Useful OpenQuake Commands#

The oq command-line script is the entry point for several commands, the most important one being oq engine, which is documented in the manual.

The commands documented here are not in the manual because they have not reached the same level of maturity and stability. Still, some of them are quite stable and quite useful for the final users, so feel free to use them.

You can see the full list of commands by running oq –help:

$ oq --help
usage: oq [-h] [-v]
          {shell,upgrade_nrml,reduce_smlt,show_attrs,prepare_site_model,nrml_from,shakemap2gmfs,importcalc,run,show,purge,renumber_sm,workers,postzip,plot_assets,db,dbserver,tidy,extract,sample,to_hdf5,ltcsv,reaggregate,restore,mosaic,check_input,dump,info,zip,abort,nrml_to,engine,reset,checksum,export,webui,compare,plot,reduce_sm}
          ...

positional arguments:
  {shell,upgrade_nrml,reduce_smlt,show_attrs,prepare_site_model,nrml_from,shakemap2gmfs,importcalc,run,show,purge,renumber_sm,workers,postzip,plot_assets,db,dbserver,tidy,extract,sample,to_hdf5,ltcsv,reaggregate,restore,mosaic,check_input,dump,info,zip,abort,nrml_to,engine,reset,checksum,export,webui,compare,plot,reduce_sm}
                        available subcommands; use oq <subcmd> --help

options:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit

This is the output that you get at the present time (engine 3.17); depending on your version of the engine you may get a different output. As you see, there are several commands, like purge, show_attrs, export, restore, … You can get information about each command with oq <command> –help; for instance, here is the help for purge:

$ oq purge --help
usage: oq purge [-h] [-f] calc_id

Remove the given calculation. If you want to remove all calculations, use oq
reset.

positional arguments:
  calc_id      calculation ID

optional arguments:
  -h, --help   show this help message and exit
  -f, --force  ignore dependent calculations

Some of these commands are highly experimental and may disappear; others are meant for debugging and are not meant to be used by end-users. Here I will document only the commands that are useful for the general public and have reached some level of stability.

Probably the most important command is oq info. It has several features.

  1. It can be invoked with a job.ini file to extract information about the logic tree of the calculation.

  2. When invoked with the –report option, it produces a .rst report with important information about the computation. It is ESSENTIAL in the case of large calculations, since it will give you an idea of the feasibility of the computation without running it. Here is an example of usage:

    $ oq info --report job.ini
Generated /tmp/report_1644.rst
<Monitor info, duration=10.910529613494873s, memory=60.16 MB>

    You can open /tmp/report_1644.rst and read the information listed there (1644 is the calculation ID, the number will be different each time).

  3. It can be invoked without a job.ini file, and it that case it provides global information about the engine and its libraries. Try, for instance:

    $ oq info calculators # list available calculators
$ oq info gsims       # list available GSIMs
$ oq info views       # list available views
$ oq info exports     # list available exports
$ oq info parameters  # list all job.ini parameters

The second most important command is oq export. It allows customization of the exports from the datastore with additional flexibility compared to the oq engine export commands. In the future the oq engine exports commands might be deprecated and oq export might become the official export command, but we are not there yet.

Here is the usage message:

$ oq export --help
usage: oq export [-h] [-e csv] [-d .] datastore_key [calc_id]

Export an output from the datastore.

positional arguments:
  datastore_key         datastore key
  calc_id               number of the calculation [default: -1]

optional arguments:
  -h, --help            show this help message and exit
  -e csv, --exports csv
                      export formats (comma separated)
  -d ., --export-dir .  export directory

The list of available exports (i.e. the datastore keys and the available export formats) can be extracted with the oq info exports command; the number of exporters defined changes at each version:

$ oq info exports
? "aggregate_by" ['csv']
? "disagg_traditional" ['csv']
? "loss_curves" ['csv']
? "losses_by_asset" ['npz']
Aggregate Asset Losses "agglosses" ['csv']
Aggregate Loss Curves Statistics "agg_curves-stats" ['csv']
Aggregate Losses "aggrisk" ['csv']
Aggregate Risk Curves "aggcurves" ['csv']
Aggregated Risk By Event "risk_by_event" ['csv']
Asset Loss Curves "loss_curves-rlzs" ['csv']
Asset Loss Curves Statistics "loss_curves-stats" ['csv']
Asset Loss Maps "loss_maps-rlzs" ['csv', 'npz']
Asset Loss Maps Statistics "loss_maps-stats" ['csv', 'npz']
Asset Risk Distributions "damages-rlzs" ['npz', 'csv']
Asset Risk Statistics "damages-stats" ['csv']
Average Asset Losses "avg_losses-rlzs" ['csv']
Average Asset Losses Statistics "avg_losses-stats" ['csv']
Average Ground Motion Field "avg_gmf" ['csv']
Benefit Cost Ratios "bcr-rlzs" ['csv']
Benefit Cost Ratios Statistics "bcr-stats" ['csv']
Disaggregation Outputs "disagg" ['csv']
Earthquake Ruptures "ruptures" ['csv']
Events "events" ['csv']
Exposure + Risk "asset_risk" ['csv']
Full Report "fullreport" ['rst']
Ground Motion Fields "gmf_data" ['csv', 'hdf5']
Hazard Curves "hcurves" ['csv', 'xml', 'npz']
Hazard Maps "hmaps" ['csv', 'xml', 'npz']
Input Files "input" ['zip']
Mean Conditional Spectra "cs-stats" ['csv']
Realizations "realizations" ['csv']
Source Loss Table "src_loss_table" ['csv']
Total Risk "agg_risk" ['csv']
Uniform Hazard Spectra "uhs" ['csv', 'xml', 'npz']
There are 44 exporters defined.

At the present the supported export types are xml, csv, rst, npz and hdf5. xml has been deprecated for some outputs and is not the recommended format for large exports. For large exports, the recommended formats are npz (which is a binary format for numpy arrays) and hdf5. If you want the data for a specific realization (say the first one), you can use:

$ oq export hcurves/rlz-0 --exports csv
$ oq export hmaps/rlz-0 --exports csv
$ oq export uhs/rlz-0 --exports csv

but currently this only works for csv and xml. The exporters are one of the most time-consuming parts on the engine, mostly because of the sheer number of them; there are more than fifty exporters and they are always increasing. If you need new exports, please add an issue on GitHub.

oq zip#

An extremely useful command if you need to copy the files associated to a computation from a machine to another is oq zip:

$ oq zip --help
usage: oq zip [-h] [-r] what [archive_zip]

positional arguments:
  what               path to a job.ini, a ssmLT.xml file, or an exposure.xml
  archive_zip        path to a non-existing .zip file [default: '']

optional arguments:
  -h, --help         show this help message and exit
  -r , --risk-file   optional file for risk

For instance, if you have two configuration files job_hazard.ini and job_risk.ini, you can zip all the files they refer to with the command:

$ oq zip job_hazard.ini -r job_risk.ini

oq zip is actually more powerful than that; other than job.ini files, it can also zip source models:

$ oq zip ssmLT.xml

and exposures:

$ oq zip my_exposure.xml

Importing a remote calculation#

The use-case is importing on your laptop a calculation that was executed on a remote server/cluster. For that to work you need to create a file a file called openquake.cfg in the virtualenv of the engine (the output of the command oq info venv, normally it is in $HOME/openquake) with the following section:

[webapi]
server = https://oq1.wilson.openquake.org/  # change this
username = michele  # change this
password = PWD # change this

Then you can import any calculation by simply giving its ID, as in this example:

$ oq importcalc 41214
INFO:root:POST https://oq2.wilson.openquake.org//accounts/ajax_login/
INFO:root:GET https://oq2.wilson.openquake.org//v1/calc/41214/extract/oqparam
INFO:root:Saving /home/michele/oqdata/calc_41214.hdf5
Downloaded 58,118,085 bytes
{'checksum32': 1949258781,
 'date': '2021-03-18T15:25:11',
 'engine_version': '3.12.0-gita399903317'}
INFO:root:Imported calculation 41214 successfully

plotting commands#

The engine provides several plotting commands. They are all experimental and subject to change. They will always be. The official way to plot the engine results is by using the QGIS plugin. Still, the oq plotting commands are useful for debugging purposes. Here I will describe the plot_assets command, which allows to plot the exposure used in a calculation together with the hazard sites:

$ oq plot_assets --help
usage: oq plot_assets [-h] [calc_id]

Plot the sites and the assets

positional arguments:
  calc_id     a computation id [default: -1]

optional arguments:
  -h, --help  show this help message and exit

This is particularly interesting when the hazard sites do not coincide with the asset locations, which is normal when gridding the exposure.

Very often, it is interesting to plot the sources. While there is a primitive functionality for that in oq plot, we recommend to convert the sources into .gpkg format and use QGIS to plot them:

$ oq nrml_to --help
usage: oq nrml_to [-h] [-o .] [-c] {csv,gpkg} fnames [fnames ...]

Convert source models into CSV files or a geopackage.

positional arguments:
  {csv,gpkg}        csv or gpkg
  fnames            source model files in XML

optional arguments:
  -h, --help        show this help message and exit
  -o ., --outdir .  output directory
  -c, --chatty      display sources in progress

For instance $ oq nrml_to gpkg source_model.xml -o source_model.gpkg will convert the sources in .gpkg format while $ oq nrml_to csv source_model.xml -o source_model.csv will convert the sources in .csv format. Both are fully supported by QGIS. The CSV format has the advantage of being transparent and easily editable; it also can be imported in a geospatial database like Postgres, if needed.

prepare_site_model#

The command oq prepare_site_model, introduced in engine 3.3, is quite useful if you have a vs30 file with fields lon, lat, vs30 and you want to generate a site model from it. Normally this feature is used for risk calculations: given an exposure, one wants to generate a collection of hazard sites covering the exposure and with vs30 values extracted from the vs30 file with a nearest neighbour algorithm:

$ oq prepare_site_model -h
usage: oq prepare_site_model [-h] [-1] [-2] [-3]
                             [-e [EXPOSURE_XML [EXPOSURE_XML ...]]]
                             [-s SITES_CSV] [-g 0] [-a 5] [-o site_model.csv]
                             vs30_csv [vs30_csv ...]

Prepare a site_model.csv file from exposure xml files/site csv files, vs30 csv
files and a grid spacing which can be 0 (meaning no grid). For each site the
closest vs30 parameter is used. The command can also generate (on demand) the
additional fields z1pt0, z2pt5 and vs30measured which may be needed by your
hazard model, depending on the required GSIMs.

positional arguments:
  vs30_csv              files with lon,lat,vs30 and no header

optional arguments:
  -h, --help            show this help message and exit
  -1, --z1pt0
  -2, --z2pt5           build the z2pt5
  -3, --vs30measured    build the vs30measured
  -e [EXPOSURE_XML [EXPOSURE_XML ...]], --exposure-xml [EXPOSURE_XML [EXPOSURE_XML ...]]
                        exposure(s) in XML format
  -s SITES_CSV, --sites-csv SITES_CSV
  -g 0, --grid-spacing 0
                        grid spacing in km (the default 0 means no grid)
  -a 5, --assoc-distance 5
                        sites over this distance are discarded
  -o site_model.csv, --output site_model.csv
                        output file

The command works in two modes: with non-gridded exposures (the default) and with gridded exposures. In the first case the assets are aggregated in unique locations and for each location the vs30 coming from the closest vs30 record is taken. In the second case, when a grid_spacing parameter is passed, a grid containing all of the exposure is built and the points with assets are associated to the vs30 records. In both cases if the closest vs30 record is over the site_param_distance - which by default is 5 km - a warning is printed.

In large risk calculations, it is quite preferable to use the gridded mode because with a well spaced grid,

  1. the results are the nearly the same than without the grid and

  2. the calculation is a lot faster and uses a lot less memory.

Gridding of the exposure makes large calculations more manageable. The command is able to manage multiple Vs30 files at once. Here is an example of usage:

$ oq prepare_site_model Vs30/Ecuador.csv Vs30/Bolivia.csv -e Exposure/Exposure_Res_Ecuador.csv Exposure/Exposure_Res_Bolivia.csv --grid-spacing=10

Reducing the source model#

Source models are usually large, at the continental scale. If you are interested in a city or in a small region, it makes sense to reduce the model to only the sources that would affect the region, within the integration distance. To fulfil this purpose there is the oq reduce_sm command. The suggestion is run a preclassical calculation (i.e. set calculation_mode=preclassical in the job.ini) with the full model in the region of interest, keep track of the calculation ID and then run:

$ oq reduce_sm <calc_id>

The command will reduce the source model files and add an extension .bak to the original ones.:

$ oq reduce_sm -h
usage: oq reduce_sm [-h] calc_id

Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.

positional arguments:
  calc_id     calculation ID

optional arguments:
  -h, --help  show this help message and exit

Comparing hazard results#

If you are interested in sensitivity analysis, i.e. in how much the results of the engine change by tuning a parameter, the oq compare command is useful. It is able to compare many things, depending on the engine version. Here are a few examples:

$ oq compare hcurves --help
usage: oq compare hcurves [-h] [-f] [-s] [-r 0] [-a 0.001] imt calc_ids [calc_ids ...]

Compare the hazard curves of two or more calculations.

positional arguments:
  imt                   intensity measure type to compare
  calc_ids              calculation IDs

optional arguments:
  -h, --help            show this help message and exit
  -f, --files           write the results in multiple files
  -s , --samplesites    sites to sample (or fname with site IDs)
  -r 0, --rtol 0        relative tolerance
  -a 0.001, --atol 0.001
                        absolute tolerance

$ oq compare hmaps --help
usage: oq compare hmaps [-h] [-f] [-s] [-r 0] [-a 0.001] imt calc_ids [calc_ids ...]

Compare the hazard maps of two or more calculations.

positional arguments:
  imt                   intensity measure type to compare
  calc_ids              calculation IDs

optional arguments:
  -h, --help            show this help message and exit
  -f, --files           write the results in multiple files
  -s , --samplesites    sites to sample (or fname with site IDs)
  -r 0, --rtol 0        relative tolerance
  -a 0.001, --atol 0.001
                        absolute tolerance

$ oq compare uhs --help
usage: oq compare uhs [-h] [-f] [-s] [-r 0] [-a 0.001] calc_ids [calc_ids ...]

Compare the uniform hazard spectra of two or more calculations.

positional arguments:
  calc_ids              calculation IDs

optional arguments:
  -h, --help            show this help message and exit
  -f, --files           write the results in multiple files
  -s , --samplesites    sites to sample (or fname with site IDs)
  -r 0, --rtol 0        relative tolerance
  -a 0.001, --atol 0.001
                        absolute tolerance

Notice the compare uhs is able to compare all IMTs at once, so it is the most convenient to use if there are many IMTs.

Showing calculation attributes#

The command oq show_attrs offers a convenient way to retrieve the attributes of a calculation without needing to open the datastore with any external tools:

$ oq show_attrs -h
usage: oq show_attrs [-h] key [calc_id]

Show the attributes of a HDF5 dataset in the datastore.

positional arguments:
  key         key of the datastore
  calc_id     calculation ID [default: -1]

options:
  -h, --help  show this help message and exit

If the key / is requested, the root attributes are retrieved. For instance:

$ oq show_attrs / 4

checksum32 1572793419
date 2023-04-25T08:19:33
engine_version 3.17.0-gitcae0748
input_size 4021

If the calculation id is not specified, the value of the requested key is retrieved for the latest calculation.

Using collect_rlzs=true in the risk calculation#

Since version 3.12 the engine recognizes a flag collect_rlzs in the risk configuration file. When the flag is set to true, then the hazard realizations are collected together when computing the risk results and considered as one.

Setting collect_rlzs=true is possible only when the weights of the realizations are all equal, otherwise, the engine raises an error. Collecting the realizations makes the calculation of the average losses and loss curves much faster and more memory efficient. It is the recommended way to proceed when you are interested only in mean results. When you have a large exposure and many realizations (say 5 million assets and 1000 realizations, as it is the case for Chile) setting collect_rlzs=true can make possible a calculation that otherwise would run out of memory.

Note 1: when using sampling, collect_rlzs is implicitly set to True, so if you want to export the individual results per realization you must set explicitly collect_rlzs=false.

Note 2: collect_rlzs is not the inverse of the individual_rlzs flag. The collect_rlzs flag indicates to the engine that it should pool together the hazard realizations into a single collective bucket that will then be used to approximate the branch-averaged risk metrics directly, without going through the process of first computing the individual branch results and then getting the weighted average results from the branch results. Whereas the individual_rlzs flag indicates to the engine that the user is interested in storing and exporting the hazard (or risk) results for every realization. Setting individual_rlzs to false means that the engine will store only the statistics (mean and quantile results) in the datastore.

Note 3: collect_rlzs is completely ignored in the hazard part of the calculation, i.e. it does not affect at all the computation of the GMFs, only the computation of the risk metrics.

ignore_covs vs ignore_master_seed#

The vulnerability functions using continuous distributions (lognormal/beta) to characterize the uncertainty in the loss ratio, specify the mean loss ratios and the corresponding coefficients of variation for a set of intensity levels.

There is clearly a performance/memory penalty associated with the propagation of uncertainty in the vulnerability to losses. You can completely remove it by setting

ignore_covs = true

in the job.ini file. Then the engine would compute just the mean loss ratios by ignoring the uncertainty i.e. the coefficients of variation. Since engine 3.12 there is a better solution: setting

ignore_master_seed = true

in the job.ini file. Then the engine will compute the mean loss ratios but also store information about the uncertainty of the results in the asset loss table, in the column “variance”, by using the formulae

\[\begin{split}variance = {\sum}_{i}{\sigma_{i}}^2\ for\ asset\_correl = 0\\ variance = ({\sum}_{i}{\sigma_{i}})^2\ for\ asset\_correl = 1\end{split}\]

in terms of the variance of each asset for the event and intensity level in consideration, extracted from the asset loss and the coefficients of variation. People interested in the details should look at the implementation in gem/oq-engine.

Aggregating by multiple tags#

The engine also supports aggregation by multiple tags. Multiple tags can be indicated as multi-tag and/or various single-tag aggregations:

aggregate_by = NAME_1, taxonomy

or

aggregate_by = NAME_1; taxonomy

Comma , separated values will generate keys for all the possible combinations of the indicated tag values, while semicolon ; will generate keys for the single tags.

For instance the second event based risk demo (the file job_eb.ini) has a line

aggregate_by = NAME_1, taxonomy

and it is able to aggregate both on geographic region (NAME_1) and on taxonomy. There are 25 possible combinations, that you can see with the command oq show agg_keys:

$ oq show agg_keys
| NAME_1_ | taxonomy_ | NAME_1      | taxonomy                   |
+---------+-----------+-------------+----------------------------+
| 1       | 1         | Mid-Western | Wood                       |
| 1       | 2         | Mid-Western | Adobe                      |
| 1       | 3         | Mid-Western | Stone-Masonry              |
| 1       | 4         | Mid-Western | Unreinforced-Brick-Masonry |
| 1       | 5         | Mid-Western | Concrete                   |
| 2       | 1         | Far-Western | Wood                       |
| 2       | 2         | Far-Western | Adobe                      |
| 2       | 3         | Far-Western | Stone-Masonry              |
| 2       | 4         | Far-Western | Unreinforced-Brick-Masonry |
| 2       | 5         | Far-Western | Concrete                   |
| 3       | 1         | West        | Wood                       |
| 3       | 2         | West        | Adobe                      |
| 3       | 3         | West        | Stone-Masonry              |
| 3       | 4         | West        | Unreinforced-Brick-Masonry |
| 3       | 5         | West        | Concrete                   |
| 4       | 1         | East        | Wood                       |
| 4       | 2         | East        | Adobe                      |
| 4       | 3         | East        | Stone-Masonry              |
| 4       | 4         | East        | Unreinforced-Brick-Masonry |
| 4       | 5         | East        | Concrete                   |
| 5       | 1         | Central     | Wood                       |
| 5       | 2         | Central     | Adobe                      |
| 5       | 3         | Central     | Stone-Masonry              |
| 5       | 4         | Central     | Unreinforced-Brick-Masonry |
| 5       | 5         | Central     | Concrete                   |

The lines in this table are associated to the generalized aggregation ID, agg_id which is an index going from 0 (meaning aggregate assets with NAME_1=*Mid-Western* and taxonomy=*Wood*) to 24 (meaning aggregate assets with NAME_1=*Central* and taxonomy=*Concrete*); moreover agg_id=25 means full aggregation.

The agg_id field enters in risk_by_event and in outputs like the aggregate losses; for instance:

$ oq show agg_losses-rlzs
| agg_id | rlz | loss_type     | value       |
+--------+-----+---------------+-------------+
| 0      | 0   | nonstructural | 2_327_008   |
| 0      | 0   | structural    | 937_852     |
+--------+-----+---------------+-------------+
| ...    + ... + ...           + ...         +
+--------+-----+---------------+-------------+
| 25     | 1   | nonstructural | 100_199_448 |
| 25     | 1   | structural    | 157_885_648 |

The exporter (oq export agg_losses-rlzs) converts back the agg_id to the proper combination of tags; agg_id=25, i.e. full aggregation, is replaced with the string *total*.

It is possible to see the agg_id field with the command $ oq show agg_id.

By knowing the number of events, the number of aggregation keys and the number of loss types, it is possible to give an upper limit to the size of risk_by_event. In the demo there are 1703 events, 26 aggregation keys and 2 loss types, so risk_by_event contains at most:

1703 * 26 * 2 = 88,556 rows

This is an upper limit, since some combination can produce zero losses and are not stored, especially if the minimum_asset_loss feature is used. In the case of the demo actually only 20,877 rows are nonzero:

$ oq show risk_by_event
       event_id  agg_id  loss_id           loss      variance
...
[20877 rows x 5 columns]

It is also possible to perform the aggregation by various single-tag aggregations, using the ; separator instead of ,. For example, a line like:

aggregate_by = NAME_1; taxonomy

would produce first the aggregation by geographic region (NAME_1), then by taxonomy. In this case, instead of producing 5 x 5 combinations, only 5 + 5 outputs would be obtained.