| 2012-02-29 15:27:12 |
Lars Butler |
bug |
|
|
added bug |
| 2012-02-29 15:27:27 |
Lars Butler |
openquake: milestone |
|
0.6.0 |
|
| 2012-02-29 15:27:44 |
John Tarter |
openquake: importance |
Undecided |
High |
|
| 2012-02-29 15:27:53 |
John Tarter |
openquake: status |
New |
In Progress |
|
| 2012-02-29 15:28:13 |
Lars Butler |
tags |
|
risk |
|
| 2012-03-05 09:30:39 |
Lars Butler |
openquake: status |
In Progress |
Confirmed |
|
| 2012-03-05 12:56:06 |
John Tarter |
openquake: assignee |
|
Andrea Cerisara (andreacerisara-b) |
|
| 2012-03-05 13:13:00 |
John Tarter |
openquake: milestone |
0.6.0 |
0.6.1 |
|
| 2012-03-07 16:09:10 |
Lars Butler |
openquake: assignee |
Andrea Cerisara (andreacerisara-b) |
Lars Butler (lars-butler) |
|
| 2012-03-07 16:14:09 |
Lars Butler |
description |
DRAFT
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results. |
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above). |
|
| 2012-03-07 16:14:15 |
Lars Butler |
openquake: status |
Confirmed |
Incomplete |
|
| 2012-03-07 16:15:07 |
Lars Butler |
description |
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above). |
DRAFT
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above).
Open Issues:
- Need to talk with Vitor to if test data/expected results are need on the Hazard side. If so, that adds significant amount of scope to this work package. |
|
| 2012-03-07 16:42:53 |
Lars Butler |
description |
DRAFT
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above).
Open Issues:
- Need to talk with Vitor to if test data/expected results are need on the Hazard side. If so, that adds significant amount of scope to this work package. |
[et=12h]
[at=]
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above).
Open Issues:
- Need to talk with Vitor to if test data/expected results are need on the Hazard side. If so, that adds significant amount of scope to this work package. |
|
| 2012-03-14 12:09:26 |
Lars Butler |
openquake: status |
Incomplete |
Confirmed |
|
| 2012-03-20 11:21:51 |
Lars Butler |
openquake: status |
Confirmed |
In Progress |
|
| 2012-03-28 07:19:48 |
Lars Butler |
attachment added |
|
Scenario Risk Calculator QA Tests.pdf https://bugs.launchpad.net/openquake/+bug/943332/+attachment/2953139/+files/Scenario%20Risk%20Calculator%20QA%20Tests.pdf |
|
| 2012-03-29 18:38:22 |
Lars Butler |
description |
[et=12h]
[at=]
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above).
Open Issues:
- Need to talk with Vitor to if test data/expected results are need on the Hazard side. If so, that adds significant amount of scope to this work package. |
[et=12h]
[at=12.5h]
The Scenario Risk calculation does not have a QA test; it needs one.
The biggest difficulty identified so far is the following:
Part of the scenario calculation includes randomly sampling a normal distribution. We're currently using scipy.stats.norm.rvs to generate random variates. Unfortunately, this means that getting consistent test results every time is impossible unless we seed the random number generator. I read the scipy documentation and some related source code and there is no obvious way to specify a seed. However, we should instead be able to use the Python standard 'random' module as a replacement. See: http://docs.python.org/dev/library/random.html#random.normalvariate. This implementation can be seeded and should enable us to get predictable test results.
Tasks:
- Create a small demo file set that can run quickly
- Gather 'expected result' test data
- Add an EPSILON_RANDOM_SEED parameter to the job profile for Scenario Risk calculations
- This seed should be used in the random normal distribution sample (see description above).
Open Issues:
- Need to talk with Vitor to if test data/expected results are need on the Hazard side. If so, that adds significant amount of scope to this work package. |
|
| 2012-04-10 10:32:08 |
Lars Butler |
openquake: status |
In Progress |
Fix Committed |
|
| 2013-04-08 10:35:34 |
Lars Butler |
openquake: status |
Fix Committed |
Fix Released |
|
