Working with the dstore#
Advanced users could find useful to work directly with the dstore*. The use of the dstore is consider experimental as the structure may change across versions. Here we document some of the most common operations for end-users.
Read dstore with python#
Read the dstore for a given calculation id and list availabe datastore keys:
>> from openquake.commonlib.datastore import read
>> dstore = read(calc_id)
>> list(dstore)
Extract the parameters used in the calculation:
>> oq = dstore["oqparam"]
>> list(oq)
>> oq.rupture_mesh_spacing
2.0
>> oq.ses_per_logic_tree_path
1000
Reading outputs with pandas#
If you are a scientist familiar with Pandas, you will be happy to know that it is possible to process the engine outputs with it. Here we will give a couple of examples
Hazard curves#
Suppose you ran the hazard AreaSourceClassicalPSHA demo, with calculation ID=42; then you can process the hazard curves as follows:
>> from openquake.commonlib.datastore import read
>> dstore = read(42)
>> df = dstore.read_df('hcurves-stats', index='lvl',
.. sel=dict(imt='PGA', stat='mean', site_id=0))
site_id stat imt value
lvl
0 0 b'mean' b'PGA' 0.999982
1 0 b'mean' b'PGA' 0.999949
2 0 b'mean' b'PGA' 0.999850
3 0 b'mean' b'PGA' 0.999545
4 0 b'mean' b'PGA' 0.998634
.. ... ... ... ...
44 0 b'mean' b'PGA' 0.000000
The dictionary dict(imt='PGA', stat='mean', site_id=0) is used to select subsets of the entire dataset: in this case
hazard curves for mean PGA for the first site.
If you do not like pandas, or for some reason you prefer plain numpy arrays, you can get a slice of hazard curves by
using the .sel method:
>> arr = dstore.sel('hcurves-stats', imt='PGA', stat='mean', site_id=0)
>> arr.shape # (num_sites, num_stats, num_imts, num_levels)
(1, 1, 1, 45)
Notice that the .sel method does not reduce the number of dimensions of the original array (4 in this case), it just
reduces the number of elements. It was inspired by a similar functionality in xarray.
Event loss table#
Suppose you ran the risk EventBasedRisk demo, with calculation ID=50; then you can process the event loss table
(dstore key risk-by-event) as follows:
>> from openquake.commonlib.datastore import read
>> dstore = read(50)
>> df = dstore.read_df('risk_by_event')
event_id agg_id loss_id variance loss
0 217 5 2 1.334203e+14 4.602987e+08
1 217 5 3 5.384151e+14 7.817219e+08
2 218 5 2 4.987701e+11 3.446305e+07
3 218 5 3 1.859565e+12 5.651559e+07
4 219 5 2 6.985281e+10 6.659544e+06
... ... ... ... ... ...
7389 1739 1 3 2.229089e+11 2.603723e+06
7390 1740 1 2 4.362298e+11 1.359160e+07
7391 1740 1 3 1.462301e+12 2.110337e+07
7392 1741 1 2 7.072199e+11 2.098369e+07
7393 1741 1 3 4.615159e+12 3.818096e+07
It is possible to extract the agg_key with:
>> agg_key = pd.DataFrame({'agg_key':dstore['agg_keys']})
>> agg_key['agg_id'] = agg_key.index
To get the corresponding loss_id, users need:
>> from openquake.risklib.scientific import LOSSID
>> pd.DataFrame.from_dict(LOSSID, orient='index')
0
business_interruption 0
contents 1
nonstructural 2
structural 3
... ...
structural_ins+contents_ins+business_interrupti... 40
nonstructural_ins+contents_ins+business_interru... 41
structural_ins+nonstructural_ins+contents_ins+b... 42
Example: how many events per magnitude?#
When analyzing an event based calculation, users are often interested in checking the magnitude-frequency distribution,
i.e. to count how many events of a given magnitude are present in the stochastic event set for a fixed investigation
time and a fixed ses_per_logic_tree_path. You can do that with code like the following:
def print_events_by_mag(calc_id):
# open the DataStore for the current calculation
dstore = datastore.read(calc_id)
# read the events table as a Pandas dataset indexed by the event ID
events = dstore.read_df('events', 'id')
# find the magnitude of each event by looking at the 'ruptures' table
events['mag'] = dstore['ruptures']['mag'][events['rup_id']]
# group the events by magnitude
for mag, grp in events.groupby(['mag']):
print(mag, len(grp)) # number of events per group
If you want to know the number of events per realization and per stochastic event set you can just refine the groupby
clause, using the list ['mag', 'rlz_id', 'ses_id'] instead of simply ['mag'].
Given an event, it is trivial to extract the ground motion field generated by that event, if it has been stored
(warning: events producing zero ground motion are not stored). It is enough to read the gmf_data table indexed by
event ID, i.e. the eid field:
>> eid = 20 # consider event with ID 20
>> gmf_data = dstore.read_df('gmf_data', index='eid') # engine>3.11
>> gmf_data.loc[eid]
sid gmv_0
eid
20 93 0.113241
20 102 0.114756
20 121 0.242828
20 142 0.111506
The gmv_0 refers to the first IMT; here I have shown an example with a single IMT, in presence of multiple IMTs you
would see multiple columns gmv_0, gmv_1, gmv_2, .... The sid column refers to the site ID.
As a following step, you can compute the hazard curves at each site from the ground motion values by using the function gmvs_to_poes, available since engine 3.10:
>> from openquake.commonlib.calc import gmvs_to_poes
>> gmf_data = dstore.read_df('gmf_data', index='sid')
>> df = gmf_data.loc[0] # first site
>> gmvs = [df[col].to_numpy() for col in df.columns
.. if col.startswith('gmv_')] # list of M arrays
>> oq = dstore['oqparam'] # calculation parameters
>> poes = gmvs_to_poes(gmvs, oq.imtls, oq.ses_per_logic_tree_path)
This will return an array of shape (M, L) where M is the number of intensity measure types and L the number of levels
per IMT. This works when there is a single realization; in presence of multiple realizations one has to collect
together set of values corresponding to the same realization (this can be done by using the relation event_id -> rlz_id)
and apply gmvs_to_poes to each set.
NB: another quantity one may want to compute is the average ground motion field, normally for plotting purposes. In
that case special care must be taken in the presence of zero events, i.e. events producing a zero ground motion value
(or below the minimum_intensity): since such values are not stored you have to enlarge the gmvs arrays with the
missing zeros, the number of which can be determined from the events table for each realization. The engine is able
to compute the avg_gmf correctly, however, since it is an expensive operation, it is done only for small
calculations.