the postfix to use (can be a realization object)
fmt – the extension (‘csv’, ‘xml’, etc)
export_dir – export directory (if None use .export_dir)

Returns:

relative pathname including the extension

calc_id = None#

clear()[source]#: Remove the datastore from the file system

close()[source]#: Close the underlying hdf5 file

closed = 0#

create_df(key, nametypes, compression=None, **kw)[source]#

Create a HDF5 datagroup readable as a pandas DataFrame

Parameters:

key – name of the dataset
nametypes – list of pairs (name, dtype) or (name, array) or DataFrame
compression – the kind of HDF5 compression to use
kw – extra attributes to store

create_dset(key, dtype, shape=(None,), compression=None, fillvalue=0, attrs=None)[source]#

Create a one-dimensional HDF5 dataset.

Parameters:

key – name of the dataset
dtype – dtype of the dataset (usually composite)
shape – shape of the dataset, possibly extendable
compression – the kind of HDF5 compression to use
attrs – dictionary of attributes of the dataset

Returns:

a HDF5 dataset

property export_dir#: Return the underlying export directory

export_path(relname, export_dir=None)[source]#

Return the path of the exported file by adding the export_dir in front, the calculation ID at the end.

Parameters:

relname – relative file name
export_dir – export directory (if None use .export_dir)

flush()[source]#: Flush the underlying hdf5 file

get(key, default)[source]#

Returns:: the value associated to the datastore key, or the default

get_attr(key, name, default=None)[source]#

Parameters:

key – dataset path
name – name of the attribute
default – value to return if the attribute is missing

get_attrs(key)[source]#

Parameters:: key – dataset path
Returns:: dictionary of attributes for that path

get_file(key)[source]#

Returns:: a BytesIO object

getitem(name)[source]#: Return a dataset by using h5py.File.__getitem__

getsize(key='/')[source]#: Return the size in byte of the output associated to the given key. If no key is given, returns the total size of all files.

job = None#

property metadata#

Returns:: datastore metadata version, date, checksum as a dictionary

open(mode)[source]#: Open the underlying .hdf5 file

opened = 0#

read_df(key, index=None, sel=(), slc=slice(None, None, None))[source]#

Parameters:

key – name of the structured dataset
index – pandas index (or multi-index), possibly None
sel – dictionary used to select subsets of the dataset
slc – slice object to extract a slice of the dataset

Returns:

pandas DataFrame associated to the dataset

read_unique(key, field)[source]#

Parameters:

key – key to a dataset containing a structured array
field – a field in the structured array

Returns:

sorted, unique values

Works with chunks of 1M records

retrieve_files(prefix='input')[source]#

Yields:: pairs (relative path, data)

sel(key, **kw)[source]#: Select a dataset with shape_descr. For instance dstore.sel(‘hcurves’, imt=’PGA’, sid=2)

set_attrs(key, **kw)[source]#: Set the HDF5 attributes of the given key

set_shape_descr(key, **kw)[source]#: Set shape attributes

store_files(fnames, where='input/')[source]#

Parameters:: fnames – a set of full pathnames

swmr_on()[source]#: Enable the SWMR mode on the underlying HDF5 file

openquake.commonlib.datastore.build_dstore_log(description='custom calculation', parent=(), ini=None)[source]#

Returns:: DataStore instance associated to the .calc_id

openquake.commonlib.datastore.extract_calc_id_datadir(filename)[source]#

Extract the calculation ID from the given filename or integer:

>>> id, datadir = extract_calc_id_datadir('/mnt/ssd/oqdata/calc_25.hdf5')
>>> id
25
>>> path_items = os.path.normpath(datadir).split(os.sep)[1:]
>>> print(path_items)
['mnt', 'ssd', 'oqdata']

>>> wrong_name = '/mnt/ssd/oqdata/wrong_name.hdf5'
>>> try:
...     extract_calc_id_datadir(wrong_name)
... except ValueError as exc:
...     assert 'Cannot extract calc_id from' in str(exc)
...     assert 'wrong_name.hdf5' in str(exc)

openquake.commonlib.datastore.hdf5new(datadir=None)[source]#: Return a new hdf5.File by instance with name determined by the last calculation in the datadir (plus one). Set the .path attribute to the generated filename.

openquake.commonlib.datastore.new(calc_id, oqparam, datadir=None, mode=None)[source]#

Parameters:

calc_id – if integer > 0 look in the database and then on the filesystem if integer < 0 look at the old calculations in the filesystem
oqparam – OqParam instance with the validated parameters of the calculation

Returns:

a DataStore instance associated to the given calc_id

openquake.commonlib.datastore.read(calc_id, mode='r', datadir=None, parentdir=None, read_parent=True)[source]#

Parameters:

calc_id – calculation ID or filename
mode – ‘r’ or ‘w’
datadir – the directory where to look
parentdir – the datadir of the parent calculation
read_parent – read the parent calculation if it is there

Returns:

the corresponding DataStore instance

Read the datastore, if it exists and it is accessible.

openquake.commonlib.datastore.read_hc_id(hdf5)[source]#: Getting the hazard_calculation_id, if any

openquake.commonlib.dbapi module#

One of the worst thing about Python is the DB API 2.0 specification, which is unusable except for building frameworks. It should have been a stepping stone for an usable DB API 3.0 that never happened. So, instead of a good low level API, we had a proliferation of Object Relational Mappers making our lives a lot harder. Fortunately, there has always been good Pythonistas in the anti-ORM camp.

This module is heavily inspired by the dbapiext module by Martin Blais, which is part of the antiorm package. The main (only) difference is that I am using the question mark (?) for the placeholders instead of the percent sign (%) to avoid confusions with other usages of the %s, in particular in LIKE queries and in expressions like strftime(‘%s’, time) used in SQLite.

In less than 200 lines of code there is enough support to build dynamic SQL queries and to make an ORM unneeded, since we do not need database independence.

dbiapi tutorial#

The only thing you must to know is the Db class, which is lazy wrapper over a database connection. You instantiate it by passing a connection function and its arguments:

>>> import sqlite3
>>> db = Db(sqlite3.connect, ':memory:')

Now you have an interface to your database, the db object. This object is lazy, i.e. the connection is not yet instantiated, but it will be when you will access its .conn attribute. This attribute is automatically accessed when you call the interface to run a query, for instance to create an empty table:

>>> curs = db('CREATE TABLE job ('
...     'id INTEGER PRIMARY KEY AUTOINCREMENT, value INTEGER)')

You can populate the table by using the .insert method:

>>> db.insert('job', ['value'], [(42,), (43,)]) 
<sqlite3.Cursor object at ...>

Notice that this method returns a standard DB API 2.0 cursor and you have access to all of its features: for instance here you could extract the lastrowid.

Then you can run SELECT queries:

>>> rows = db('SELECT * FROM job')

The dbapi provides a Row class which is used to hold the results of SELECT queries and is working as one would expect:

>>> rows
[<Row(id=1, value=42)>, <Row(id=2, value=43)>]
>>> tuple(rows[0])
(1, 42)
>>> rows[0].id
1
>>> rows[0].value
42
>>> rows[0]._fields
['id', 'value']

The queries can have different kind of ? parameters:

?s is for interpolated string parameters:

>>> db('SELECT * FROM ?s', 'job')  # ?s is replaced by 'job'
[<Row(id=1, value=42)>, <Row(id=2, value=43)>]

?x is for escaped parameters (to avoid SQL injection):

>>> db('SELECT * FROM job WHERE id=?x', 1)  # ?x is replaced by 1
[<Row(id=1, value=42)>]

?s and ?x are for scalar parameters; ?S and ?X are for sequences:

>>> db('INSERT INTO job (?S) VALUES (?X)', ['id', 'value'], (3, 44)) 
<sqlite3.Cursor object at ...>

You can see how the interpolation works by calling the expand method that returns the interpolated template (alternatively, there is a debug=True flag when calling db that prints the same info). In this case

>>> db.expand('INSERT INTO job (?S) VALUES (?X)', ['id', 'value'], [3, 44])
'INSERT INTO job (id, value) VALUES (?, ?)'

As you see, ?S parameters work by replacing a list of strings with a comma separated string, where ?X parameters are replaced by a comma separated sequence of question marks, i.e. the low level placeholder for SQLite. The interpolation performs a regular search and replace, so if you have a ?- string in your template that must not be escaped, you can run into issues. This is an error:

>>> match("SELECT * FROM job WHERE id=?x AND description='Lots of ?s'", 1)
Traceback (most recent call last):
   ...
ValueError: Incorrect number of ?-parameters in SELECT * FROM job WHERE id=?x AND description='Lots of ?s', expected 1

This is correct:

>>> match("SELECT * FROM job WHERE id=?x AND description=?x", 1, 'Lots of ?s')
('SELECT * FROM job WHERE id=? AND description=?', (1, 'Lots of ?s'))

There are three other ? parameters:

?D is for dictionaries and it is used mostly in UPDATE queries:

>>> match('UPDATE mytable SET ?D WHERE id=?x', dict(value=33, other=5), 1)
('UPDATE mytable SET other=?, value=? WHERE id=?', (5, 33, 1))

?A is for dictionaries and it is used in AND queries:

>>> match('SELECT * FROM job WHERE ?A', dict(value=33, id=5))
('SELECT * FROM job WHERE id=? AND value=?', (5, 33))

?O is for dictionaries and it is used in OR queries:

>>> match('SELECT * FROM job WHERE ?O', dict(value=33, id=5))
('SELECT * FROM job WHERE id=? OR value=?', (5, 33))

The dictionary parameters are ordered per field name, just to make the templates reproducible. ?A and ?O are smart enough to treat specially None parameters, that are turned into NULL:

>>> match('SELECT * FROM job WHERE ?A', dict(value=None, id=5))
('SELECT * FROM job WHERE id=? AND value IS NULL', (5,))

The ? parameters are matched positionally; it is also possible to pass to the db object a few keyword arguments to tune the standard behavior. In particular, if you know that a query must return a single row you can do the following:

>>> db('SELECT * FROM job WHERE id=?x', 1, one=True)
<Row(id=1, value=42)>

Without the one=True the query would have returned a list with a single element. If you know that the query must return a scalar you can do the following:

>>> db('SELECT value FROM job WHERE id=?x', 1, scalar=True)
42

If a query that should return a scalar returns something else, or if a query that should return a row returns a different number of rows, appropriate errors are raised:

>>> db('SELECT * FROM job WHERE id=?x', 1, scalar=True) 
Traceback (most recent call last):
   ...
TooManyColumns: 2, expected 1

>>> db('SELECT * FROM job', None, one=True) 
Traceback (most recent call last):
   ...
TooManyRows: 3, expected 1

If a row is expected but not found, a NotFound exception is raised:

>>> db('SELECT * FROM job WHERE id=?x', None, one=True) 
Traceback (most recent call last):
   ...
NotFound

class openquake.commonlib.dbapi.Db(connect, *args, **kw)[source]#

Bases: object

A wrapper over a DB API 2 connection. See the tutorial.

close()[source]#: Close the main thread connection and refresh the threadlocal object

property conn#

classmethod expand(m_templ, *m_args)[source]#: Performs partial interpolation of the template. Used for debugging.

insert(table, columns, rows)[source]#: Insert several rows with executemany. Return a cursor.

property path#: Path to the underlying sqlite file

exception openquake.commonlib.dbapi.NotFound[source]#

Bases: Exception

Raised when a scalar query has not output

class openquake.commonlib.dbapi.Row(fields, values)[source]#

Bases: Sequence

A pickleable row, working both as a tuple and an object:

>>> row = Row(['id', 'value'], (1, 2))
>>> tuple(row)
(1, 2)
>>> assert row[0] == row.id and row[1] == row.value

Parameters:

fields – a sequence of field names
values – a sequence of values (one per field)

class openquake.commonlib.dbapi.Table(fields, rows)[source]#

Bases: list

Just a list of Rows with an attribute _fields

exception openquake.commonlib.dbapi.TooManyColumns[source]#

Bases: Exception

Raised when a scalar query has more than one column

exception openquake.commonlib.dbapi.TooManyRows[source]#

Bases: Exception

Raised when a scalar query produces more than one row

openquake.commonlib.dbapi.match(m_templ, *m_args)[source]#

Parameters:

m_templ – a meta template string
m_args – all arguments

Returns:

template, args

Here is an example of usage:

>>> match('SELECT * FROM job WHERE id=?x', 1)
('SELECT * FROM job WHERE id=?', (1,))

calc module#

class openquake.commonlib.calc.RuptureImporter(dstore)[source]#

Bases: object

Import an array of ruptures correctly, i.e. by populating the datasets ruptures, rupgeoms, events.

check_overflow(E)[source]#: Raise a ValueError if the number of IMTs is larger than 256 or the number of events is larger than 4,294,967,296. The limits are due to the numpy dtype used to store the GMFs (gmv_dt). There also a limit of max_potential_gmfs on the number of sites times the number of events, to avoid producing too many GMFs. In that case split the calculation or be smarter.

get_eid_rlz(proxies, rlzs_by_gsim, ordinal)[source]#

Returns:: a composite array with the associations eid->rlz

import_rups_events(rup_array, get_rupture_getters)[source]#: Import an array of ruptures and store the associated events. :returns: (number of imported ruptures, number of imported events)

openquake.commonlib.calc.build_slice_by_event(eids, offset=0)[source]#

openquake.commonlib.calc.compactify(arrayN2)[source]#

Parameters:: arrayN2 – an array with columns (start, stop)
Returns:: a shorter array with the same structure

Here is how it works in an example where the first three slices are compactified into one while the last slice stays as it is:

>>> arr = numpy.array([[84384702, 84385520],
...                    [84385520, 84385770],
...                    [84385770, 84386062],
...                    [84387636, 84388028]])
>>> compactify(arr)
array([[84384702, 84386062],
       [84387636, 84388028]])

openquake.commonlib.calc.compactify3(arrayN3, maxsize=1000000)[source]#

Parameters:: arrayN3 – an array with columns (idx, start, stop)
Returns:: a shorter array with columns (start, stop)

openquake.commonlib.calc.convert_to_array(pmap, nsites, imtls, inner_idx=0)[source]#

Convert the probability map into a composite array with header of the form PGA-0.1, PGA-0.2 …

Parameters:

pmap – probability map
nsites – total number of sites
imtls – a DictArray with IMT and levels

Returns:

a composite array of lenght nsites

openquake.commonlib.calc.get_counts(idxs, N)[source]#

Parameters:

idxs – indices in the range 0..N-1
N – size of the returned array

Returns:

an array of size N with the counts of the indices

openquake.commonlib.calc.get_slices(sbe, data, num_assets)[source]#

Returns:: a list of triple (start, stop, weight)

openquake.commonlib.calc.gmvs_to_poes(df, imtls, ses_per_logic_tree_path)[source]#

Parameters:

df – a DataFrame with fields gmv_0, .. gmv_{M-1}
imtls – a dictionary imt -> imls with M IMTs and L levels
ses_per_logic_tree_path – a positive integer

Returns:

an array of PoEs of shape (M, L)

openquake.commonlib.calc.make_hmaps(pmaps, imtls, poes)[source]#

Compute the hazard maps associated to the passed probability maps.

Parameters:

pmaps – a list of Pmaps of shape (N, M, L1)
imtls – DictArray with M intensity measure types
poes – P PoEs where to compute the maps

Returns:

a list of Pmaps with size (N, M, P)

openquake.commonlib.calc.make_uhs(hmap, info)[source]#

Make Uniform Hazard Spectra curves for each location.

Parameters:

hmap – array of shape (N, M, P)
info – a dictionary with keys poes, imtls, uhs_dt

Returns:

a composite array containing uniform hazard spectra

openquake.commonlib.calc.starmap_from_gmfs(task_func, oq, dstore, mon)[source]#

Parameters:

task_func – function or generator with signature (gmf_df, oq, dstore)
oq – an OqParam instance
dstore – DataStore instance where the GMFs are stored

Returns:

a Starmap object used for event based calculations

hazard_writers module#

Classes for serializing various NRML XML artifacts.

class openquake.commonlib.hazard_writers.BaseCurveWriter(dest, **metadata)[source]#

Bases: object

Base class for curve writers.

Parameters:

dest – File path (including filename) or file-like object for results to be saved to.
metadata –
The following keyword args are required:
- investigation_time: Investigation time (in years) defined in the calculation which produced these results.
The following are more or less optional (combinational rules noted below where applicable):
- statistics: ‘mean’ or ‘quantile’
- quantile_value: Only required if statistics = ‘quantile’.
- smlt_path: String representing the logic tree path which produced these curves. Only required for non-statistical curves.
- gsimlt_path: String represeting the GSIM logic tree path which produced these curves. Only required for non-statisical curves.

serialize(_data)[source]#: Implement in subclasses.

class openquake.commonlib.hazard_writers.HazardCurveXMLWriter(dest, **metadata)[source]#

Bases: BaseCurveWriter

Hazard Curve XML writer. See BaseCurveWriter for a list of general constructor inputs.

The following additional metadata params are required:

imt: Intensity measure type used to compute these hazard curves.
imls: Intensity measure levels, which represent the x-axis values of each curve.

The following parameters are optional:

sa_period: Only used with imt = ‘SA’.
sa_damping: Only used with imt = ‘SA’.

add_hazard_curves(root, metadata, data)[source]#: Add hazard curves stored into data as child of the root element with metadata. See the documentation of the method serialize and the constructor for a description of data and metadata, respectively.

serialize(data)[source]#

Write a sequence of hazard curves to the specified file.

Parameters:

data –

Iterable of hazard curve data. Each datum must be an object with the following attributes:

poes: A list of probability of exceedence values (floats).
location: An object representing the location of the curve; must have x and y to represent lon and lat, respectively.

class openquake.commonlib.hazard_writers.HazardMapWriter(dest, **metadata)[source]#

Bases: object

Parameters:

dest – File path (including filename) or a file-like object for results to be saved to.
metadata –
The following keyword args are required:
- investigation_time: Investigation time (in years) defined in the calculation which produced these results.
- imt: Intensity measure type used to compute these hazard curves.
- poe: The Probability of Exceedance level for which this hazard map was produced.
The following are more or less optional (combinational rules noted below where applicable):
- statistics: ‘mean’ or ‘quantile’
- quantile_value: Only required if statistics = ‘quantile’.
- smlt_path: String representing the logic tree path which produced these curves. Only required for non-statistical curves.
- gsimlt_path: String represeting the GSIM logic tree path which produced these curves. Only required for non-statisical curves.
- sa_period: Only used with imt = ‘SA’.
- sa_damping: Only used with imt = ‘SA’.

serialize(data)[source]#

Write a sequence of hazard map data to the specified file.

Parameters:: data – Iterable of hazard map data. Each datum should be a triple of (lon, lat, iml) values.

class openquake.commonlib.hazard_writers.HazardMapXMLWriter(dest, **metadata)[source]#

Bases: HazardMapWriter

NRML/XML implementation of a HazardMapWriter.

See HazardMapWriter for information about constructor parameters.

serialize(data)[source]#

Serialize hazard map data to XML.

See HazardMapWriter.serialize() for details about the expected input.

class openquake.commonlib.hazard_writers.UHSXMLWriter(dest, **metadata)[source]#

Bases: BaseCurveWriter

UHS curve XML writer. See BaseCurveWriter for a list of general constructor inputs.

The following additional metadata params are required:

poe: Probability of exceedance for which a given set of UHS have been
computed
periods: A list of SA (Spectral Acceleration) period values, sorted
ascending order

serialize(data)[source]#

Write a sequence of uniform hazard spectra to the specified file.

Parameters:

data –

Iterable of UHS data. Each datum must be an object with the following attributes:

imls: A sequence of Intensity Measure Levels
location: An object representing the location of the curve; must have x and y to represent lon and lat, respectively.

openquake.commonlib.hazard_writers.gen_gmfs(gmf_set)[source]#: Generate GMF nodes from a gmf_set :param gmf_set: a sequence of GMF objects with attributes imt, sa_period, sa_damping, event_id and containing a list of GMF nodes with attributes gmv and location. The nodes are sorted by lon/lat.

openquake.commonlib.hazard_writers.rupture_to_element(rup, parent=None)[source]#

Convert a rupture object into an Element object.

Parameters:

rup – must have attributes .rupid, .events_by_ses and .seed
parent – parent of the returned element, or None

openquake.commonlib.hazard_writers.sub_elems(elem, rup, *names)[source]#

logs module#

Set up some system-wide loggers

class openquake.commonlib.logs.LogContext(job_ini, calc_id, log_level='info', log_file=None, user_name=None, hc_id=None, host=None, tag='')[source]#

Bases: object

Context manager managing the logging functionality

get_oqparam(validate=True)[source]#

Returns:: an OqParam instance

multi = False#

oqparam = None#

class openquake.commonlib.logs.LogDatabaseHandler(job_id)[source]#

Bases: Handler

Log stream handler

emit(record)[source]#

Do whatever it takes to actually log the specified logging record.

This version is intended to be implemented by subclasses and so raises a NotImplementedError.

class openquake.commonlib.logs.LogFileHandler(job_id, log_file)[source]#

Bases: FileHandler

Log file handler

emit(record)[source]#

Emit a record.

If the stream was not opened because ‘delay’ was specified in the constructor, open it before calling the superclass’s emit.

If stream is not open, current mode is ‘w’ and _closed=True, record will not be emitted (see Issue #42378).

class openquake.commonlib.logs.LogStreamHandler(job_id)[source]#

Bases: StreamHandler

Log stream handler

emit(record)[source]#

Emit a record.

If a formatter is specified, it is used to format the record. The record is then written to the stream with a trailing newline. If exception information is present, it is formatted using traceback.print_exception and appended to the stream. If the stream has an ‘encoding’ attribute, it is used to determine how to do the output to the stream.

openquake.commonlib.logs.dbcmd(action, *args)[source]#

A dispatcher to the database server.

Parameters:

action (string) – database action to perform
args (tuple) – arguments

openquake.commonlib.logs.dblog(level: str, job_id: int, task_no: int, msg: str)[source]#: Log on the database

openquake.commonlib.logs.get_calc_ids(datadir=None)[source]#: Extract the available calculation IDs from the datadir, in order.

openquake.commonlib.logs.get_datadir()[source]#: Extracts the path of the directory where the openquake data are stored from the environment ($OQ_DATADIR) or from the shared_dir in the configuration file.

openquake.commonlib.logs.get_last_calc_id(datadir=None)[source]#: Extract the latest calculation ID from the given directory. If none is found, return 0.

openquake.commonlib.logs.get_tag(job_ini)[source]#

Returns:: the name of the model if job_ini belongs to the mosaic_dir

openquake.commonlib.logs.init(job_ini, dummy=None, log_level='info', log_file=None, user_name=None, hc_id=None, host=None, tag='')[source]#

Parameters:

job_ini – path to the job.ini file or dictionary of parameters
dummy – ignored parameter, exists for backward compatibility
log_level – the log level as a string or number
log_file – path to the log file (if any)
user_name – user running the job (None means current user)
hc_id – parent calculation ID (default None)
host – machine where the calculation is running (default None)
tag – tag (for instance the model name) to show before the log message

Returns:

a LogContext instance

initialize the root logger (if not already initialized)
set the format of the root log handlers (if any)
create a job in the database if job_or_calc == “job”
return a LogContext instance associated to a calculation ID

oqvalidation module#

Full list of configuration parameters#

Engine Version: 3.19.1

Some parameters have a default that it is used when the parameter is not specified in the job.ini file. Some other parameters have no default, which means that not specifying them will raise an error when running a calculation for which they are required.

override_vs30:: Optional Vs30 parameter to override the site model Vs30 Example: override_vs30 = 800 Default: None
aggregate_by:: Used to compute aggregate losses and aggregate loss curves in risk calculations. Takes in input one or more exposure tags. Example: aggregate_by = region, taxonomy. Default: empty list
aggregate_loss_curves_types:: Used for event-based risk and damage calculations, to estimate the aggregated loss Exceedance Probability (EP) only or to also calculate (if possible) the Occurrence Exceedance Probability (OEP) and/or the Aggregate Exceedance Probability (AEP). Example: aggregate_loss_curves_types = aep, oep. Default: ep
reaggregate_by:: Used to perform additional aggregations in risk calculations. Takes in input a proper subset of the tags in the aggregate_by option. Example: reaggregate_by = region. Default: empty list
amplification_method:: Used in classical PSHA calculations to amplify the hazard curves with the convolution or kernel method. Example: amplification_method = kernel. Default: “convolution”
area_source_discretization:: Discretization parameters (in km) for area sources. Example: area_source_discretization = 10. Default: 10
ash_wet_amplification_factor:: Used in volcanic risk calculations. Example: ash_wet_amplification_factor=1.0. Default: 1.0
asset_correlation:: Used in risk calculations to take into account asset correlation. Accepts only the values 1 (full correlation) and 0 (no correlation). Example: asset_correlation=1. Default: 0
asset_hazard_distance:: In km, used in risk calculations to print a warning when there are assets too distant from the hazard sites. Example: asset_hazard_distance = 5. Default: 15
asset_life_expectancy:: Used in the classical_bcr calculator. Example: asset_life_expectancy = 50. Default: no default
assets_per_site_limit:: INTERNAL
gmf_max_gb:: If the size (in GB) of the GMFs is below this value, then compute avg_gmf Example: gmf_max_gb = 1. Default: 0.1
avg_losses:: Used in risk calculations to compute average losses. Example: avg_losses=false. Default: True
base_path:: INTERNAL
cachedir:: INTERNAL
cache_distances:: Useful in UCERF calculations. Example: cache_distances = true. Default: False
calculation_mode:: One of classical, disaggregation, event_based, scenario, scenario_risk, scenario_damage, event_based_risk, classical_risk, classical_bcr. Example: calculation_mode=classical Default: no default
collapse_gsim_logic_tree:: INTERNAL
collapse_level:: INTERNAL
collect_rlzs:: Collect all realizations into a single effective realization. If not given it is true for sampling and false for full enumeration. Example: collect_rlzs=true. Default: None
compare_with_classical:: Used in event based calculation to perform also a classical calculation, so that the hazard curves can be compared. Example: compare_with_classical = true. Default: False
complex_fault_mesh_spacing:: In km, used to discretize complex faults. Example: complex_fault_mesh_spacing = 15. Default: 5
concurrent_tasks:: A hint to the engine for the number of tasks to generate. Do not set it unless you know what you are doing. Example: concurrent_tasks = 100. Default: twice the number of cores
conditional_loss_poes:: Used in classical_risk calculations to compute loss curves. Example: conditional_loss_poes = 0.01 0.02. Default: empty list
cholesky_limit:: When generating the GMFs from a ShakeMap the engine needs to perform a Cholesky decomposition of a matrix of size (M x N)^2, being M the number of intensity measure types and N the number of sites. The decomposition can become ultra-slow, run out of memory, or produce bogus negative eigenvalues, therefore there is a limit on the maximum size of M x N. Example: cholesky_limit = 1000. Default: 10,000
continuous_fragility_discretization:: Used when discretizing continuuos fragility functions. Example: continuous_fragility_discretization = 10. Default: 20
coordinate_bin_width:: Used in disaggregation calculations. Example: coordinate_bin_width = 1.0. Default: 100 degrees, meaning don’t disaggregate by lon, lat
cross_correlation:: When used in Conditional Spectrum calculation is the name of a cross correlation class (i.e. “BakerJayaram2008”). When used in ShakeMap calculations the valid choices are “yes”, “no” “full”, same as for spatial_correlation. Example: cross_correlation = no. Default: “yes”
description:: A string describing the calculation. Example: description = Test calculation. Default: “no description”
disagg_bin_edges:: A dictionary where the keys can be: mag, dist, lon, lat, eps and the values are lists of floats indicating the edges of the bins used to perform the disaggregation. Example: disagg_bin_edges = {‘mag’: [5.0, 5.5, 6.0, 6.5]}. Default: empty dictionary
disagg_by_src:: Flag used to enable disaggregation by source when possible. Example: disagg_by_src = true. Default: False
disagg_outputs:: Used in disaggregation calculations to restrict the number of exported outputs. Example: disagg_outputs = Mag_Dist Default: list of all possible outputs
discard_assets:: Flag used in risk calculations to discard assets from the exposure. Example: discard_assets = true. Default: False
discard_trts:: Used to discard tectonic region types that do not contribute to the hazard. Example: discard_trts = Volcanic. Default: empty list
discrete_damage_distribution:: Make sure the damage distribution contain only integers (require the “number” field in the exposure to be integer). Example: discrete_damage_distribution = true Default: False
distance_bin_width:: In km, used in disaggregation calculations to specify the distance bins. Example: distance_bin_width = 20. Default: no default
ebrisk_maxsize:: INTERNAL
epsilon_star:: A boolean controlling the typology of disaggregation output to be provided. When True disaggregation is perfomed in terms of epsilon* rather then epsilon (see Bazzurro and Cornell, 1999) Example: epsilon_star = true Default: False
extreme_gmv:: A scalar on an IMT-keyed dictionary specifying when a ground motion value is extreme and the engine has to treat is specially. Example: extreme_gmv = 5.0 Default: {‘default’: numpy.inf} i.e. no values are extreme
floating_x_step:: Float, used in rupture generation for kite faults. indicates the fraction of fault length used to float ruptures along strike by the given float (i.e. “0.5” floats the ruptures at half the rupture length). Uniform distribution of the ruptures is maintained, such that if the mesh spacing and rupture dimensions prohibit the defined overlap fraction, the fraction is increased until uniform distribution is achieved. The minimum possible value depends on the rupture dimensions and the mesh spacing. If 0, standard rupture floating is used along-strike (i.e. no mesh nodes are skipped). Example: floating_x_step = 0.5 Default: 0
floating_y_step:: Float, used in rupture generation for kite faults. indicates the fraction of fault width used to float ruptures down dip. (i.e. “0.5” floats that half the rupture length). Uniform distribution of the ruptures is maintained, such that if the mesh spacing and rupture dimensions prohibit the defined overlap fraction, the fraction is increased until uniform distribution is achieved. The minimum possible value depends on the rupture dimensions and the mesh spacing. If 0, standard rupture floating is used along-strike (i.e. no mesh nodes on the rupture dimensions and the mesh spacing. Example: floating_y_step = 0.5 Default: 0
ignore_encoding_errors:: If set, skip characters with non-UTF8 encoding Example: ignore_encoding_errors = true. Default: False
ignore_master_seed:: If set, estimate analytically the uncertainty on the losses due to the uncertainty on the vulnerability functions. Example: ignore_master_seed = vulnerability. Default: None
export_dir:: Set the export directory. Example: export_dir = /tmp. Default: the current directory, “.”
exports:: Specify what kind of outputs to export by default. Example: exports = csv, rst. Default: empty list
ground_motion_correlation_model:: Enable ground motion correlation. Example: ground_motion_correlation_model = JB2009. Default: None
ground_motion_correlation_params:: To be used together with ground_motion_correlation_model. Example: ground_motion_correlation_params = {“vs30_clustering”: False}. Default: empty dictionary
ground_motion_fields:: Flag to turn on/off the calculation of ground motion fields. Example: ground_motion_fields = false. Default: True
gsim:: Used to specify a GSIM in scenario or event based calculations. Example: gsim = BooreAtkinson2008. Default: “[FromFile]”
hazard_calculation_id:: Used to specify a previous calculation from which the hazard is read. Example: hazard_calculation_id = 42. Default: None
hazard_curves:: Used to disable the calculation of hazard curves when there are too many realizations. Example: hazard_curves = false Default: True
hazard_curves_from_gmfs:: Used in scenario/event based calculations. If set, generates hazard curves from the ground motion fields. Example: hazard_curves_from_gmfs = true. Default: False
hazard_maps:: Set it to true to export the hazard maps. Example: hazard_maps = true. Default: False
horiz_comp_to_geom_mean:: Apply the correction to the geometric mean when possible, depending on the GMPE and the Intensity Measure Component Example: horiz_comp_to_geom_mean = true. Default: False
ignore_covs:: Used in risk calculations to set all the coefficients of variation of the vulnerability functions to zero. Example ignore_covs = true Default: False
ignore_missing_costs:: Accepts exposures with missing costs (by discarding such assets). Example: ignore_missing_costs = nonstructural, business_interruption. Default: False
iml_disagg:: Used in disaggregation calculations to specify an intensity measure type and level. Example: iml_disagg = {‘PGA’: 0.02}. Default: no default
imt_ref:: Reference intensity measure type used to compute the conditional spectrum. The imt_ref must belong to the list of IMTs of the calculation. Example: imt_ref = SA(0.15). Default: empty string
individual_rlzs:: When set, store the individual hazard curves and/or individual risk curves for each realization. Example: individual_rlzs = true. Default: False
individual_curves:: Legacy name for individual_rlzs, it should not be used. Example: individual_curves = true. Default: False
infer_occur_rates:: If set infer the occurrence rates from the first probs_occur in nonparametric sources. Example: infer_occur_rates = true Default: False
infrastructure_connectivity_analysis:: If set, run the infrastructure connectivity analysis. Example: infrastructure_connectivity_analysis = true Default: False
inputs:: INTERNAL. Dictionary with the input files paths.
intensity_measure_types:: List of intensity measure types in an event based calculation. Example: intensity_measure_types = PGA SA(0.1). Default: empty list
intensity_measure_types_and_levels:: List of intensity measure types and levels in a classical calculation. Example: intensity_measure_types_and_levels={“PGA”: logscale(0.1, 1, 20)}. Default: empty dictionary
interest_rate:: Used in classical_bcr calculations. Example: interest_rate = 0.05. Default: no default
investigation_time:: Hazard investigation time in years, used in classical and event based calculations. Example: investigation_time = 50. Default: no default
limit_states:: Limit states used in damage calculations. Example: limit_states = moderate, complete Default: no default
lrem_steps_per_interval:: Used in the vulnerability functions. Example: lrem_steps_per_interval = 1. Default: 0
mag_bin_width:: Width of the magnitude bin used in disaggregation calculations. Example: mag_bin_width = 0.5. Default: 1.
master_seed:: Seed used to control the generation of the epsilons, relevant for risk calculations with vulnerability functions with nonzero coefficients of variation. Example: master_seed = 1234. Default: 123456789
max:: Compute the maximum across realizations. Akin to mean and quantiles. Example: max = true. Default: False
max_aggregations:: Maximum number of aggregation keys. Example: max_aggregations = 200_000 Default: 100_000
max_data_transfer:: INTERNAL. Restrict the maximum data transfer in disaggregation calculations.
max_gmvs_chunk:: Maximum number of rows of the gmf_data table per task. Example: max_gmvs_chunk = 200_000 Default: 100_000
max_potential_gmfs:: Restrict the product num_sites * num_events. Example: max_potential_gmfs = 1E9. Default: 2E11
max_potential_paths:: Restrict the maximum number of realizations. Example: max_potential_paths = 200. Default: 15000
max_sites_disagg:: Maximum number of sites for which to store rupture information. In disaggregation calculations with many sites you may be forced to raise max_sites_disagg, that must be greater or equal to the number of sites. Example: max_sites_disagg = 100 Default: 10
max_weight:: INTERNAL
maximum_distance:: Integration distance. Can be give as a scalar, as a dictionary TRT -> scalar or as dictionary TRT -> [(mag, dist), …] Example: maximum_distance = 200. Default: no default
maximum_distance_stations:: Applies only to scenario calculations with conditioned GMFs to discard stations. Example: maximum_distance_stations = 100. Default: None
mean:: Flag to enable/disable the calculation of mean curves. Example: mean = false. Default: True
minimum_asset_loss:: Used in risk calculations. If set, losses smaller than the minimum_asset_loss are consider zeros. Example: minimum_asset_loss = {“structural”: 1000}. Default: empty dictionary
minimum_distance:: If set, distances below the minimum are rounded up. Example: minimum_distance = 5 Default: 0
minimum_intensity:: If set, ground motion values below the minimum_intensity are considered zeros. Example: minimum_intensity = {‘PGA’: .01}. Default: empty dictionary
minimum_magnitude:: If set, ruptures below the minimum_magnitude are discarded. Example: minimum_magnitude = 5.0. Default: 0
modal_damage_state:: Used in scenario_damage calculations to export only the damage state with the highest probability. Example: modal_damage_state = true. Default: false
mosaic_model:: Used to restrict the ruptures to a given model Example: mosaic_model = ZAF Default: empty string
num_epsilon_bins:: Number of epsilon bins in disaggregation calculations. Example: num_epsilon_bins = 3. Default: 1
num_rlzs_disagg:: Used in disaggregation calculation to specify how many outputs will be generated. 0 means all realizations, n means the n closest to the mean hazard curve. Example: num_rlzs_disagg=1. Default: 0
number_of_ground_motion_fields:: Used in scenario calculations to specify how many random ground motion fields to generate. Example: number_of_ground_motion_fields = 100. Default: no default
number_of_logic_tree_samples:: Used to specify the number of realizations to generate when using logic tree sampling. If zero, full enumeration is performed. Example: number_of_logic_tree_samples = 0. Default: 0
oversampling:: When equal to “forbid” raise an error if tot_samples > num_paths in classical calculations; when equal to “tolerate” do not raise the error (the default); when equal to “reduce_rlzs” reduce the realizations to the unique paths with weights num_samples/tot_samples Example: oversampling = reduce_rlzs Default: tolerate
poes:: Probabilities of Exceedance used to specify the hazard maps or hazard spectra to compute. Example: poes = 0.01 0.02. Default: empty list
poes_disagg:: Alias for poes.
pointsource_distance:: Used in classical calculations to collapse the point sources. Can also be used in conjunction with ps_grid_spacing. Example: pointsource_distance = 50. Default: {‘default’: 100}
postproc_func:: Specify a postprocessing function in calculators/postproc. Example: postproc_func = compute_mrd.main Default: ‘dummy.main’ (no postprocessing)
postproc_args:: Specify the arguments to be passed to the postprocessing function Example: postproc_args = {‘imt’: ‘PGA’} Default: {} (no arguments)
prefer_global_site_params:: INTERNAL. Automatically set by the engine.
ps_grid_spacing:: Used in classical calculations to grid the point sources. Requires the pointsource_distance to be set too. Example: ps_grid_spacing = 50. Default: 0, meaning no grid
quantiles:: List of probabilities used to compute the quantiles across realizations. Example: quantiles = 0.15 0.50 0.85 Default: empty list
random_seed:: Seed used in the sampling of the logic tree. Example: random_seed = 1234. Default: 42
reference_backarc:: Used when there is no site model to specify a global backarc parameter, used in some GMPEs. Can be True or False Example: reference_backarc = true. Default: False
reference_depth_to_1pt0km_per_sec:: Used when there is no site model to specify a global z1pt0 parameter, used in some GMPEs. Example: reference_depth_to_1pt0km_per_sec = 100. Default: no default
reference_depth_to_2pt5km_per_sec:: Used when there is no site model to specify a global z2pt5 parameter, used in some GMPEs. Example: reference_depth_to_2pt5km_per_sec = 5. Default: no default
reference_vs30_type:: Used when there is no site model to specify a global vs30 type. The choices are “inferred” or “measured” Example: reference_vs30_type = measured”. Default: “inferred”
reference_vs30_value:: Used when there is no site model to specify a global vs30 value. Example: reference_vs30_value = 760. Default: no default
region:: A list of lon/lat pairs used to specify a region of interest Example: region = 10.0 43.0, 12.0 43.0, 12.0 46.0, 10.0 46.0 Default: None
region_grid_spacing:: Used together with the region option to generate the hazard sites. Example: region_grid_spacing = 10. Default: None
return_periods:: Used in the computation of the loss curves. Example: return_periods = 200 500 1000. Default: empty list.
reqv_ignore_sources:: Used when some sources in a TRT that uses the equivalent distance term should not be collapsed. Example: reqv_ignore_sources = src1 src2 src3 Default: empty list
risk_imtls:: INTERNAL. Automatically set by the engine.
risk_investigation_time:: Used in risk calculations. If not specified, the (hazard) investigation_time is used instead. Example: risk_investigation_time = 50. Default: None
rlz_index:: Used in disaggregation calculations to specify the realization from which to start the disaggregation. Example: rlz_index = 0. Default: None
rupture_dict:: Dictionary with rupture parameters lon, lat, dep, mag, rake, strike, dip Example: rupture_dict = {‘lon’: 10, ‘lat’: 20, ‘dep’: 10, ‘mag’: 6, ‘rake’: 0} Default: {}
rupture_mesh_spacing:: Set the discretization parameter (in km) for rupture geometries. Example: rupture_mesh_spacing = 2.0. Default: 5.0
sampling_method:: One of early_weights, late_weights, early_latin, late_latin) Example: sampling_method = early_latin. Default: ‘early_weights’
sec_peril_params:: INTERNAL
secondary_perils:: INTERNAL
secondary_simulations:: INTERNAL
sensitivity_analysis:: Dictionary describing a sensitivity analysis. Example: sensitivity_analysis = {‘maximum_distance’: [200, 300]}. Default: empty dictionary
ses_per_logic_tree_path:: Set the number of stochastic event sets per logic tree realization in event based calculations. Example: ses_per_logic_tree_path = 100. Default: 1
ses_seed:: Seed governing the generation of the ground motion field. Example: ses_seed = 123. Default: 42
rupture_usgs_id:: Used in Aristotle calculations to download a rupture from the USGS site Example: rupture_usgs_id = usp000fjta. Default: no default
shakemap_id:: Used in ShakeMap calculations to download a ShakeMap from the USGS site Example: shakemap_id = usp000fjta. Default: no default
shakemap_uri:: Dictionary used in ShakeMap calculations to specify a ShakeMap. Must contain a key named “kind” with values “usgs_id”, “usgs_xml” or “file_npy”. Example: shakemap_uri = { “kind”: “usgs_xml”, “grid_url”: “file:///home/michele/usp000fjta/grid.xml”, “uncertainty_url”: “file:///home/michele/usp000fjta/uncertainty.xml”}. Default: empty dictionary
shift_hypo:: Used in classical calculations to shift the rupture hypocenter. Example: shift_hypo = true. Default: false
site_effects:: Used in ShakeMap calculations to turn on GMF amplification based on the vs30 values in the ShakeMap (site_effects=’shakemap’) or in the site collection (site_effects=’sitecol’). Example: site_effects = ‘shakemap’. Default: ‘no’
sites:: Used to specify a list of sites. Example: sites = 10.1 45, 10.2 45.
sites_slice:: INTERNAL
smlt_branch:: Used to restrict the source model logic tree to a specific branch Example: smlt_branch=b1 Default: empty string, meaning all branches
soil_intensities:: Used in classical calculations with amplification_method = convolution
source_id:: Used for debugging purposes. When given, restricts the source model to the given source IDs. Example: source_id = src001 src002. Default: empty list
source_nodes:: INTERNAL
spatial_correlation:: Used in the ShakeMap calculator. The choics are “yes”, “no” and “full”. Example: spatial_correlation = full. Default: “yes”
specific_assets:: INTERNAL
split_sources:: INTERNAL
outs_per_task:: How many outputs per task to generate (honored in some calculators) Example: outs_per_task = 3 Default: 4
std:: Compute the standard deviation across realizations. Akin to mean and max. Example: std = true. Default: False
steps_per_interval:: Used in the fragility functions when building the intensity levels Example: steps_per_interval = 4. Default: 1
time_event:: Used in scenario_risk calculations when the occupancy depend on the time. Valid choices are “day”, “night”, “transit”. Example: time_event = day. Default: “avg”
time_per_task:: Used in calculations with task splitting. If a task slice takes longer then time_per_task seconds, then spawn subtasks for the other slices. Example: time_per_task=1000 Default: 1200
total_losses:: Used in event based risk calculations to compute total losses and and total curves by summing across different loss types. Possible values are “structural+nonstructural”, “structural+contents”, “nonstructural+contents”, “structural+nonstructural+contents”. Example: total_losses = structural+nonstructural Default: None
truncation_level:: Truncation level used in the GMPEs. Example: truncation_level = 0 to compute median GMFs. Default: no default
uniform_hazard_spectra:: Flag used to generated uniform hazard specta for the given poes Example: uniform_hazard_spectra = true. Default: False
use_rates:: When set, convert to rates before computing the statistical hazard curves Example: use_rates = true. Default: False
vs30_tolerance:: Used when amplification_method = convolution. Example: vs30_tolerance = 20. Default: 0
width_of_mfd_bin:: Used to specify the width of the Magnitude Frequency Distribution. Example: width_of_mfd_bin = 0.2. Default: None

class openquake.commonlib.oqvalidation.OqParam(**names_vals)[source]#

Bases: ParamSet

ALIASES = {'individual_curves': 'individual_rlzs', 'max_hazard_curves': 'max', 'mean_hazard_curves': 'mean', 'quantile_hazard_curves': 'quantiles'}#

KNOWN_INPUTS = {'amplification', 'area_vulnerability', 'business_interruption_consequence', 'business_interruption_fragility', 'business_interruption_vulnerability', 'consequence', 'contents_consequence', 'contents_fragility', 'contents_vulnerability', 'delta_rates', 'exposure', 'fragility', 'geometry', 'gmfs', 'gsim_logic_tree', 'hazard_curves', 'input_zip', 'ins_loss', 'insurance', 'job_ini', 'multi_peril', 'nonstructural_consequence', 'nonstructural_fragility', 'nonstructural_vulnerability', 'number_vulnerability', 'occupants_vulnerability', 'reinsurance', 'reqv', 'reqv_ignore_sources', 'residents_vulnerability', 'rupture_model', 'shakemap', 'site_model', 'source_model', 'source_model_logic_tree', 'station_data', 'structural_consequence', 'structural_fragility', 'structural_vulnerability', 'structural_vulnerability_retrofitted', 'taxonomy_mapping'}#

aggregate_by#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

aggregate_loss_curves_types#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

all_imts()[source]#

Returns:: gmv_0, … gmv_M, sec_imt…

amplification_method#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

area_source_discretization#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ash_wet_amplification_factor#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

asset_correlation#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

asset_hazard_distance#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

asset_life_expectancy#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

assets_per_site_limit#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

avg_losses#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

base_path#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

cache_distances#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

cachedir#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

calculation_mode#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

check_aggregate_by()[source]#

check_gsims(gsims)[source]#

Parameters:: gsims – a sequence of GSIM instances

check_missing(param, action)[source]#: Make sure the given parameter is missing in the job.ini file

check_reinsurance()[source]#

check_source_model()[source]#

check_uniform_hazard_spectra()[source]#

cholesky_limit#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

collapse_gsim_logic_tree#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

collapse_level#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

collect_rlzs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

compare_with_classical#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

complex_fault_mesh_spacing#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

concurrent_tasks#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

conditional_loss_poes#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

continuous_fragility_discretization#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

coordinate_bin_width#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property correl_model#: Return a correlation object. See openquake.hazardlib.correlation for more info.

property cross_correl#: Return a cross correlation object (or None). See openquake.hazardlib.cross_correlation for more info.

cross_correlation#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

description#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

disagg_bin_edges#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

disagg_by_src#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

disagg_outputs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

discard_assets#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

discard_trts#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

discrete_damage_distribution#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

distance_bin_width#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

classmethod docs()[source]#

Returns:: a dictionary parameter name -> parameter documentation

ebrisk_maxsize#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

epsilon_star#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

export_dir#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

exports#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property ext_loss_types#

Returns:: list of loss types + secondary loss types

extreme_gmv#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

fix_legacy_names(dic)[source]#

floating_x_step#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

floating_y_step#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

get_input_size()[source]#

Returns:: the total size in bytes of the input files

NB: this will fail if the files are not available, so it should be called only before starting the calculation. The same information is stored in the datastore.

get_kinds(kind, R)[source]#: Yield ‘rlz-000’, ‘rlz-001’, …’, ‘mean’, ‘quantile-0.1’, …

get_max_iml()[source]#

Returns:: a vector of extreme intensities, one per IMT

get_primary_imtls()[source]#

Returns:: IMTs and levels which are not secondary

get_reqv()[source]#

Returns:: an instance of class:RjbEquivalent if reqv_hdf5 is set

get_sec_perils()[source]#

Returns:: a list of secondary perils

gmf_data_dt()[source]#

Returns:: a composite data type for the GMFs

gmf_max_gb#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ground_motion_correlation_model#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ground_motion_correlation_params#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ground_motion_fields#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

gsim#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

hazard_calculation_id#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

hazard_curves#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

hazard_curves_from_gmfs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

hazard_imtls = {}#

hazard_maps#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

hazard_precomputed()[source]#

Returns:: True if the hazard is precomputed

hazard_stats()[source]#: Return a dictionary stat_name -> stat_func

hmap_dt()[source]#

Returns:: a composite dtype (imt, poe)

horiz_comp_to_geom_mean#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ignore_covs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ignore_encoding_errors#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ignore_master_seed#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ignore_missing_costs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

iml_disagg#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

imt_dt(dtype=<class 'numpy.float64'>)[source]#

Returns:: a numpy dtype {imt: float}

imt_periods()[source]#

Returns:: the IMTs with a period, to be used in an UHS calculation

imt_ref#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property imtls#: Returns a DictArray with the risk intensity measure types and levels, if given, or the hazard ones.

individual_rlzs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

infer_occur_rates#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

infrastructure_connectivity_analysis#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property input_dir#

Returns:: absolute path to where the job.ini is

inputs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

intensity_measure_types#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

intensity_measure_types_and_levels#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

interest_rate#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

investigation_time#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

is_event_based()[source]#: The calculation mode is event_based, event_based_risk or ebrisk

is_valid_collect_rlzs()[source]#: sampling_method must be early_weights with collect_rlzs=true

is_valid_complex_fault_mesh_spacing()[source]#: The complex_fault_mesh_spacing parameter can be None only if rupture_mesh_spacing is set. In that case it is identified with it.

is_valid_disagg_by_src()[source]#: disagg_by_src can be set only if ps_grid_spacing = 0

is_valid_export_dir()[source]#: export_dir={export_dir} must refer to a directory, and the user must have the permission to write on it.

is_valid_geometry()[source]#: It is possible to infer the geometry only if exactly one of sites, sites_csv, hazard_curves_csv, region is set. You did set more than one, or nothing.

is_valid_intensity_measure_levels()[source]#: In order to compute hazard curves, intensity_measure_types_and_levels must be set or extracted from the risk models.

is_valid_intensity_measure_types()[source]#: If the IMTs and levels are extracted from the risk models, they must not be set directly. Moreover, if intensity_measure_types_and_levels is set directly, intensity_measure_types must not be set.

is_valid_maximum_distance()[source]#: Invalid maximum_distance={maximum_distance}: {error}

is_valid_poes()[source]#: When computing hazard maps and/or uniform hazard spectra, the poes list must be non-empty.

is_valid_shakemap()[source]#: hazard_calculation_id must be set if shakemap_id is set

is_valid_soil_intensities()[source]#: soil_intensities must be defined only in classical calculations with amplification_method=convolution

is_valid_specific_assets()[source]#: Read the special assets from the parameters specific_assets or specific_assets_csv, if present. You cannot have both. The concept is meaninful only for risk calculators.

is_valid_truncation_level()[source]#: In presence of a correlation model the truncation level must be nonzero

property job_type#: ‘hazard’ or ‘risk’

levels_per_imt()[source]#

Returns:: the number of levels per IMT (a.ka. L1)

limit_states#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

loss_dt(dtype=<class 'numpy.float64'>)[source]#

Returns:: a composite dtype based on the loss types including occupants

loss_dt_list(dtype=<class 'numpy.float64'>)[source]#

Returns:: a data type list [(loss_name, dtype), …]

loss_maps_dt(dtype=<class 'numpy.float32'>)[source]#: Return a composite data type for loss maps

property loss_types#

Returns:: list of loss types (empty for hazard)

lrem_steps_per_interval#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property lti#: Dictionary extended_loss_type -> extended_loss_type index

mag_bin_width#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

master_seed#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max_aggregations#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max_data_transfer#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max_gmvs_chunk#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max_potential_gmfs#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max_potential_paths#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

max_sites_disagg#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

maximum_distance#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

maximum_distance_stations#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

mean#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

mean_hazard_curves#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property min_iml#

Returns:: a vector of minimum intensities, one per IMT

minimum_asset_loss#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

minimum_distance#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

minimum_intensity#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

minimum_magnitude#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

modal_damage_state#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

mosaic_model#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

no_imls()[source]#: Return True if there are no intensity measure levels

property no_pointsource_distance#

Returns:: True if the pointsource_distance is 1000 km

num_epsilon_bins#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

num_rlzs_disagg#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

number_of_ground_motion_fields#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

number_of_logic_tree_samples#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

outs_per_task#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

override_vs30#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

oversampling#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

poes#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

poes_disagg#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

pointsource_distance#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

postproc_args#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

postproc_func#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

prefer_global_site_params#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ps_grid_spacing#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

quantile_hazard_curves#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

quantiles#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

random_seed#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reaggregate_by#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reference_backarc#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reference_depth_to_1pt0km_per_sec#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reference_depth_to_2pt5km_per_sec#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reference_vs30_type#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reference_vs30_value#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

region#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

region_grid_spacing#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

reqv_ignore_sources#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

return_periods#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

risk_event_rates(num_events, num_haz_rlzs)[source]#

Parameters:

num_events – the number of events per risk realization
num_haz_rlzs – the number of hazard realizations

If risk_investigation_time is 1, returns the annual event rates for each realization as a list, possibly of 1 element.

property risk_files#

risk_imtls#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

risk_investigation_time#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

rlz_index#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

rupture_dict#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

rupture_mesh_spacing#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

rupture_usgs_id#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

sampling_method#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property sec_imts#

Returns:: a list of secondary outputs

sec_peril_params#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

secondary_perils#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

secondary_simulations#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

sensitivity_analysis#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ses_per_logic_tree_path#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

ses_seed#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

set_risk_imts(risklist)[source]#

Parameters:: risklist – a list of risk functions with attributes .id, .loss_type, .kind

Set the attribute risk_imtls.

shakemap_id#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

shakemap_uri#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

shift_hypo#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

site_effects#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

sites#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

sites_slice#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

smlt_branch#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

soil_intensities#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

source_id#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

source_nodes#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

spatial_correlation#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

specific_assets#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

split_sources#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

std#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

steps_per_interval#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

time_event#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

time_per_task#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property time_ratio#: The ratio risk_investigation_time / eff_investigation_time per rlz

total_losses#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

truncation_level#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

property tses#: Return the total time as investigation_time * ses_per_logic_tree_path * (number_of_logic_tree_samples or 1)

uhs_dt()[source]#

Returns:: a composity dtype (poe, imt)

uniform_hazard_spectra#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

use_rates#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

validate()[source]#: Set self.loss_types

vs30_tolerance#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

width_of_mfd_bin#

A descriptor for validated parameters with a default, to be used as attributes in ParamSet objects.

Parameters:

validator – the validator
default – the default value

openquake.commonlib.oqvalidation.check_same_levels(imtls)[source]#

Parameters:: imtls – a dictionary (or dict-like) imt -> imls
Returns:: the periods and the levels
Raises:: a ValueError if the levels are not the same across all IMTs

readinput module#

exception openquake.commonlib.readinput.DuplicatedPoint[source]#

Bases: Exception

Raised when reading a CSV file with duplicated (lon, lat) pairs

class openquake.commonlib.readinput.Global[source]#

Bases: object

Global variables to be reset at the end of each calculation/test

exposure = None#

gsim_lt_cache = {}#

pmap = None#

classmethod reset()[source]#

class openquake.commonlib.readinput.Site(sid, lon, lat)#

Bases: tuple

lat#: Alias for field number 2

lon#: Alias for field number 1

sid#: Alias for field number 0

openquake.commonlib.readinput.check_min_mag(sources, minimum_magnitude)[source]#: Raise an error if all sources are below the minimum_magnitude

openquake.commonlib.readinput.check_params(cp, fname)[source]#

openquake.commonlib.readinput.collect_files(dirpath, cond=<function <lambda>>)[source]#

Recursively collect the files contained inside dirpath.

Parameters:

dirpath – path to a readable directory
cond – condition on the path to collect the file

openquake.commonlib.readinput.extract_from_zip(path, ext='.ini', targetdir=None)[source]#

Given a zip archive and an extension (by default .ini), unzip the archive into the target directory and the files with the given extension.

Parameters:

path – pathname of the archive
ext – file extension to search for

Returns:

filenames

openquake.commonlib.readinput.get_cache_path(oqparam, h5=None)[source]#

Returns:: cache path of the form OQ_DATA/csm_<checksum>.hdf5

openquake.commonlib.readinput.get_checksum32(oqparam, h5=None)[source]#

Build an unsigned 32 bit integer from the hazard input files

Parameters:: oqparam – an OqParam instance

openquake.commonlib.readinput.get_composite_source_model(oqparam, dstore=None)[source]#

Parse the XML and build a complete composite source model in memory.

Parameters:

oqparam – an openquake.commonlib.oqvalidation.OqParam instance
dstore – an open datastore where to save the source info

openquake.commonlib.readinput.get_crmodel(oqparam)[source]#

Return a openquake.risklib.riskinput.CompositeRiskModel instance

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance

openquake.commonlib.readinput.get_csv_header(fname, sep=',')[source]#

Parameters:

fname – a CSV file
sep – the separator (default comma)

Returns:

the first non-commented line of fname and the file object

openquake.commonlib.readinput.get_exposure(oqparam, h5=None)[source]#

Read the full exposure in memory and build a list of openquake.risklib.asset.Asset instances.

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance
Returns:: an Exposure instance or a compatible AssetCollection

openquake.commonlib.readinput.get_full_lt(oqparam)[source]#

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance
Returns:: a openquake.hazardlib.logictree.FullLogicTree instance

openquake.commonlib.readinput.get_gsim_lt(oqparam, trts=('*',))[source]#

Parameters:

oqparam – an openquake.commonlib.oqvalidation.OqParam instance
trts – a sequence of tectonic region types as strings; trts=[‘*’] means that there is no filtering

Returns:

a GsimLogicTree instance obtained by filtering on the provided tectonic region types.

openquake.commonlib.readinput.get_imts(oqparam)[source]#: Return a sorted list of IMTs as hazardlib objects

openquake.commonlib.readinput.get_input_files(oqparam)[source]#

Parameters:

oqparam – an OqParam instance
hazard – if True, consider only the hazard files

Returns:

input path names in a specific order

openquake.commonlib.readinput.get_logic_tree(oqparam)[source]#

Returns:: a CompositeLogicTree instance

openquake.commonlib.readinput.get_mesh(oqparam, h5=None)[source]#

Extract the mesh of points to compute from the sites, the sites_csv, the region, the site model, the exposure in this order.

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance

openquake.commonlib.readinput.get_no_vect(gsim_lt)[source]#

Returns:: the names of the non-vectorized GMPEs

openquake.commonlib.readinput.get_oqparam(job_ini, pkg=None, kw={}, validate=True)[source]#

Parse a dictionary of parameters from an INI-style config file.

Parameters:

job_ini – Path to configuration file/archive or dictionary of parameters with a key “calculation_mode”
pkg – Python package where to find the configuration file (optional)
kw – Dictionary of strings to override the job parameters

Returns:

An openquake.commonlib.oqvalidation.OqParam instance containing the validated and casted parameters/values parsed from the job.ini file as well as a subdictionary ‘inputs’ containing absolute paths to all of the files referenced in the job.ini, keyed by the parameter name.

openquake.commonlib.readinput.get_params(job_ini, kw={})[source]#

Parse a .ini file or a .zip archive

Parameters:

job_ini – Configuration file | zip archive | URL
kw – Optionally override some parameters

Returns:

A dictionary of parameters

openquake.commonlib.readinput.get_pmap_from_csv(oqparam, fnames)[source]#

Parameters:

oqparam – an openquake.commonlib.oqvalidation.OqParam instance
fnames – a space-separated list of .csv relative filenames

Returns:

the site mesh and the hazard curves read by the .csv files

openquake.commonlib.readinput.get_poor_site_model(fname)[source]#

Returns:: a poor site model with only lon, lat fields

openquake.commonlib.readinput.get_reinsurance(oqparam, assetcol=None)[source]#

Returns:: (policy_df, treaty_df, field_map)

openquake.commonlib.readinput.get_rupture(oqparam)[source]#

Read the rupture_model XML file and by filter the site collection

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance
Returns:: an hazardlib rupture

openquake.commonlib.readinput.get_shapefiles(dirname)[source]#

Parameters:: dirname – directory containing the shapefiles
Returns:: list of shapefiles

openquake.commonlib.readinput.get_site_collection(oqparam, h5=None)[source]#

Returns a SiteCollection instance by looking at the points and the site model defined by the configuration parameters.

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance

openquake.commonlib.readinput.get_site_model(oqparam)[source]#

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance
Returns:: an array with fields lon, lat, vs30, …

openquake.commonlib.readinput.get_site_model_around(site_model_hdf5, rup, dist)[source]#

Returns:: site model close to the rupture

openquake.commonlib.readinput.get_sitecol_assetcol(oqparam, haz_sitecol=None, exp_types=(), h5=None)[source]#

Parameters:

oqparam – calculation parameters
haz_sitecol – the hazard site collection
exp_types – the expected loss types

Returns:

(site collection, asset collection, discarded)

openquake.commonlib.readinput.get_source_model_lt(oqparam)[source]#

Parameters:: oqparam – an openquake.commonlib.oqvalidation.OqParam instance
Returns:: a openquake.hazardlib.logictree.SourceModelLogicTree instance

openquake.commonlib.readinput.get_station_data(oqparam, sitecol)[source]#

Read the station data input file and build a list of ground motion stations and recorded ground motion values along with their uncertainty estimates

Parameters:

oqparam – an openquake.commonlib.oqvalidation.OqParam instance
sitecol – the hazard site collection

Returns:

station_data, observed_imts

openquake.commonlib.readinput.levels_from(header)[source]#

openquake.commonlib.readinput.normalize(key, fnames, base_path)[source]#

openquake.commonlib.readinput.normpath(fnames, base_path)[source]#

openquake.commonlib.readinput.read_countries_df(buffer=0.1)[source]#

Returns:: a DataFrame of geometries for the world countries

openquake.commonlib.readinput.read_delta_rates(fname, idx_nr)[source]#

Parameters:

fname – path to a CSV file with fields (source_id, rup_id, delta)
idx_nr – dictionary source_id -> (src_id, num_ruptures) with Ns sources

Returns:

list of Ns floating point arrays of different lenghts

openquake.commonlib.readinput.read_geometries(fname, code, buffer=0)[source]#

Parameters:

fname – path of the file containing the geometries
code – name of the primary key field
buffer – shapely buffer in degrees

Returns:

data frame with codes and geometries

openquake.commonlib.readinput.read_mosaic_df(buffer=0.1)[source]#

Returns:: a DataFrame of geometries for the mosaic models

openquake.commonlib.readinput.reduce_sm(paths, source_ids)[source]#

Parameters:

paths – list of source_model.xml files
source_ids – dictionary src_id -> array[src_id, code]

Returns:

dictionary with keys good, total, model, path, xmlns

NB: duplicate sources are not removed from the XML

openquake.commonlib.readinput.reduce_source_model(smlt_file, source_ids, remove=True)[source]#

Extract sources from the composite source model.

Parameters:

smlt_file – path to a source model logic tree file
source_ids – dictionary source_id -> records (src_id, code)
remove – if True, remove sm.xml files containing no sources

Returns:

the number of sources satisfying the filter vs the total

openquake.commonlib.readinput.taxonomy_mapping(oqparam, taxonomies)[source]#

Parameters:

oqparam – OqParam instance
taxonomies – array of strings tagcol.taxonomy

Returns:

a dictionary loss_type -> [[(taxonomy, weight), …], …]

openquake.commonlib.readinput.unzip_rename(zpath, name)[source]#

Parameters:

zpath – full path to a .zip archive
name – exposure.xml or ssmLT.xml

Returns:

path to an .xml file with the same name of the archive

openquake.commonlib.readinput.update(params, items, base_path)[source]#: Update a dictionary of string parameters with new parameters. Manages correctly file parameters.

util module#

openquake.commonlib.util.closest_to_ref(arrays, ref, cutoff=1e-12)[source]#

Parameters:

arrays – a sequence of arrays
ref – the reference array

Returns:

a list of indices ordered by closeness

This function is used to extract the realization closest to the mean in disaggregation. For instance, if there are 2 realizations with indices 0 and 1, the first hazard curve having values

>>> c0 = numpy.array([.99, .97, .5, .1])

and the second hazard curve having values

>>> c1 = numpy.array([.98, .96, .45, .09])

with weights 0.6 and 0.4 and mean

>>> mean = numpy.average([c0, c1], axis=0, weights=[0.6, 0.4])

then calling closest_to_ref will returns the indices 0 and 1 respectively:

>>> closest_to_ref([c0, c1], mean)
[0, 1]

This means that the realization 0 is the closest to the mean, as expected, since it has a larger weight. You can check that it is indeed true by computing the sum of the quadratic deviations:

>>> ((c0 - mean)**2).sum()
0.0004480000000000008
>>> ((c1 - mean)**2).sum()
0.0010079999999999985

If the 2 realizations have equal weights the distance from the mean will be the same. In that case both the realizations will be okay; the one that will be chosen by closest_to_ref depends on the magic of floating point approximation (theoretically identical distances will likely be different as numpy.float64 numbers) or on the magic of Python list.sort.

openquake.commonlib.util.compose_arrays(a1, a2, firstfield='etag')[source]#: Compose composite arrays by generating an extended datatype containing all the fields. The two arrays must have the same length.

openquake.commonlib.util.get_assets(dstore)[source]#

Parameters:: dstore – a datastore with keys ‘assetcol’
Returns:: an array of records (id, tag1, …, tagN, lon, lat)

openquake.commonlib.util.log(array, cutoff)[source]#: Compute the logarithm of an array with a cutoff on the small values

openquake.commonlib.util.max_rel_diff(curve_ref, curve, min_value=0.01)[source]#

Compute the maximum relative difference between two curves. Only values greather or equal than the min_value are considered.

>>> curve_ref = [0.01, 0.02, 0.03, 0.05, 1.0]
>>> curve = [0.011, 0.021, 0.031, 0.051, 1.0]
>>> round(max_rel_diff(curve_ref, curve), 2)
0.1

openquake.commonlib.util.max_rel_diff_index(curve_ref, curve, min_value=0.01)[source]#

Compute the maximum relative difference between two sets of curves. Only values greather or equal than the min_value are considered. Return both the maximum difference and its location (array index).

>>> curve_refs = [[0.01, 0.02, 0.03, 0.05], [0.01, 0.02, 0.04, 0.06]]
>>> curves = [[0.011, 0.021, 0.031, 0.051], [0.012, 0.022, 0.032, 0.051]]
>>> max_rel_diff_index(curve_refs, curves)
(0.2, 1)

openquake.commonlib.util.rmsep(array_ref, array, min_value=0)[source]#

Root Mean Square Error Percentage for two arrays.

Parameters:

array_ref – reference array
array – another array
min_value – compare only the elements larger than min_value

Returns:

the relative distance between the arrays

>>> curve_ref = numpy.array([[0.01, 0.02, 0.03, 0.05],
... [0.01, 0.02, 0.04, 0.06]])
>>> curve = numpy.array([[0.011, 0.021, 0.031, 0.051],
... [0.012, 0.022, 0.032, 0.051]])
>>> str(round(rmsep(curve_ref, curve, .01), 5))
'0.11292'

openquake.commonlib.util.shared_dir_on()[source]#

Returns:: True if a shared_dir has been set in openquake.cfg, else False