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Reformat markdown for ruff 0.16 and pin ruff in CI #14

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5 changes: 5 additions & 0 deletions .github/workflows/formatting.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -13,11 +13,16 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
# Pin the ruff version: the action otherwise resolves to the latest
# release, so a new ruff can fail CI on unchanged code (0.16 began
# formatting fenced code blocks in Markdown).
- uses: astral-sh/ruff-action@v3
with:
version: "0.16.0"
args: "check"
src: "."
- uses: astral-sh/ruff-action@v3
with:
version: "0.16.0"
args: "format --check"
src: "."
69 changes: 38 additions & 31 deletions README.md
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Expand Up @@ -46,7 +46,8 @@ For most users, the interactive interface provides the easiest way to process as

```python
import cutana_ui
cutana_ui.start() # optionally can specify e.g. ui_scale=0.6 for smaller UI

cutana_ui.start() # optionally can specify e.g. ui_scale=0.6 for smaller UI
```

This launches a step-by-step interface where you can:
Expand All @@ -63,16 +64,16 @@ from cutana import get_default_config, Orchestrator

# Configure processing
config = get_default_config()
config.source_catalogue = "sources.csv" # See format below
config.source_catalogue = "sources.csv" # See format below
config.output_dir = "cutouts_output/"
config.output_format = "zarr" # or "fits"
config.target_resolution = 256
config.selected_extensions = [{'name': 'VIS', 'ext': 'PrimaryHDU'}] # Extensions to process
config.selected_extensions = [{"name": "VIS", "ext": "PrimaryHDU"}] # Extensions to process
# 1 output channel for VIS, details explained below
config.channel_weights = {
"VIS": [1.0],
}
config.console_log_level = "INFO" # Show INFO logs in console
"VIS": [1.0],
}
config.console_log_level = "INFO" # Show INFO logs in console

# Process cutouts
orchestrator = Orchestrator(config)
Expand Down Expand Up @@ -151,9 +152,9 @@ The `channel_weights` parameter controls how multiple FITS files are combined in
```python
# Configure channel weights (ordered dictionary format)
config.channel_weights = {
"VIS": [1.0, 0.0, 0.5], # RGB weights for VIS band
"VIS": [1.0, 0.0, 0.5], # RGB weights for VIS band
"NIR-H": [0.0, 1.0, 0.3], # RGB weights for NIR H-band
"NIR-J": [0.0, 0.0, 0.8] # RGB weights for NIR J-band
"NIR-J": [0.0, 0.0, 0.8], # RGB weights for NIR J-band
}
```

Expand Down Expand Up @@ -182,9 +183,9 @@ config.normalisation_method = "asinh" # "linear", "log", "asinh", "zscale"

# Configure normalisation parameters (method-specific defaults applied automatically)
config.normalisation.percentile = 99.8 # Data clipping percentile
config.normalisation.a = 0.7 # Transition parameter (asinh/log)
config.normalisation.n_samples = 1000 # ZScale samples
config.normalisation.contrast = 0.25 # ZScale contrast
config.normalisation.a = 0.7 # Transition parameter (asinh/log)
config.normalisation.n_samples = 1000 # ZScale samples
config.normalisation.contrast = 0.25 # ZScale contrast
```

**Image stretching is powered by [fitsbolt](https://github.com/Lasloruhberg/fitsbolt) for consistent processing.**
Expand Down Expand Up @@ -283,38 +284,38 @@ from cutana import get_default_config

config = get_default_config()
config.normalisation_method = "linear"
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.percentile = 99.8 # Percentile clipping (default)
```

### ASINH Stretch (Recommended)
```python
from cutana import get_default_config

config = get_default_config()
config.normalisation_method = 'asinh'
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.a = 0.7 # Transition parameter (default for asinh)
config.normalisation_method = "asinh"
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.a = 0.7 # Transition parameter (default for asinh)
```

### Log Stretch
```python
from cutana import get_default_config

config = get_default_config()
config.normalisation_method = 'log'
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.a = 1000.0 # Scale factor (default for log)
config.normalisation_method = "log"
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.a = 1000.0 # Scale factor (default for log)
```

### ZScale Stretch
```python
from cutana import get_default_config

config = get_default_config()
config.normalisation_method = 'zscale'
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.n_samples = 1000 # Number of samples (default)
config.normalisation.contrast = 0.25 # Contrast parameter (default)
config.normalisation_method = "zscale"
config.normalisation.percentile = 99.8 # Percentile clipping (default)
config.normalisation.n_samples = 1000 # Number of samples (default)
config.normalisation.contrast = 0.25 # Contrast parameter (default)
```

## Performance Considerations
Expand Down Expand Up @@ -462,11 +463,15 @@ config = get_default_config()
config.output_dir = "cutouts_output/"
config.output_format = "zarr"
config.target_resolution = 256
config.selected_extensions = [{'name': 'VIS', 'ext': 'PrimaryHDU'}, {'name': 'NIR-H', 'ext': 'PrimaryHDU'},{'name': 'NIR-J', 'ext': 'PrimaryHDU'}]
config.selected_extensions = [
{"name": "VIS", "ext": "PrimaryHDU"},
{"name": "NIR-H", "ext": "PrimaryHDU"},
{"name": "NIR-J", "ext": "PrimaryHDU"},
]
config.channel_weights = {
"VIS": [1.0, 0.0, 0.5],
"NIR-H": [0.0, 1.0, 0.3],
"NIR-J": [0.0, 0.0, 0.8]
"NIR-J": [0.0, 0.0, 0.8],
}

# Process cutouts
Expand Down Expand Up @@ -511,15 +516,15 @@ from cutana import create_cutouts_direct, get_default_config

config = get_default_config()
config.target_resolution = 256
config.selected_extensions = [{'name': 'VIS', 'ext': 'PrimaryHDU'}]
config.selected_extensions = [{"name": "VIS", "ext": "PrimaryHDU"}]
config.channel_weights = {"VIS": [1.0]}

catalogue_df = pd.read_csv("sources.csv")
results = create_cutouts_direct(catalogue_df, config)

for result in results:
cutouts = result["cutouts"] # ndarray (N, H, W, C)
metadata = result["metadata"] # list of per-source dicts
cutouts = result["cutouts"] # ndarray (N, H, W, C)
metadata = result["metadata"] # list of per-source dicts
```

**When to use which API:**
Expand All @@ -542,9 +547,11 @@ config = get_default_config()
config.source_catalogue = "sources.csv"
config.output_dir = "streaming_output/"
config.target_resolution = 256
config.selected_extensions = [{'name': 'VIS', 'ext': 'PrimaryHDU'}, {'name': 'NIR-H', 'ext': 'PrimaryHDU'}]
config.channel_weights = {"VIS": [1.0,0.0],
"NIR-H": [0.0,1.0]}
config.selected_extensions = [
{"name": "VIS", "ext": "PrimaryHDU"},
{"name": "NIR-H", "ext": "PrimaryHDU"},
]
config.channel_weights = {"VIS": [1.0, 0.0], "NIR-H": [0.0, 1.0]}

# Create streaming orchestrator
orchestrator = StreamingOrchestrator(config)
Expand All @@ -564,7 +571,7 @@ for i in range(orchestrator.get_batch_count()):
# result['batch_number']: 1-indexed batch number

# Your ML inference or analysis here...
process_cutouts(result['cutouts'])
process_cutouts(result["cutouts"])

# The next batches are already being prepared in background!

Expand Down
1 change: 1 addition & 0 deletions cutana_ui/README.md
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Expand Up @@ -20,6 +20,7 @@ The Cutana UI guides you through a simple 3-step process to generate cutouts:
### Launch the Interface
```python
import cutana_ui

cutana_ui.start(ui_scale=0.75)
```
You can vary the UI scale to fit your screen if necessary.
Expand Down
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