157 lines
3.0 KiB
Markdown
157 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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# Quickstart
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## Installation
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```bash
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pip install qubed
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```
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## Usage
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Make an uncompressed qube:
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```{code-cell} python3
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from qubed import Qube
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q = Qube.from_dict({
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"class=od" : {
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"expver=0001": {"param=1":{}, "param=2":{}},
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"expver=0002": {"param=1":{}, "param=2":{}},
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},
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"class=rd" : {
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"expver=0001": {"param=1":{}, "param=2":{}, "param=3":{}},
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"expver=0002": {"param=1":{}, "param=2":{}},
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},
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})
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print(f"{q.n_leaves = }, {q.n_nodes = }")
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q
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```
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Compress it:
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```{code-cell} python3
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cq = q.compress()
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assert cq.n_leaves == q.n_leaves
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print(f"{cq.n_leaves = }, {cq.n_nodes = }")
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cq
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```
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### Quick Tree Construction
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One of the quickest ways to construct non-trivial trees is to use the `Qube.from_datacube` method to construct dense trees and then use the set operations to combine or intersect them:
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```{code-cell} python3
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q = Qube.from_datacube({
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"class": "d1",
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"dataset": ["climate-dt", "another-value"],
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'generation': ['1', "2", "3"],
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})
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r = Qube.from_datacube({
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"class": "d1",
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"dataset": ["weather-dt", "climate-dt"],
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'generation': ['1', "2", "3", "4"],
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})
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q | r
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```
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### Iteration / Flattening
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Iterate over the leaves:
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```{code-cell} python3
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for i, identifier in enumerate(cq.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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Iterate over the datacubes:
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```{code-cell} python3
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cq.datacubes()
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```
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### A Real World Example
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Load a larger example qube:
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```{code-cell} python3
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import requests
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qube_json = requests.get("https://github.com/ecmwf/qubed/raw/refs/heads/main/tests/example_qubes/climate_dt.json").json()
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climate_dt = Qube.from_json(qube_json)
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# Using the html or print methods is optional but lets you specify things like the depth of the tree to display.
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print(f"{climate_dt.n_leaves = }, {climate_dt.n_nodes = }")
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climate_dt.html(depth=1) # Limit how much is open initially, click leave to see more.
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```
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Select a subset of the tree:
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```{code-cell} python3
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climate_dt.select({
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"activity": "scenariomip"
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}).html(depth=1)
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```
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Use `.span("key")` to get the set of possibles values for a key, note this includes anywhere this key appears in the tree.
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```{code-cell} python3
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climate_dt.span("activity")
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```
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Use `.axes()` to get the span of every key in one go.
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```{code-cell} python3
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axes = climate_dt.axes()
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for key, values in axes.items():
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print(f"{key} : {list(values)[:10]}")
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```
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### Set Operations
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The union/intersection/difference of two dense datacubes is not itself dense.
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```{code-cell} python3
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A = Qube.from_dict({"a=1/2/3" : {"b=i/j/k" : {}},})
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B = Qube.from_dict({"a=2/3/4" : {"b=j/k/l" : {}},})
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A.print(), B.print();
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```
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Union:
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```{code-cell} python3
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(A | B).print();
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```
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Intersection:
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```{code-cell} python3
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(A & B).print();
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```
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Difference:
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```{code-cell} python3
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(A - B).print();
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```
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Symmetric Difference:
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```{code-cell} python3
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(A ^ B).print();
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```
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