Feature: store "aggregate" disaggregation

[chrisdicaprio]

We want to be able to store an aggregate disagg (e.g. typically, a mean of all disaggregations of a logic tree)

unique identifiers of a disaggregation are:

• vs30
• location lat,, lon
• hazard_model_id (THS)
• annual PoE (what about PoO)
• IMT
• agg of the hazard curve (e.g. 'mean', '0.5', etc)
• agg of the disaggregation

Data to be stored

• hazard level of shaking. This is a single floating point value
• the bins (i.e. the values of magnitude, distance, etc) at which we've calculated the disagg: equivalent to levels in a hazard aggregation
• the disaggregation probabilities: equivalent to values in a hazard aggregation

Data Structure

The diaggregation is a multi-dimensional matrix (typically 4 dimensions). In the example data provided the dimensions are

• magnitude
• distance
• trt (tectonic region type)
• epsilon

It is possible to have a disagg over a subset of those dimensions. It may also be possible that we have more or other dimensions (e.g. lat and lon)

Code References

• saving data: https://github.com/GNS-Science/toshi-hazard-post/blob/f8c0190be6ea1d03deae0a0ff4d8285850dff0c2/toshi_hazard_post/util/file_utils.py#L179
• loading data: https://github.com/chrisdicaprio/nzshm-hazlab/blob/8c518f671c93eb9142e61a85596c812b21673a3e/nzshm_hazlab/hazard_report/disagg_report_builder.py#L66
• using data: https://github.com/chrisdicaprio/nzshm-hazlab/blob/8c518f671c93eb9142e61a85596c812b21673a3e/nzshm_hazlab/disagg_data_functions.py#L50

Sample Data

sample_data.zip

For the sample data provided, the axis of the 4d disagg array are:

• 0: magnitude
• 1: distance
• 2: trt
• 3: epsilon

Similarly, bins[i] are the values of the bins for the dimension i
bins[4] stores the value of the hazard level of shaking

vs30 = 750
location = lat: 39.000 lon:175.930
hazard_model_id = 'SLT_v8_gmm_v2_FINAL'
annual probability = 0.038559172
IMT = 'SA(0.5)'
agg = 'mean'
deagg_SLT_v8_gmm_v2_FINAL_-39.000~175.930_750_SA(0.5)86_eps-dist-mag-trt.npy
bins_SLT_v8_gmm_v2_FINAL-39.000~175.930_750_SA(0.5)_86_eps-dist-mag-trt.npy

vs30 = 400
location = lat:-42.780 lon:171.540'
hazard_model_id = 'SLT_v8_gmm_v2_FINAL'
annual probability = 0.019688642
IMT = 'SA(1.5)'
agg = 'mean'
deagg_SLT_v8_gmm_v2_FINAL_-42.780~171.540_400_SA(1.5)63_eps-dist-mag-trt.npy
bins_SLT_v8_gmm_v2_FINAL-42.780~171.540_400_SA(1.5)_63_eps-dist-mag-trt.npy

[chrisdicaprio]

aggregate possibilities for Enum
["mean", "cov", "std", "0.005", "0.01", "0.025", "0.05", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6", "0.7", "0.8", "0.9", "0.95", "0.975", "0.99", "0.995"]

[chrisdicaprio]

probabilities

Probability % in 50	probability fraction in 1 year	probability fraction in 1 year (sci notation)
86	0.038559171941116	3.8559E-02
63	0.0196886419357	1.9689E-02
39	0.009837221296157	9.8372E-03
18	0.00396115262986	3.9612E-03
10	0.002104991704137	2.1050E-03
5	0.001025339867232	1.0253E-03
2	0.000403972527467	4.0397E-04
1	0.000200986516573	2.0099E-04