138 lines
3.0 KiB
Markdown
138 lines
3.0 KiB
Markdown
---
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jupytext:
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text_representation:
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extension: .md
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format_name: myst
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format_version: 0.13
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jupytext_version: 1.16.4
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---
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# Fiab
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## Model Selection
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This is a demo of using qubed to select from a set of forecast models that each produce a set of output variables.
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First let's construct some models represented as qubes:
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```{code-cell} python3
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from qubed import Qube
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model_1 = Qube.from_datacube({
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"levtype": "pl",
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"param" : ["q", "t", "u", "v", "w", "z"],
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"level" : [100, 200, 300, 400, 50, 850, 500, 150, 600, 250, 700, 925, 1000],
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}) | Qube.from_datacube({
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"levtype": "sfc",
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"param" : ["10u", "10v", "2d", "2t", "cp", "msl", "skt", "sp", "tcw", "tp"],
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})
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model_1 = "model=1" / ("frequency=6h" / model_1)
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model_1
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```
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This is the most complete model. Now let's do one with fewer variables and levels:
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```{code-cell} python3
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model_2 = Qube.from_datacube({
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"levtype": "pl",
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"param" : ["q", "t"],
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"level" : [100, 200, 300, 400, 50, 850, 500, 150, 600, 250, 700, 925, 1000],
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}) | Qube.from_datacube({
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"levtype": "sfc",
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"param" : ["2t", "cp", "msl"],
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})
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model_2 = "model=2" / ("frequency=continuous" / model_2)
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```
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```{code-cell} python3
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model_3 = Qube.from_datacube({
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"levtype": "pl",
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"param" : ["q", "t"],
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"level" : [100, 200, 300, 400, 50, 850, 500, 150, 600, 250, 700, 925, 1000],
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}) | Qube.from_datacube({
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"levtype": "sfc",
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"param" : ["2t", "cp", "msl"],
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})
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model_3 = "model=3" / ("frequency=6h" / model_3)
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model_3
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```
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Now we can combine the three models into a single qube:
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```{code-cell} python3
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all_models = model_1 | model_2 | model_3
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all_models
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```
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Now we can perform queries over the models. We can get all models that produce 2m temperature:
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```{code-cell} python3
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all_models.select({
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"param" : "2t",
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})
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```
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Filter on both parameter and frequency:
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```{code-cell} python3
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all_models.select({
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"param" : "2t",
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"frequency": "continuous",
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})
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```
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Find all models that have some overlap with this set of parameters:
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```{code-cell} python3
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all_models.select({
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"param" : ["q", "t", "u", "v"],
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})
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```
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## Choosing a set of models based on the requested parameter set
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```{code-cell} python3
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all_models.select({
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"param" : ["q", "t", "u", "v"],
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"frequency": "6h",
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})
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```
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## Using WildCards
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```{code-cell} python3
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daily_surface_means = Qube.from_datacube({
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"model": "*",
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"frequency": "*",
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"levtype": "sfc",
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"param": "*",
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})
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all_models & daily_surface_means
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```
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```{code-cell} python3
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daily_level_means = Qube.from_datacube({
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"model": "*",
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"frequency": "*",
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"levtype": "pl",
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"param": "*",
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"level": "*"
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})
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all_models & daily_level_means
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```
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```{code-cell} python3
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daily_level_mean_products = all_models & daily_surface_means
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for i, identifier in enumerate(daily_level_mean_products.leaves()):
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print(identifier)
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if i > 10:
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print("...")
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break
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```
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<!-- ## Choosing the fewest models needed to cover the requested parameter set -->
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<!-- ```{code-cell} python3 -->
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