# Developing with the engine¶

Some advanced users are interested in developing with the engine, usually to contribute new GMPEs and sometimes to submit a bug fix. There are also users interested in implementing their own customizations of the engine. This part of the manual is for them.

## Prerequisites¶

It is assumed here that you are a competent scientific Python programmer, i.e. that you have a good familiarity with the Python ecosystem (including pip and virtualenv) and its scientific stack (numpy, scipy, h5py, …). It should be noticed that since engine v2.0 there is no need to know anything about databases and web development (unless you want to develop on the WebUI part) so the barrier for contribution to the engine is much lower than it used to be. However, contributing is still nontrivial, and it absolutely necessary to know git and the tools of Open Source development in general, in particular about testing. If this is not the case, you should do some study on your own and come back later. There is a huge amount of resources on the net about these topics. This manual will focus solely on the OpenQuake engine and it assume that you already know how to use it, i.e. you have read the User Manual first.

Before starting, it may be useful to have an idea of the architecture of the engine and its internal components, like the DbServer and the WebUI. For that you should read the Architecture of the OpenQuake engine document.

There are also external tools which are able to interact with the engine, like the QGIS plugin to run calculations and visualize the outputs and the IPT tool to prepare the required input files (except the hazard models). Unless you are developing for such tools you can safely ignore them.

## The first thing to do¶

The first thing to do if you want to develop with the engine is to remove any non-development installation of the engine that you may have. While it is perfectly possible to install on the same machine both a development and a production instance of the engine (it is enough to configure the ports of the DbServer and WebUI) it is easier to work with a single instance. In that way you will have a single code base and no risks of editing the wrong code. A development installation the engine works as any other development installation in Python: you should clone the engine repository, create and activate a virtualenv and then perform a pip install -e . from the engine main directory, as normal. You can find the details here:

https://github.com/gem/oq-engine/blob/master/doc/installing/development.md

It is also possible to develop on Windows ( https://github.com/gem/oq-engine/blob/master/doc/installing/development.md) but very few people in GEM are doing that, so you are on your own, should you encounter difficulties. We recommend Linux, but Mac also works.

Since you are going to develop with the engine, you should also install the development dependencies that by default are not installed. They are listed in the setup.py file, and currently (January 2020) they are pytest, flake8, pdbpp, silx and ipython. They are not required but very handy and recommended. It is the stack we use daily for development.

## Understanding the engine¶

Once you have the engine installed you can run calculations. We recommend starting from the demos directory which contains example of hazard and risk calculations. For instance you could run the area source demo with the following command:

$oq run demos/hazard/AreaSourceClassicalPSHA/job.ini  You should notice that we used here the command oq run while the engine manual recommend the usage of oq engine --run. There is no contradiction. The command oq engine --run is meant for production usage, but here we are doing development, so the recommended command is oq run which will will be easier to debug thanks to the flag --pdb, which will start the python debugger should the calculation fail. Since during development is normal to have errors and problems in the calculation, this ability is invaluable. If you want to understand what happened during the calculation you should generate the associated .rst report, which can be seen with the command $ oq show fullreport

There you will find a lot of interesting information that it is worth studying and we will discuss in detail in the rest of this manual. The most important section of the report is probably the last one, titled “Slowest operations”. For that one can understand the bottlenecks of a calculation and, with experience, he can understand which part of the engine he needs to optimize. Also, it is very useful to play with the parameters of the calculation (like the maximum distance, the area discretization, the magnitude binning, etc etc) and see how the performance change. There is also a command to plot hazard curves and a command to compare hazard curves between different calculations: it is common to be able to get big speedups simply by changing the input parameters in the job.ini of the model, without changing much the results.

There a lot of oq commands: if you are doing development you should study all of them. They are documented here.

## Running calculations programmatically¶

Starting from engine 3.12 the recommended way to run a job programmaticaly is the following:

>> from openquake.commonlib import logs
>> from openquake.calculators.base import calculators
>> with logs.init('job', 'job_ini') as log: # initialize logs
...   calc = calculators(log.get_oqparam(), log.calc_id)
...   calc.run()  # run the calculator


Then the results can be read from the datastore by using the extract API:

>> from openquake.calculators.extract import extract
>> extract(calc.datastore, 'something')


## Case study: computing the impact of a source on a site¶

As an exercise showing off how to use the engine as a library, we will solve the problem of computing the hazard on a given site generated by a given source, with a given GMPE logic tree and a few parameters, i.e. the intensity measure levels and the maximum distance.

The first step is to specify the site and the parameters; let’s suppose that we want to compute the probability of exceeding a Peak Ground Accelation (PGA) of 0.1g by using the ToroEtAl2002SHARE GMPE:

>>> from openquake.commonlib import readinput
... calculation_mode='classical',
... sites='15.0 45.2',
... reference_vs30_type='measured',
... reference_vs30_value='600.0',
... intensity_measure_types_and_levels="{'PGA': [0.1]}",
... investigation_time='50.0',
... gsim='ToroEtAl2002SHARE',
... maximum_distance='200.0'))


Then we need to specify the source:

>>> from openquake.hazardlib import nrml
>>> src = nrml.get('''
...         <areaSource
...         id="126"
...         name="HRAS195"
...         >
...             <areaGeometry discretization="10">
...                 <gml:Polygon>
...                     <gml:exterior>
...                         <gml:LinearRing>
...                             <gml:posList>
...                                 1.5026169E+01 4.5773603E+01
...                                 1.5650548E+01 4.6176279E+01
...                                 1.6273108E+01 4.6083465E+01
...                                 1.6398742E+01 4.6024744E+01
...                                 1.5947759E+01 4.5648318E+01
...                                 1.5677179E+01 4.5422577E+01
...                             </gml:posList>
...                         </gml:LinearRing>
...                     </gml:exterior>
...                 </gml:Polygon>
...                 <upperSeismoDepth>0</upperSeismoDepth>
...                 <lowerSeismoDepth>30</lowerSeismoDepth>
...             </areaGeometry>
...             <magScaleRel>WC1994</magScaleRel>
...             <ruptAspectRatio>1</ruptAspectRatio>
...             <incrementalMFD binWidth=".2" minMag="4.7">
...                 <occurRates>
...                     1.4731083E-02 9.2946848E-03 5.8645496E-03
...                     3.7002807E-03 2.3347193E-03 1.4731083E-03
...                     9.2946848E-04 5.8645496E-04 3.7002807E-04
...                     2.3347193E-04 1.4731083E-04 9.2946848E-05
...                     1.7588460E-05 1.1097568E-05 2.3340307E-06
...                 </occurRates>
...             </incrementalMFD>
...             <nodalPlaneDist>
...                 <nodalPlane dip="5.7596810E+01" probability="1"
...                             rake="0" strike="6.9033586E+01"/>
...             </nodalPlaneDist>
...             <hypoDepthDist>
...                 <hypoDepth depth="1.0200000E+01" probability="1"/>
...             </hypoDepthDist>
...         </areaSource>
... ''')


Then the hazard curve can be computed as follows:

>>> from openquake.hazardlib.calc.hazard_curve import calc_hazard_curve
>>> from openquake.hazardlib import valid
>>> calc_hazard_curve(sitecol, src, gsims, oq)
<ProbabilityCurve
[[0.00508693]]>


## Working with GMPEs directly: the ContextMaker¶

If you are an hazard scientist, you will likely want to interact with the GMPE library in openquake.hazardlib.gsim. The recommended way to do so is in terms of a ContextMaker object.

>>> from openquake.hazardlib.contexts import ContextMaker


In order to instantiate a ContextMaker you first need to populate a dictionary of parameters:

>>> param = dict(maximum_distance=oq.maximum_distance, imtls=oq.imtls,
...              truncation_level=oq.truncation_level,
...              investigation_time=oq.investigation_time)
>>> cmaker = ContextMaker(src.tectonic_region_type, gsims, param)


Then you can use the ContextMaker to generate context objects from the sources:

>>> ctxs = cmaker.from_srcs([src], sitecol)


There is a context for each rupture in the source. In our example, there are 15 magnitudes

>>> len(src.get_annual_occurrence_rates())
15


and the area source contains 47 point sources

>>> len(list(src))
47


so in total there are 15 x 47 = 705 ruptures:

>>> len(ctxs)
705


The ContextMaker takes care of the maximum_distance filtering, so in general the number of contexts is lower than the total number of ruptures, since some ruptures are normally discarded, being distant from the sites.

The contexts contains all the rupture, site and distance parameters. Consider for instance the first context:

>>> ctx = ctxs[0]


Then you have

>>> ctx.mag
4.7
>>> ctx.rrup
array([106.40112646])
>>> ctx.rjb
array([105.8963247])


In this example, the GMPE ToroEtAl2002SHARE does not require site parameters, so calling ctx.vs30 will raise an AttributeError but in general the contexts contains also arrays of site parameters. There is also an array of indices telling which are the sites affected by the rupture associated to the context:

>>> ctx.sids
array([0], dtype=uint32)


Once you have the contexts, the ContextMaker is able to compute means and standard deviations from the underlying GMPEs as follows (for engine version >= 3.13):

>>> mean, sig, tau, phi = cmaker.get_mean_stds(ctxs)


Since in this example there is a single gsim and a single IMT you will get:

>>> mean.shape
(1, 1, 705)
>>> sig.shape
(1, 1, 705)


The shape of the arrays in general is (G, M, N) where G is the number of GSIMs, M the number of intensity measure types and N the total size of the contexts. Since this is an example with a single site, each context has size 1, therefore N = 705 * 1 = 705. In general if there are multiple sites a context M is the total number of affected sites. For instance if there are two contexts and the first affect 1 sites and the second 2 sites then N would be 1 + 2 = 3. This example correspond to 1 + 1 + … + 1 = 705.

From the mean and standard deviation is possible to compute the probabilities of exceedence. The ContextMaker provides a method to compute directly the probability map, which internally calls cmaker.get_mean_stds(ctxs):

>>> cmaker.get_pmap(ctxs)
{0: <ProbabilityCurve
[[0.00508693]]>}


This is exactly the result provided by calc_hazard_curve(sitecol, src, gsims, oq) in the section before.

If you want to know exactly how get_pmap works you are invited to look at the source code in openquake.hazardlib.contexts.

## Working with verification tables¶

Hazard scientists implementing a new GMPE must provide verification tables, i.e. CSV files containing inputs and expected outputs.

For instance, for the Atkinson2015 GMPE (chosen simply because is the first GMPE in lexicographic order in hazardlib) the verification table has a structure like this:

rup_mag,dist_rhypo,result_type,pgv,pga,0.03,0.05,0.1,0.2,0.3,0.5
2.0,1.0,MEAN,5.50277734e-02,3.47335058e-03,4.59601700e-03,7.71361460e-03,9.34624779e-03,4.33207607e-03,1.75322233e-03,3.44695521e-04
2.0,5.0,MEAN,6.43850933e-03,3.61047741e-04,4.57949482e-04,7.24558049e-04,9.44495571e-04,5.11252304e-04,2.21076069e-04,4.73435138e-05
...


The columns starting with rup_ contains rupture parameters (the magnitude in this example) while the columns starting with dist_ contains distance parameters. The column result_type is a string in the set {“MEAN”, “INTER_EVENT_STDDEV”, “INTRA_EVENT_STDDEV”, “TOTAL_STDDEV”}. The remaining columns are the expected results for each intensity measure type; in the the example the IMTs are PGV, PGA, SA(0.03), SA(0.05), SA(0.1), SA(0.2), SA(0.3), SA(0.5).

Starting from engine version 3.13, it is possible to instantiate a ContextMaker and the associated contexts from a GMPE and its verification tables with a few simple steps. First of all one must instantiate the GMPE:

>>> from openquake.hazardlib import valid
>>> gsim = valid.gsim("Atkinson2015")


Second, one can determine the path names to the verification tables as follows (they are in a subdirectory of hazardlib/tests/gsim/data):

>>> import os
>>> from openquake.hazardlib.tests.gsim import data
>>> fnames = [os.path.join(datadir, f) for f in ["ATKINSON2015_MEAN.csv",
...           "ATKINSON2015_STD_INTER.csv", "ATKINSON2015_STD_INTRA.csv",
...           "ATKINSON2015_STD_TOTAL.csv"]]


Then it is possible to instantiate the ContextMaker associated to the GMPE and a pandas DataFrame associated to the verification tables in a single step:

>>> from openquake.hazardlib.tests.gsim.utils import read_cmaker_df, gen_ctxs
>>> cmaker, df = read_cmaker_df(gsim, fnames)
>>> list(df.columns)
['rup_mag', 'dist_rhypo', 'result_type', 'damping', 'PGV', 'PGA', 'SA(0.03)', 'SA(0.05)', 'SA(0.1)', 'SA(0.2)', 'SA(0.3)', 'SA(0.5)', 'SA(1.0)', 'SA(2.0)', 'SA(3.0)', 'SA(5.0)']


Then you can immediately compute mean and standard deviations and compare with the values in the verification table:

>>> mean, sig, tau, phi = cmaker.get_mean_stds(gen_ctxs(df))


sig refers to the “TOTAL_STDDEV”, tau to the “INTER_EVENT_STDDEV” and phi to the “INTRA_EVENT_STDDEV”. This is how the tests in hazardlib are implemented. Interested users should look at the code in https://github.com/gem/oq-engine/blob/master/openquake/hazardlib/tests/gsim/utils.py.