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The `channels4world` library (1.0.0) requires Python ≥3.11, NumPy and PyArrow. Install it
with `pip install channels4world`; its full documentation is at <https://github.com/JoonkyuJang/channels4world#readme>. It reads local
files and never downloads them. A download into the Hugging Face cache
(`snapshot_download` without `local_dir`) works too: open the snapshot folder it returns.
## Partial Tiny and direct reads
Opening a root reads `c4w-root.json` and **every table referenced by that manifest**.
For Tiny these include scenes, files and included records. Payloads and group
metadata load later. Downloading those root tables, shared schemas, the chosen
scene's group metadata and its payloads permits direct access to that explicit
scene even when the other Tiny payloads are absent. Do not select an arbitrary first
scene when only one known scene has been downloaded.
```python
from pathlib import Path
import channels4world as c4w
TINY_ROOT = Path("c4w/tiny")
SCENE_ID, CARRIER_HZ, BS_ID, UE_ID = "urban_scene_019234", 3_500_000_000, 0, 14
ds = c4w.open(TINY_ROOT)
scene = ds.scene(SCENE_ID)
record = scene.frequencies[CARRIER_HZ].base_stations[BS_ID].read()
positions = scene.read_positions()
link = record.get_link(UE_ID) # BS 0 → UE 14: 16 retained paths
```
A loaded record's absent UE IDs mean no retained link; missing files and unpublished
components raise separate availability errors. Selecting across partially downloaded
**loose** Tiny before group metadata is available can conservatively plan other files
in a touched group. For the first scene, use the card's narrow include command and
explicit scene read; direct reads do not require every scene's payload.
## Root-first planning for TAR releases
Download the release catalog (`c4w-root.json` and `c4w-root/`) at the release revision.
`missing()` can then plan from the catalog alone.
For groups whose members/snapshot are absent, it lists matching component/carrier
TARs and requested scene assets, plus required metadata and schemas. Once metadata
is local, it maps selected records to their TARs. Because a TAR contains the group's
records for one component/carrier, either route uses archives as download units.
Rerun after downloading to resolve unpublished requests and confirm completeness.
```python
import channels4world as c4w
ds = c4w.open("c4w") # the release root is the repository top level
selection = ds.select(
tier="small", frequencies_hz=[3_500_000_000], components=("positions", "core"),
)
report = selection.missing()
print(report) # files and total bytes
if report.files:
print(report.hf_command(revision="v1.0.0"))
```
`report.files` are paths inside the opened root. `report.repo_files` are the same files
as repository paths (identical for the release root; prefixed with `tiny/` for Tiny), and
`report.local_dir` is the folder that corresponds to the repository's top level.
`hf_command(revision=...)` uses both and quotes every argument. It refuses an empty plan
and roots that are not published as-is, such as a subset you extracted yourself, whose
loose files exist only on your disk.
For long plans, avoid putting thousands of paths on a shell command line:
```python
from huggingface_hub import snapshot_download
if report.files:
snapshot_download(
repo_id="Intelligence-Networking-Lab/Channels4World", repo_type="dataset",
revision="v1.0.0", local_dir=report.local_dir,
allow_patterns=report.repo_files,
)
report = selection.missing()
if report.files:
raise RuntimeError("Files are still missing; re-plan the download.")
if report.unpublished:
raise RuntimeError(f"Requested components are not published: {report.unpublished}")
```
The published catalogs at that revision are the source of truth for paths, sizes and
hashes. Without the library, the increment-folder patterns in the
[file guide](without-library.md#download-a-tiers-archives) select a whole tier.
Selections combine filters. `ds.profiles` lists the catalog's profile names:
```python
metro_train = ds.select(tier="medium", split="train", profile="metropolitan",
frequencies_hz=[7_000_000_000], components=("positions", "core"))
```
### Share a selection as a recipe
A recipe is a small JSON file that records a selection (tier, split, profiles, scene IDs,
carriers, BS IDs and components) and the release it refers to. It holds no paths or
commands, so whoever replays it plans the download from their own catalog:
```python
with open("selection.json", "w") as fh:
fh.write(c4w.recipe.dumps(selection.to_recipe(revision="v1.0.0")))
same = c4w.open("c4w").select_recipe("selection.json")
```
The command line does the same. `c4w recipe` never overwrites a file, so choose a new name:
```bash
c4w recipe c4w --tier small --frequency 3500000000 --components positions,core \
--revision v1.0.0 -o small-core.json
c4w missing c4w --recipe small-core.json # prints a pinned hf download command, or a snapshot_download hint for long plans
```
The [download helper](https://cbchae.yonsei.ac.kr/channels4world/download.html) on the
project website writes the same recipes. A recipe for another release is refused, and every
filter is checked against the catalog exactly as `select()` checks it.
The examples below continue with the Small selection from above.
## Iterate, extract or cache
```python
for scene in selection.scenes():
positions = scene.read_positions()
for record in scene.records(): # selected scene preserves carrier/BS/component filters
print(record.scene_id, record.frequency_hz, record.bs_id, record.n_links)
# Use new destinations after missing()/coverage checks have passed.
c4w.extract(selection, "small-loose", verify="basic", check_space=True)
c4w.to_cache(selection, "small-npy", verify="basic", check_space=True)
```
Packed TARs preserve original NPZ/ZIP records. `extract` preserves selected
`member_path` values and original payload bytes and writes a common loose subset
root. The optional NPY cache instead stores arrays plus indexes for memory-mapped
repeated access; values, IDs, dtypes and ragged associations stay the same. Geometry
remains ZIP. A cache is not another scene tier.
## Arrays are read-only
Arrays returned by the library are read-only views of the stored data. Arithmetic such as
`record.core["tau"] * 1e9` creates a new array; to modify values in place, copy first
(`record.copy()` or `np.array(...)`).
## Reads, file handles and disk writes
| Verb | Meaning | Examples |
|---|---|---|
| `read` / `read_*` | Load data into memory | `bs.read()`, `scene.read_positions()` |
| `open_*` | Return a handle to close | `scene.open_geometry()` returns `ZipFile` |
| `extract` / `extract_*` | Write files and return a path | `c4w.extract(selection, dest)`, `scene.extract_geometry(dest)` |
Geometry extraction requires a destination because renderers load XML and referenced
meshes from disk. Positions are arrays. No hidden temporary geometry location is
assumed.
`verify="basic"` checks declared bytes and array structure. `verify="full"` adds
scientific/content-proof checks for loaded components. A full core+detail read checks
their association together. Extraction/cache conversion does not replace that
pairwise read check. See the [schema reference](schema-reference.md).
## Optional transforms
```python
import numpy as np
from channels4world import transforms as T
rms_delay_s = T.delay_spread(record)
response = T.frequency_response(
record, np.array([-1e6, 0.0, 1e6]), delay_reference="absolute",
)
```
These return new writable arrays describing retained paths. Frequency inputs above
are **offsets from the record's carrier**, not absolute frequencies. `T.pad_paths(record.core,
"tau")` gives one row per link, `(links, 16)`, with a mask of stored paths, and
`T.pad_interactions(record.detail, "interaction_vertex")` one row per path, `(paths, 4, 3)`;
both match the card's padding snippet. Stored
`a_real + 1j*a_imag` is a scalar passband coefficient excluding carrier-delay phase;
`tau` is absolute propagation delay. Do not add antenna effects twice. The
[conventions reference](schema-reference.md) provides the details.
[Return to card](../README.md) · [Without the library](without-library.md)
|