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Concurrent CDS API downloader with a textual TUI, script mode, and Python library interface.

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# <img src="https://raw.githubusercontent.com/bgiebl/cdsswarm/main/img/logo.png" alt="cdsswarm logo" height="30"> cdsswarm

[![CI](https://github.com/bgiebl/cdsswarm/actions/workflows/ci.yml/badge.svg)](https://github.com/bgiebl/cdsswarm/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/bgiebl/cdsswarm/branch/main/graph/badge.svg)](https://codecov.io/gh/bgiebl/cdsswarm)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![PyPI Downloads](https://static.pepy.tech/personalized-badge/cdsswarm?period=total&units=INTERNATIONAL_SYSTEM&left_color=BLACK&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/cdsswarm)

Concurrent [CDS API](https://cds.climate.copernicus.eu/) downloader with an interactive Textual TUI and script mode.

Submit multiple CDS API requests and download them in parallel with a configurable number of workers. Monitor progress through an interactive terminal UI with an htop-style worker table, or run headless in script mode for CI/cron jobs.

> **Feedback welcome!** This project is under active development. If you have suggestions, feature requests, or run into any issues, please open an issue on [GitHub](https://github.com/bgiebl/cdsswarm/issues) or send an email to [b.giebl@protonmail.com](mailto:b.giebl@protonmail.com).

![TUI demo](https://raw.githubusercontent.com/bgiebl/cdsswarm/main/img/demo.gif)

## Performance

How much faster is parallel downloading? Benchmark with 4 monthly ERA5 files (2m temperature, Germany bounding box):

| Workers | Wall time | Speedup |
|---------|-----------|---------|
| 1       | 24m02s    | 1.0x    |
| 2       | 8m58s     | 2.7x   |
| 4       | 7m39s     | 3.1x   |
| 8       | 7m54s     | 3.0x   |

Each CDS request spends most of its time waiting in the server queue and being processed — parallel workers overlap that wait. For large files, download parallelism helps too. Results vary with server load and time of day; larger batches benefit more from higher worker counts.

## Installation

```bash
pip install cdsswarm
```

Or via conda-forge:

```bash
conda install -c conda-forge cdsswarm
```

For YAML request file support:

```bash
pip install "cdsswarm[yaml]"
```

For development (tests, pre-commit):

```bash
git clone https://github.com/bgiebl/cdsswarm.git
cd cdsswarm
pip install -e ".[dev]"
```

## Prerequisites

A valid CDS API configuration file at `~/.cdsapirc`:

```
url: https://cds.climate.copernicus.eu/api
key: <your-personal-access-token>
```

See the [CDS API documentation](https://cds.climate.copernicus.eu/how-to-api) for setup instructions.

## Quick Start

### Command Line

Create a request file `requests.json`:

```json
[
  {
    "dataset": "reanalysis-era5-single-levels",
    "request": {
      "product_type": ["reanalysis"],
      "variable": ["2m_temperature"],
      "year": ["2024"],
      "month": ["01"],
      "day": ["01", "02", "03"],
      "time": ["12:00"],
      "data_format": "grib"
    },
    "target": "temperature_jan.grib"
  },
  {
    "dataset": "reanalysis-era5-single-levels",
    "request": {
      "product_type": ["reanalysis"],
      "variable": ["total_precipitation"],
      "year": ["2024"],
      "month": ["01"],
      "day": ["01", "02", "03"],
      "time": ["12:00"],
      "data_format": "grib"
    },
    "target": "precipitation_jan.grib"
  }
]
```

Run with 4 workers:

```bash
cdsswarm requests.json --workers 4
```

### Python API

```python
import cdsswarm

tasks = [
    cdsswarm.Task(
        dataset="reanalysis-era5-single-levels",
        request={
            "product_type": ["reanalysis"],
            "variable": ["2m_temperature"],
            "year": ["2024"],
            "month": ["01"],
            "day": ["01", "02", "03"],
            "time": ["12:00"],
            "data_format": "grib",
        },
        target="temperature_jan.grib",
    ),
    cdsswarm.Task(
        dataset="reanalysis-era5-single-levels",
        request={
            "product_type": ["reanalysis"],
            "variable": ["total_precipitation"],
            "year": ["2024"],
            "month": ["01"],
            "day": ["01", "02", "03"],
            "time": ["12:00"],
            "data_format": "grib",
        },
        target="precipitation_jan.grib",
    ),
]

results = cdsswarm.download(tasks, num_workers=4)

for r in results:
    if r.success:
        print(f"Downloaded {r.task.target}")
    else:
        print(f"Failed {r.task.target}: {r.error}")
```

## CLI Reference

```
usage: cdsswarm [-h] [--version] [-w WORKERS] [-m {interactive,script,auto}]
                [--no-skip] [--resume | --no-resume] [--reuse | --no-reuse]
                [--max-retries MAX_RETRIES] [--output-dir OUTPUT_DIR]
                [--dry-run] [--log FILE] [--summary FILE] [--post-hook CMD]
                requests_file
```

| Argument | Description |
|---|---|
| `requests_file` | Path to a JSON or YAML file with download requests |
| `-w`, `--workers` | Number of parallel download workers (default: 4) |
| `-m`, `--mode` | Display mode: `interactive` (TUI), `script` (plain text), or `auto` (default) |
| `--no-skip` | Re-download files that already exist on disk |
| `--resume` / `--no-resume` | Resume an interrupted session if state file exists (default: enabled) |
| `--reuse` / `--no-reuse` | Reuse existing CDS jobs with matching parameters (default: enabled) |
| `--max-retries` | Max retry attempts per task (default: 3, 1 to disable) |
| `--output-dir` | Prepend directory to relative target paths |
| `--dry-run` | Show what would be downloaded without actually downloading |
| `--log FILE` | Write timestamped log to a file |
| `--summary FILE` | Export summary as JSON (`.json`) or CSV (`.csv`) |
| `--post-hook CMD` | Shell command to run after each successful download (see below) |

In `auto` mode, the TUI is used when stdout is a TTY; otherwise it falls back to script mode.

### Post-download hooks

The `--post-hook` option runs a shell command after each file is successfully downloaded. Use `{file}` and `{dataset}` as placeholders:

```bash
# Compress each file after download
cdsswarm requests.json --post-hook "gzip {file}"

# Convert GRIB to NetCDF with CDO
cdsswarm requests.json --post-hook "cdo -f nc copy {file} {file}.nc"

# Upload to S3
cdsswarm requests.json --post-hook "aws s3 cp {file} s3://my-bucket/cds/"
```

Hook failures produce a warning but do not mark the download as failed — the file is already on disk.

### Request generation

The `generate` subcommand expands a template file into a full request file using Cartesian product expansion:

```bash
cdsswarm generate template.json -o requests.json
cdsswarm generate template.json --dry-run          # preview without writing
```

The template file must contain a **single JSON object** (not a list). If you pass a single-element list `[{...}]`, it will be auto-unwrapped with a warning.

A template looks like a single request with a `split_by` field that lists which dimensions to expand:

```json
{
  "dataset": "reanalysis-era5-single-levels",
  "request": {
    "product_type": ["reanalysis"],
    "variable": ["2m_temperature", "total_precipitation"],
    "year": ["2023", "2024"],
    "month": ["01", "02", "03"],
    "day": ["01", "02", "03"],
    "time": ["12:00"],
    "data_format": "grib"
  },
  "target": "output/{variable}_{year}_{month}.grib",
  "split_by": ["variable", "year", "month"]
}
```

This generates 2 &times; 2 &times; 3 = 12 separate tasks, one for each combination of variable, year, and month. Non-split fields (`day`, `time`, etc.) are shared across all tasks. The `{placeholder}` syntax in `target` fills in each combination's values.

| Option | Description |
|---|---|
| `--split-by FIELDS` | Override the template's `split_by` (comma-separated) |
| `-o`, `--output FILE` | Output file path (default: stdout) |
| `--dry-run` | Show task count and target filenames without writing output |

### Cancelling requests

The `cancel` subcommand cancels active CDS API requests on the server — useful for cleaning up after a crashed session or accidental submissions:

```bash
cdsswarm cancel                        # cancel all queued/running requests (new API only)
cdsswarm cancel abc-123 def-456        # cancel specific request IDs (both APIs)
cdsswarm cancel --yes                  # skip confirmation prompt
```

When no IDs are given, cdsswarm queries the CDS server for all active (accepted/running) requests and presents them for confirmation before cancelling. This requires the new CADS API (`ecmwf-datastores`). With the old `cdsapi`, you must provide specific request IDs.

| Option | Description |
|---|---|
| `request_ids` | Specific request IDs to cancel (omit to cancel all active) |
| `-y`, `--yes` | Skip confirmation prompt |

### Shell completion

Tab-completion for subcommands and flags is available for bash and zsh:

```bash
# Bash (add to ~/.bashrc)
eval "$(cdsswarm completion bash)"

# Zsh (add to ~/.zshrc)
eval "$(cdsswarm completion zsh)"
```

### Session resume

cdsswarm automatically saves session state after each task completes. If a download session is interrupted (e.g. by `Ctrl+C` or a network failure), rerunning the same command picks up where it left off — completed tasks are skipped and failed/pending tasks are retried.

State files are stored in `~/.cache/cdsswarm/sessions/` (or `$XDG_CACHE_HOME`), keyed by request file path and output directory. Run logs are automatically saved to `~/.local/state/cdsswarm/logs/` (or `$XDG_STATE_HOME`).

```bash
cdsswarm requests.json -w 4             # interrupted — 50 of 100 tasks done
cdsswarm requests.json -w 4             # resumes from task 51
cdsswarm requests.json -w 4 --no-resume # force a fresh start
```

### Configuration file

Settings can be stored in a `.cdsswarm.toml` file instead of passing CLI flags every time. CLI flags always take precedence.

| Location | Scope |
|---|---|
| `~/.cdsswarm.toml` | User-global defaults |
| `.cdsswarm.toml` (working directory) | Project-level overrides |

Example `.cdsswarm.toml`:

```toml
workers = 8
max-retries = 5
mode = "script"
output-dir = "/data/downloads"
post-hook = "gzip {file}"
```

All CLI flags are supported as config keys (use hyphens, e.g. `max-retries`, `post-hook`, `skip-existing`).

## Request File Format

### List format

Each entry specifies its own dataset:

```json
[
  {
    "dataset": "reanalysis-era5-single-levels",
    "request": { ... },
    "target": "output1.grib"
  },
  {
    "dataset": "reanalysis-era5-pressure-levels",
    "request": { ... },
    "target": "output2.grib"
  }
]
```

### Compact format

Share a dataset across all requests:

```json
{
  "dataset": "reanalysis-era5-single-levels",
  "requests": [
    { "request": { ... }, "target": "output1.grib" },
    { "request": { ... }, "target": "output2.grib" }
  ]
}
```

### YAML

Both formats also work in YAML (requires `pip install cdsswarm[yaml]`):

```yaml
dataset: reanalysis-era5-single-levels
requests:
  - request:
      product_type: [reanalysis]
      variable: [2m_temperature]
      year: ["2024"]
      month: ["01"]
      day: ["01"]
      time: ["12:00"]
      data_format: grib
    target: temperature.grib
```

The `request` dict accepts the same parameters as `cdsapi.Client.retrieve()`.

## Python API Reference

### `cdsswarm.Task(dataset, request, target)`

A single CDS API download request.

| Field | Type | Description |
|---|---|---|
| `dataset` | `str` | CDS dataset name (e.g. `"reanalysis-era5-single-levels"`) |
| `request` | `dict` | Request parameters, same format as `cdsapi.Client.retrieve()` |
| `target` | `str` | Local file path to save the downloaded data |

### `cdsswarm.download(tasks, num_workers=4, skip_existing=True, reuse_jobs=True, max_retries=3, on_message=None, post_hook="")`

Download multiple CDS API requests concurrently.

| Parameter | Type | Default | Description |
|---|---|---|---|
| `tasks` | `list[Task]` | required | List of download tasks |
| `num_workers` | `int` | `4` | Number of parallel workers |
| `skip_existing` | `bool` | `True` | Skip files that already exist |
| `reuse_jobs` | `bool` | `True` | Reuse existing CDS jobs with matching parameters |
| `max_retries` | `int` | `3` | Max retry attempts per task (1 to disable) |
| `on_message` | `callable` | `None` | Callback `fn(message: str)` for status updates |
| `post_hook` | `str` | `""` | Shell command to run after each successful download (`{file}`, `{dataset}`) |

Returns a `list[Result]`. Returns an empty list if interrupted by `KeyboardInterrupt`.

### `cdsswarm.Result`

| Field | Type | Description |
|---|---|---|
| `task` | `Task` | The original task |
| `success` | `bool` | Whether the download succeeded |
| `error` | `str` | Error message (empty on success) |

### `cdsswarm.expand_template(template, split_by=None)`

Expand a template dict into a list of `Task` objects via Cartesian product of the `split_by` dimensions.

### `cdsswarm.ConfigError`

Raised for invalid configuration values in config files or CLI flags. Subclass of `ValueError`.

### `cdsswarm.RequestFileError`

Raised for invalid or unrecognized request file formats. Subclass of `ValueError`.

## TUI

The interactive TUI (terminal user interface) is built with [Textual](https://textual.textualize.io/) and is available via the CLI only. It shows an htop-style `DataTable` with one row per worker:

```
W  │W-State  │CDS Status │Filename          │Started  │Elapsed  │Size    │DL %   │Request ID
0  │ active  │ running    │era5_2024_01.grib │22:31:24 │2h30m05s │15.2 GB│48%    │af1e2306-28c3...
1  │ active  │ successful │era5_2024_02.nc   │22:31:25 │1h15m00s │8.1 GB │100% ✓ │b2f4a891-...
```

The layout has two tabs (Workers and Files), an info panel above the table, and a progress footer with an overall progress bar and ETA.

**Key bindings:**

| Key | Action |
|---|---|
| `q` | Quit |
| `t` / `Tab` | Switch tab |
| `Enter` | Open scrollable log for the selected worker |
| `a` | Show full request parameters |
| `Esc` | Dismiss screen / go back |
| `Ctrl+C` | Cancel — in-flight CDS API requests are cancelled on the server |

## Running Tests

```bash
pip install -e ".[dev]"
pytest -v
```

## License

MIT

cdsswarm

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