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Frequently Asked Questions (FAQ)#

FAQ for IT issues#

Help! Should I disable hyperthreading on my laptop/desktop?#

Disabling hyperthreading - when possible - is recommended since it will save memory. Suppose for instance that you have a laptop with a i9 processor with 20 threads and 16 GB of RAM. It seems a lot at the time of this writing (early 2022). In reality it is not. The operating system will consume some memory, the browser will consume a lot of memory, you may have other applications open and you may end up with less than 10 GB of available memory. If hyperthreading is enabled the engine will see 10x2 = 20 cores; running parallel computations may easily consume 0.5 GB per core, i.e. 10 GB, so you will run out of memory. With hyperthreading disabled you will still have 5 GB of available RAM.

Note: on a linux machine you can try disable hyperthreading temporarily with the command sudo echo off > /sys/devices/system/cpu/smt/control: however, this setting will not survive a reboot. Also, on some systems this command will not work. If you cannot disable hyperthreading just make sure that if you have enough memory: we recommend at least 2 GB per thread.

Help! My windows server with 32/64 or more cores hangs!#

Some users reported this issue. It is due to a limitation of Python multiprocessing module on Windows. In all cases we have seen, the problem was solved by disabling hyperthreading. Otherwise you can reduce the number of used cores by setting the parameter num_cores in the file openquake.cfg as explained below.

Help! I want to limit the number of cores used by the engine#

This is another way to save memory. If you are on a single machine, the way to do it is to edit the file openquake.cfg and add the lines (if for instance you want to use 8 cores):

[distribution]
num_cores = 8

If you are on a cluster you must edit the section [zworkers] and the parameter host_cores, replacing the -1 with the number of cores to be used on each machine.

Help! I am running out of memory!#

If you are on a laptop, the first thing to do is close all memory consuming applications. Remember that running the engine from the command-line is the most memory-efficient way to run calculations (browsers can use significant memory from your laptop). You can also limit the number of parallel threads as explained before (i.e. disable hyperthreading, reduce num_cores) or disable parallelism altogether. If you still run out of memory, then you must reduce your calculation or upgrade your system.

Help! I am running out of disk space!#

By default the engine stores the calculations in the directory $HOME/oqdata. If there is not much space on that partition you may run out of disk space. The solution is to change the location where the calculations are stored, pointing to an external disk or in general to a partition with a lot of space. You can do it temporarily by setting the environment variable OQ_DATADIR or permanently by changing the openquake.cfg file and adding a couple of lines like:

[directory]
shared_dir = /mnt/largedisk

Then the data will be stored in /mnt/largedisk/<username>/oqdata.

Help! Is it possible to configure multiple installations of the engine to run independently on the same computer?#

Yes, it is possible, as long as their virtual environments are stored in different directories.

When you install the engine using the install.py script, you may specify the --venv parameter to choose in which directory the engine virtual environment must be stored. On an existing installation of the engine, you can run the command:

$ oq info venv

to retrieve the path of its virtual environment.

Another parameter accepted by the install.py script is --dbport, that specifies the port number used by the engine dbserver. This is only relevant for server installations. By default, the port is set to 1907. The port can be customized through the attribute port of section [dbserver] in the configuration file openquake.cfg, placed inside the virtual environment directory, e.g.:

[dbserver]
port = 1908

Can two installations of the engine share the same oqdata directory?#

The oqdata directory, that stores calculation data, can safely be shared between two different instances of the engine working on a same computer. Each HDF5 dataset is independent from all others in the datastore and it has a unique identifier. It is possible to determine the version of the engine that produced each HDF5 file (calc_<calc_id>.hdf5) using the command:

$ oq show_attrs / <calc_id>

where / indicates the root attributes (date, engine_version, etc.) and <calc_id> (an integer number) is the calculation identifier. In case the calculation id is not specified, the attributes are retrieved for the latest calculation.

Different installation methods#

The OpenQuake engine has several installation methods. To choose the one that best fits your needs take a look at the installation overview.

Supported operating systems#

Binary packages are provided for Windows. For all other systems use the universal installer. We also provide Docker containers.

Binary packages are provided for the following 64bit operating systems:

- Windows 10
- macOS 11.6+
- Linux Ubuntu 18.04+ and RedHat/CentOS 7/RockyLinux 8 via deb and rpm
- Any other generic Linux distribution via the universal installer
- Docker hosts

A 64bit operating system is required. Please refer to each OS specific page for details about requirements.

Unsupported operating systems#

  • Windows 8 may or may not work and we will not provide support for it Binary packages may work on Ubuntu derivatives and Debian if the dependencies are satisfied; these configurations are known to work:

  • Ubuntu 18.04 (Bionic) packages work on Debian 10.0 (Buster)

  • Ubuntu 20.04 (Focal) packages work on Debian 11.0 (Bullseye)

These configurations however are not tested and we cannot guarantee on the quality of the results. Use at your own risk.

32bit support#

The OpenQuake engine requires a 64bit operating system. Starting with version v2.3 of the Engine binary installers and packages aren’t provided for 32bit operating systems anymore.

MPI support#

MPI is not supported by the OpenQuake engine. Task distribution across network interconnected nodes is done via zmq. The worker nodes must have read access to a shared file system writeable from the master node. Data transfer is made on TCP/IP connection.

MPI support may be added in the future if sponsored by someone. If you would like to help support development of OpenQuake engine, please contact us at partnership@globalquakemodel.org.

Python 2.7 compatibility#

Support for Python 2.7 has been dropped. The last version of the Engine compatible with Python 2.7 is OpenQuake engine version 2.9 (Jeffreys).

Python scripts that import openquake#

If a third party python script (or a Jupyter notebook) needs to import openquake as a library (as an example: from openquake.commonlib import readinput) you must use a virtual environment and install a local copy of the Engine:

$ python3 -m venv </path/to/myvenv>
$ . /path/to/myvenv/bin/activate
$ pip3 install openquake.engine

‘The openquake master lost its controlling terminal’ error#

When the OpenQuake engine is driven via the oq command over an SSH connection an associated terminal must exist throughout the oq calculation lifecycle. The openquake.engine.engine.MasterKilled: The openquake master lost its controlling terminal error usually means that the SSH connection has dropped or the controlling terminal has been closed having a running computation attached to it.

To avoid this error please use nohup, screen, tmux or byobu when using oq via SSH. More information is available on Running the OpenQuake engine.

Certificate verification on macOS#

On macOS you can get the following error:

Traceback (most recent call last):
  File "/Users/openquake/py36/bin/oq", line 11, in <module>
    load_entry_point('openquake.engine', 'console_scripts', 'oq')()
  File "/Users/openquake/openquake/oq-engine/openquake/commands/__main__.py", line 53, in oq
    parser.callfunc()
  File "/Users/openquake/openquake/oq-engine/openquake/baselib/sap.py", line 181, in callfunc
    return self.func(**vars(namespace))
  File "/Users/openquake/openquake/oq-engine/openquake/baselib/sap.py", line 251, in main
    return func(**kw)
  File "/Users/openquake/openquake/oq-engine/openquake/commands/engine.py", line 210, in engine
    exports, hazard_calculation_id=hc_id)
  File "/Users/openquake/openquake/oq-engine/openquake/commands/engine.py", line 70, in run_job
    eng.run_calc(job_id, oqparam, exports, **kw)
  File "/Users/openquake/openquake/oq-engine/openquake/engine/engine.py", line 341, in run_calc
    close=False, **kw)
  File "/Users/openquake/openquake/oq-engine/openquake/calculators/base.py", line 192, in run
    self.pre_execute()
  File "/Users/openquake/openquake/oq-engine/openquake/calculators/scenario_damage.py", line 85, in pre_execute
    super().pre_execute()
  File "/Users/openquake/openquake/oq-engine/openquake/calculators/base.py", line 465, in pre_execute
    self.read_inputs()
  File "/Users/openquake/openquake/oq-engine/openquake/calculators/base.py", line 398, in read_inputs
    self._read_risk_data()
  File "/Users/openquake/openquake/oq-engine/openquake/calculators/base.py", line 655, in _read_risk_data
    haz_sitecol, assetcol)
  File "/Users/openquake/openquake/oq-engine/openquake/calculators/base.py", line 821, in read_shakemap
    oq.discard_assets)
  File "/Users/openquake/openquake/oq-engine/openquake/hazardlib/shakemap.py", line 100, in get_sitecol_shakemap
    array = download_array(array_or_id)
  File "/Users/openquake/openquake/oq-engine/openquake/hazardlib/shakemap.py", line 74, in download_array
    contents = json.loads(urlopen(url).read())[
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py", line 223, in urlopen
    return opener.open(url, data, timeout)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py", line 526, in open
    response = self._open(req, data)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py", line 544, in _open
    '_open', req)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py", line 504, in _call_chain
    result = func(*args)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py", line 1361, in https_open
    context=self._context, check_hostname=self._check_hostname)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/urllib/request.py", line 1320, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)>

Please have a look at /Applications/Python 3.8/ReadMe.rtf for possible solutions. If unsure run from a terminal the following command:

sudo /Applications/Python\ 3.8/install_certificates.command  # NB: use the appropriate Python version!

FAQ about running hazard calculations#

Can I estimate the runtime of a classical calculation without running it?#

Since engine v3.15 you can. The trick is to run a reduced calculation first, by using the command:

$ oq engine --run job.ini --sample-sources=0.01

This will reduce the number of ruptures by ~100 times so that the reduced calculation will complete in a reasonable amount of time. Then in the log you will see the estimate runtime for the full calculation. For instance for the SHARE model on a computer with an i7 processor you will see something like this:

[2022-04-19 08:57:05 #4054 INFO] Estimated time 72.3 hours

The estimate is rather rough, so do not take it at the letter. The runtime can be reduced by orders of magnitude by tuning parameters like the pointsource_distance and ps_grid_spacing, discussed at length in the advanced manual.

How should I interpret the “Realizations” output?#

This is explained in the logic trees section

How do I export the hazard curves/maps/uhs for each realization?#

By default the engine only exports statistical results, i.e. the mean hazard curves/maps/uhs. If you want the individual results you must set individual_rlzs=true in the job.ini files. Please take care: if you have thousands of realizations (which is quite common) the data transfer and disk space requirements will be thousands of times larger than just returning the mean results: the calculation might fail. This is why by default individual_rlzs is false.

Argh, I forgot to set individual_rlzs! Must I repeat the calculation?#

No, just set individual_rlzs=true in the job.ini and run:

$ oq engine --run job.ini --hc=<ID> --exports csv

where <ID> must be replaced with the ID of the original calculation. The individual outputs will be regenerated by reusing the result of the previous calculation: it will be a lot faster than repeating the calculation from scratch.

Argh, I set the wrong poes in the job.ini? Must I repeat the calculation?#

No, set the right poes in the job.ini and as before run:

$ oq engine --run job.ini --hc=<ID> --exports csv

where <ID> must be replaced with the ID of the original calculation. Hazard maps and UHS can be regenerated from an existing calculation quite efficiently.

I am getting an error “disaggregation matrix is too large”!#

This means that you have too many disaggregation bins. Please act on the binning parameters, i.e. on mag_bin_width, distance_bin_width, coordinate_bin_width, num_epsilon_bins. The most relevant parameter is coordinate_bin_width which is quadratic: for instance by changing from coordinate_bin_width=0.1 to coordinate_bin_width=1.0 the size of your disaggregation matrix will be reduced by 100 times.

What is the relation between sources, ruptures, events and realizations?#

A single rupture can produce multiple seismic events during the investigation time. How many depends on the number of stochastic event sets, on the rupture occurrence rate and on the ses_seed parameters, as explained here. In the engine a rupture is uniquely identified by a rupture ID, which is a 32 bit positive integer. Starting from engine v3.7, seismic events are uniquely identified by an event ID, which is a 32 bit positive integer. The relation between event ID and rupture ID is given encoded in the events table in the datastore, which also contains the realization associated to the event. The properties of the rupture generating the events can be ascertained by looking inside the ruptures table. In particular ther srcidx contains the index of the source that generated the rupture. The srcidx can be used to extract the properties of the sources by looking inside the source_info table, which contains the source_id string used in the XML source model.

Can I run a calculation from a Jupyter notebook?#

You can run any kind of calculation from a Jupyter notebook, but usually calculations are long and it is not convenient to run them interactively. Scenarios are an exception, since they are usually fast, unless you use spatial correlation with a lot of sites. Assuming the parameters of the calculation are in a job.ini file you can run the following lines in the notebook:

In[1]: from openquake.calculators.base import run_calc
In[2]: calc = run_calc('job.ini')

Then you can inspect the contents of the datastore and perform your postprocessing:

In[3]: calc.datastore.open('r')  # open the datastore for reading

The inner format of the datastore is not guaranteed to be the same across releases and it is not documented, so this approach is recommended to the most adventurous people.

how do I plot/analyze/postprocess the results of a calculation?#

The official way to plot the result of a calculation is to use the QGIS plugin. There is also a command oq plot included with the engine distribution with some capabilities, please run

$ oq plot examples

to get the full list of available plots.

However you may want a kind of plot which is not available, or you may want to batch-produce hundreds of plots, or you may want to plot the results of a postprocessing operation. In such cases you need to use the extract API and to write your own plotting/postprocessing code.

FAQ about running risk calculations#

What implications do random_seed, ses_seed, and master_seed have on my calculation?#

The OpenQuake engine uses (Monte Carlo) sampling strategies for propagating epistemic uncertainty at various stages in a calculation. The sampling is based on numpy’s pseudo-random number generator. Setting a ‘seed’ is useful for controlling the initialization of the random number generator, and repeating a calculation using the same seed should result in identical random numbers being generated each time.

Three different seeds are currently recognized and used by the OpenQuake engine.

  • random_seed is the seed that controls the sampling of branches from both the source model logic tree and the ground motion model logic tree, when the parameter number_of_logic_tree_samples is non-zero. It affects both classical calculations and event based calculations.

  • ses_seed is the seed that controls the sampling of the ruptures in an event based calculation (but notice that the generation of ruptures is also affected by the random_seed, unless full enumeration of the logic tree is used, due to the reasons mentioned in the previous paragraph). It is also used to generate rupture seeds for both event based and scenario calculations, which are in turn used for sampling ground motion values / intensities from a Ground Motion Model, when the parameter truncation_level is non-zero. NB: before engine v3.11, sampling ground motion values / intensities from a GMM in a scenario calculation was incorrectly controlled by the random_seed and not the ses_seed.

  • master_seed is used when generating the epsilons in a calculation involving vulnerability functions with non-zero coefficients of variations. This is a purely risk-related seed, while the previous two are hazard-related seeds.

What values should I use for investigation_time, ses_per_logic_tree_path, and number_of_logic_tree_samples in my calculation? And what does the risk_investigation_time parameter for risk calculations do?#

Setting the number_of_logic_tree_samples is relatively straightforward. This parameter controls the method used for propagation of epistemic uncertainty represented in the logic-tree structure and calculation of statistics such as the mean, median, and quantiles of key results.

number_of_logic_tree_samples = 0 implies that the engine will perform a so-called ‘full-enumeration’ of the logic-tree, i.e., it will compute the requested results for every end-branch, or ‘path’ in the logic-tree. Statistics are then computed with consideration of the relative weights assigned to each end-branch.

For models that have complex logic-trees containing thousands, or even millions of end-branches, a full-enumeration calculation will be computationally infeasible. In such cases, a sampling strategy might be more preferable and much more tractable. Setting, for instance, number_of_logic_tree_samples = 100 implies that the engine will randomly choose (i.e., ‘sample’) 100 end-branches from the complete logic-tree based on the weight assignments. The requested results will be computed for each of these 100 sampled end-branches. Statistics are then computed using the results from the 100 sampled end-branches, where the 100 sampled end-branches are considered to be equi-weighted (1/100 weight for each sampled end-branch). Note that once the end-branches have been chosen for the calculation, the initial weights assigned in the logic-tree files have no further role to play in the computation of the statistics of the requested results. As mentioned in the previous section, changing the random_seed will result in a different set of paths or end-branches being sampled.

The risk_investigation_time parameter is also fairly straightforward. It affects only the risk part of the computation and does not affect the hazard calculations or results. Two of the most common risk metrics are (1) the time-averaged risk value (damages, losses, fatalities) for a specified time-window, and (2) the risk values (damages, losses, fatalities) corresponding to a set of return periods. The risk_investigation_time parameter controls the time-window used for computing the former category of risk metrics. Specifically, setting risk_investigation_time = 1 will produce average annual risk values; such as average annual collapses, average annual losses, and average annual fatalities. This parameter does not affect the computation of the latter category of risk metrics. For example, the loss exceedance curves will remain the same irrespective of the value set for risk_investigation_time, provided all other parameters are kept the same.

Next, we come to the two parameters investigation_time and ses_per_logic_tree_path.

If the hazard model includes time-dependent sources, the choice of the investigation_time will most likely be dictated by the source model(s), and the engine will raise an error unless you set the value to that required by the source model(s). In this case, the ses_per_logic_tree_path parameter can be used to control the effective length of the stochastic event-set (or event catalog) for each end-branch, or ‘path’, for both full-enumeration and sampling-based calculations. As an example, suppose that the hazard model requires you to set investigation_time = 1, because the source model defines 1-year occurrence probabilities for the seismic sources. Further, suppose you have decided to sample 100 branches from the complete logic-tree as your strategy to propagate epistemic uncertainty. Now, setting ses_per_logic_tree_path = 10000 will imply that the engine will generate 10,000 ‘event-sets’ for each of the 100 sampled branches, where each ‘event-set’ spans 1 year. Note that some of these 1-year event-sets could be empty, implying that no events were generated in those particular 1-year intervals.

On the other hand, if the hazard model contains only time-independent sources, there is no hard constraint on the investigation_time parameter. In this case, the ses_per_logic_tree_path parameter can be used in conjunction with the investigation_time to control the effective length of the stochastic event-set (or event catalog) for each end-branch, or ‘path’, for both full-enumeration and sampling-based calculations. For instance, the following three calculation settings would produce statistically equivalent risk results:

Calculation 1

number_of_logic_tree_samples = 0
investigation_time = 1
ses_per_logic_tree_path = 10000
risk_investigation_time = 1

Calculation 2

number_of_logic_tree_samples = 0
investigation_time = 50
ses_per_logic_tree_path = 200
risk_investigation_time = 1

Calculation 3

number_of_logic_tree_samples = 0
investigation_time = 10000
ses_per_logic_tree_path = 1
risk_investigation_time = 1

The effective catalog length per branch in such cases is investigation_time × ses_per_logic_tree_path. The choice of how to split the effective catalog length amongst the two parameters is up to the modeller/analyst’s preferrence, and there are no performance implications for perferring particular choices.

Note that if you were also computing hazard curves and maps in the above example calculations, the hazard curves output in the first calculation would provide probabilities of exceedance in 1 year, whereas the hazard curves output in the second calculation would provide probabilities of exceedance in 50 years. All risk results for the three calculations will be statistically identical.

Why I am getting the warning “A big variation in the losses is expected”?#

In event based risk calculations the warning means that your effective investigation time is too small, you do not have enough events to have sensible statistics and therefore your loss curves will strongly depend on the choice of the ses_seed. The solution is to increase the parameters number_of_logic_tree_samples, ses_per_logic_tree_path or investigation_time.

The way the engine determines that the effective investigation time is insufficient is to split the event IDs in two sets of odd and even IDs. If the number of relevant events is large, you expect the two sets to be statistically equivalent and to produce very similar loss curves; on the other hand, if you get the warning, it means that the odd and even loss curves are quite different. Notice that the relevant events are the ones corresponding to nonzero losses, therefore for fatalities it is quite common to get the warning. In that case you can accept that the precision on such curves is low and go on, since it could be impractical to increase the effective investigation time (in the sense that the calculation could get too slow or could even not run due to out-of-memory/out-of-disk-space errors).

The command oq show delta_loss:<loss_index> displays the loss curves for the odd and even sets of relevant events, so that you can get an idea of the discrepancies. It is always available, even if the warning is not displayed. The loss indexes corresponding to nonzero losses can be extracted with the command:

$ oq show loss_ids
| loss_type     | loss_id |
|---------------+---------|
| nonstructural | 2       |
| structural    | 3       |

For instance the even/odd loss curve for nonstructural can be displayed as follows:

$ oq show delta_loss:2
              loss          even           odd     delta
5     9.794486e+07  8.659461e+07  1.026752e+08  0.084961
10    2.627667e+08  2.463170e+08  2.913166e+08  0.083699
20    5.115378e+08  5.389827e+08  5.060021e+08  0.031561
50    9.174307e+08  9.665251e+08  8.286646e+08  0.076794
100   1.214801e+09  1.126333e+09  1.305085e+09  0.073518
200   2.018548e+09  1.718065e+09  2.063922e+09  0.091449
500   3.366133e+09  2.235775e+09  5.022807e+09  0.383964
1000  5.022807e+09  3.366133e+09  8.697419e+09  0.441933

That gives an indication of the error on the loss curve, which is normally quite large. The delta is the relative error computed with the formula:

delta = |loss_even - loss_odd| / (loss_even + loss_odd)

In many cases there is nothing you can do about that since the statistical error goes down with 1 / sqrt(num_events) and therefore it requires a quadratic effort to reduce it (i.e. 100 times more computations only reduce the error 10 times).

In scenario risk calculations there are no loss curves, however you can still get the same warning if the average losses (averaged over the number of events) are quite different between odd and even events. In that case you can get something as follows:

$ oq show delta_loss:1
           even           odd     delta
0  5.242724e+09  5.175095e+09  0.006492
1  4.857120e+09  5.470883e+09  0.059427

where the index correspond to the realization index (i.e. the GSIM).

Can I disaggregate my losses by source?#

Starting from engine v3.10 you can get a summary of the total losses across your portfolio of assets arising from each seismic source, over the effective investigation time. For instance run the event based risk demo as follows:

$ oq engine --run job.ini

and export the output “Source Loss Table”. You should see a table like the one below:

source

loss_type

loss_value

231

nonstructural

1.07658E+10

231

structural

1.63773E+10

386

nonstructural

3.82246E+07

386

structural

6.18172E+07

238

nonstructural

2.75016E+08

238

structural

4.58682E+08

239

nonstructural

4.51321E+05

239

structural

7.62048E+05

240

nonstructural

9.49753E+04

240

structural

1.58884E+05

280

nonstructural

6.44677E+03

280

structural

1.14898E+04

374

nonstructural

8.14875E+07

374

structural

1.35158E+08

⋮

⋮

⋮

from which one can infer the sources causing the highest total losses for the portfolio of assets within the specified effective investigation time.

How does the engine compute loss curves (a.k.a. Probable Maximum Losses)?#

The PML for a given return period is built from the losses in the event loss table. The algorithm used is documented in detail in the advanced manual at the end of the section about risk calculations. The section also explains why sometimes the PML or the loss curves contain NaN values (the effective investigation time is too short compared to the return period). Finally, it also explains why the PML is not additive.

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