### Install SPXQuery Package

Source: https://github.com/wenkeren/spxquery/blob/main/docs/index.md

Install the SPXQuery package using pip. This command is used for initial setup.

```bash
pip install spxquery
```

--------------------------------

### Install SPXQuery with Poetry

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

After cloning the repository and installing Poetry, use this command to install SPXQuery and its development dependencies.

```bash
git clone https://github.com/WenkeRen/spxquery.git
cd spxquery
poetry install
```

--------------------------------

### Import and Setup SPXQuery Pipeline

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Imports necessary modules from the spxquery package and sets up basic logging. Ensure spxquery is installed.

```python
# Import required modules
from pathlib import Path

from spxquery import QueryConfig, Source, SPXQueryPipeline
from spxquery.core.pipeline import run_pipeline
from spxquery.utils.helpers import setup_logging

# Set up logging
setup_logging("INFO")
```

--------------------------------

### Initialize SPXQuery Pipeline with pip

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Example of how to use SPXQuery in a Python script after installation with pip. It demonstrates creating a source, configuration, and running the pipeline.

```python
from spxquery import Source, QueryConfig, SPXQueryPipeline

# Create a source and configuration
source = Source(ra=304.69, dec=42.44, name="my_target")
config = QueryConfig(
    source=source,
    output_dir="output",
    cutout_size="200px"
)

# Run the pipeline
pipeline = SPXQueryPipeline(config)
pipeline.run_full_pipeline()
```

--------------------------------

### Install SPXQuery from Source

Source: https://github.com/wenkeren/spxquery/blob/main/README.md

Clone the repository and install SPXQuery locally if you need the latest development version or want to contribute.

```bash
git clone https://github.com/WenkeRen/spxquery.git
cd spxquery
pip install .
```

--------------------------------

### Install Poetry

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Install Poetry using the provided script. This is a prerequisite for using the Poetry installation method.

```bash
curl -sSL https://install.python-poetry.org | python3 -
```

```powershell
(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | py -
```

--------------------------------

### Install Poetry

Source: https://github.com/wenkeren/spxquery/blob/main/CONTRIBUTING.md

Installs Poetry, the recommended package manager for SPXQuery. Run this command in your terminal.

```bash
curl -sSL https://install.python-poetry.org | python3 -
```

--------------------------------

### Install SPXQuery from Source using pip

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Install SPXQuery and its dependencies directly from the pyproject.toml file. This is the recommended method for most users.

```bash
pip install .
```

--------------------------------

### Install Package with pip

Source: https://github.com/wenkeren/spxquery/blob/main/CONTRIBUTING.md

Installs the SPXQuery package in development mode along with its dev dependencies using pip. This is used with the pip alternative setup.

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

--------------------------------

### Verify SPXQuery Installation

Source: https://github.com/wenkeren/spxquery/blob/main/README.md

Run this command in your terminal to confirm that SPXQuery has been installed correctly and to check its version.

```bash
python -c "import spxquery; print(spxquery.__version__)"
```

--------------------------------

### Install Dependencies with Poetry

Source: https://github.com/wenkeren/spxquery/blob/main/README.md

For developers managing dependencies with Poetry, use these commands to install Poetry and then the project's dependencies.

```bash
# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -

# Install with Poetry
poetry install
```

--------------------------------

### AdvancedConfig: Factory Method for Quick Setup

Source: https://context7.com/wenkeren/spxquery/llms.txt

Uses the `AdvancedConfig.create` factory method for a streamlined setup of pipeline configuration. Parameters are automatically routed to the appropriate sub-configuration objects.

```python
from spxquery.core.config import (
    AdvancedConfig, QueryConfig, PhotometryConfig,
    VisualizationConfig, DownloadConfig, Source
)
from pathlib import Path

# Method 1: Factory method for quick setup
source = Source(ra=304.69, dec=42.44, name="My_Star")
config = AdvancedConfig.create(
    source=source,
    output_dir=Path("output"),
    bands=['D1', 'D2'],
    aperture_diameter=5.0,      # Routes to PhotometryConfig
    use_magnitude=True,         # Routes to VisualizationConfig
    cutout_size='200px'         # Routes to DownloadConfig
)
```

--------------------------------

### Verify SPXQuery Installation (Poetry)

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Verify the SPXQuery installation when using Poetry by running the version check within the Poetry environment.

```python
poetry run python -c "import spxquery; print(spxquery.__version__)"
```

--------------------------------

### Verify Poetry Installation

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Check if Poetry has been installed successfully by verifying its version.

```bash
poetry --version
```

--------------------------------

### Cutout Parameter Validation Examples

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/cutouts.md

Examples of valid and invalid `cutout_size` parameters, highlighting constraints and potential warnings.

```python
# Valid
cutout_size="200px"          # ✓
cutout_size="3arcmin"        # ✓
cutout_size="0.1"            # ✓
cutout_size="100,200px"      # ✓

# Invalid (raises ValueError)
cutout_size="0px"            # ✗ Zero size
cutout_size="-100px"         # ✗ Negative size
cutout_size="2040,2040px"    # ⚠ Warning: same as full image
cutout_size="5000px"         # ✗ Exceeds detector size
```

--------------------------------

### Install Development Dependencies for SPXQuery

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Install SPXQuery along with development tools like pytest, black, and flake8, which are necessary for contributing to the project.

```bash
poetry install --with dev
```

--------------------------------

### SPXQuery Pipeline State File Example

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/pipeline.md

This YAML structure represents the state of the SPXQuery pipeline, including completed stages, query results, and downloaded file information. It enables resuming after interruptions and configuration recovery.

```yaml
# Pipeline state
stage: complete
completed_stages:
  - query
  - download
  - processing
  - visualization
pipeline_stages:
  - query
  - download
  - processing
  - visualization

# Query results
query_results:
  observations: [...]
  time_span_days: 33.1
  total_size_gb: 0.51

# Downloaded files and photometry
downloaded_files: [...]
photometry_results: [...]
```

--------------------------------

### Clone SPXQuery Repository

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Use this command to clone the SPXQuery repository from GitHub. Navigate into the cloned directory to proceed with installation.

```bash
git clone https://github.com/WenkeRen/spxquery.git
cd spxquery
```

--------------------------------

### Setup Fixed Aperture Configuration

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Creates a configuration object for fixed aperture photometry using the 'fixed' method. This method uses a user-specified aperture diameter and a window-based background subtraction. Requires the AdvancedConfig class and a YAML file.

```python
# Setup: Create configurations for both aperture methods

# Configuration with fixed aperture (traditional)
advanced_fixed = AdvancedConfig.from_yaml_file(output_dir / "cloverleaf.yaml")
advanced_fixed.update(aperture_method="fixed", aperture_diameter=3.0, background_method="window", window_size=50)
```

--------------------------------

### Initialize and Run SPXQuery Pipeline

Source: https://context7.com/wenkeren/spxquery/llms.txt

Demonstrates initializing the SPXQuery pipeline with an AdvancedConfig object and running the full pipeline. It also shows how to run pipeline stages individually and how to resume from a saved state.

```python
source = Source(ra=213.9427080, dec=11.4953890, name="cloverleaf")
output_dir = Path("./demo_data/cloverleaf")

config = AdvancedConfig(
    query=QueryConfig(
        source=source,
        output_dir=output_dir,
        bands=["D1", "D2", "D3", "D4", "D5", "D6"]
    ),
    download=DownloadConfig(
        cutout_size="60px",
        max_download_workers=5,
        max_retries=3,
        timeout=300
    ),
    photometry=PhotometryConfig(
        aperture_method="fwhm",       # Adaptive aperture sizing
        fwhm_multiplier=2.5,          # Aperture = FWHM * 2.5
        background_method="window",   # Window background estimation
        window_size=50,               # 50x50 pixel window
        subtract_zodi=True,           # Subtract zodiacal light
        max_processing_workers=10
    ),
    visualization=VisualizationConfig(
        sigma_threshold=5.0,
        use_magnitude=False,
        show_errorbars=True,
        figsize=(10, 8),
        dpi=150
    )
)

# Initialize and run full pipeline
pipeline = SPXQueryPipeline(config)
pipeline.run_full_pipeline(skip_existing_downloads=True)

# Or run stages individually with dependency checking
pipeline = SPXQueryPipeline(config)
pipeline.run_query()          # Query IRSA archive
pipeline.run_download()       # Download FITS files
pipeline.run_processing()     # Extract photometry
pipeline.run_visualization()  # Create plots

# Check pipeline status
pipeline.print_status()
# Output:
# Pipeline Status:
#   Completed stages: ['query', 'download', 'processing', 'visualization']
#   Pending stages: None
#   Current stage: complete

# Resume from saved state (after interruption)
pipeline_resumed = SPXQueryPipeline(
    AdvancedConfig.from_yaml_file(output_dir / "cloverleaf.yaml")
)
pipeline_resumed.resume(skip_existing_downloads=True)
```

--------------------------------

### Get SPXQuery Version

Source: https://github.com/wenkeren/spxquery/blob/main/CONTRIBUTING.md

Retrieves the currently installed version of the SPXQuery library using Python. This is useful for reporting issues.

```python
import spxquery
print(spxquery.__version__)
```

--------------------------------

### Run SPXQuery Pipeline with Basic Parameters

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/parameters.md

Use this to initiate the SPXQuery pipeline with essential parameters like source coordinates, output directory, and cutout size.

```python
from spxquery.core.pipeline import run_pipeline

run_pipeline(
    ra=213.94,
    dec=11.50,
    output_dir="output",
    cutout_size="200px"
)
```

--------------------------------

### Basic SPXQuery Pipeline Usage

Source: https://github.com/wenkeren/spxquery/blob/main/docs/index.md

Demonstrates basic usage of the SPXQuery pipeline. Configure a source, set query parameters including cutout size and SNR threshold, and run the full pipeline.

```python
from spxquery import SPXQueryPipeline, Source, QueryConfig

# Define your astronomical source
source = Source(ra=304.69, dec=42.44, name="My_Star")

# Configure the query with cutout support
config = QueryConfig(
    source=source,
    output_dir="output",
    cutout_size="200px",  # Download only 200×200 pixel region
    sigma_threshold=5.0,  # Minimum SNR for quality control
)

# Run the full pipeline
pipeline = SPXQueryPipeline(config)
pipeline.run_full_pipeline()
```

--------------------------------

### Run SPxQuery Pipeline (Advanced Parameters)

Source: https://github.com/wenkeren/spxquery/blob/main/docs/api/core.md

Execute the pipeline using advanced parameters loaded from a JSON file. First, export default parameters to a file, then edit the file to customize settings before passing its path to `advanced_params_file`. This allows for fine-grained control over photometry, visualization, and download settings.

```python
>>> # With advanced parameters
>>> from spxquery.utils.params import export_default_parameters
>>> params_file = export_default_parameters("output")
>>> # Edit output/spxquery_default_params.json
>>> run_pipeline(ra=304.69, dec=42.44, advanced_params_file=params_file)
```

--------------------------------

### Configure and Run SPXQuery Pipeline

Source: https://github.com/wenkeren/spxquery/blob/main/README.md

Set up the analysis configuration for a specific astronomical source and then run the full SPXQuery pipeline. This provides more control over the analysis parameters.

```python
from spxquery import Source, QueryConfig, SPXQueryPipeline

# Configure your analysis for Cloverleaf quasar
source = Source(ra=213.9427080, dec=11.4953890, name="cloverleaf")
config = QueryConfig(
    source=source,
    output_dir="output",
    aperture_diameter=2.0,
    cutout_size="60px"
)

# Run the pipeline
pipeline = SPXQueryPipeline(config)
pipeline.run_full_pipeline()
```

--------------------------------

### SPXQuery Pipeline Initialization and Stages

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Logs showing the initialization of the SPXQuery pipeline, including RA/Dec coordinates, pipeline stages, and state file.

```log
2025-11-13 00:33:00 - spxquery.core.pipeline - INFO - Initialized pipeline for source at RA=213.942708, Dec=11.495389
2025-11-13 00:33:00 - spxquery.core.pipeline - INFO - Pipeline stages: ['query', 'download', 'processing', 'visualization']
2025-11-13 00:33:00 - spxquery.core.pipeline - INFO - State file: cloverleaf.yaml
2025-11-13 00:33:00 - spxquery.core.pipeline - INFO - Starting full pipeline execution
2025-11-13 00:33:00 - spxquery.core.pipeline - INFO - Pipeline stages: ['query', 'download', 'processing', 'visualization']
2025-11-13 00:33:00 - spxquery.core.pipeline - INFO - Running query stage
2025-11-13 00:33:00 - spxquery.core.query - INFO - Querying SPHEREx observations for source at RA=213.942708, Dec=11.495389
```

--------------------------------

### Print Configuration Details

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Prints the current configuration details for annulus and window background methods. Useful for verifying settings after updates.

```python
print("✓ Configurations created:")
print(f"  Annulus: method={advanced_annulus.photometry.background_method}")
print(f"  Window: method={advanced_window.photometry.background_method}, size={advanced_window.photometry.window_size}")
```

--------------------------------

### Conventional Commit Examples

Source: https://github.com/wenkeren/spxquery/blob/main/CONTRIBUTING.md

Examples of commit messages following the conventional commits format. This standardizes commit history for better understanding and automation.

```bash
git commit -m "feat: add cutout size validation"
git commit -m "fix: correct wavelength calculation in photometry"
git commit -m "docs: update installation instructions for Poetry"
```

--------------------------------

### Configure SPXQuery with Default Parameters

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/parameters.md

Initialize QueryConfig using only source and output directory to leverage all built-in default settings for advanced parameters.

```python
# Scenario 1: Use defaults
config = QueryConfig(source=source, output_dir="output")
# Result: Uses all built-in defaults
```

--------------------------------

### Troubleshoot Permission Errors with pip

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

If you encounter permission errors during pip installation, try installing the package for the current user only or use a virtual environment.

```bash
pip install --user .

# Or use a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install .
```

--------------------------------

### Use Custom Parameters with SPXQueryPipeline

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Initializes `SPXQueryPipeline` with custom parameters loaded from a file. Verifies that parameters like visualization DPI and photometry settings are correctly applied.

```python
# Method 1: Using SPXQueryPipeline with advanced_params_file
print("\nMethod 1: Using SPXQueryPipeline with advanced_params_file")
print("=" * 70)

# Create source
pipeline_custom = SPXQueryPipeline(config_all)

# Verify parameters were loaded
print("\n✓ Custom parameters loaded!")
print(f"  Visualization DPI: {pipeline_custom.config.visualization.dpi}")
print(f"  Figure size: {pipeline_custom.config.visualization.figsize}")
print(f"  Wavelength colormap: {pipeline_custom.config.visualization.wavelength_cmap}")
print(f"  BG sigma clipping: {pipeline_custom.config.photometry.bg_sigma_clip_sigma}")

# Create and run pipeline
```

--------------------------------

### Run SPXQuery Pipeline with Specific Stages

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/pipeline.md

Instantiate SPXQueryPipeline with a configuration and specify 'query' and 'download' as the only pipeline stages to execute. This is useful for downloading data for later analysis or integrating SPXQuery into custom workflows.

```python
pipeline = SPXQueryPipeline(config, pipeline_stages=["query", "download"])
pipeline.run_full_pipeline()
```

--------------------------------

### Install Python 3.11 with pyenv

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Install a specific Python version (3.11 in this case) using pyenv, a tool for managing multiple Python versions.

```bash
# Install pyenv (macOS/Linux)
curl https://pyenv.run | bash

# Install Python 3.11
pyenv install 3.11
```

--------------------------------

### CSV Light Curve Data Example

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Example of the CSV format for light curve measurements, including observation ID, time, flux, errors, and quality flags.

```csv
obs_id,mjd,flux,flux_error,wavelength,bandwidth,band,flag,snr,is_upper_limit
2025W25_1B_0062_1,60842.269794,1007.005,43.199,1.940,0.048,D3,2097152,23.3,False
...
```

--------------------------------

### AdvancedConfig: Full Control with Explicit Sub-configs

Source: https://context7.com/wenkeren/spxquery/llms.txt

Demonstrates creating an `AdvancedConfig` object by explicitly defining each sub-configuration (QueryConfig, PhotometryConfig, VisualizationConfig, DownloadConfig).

```python
# Method 2: Full control with explicit sub-configs
config = AdvancedConfig(
    query=QueryConfig(
        source=Source(ra=213.94, dec=11.49, name="cloverleaf"),
        output_dir=Path("output"),
        bands=["D1", "D2", "D3"]
    ),
    photometry=PhotometryConfig(
        aperture_method="fwhm",
        fwhm_multiplier=2.5,
        background_method="annulus",
        bad_flags=[0, 1, 2, 6, 7, 9, 10, 11, 14, 15, 17, 19]
    ),
    visualization=VisualizationConfig(
        sigma_threshold=5.0,
        wavelength_cmap="rainbow",
        dpi=300
    ),
    download=DownloadConfig(
        cutout_size="100px",
        max_download_workers=4
    )
)
```

--------------------------------

### SPXQuery Pixel Flag Filtering Logic Example

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Illustrates the core logic of SPXQuery's flag filtering: a pixel is rejected if any of its set flag bits are present in the 'bad_flags' list. This example checks a specific pixel flag against a defined set of bad flags.

```default
pixel_flag = 2097152  # Binary: 1000000000000000000000 (bit 21 set)
bad_flags = [0, 1, 2]

# Check if any bad flags are set
for bit in bad_flags:
    if pixel_flag & (1 << bit):
        reject_pixel()  # Reject if bit is set

# Result: Not rejected (bit 21 is not in bad_flags)
```

--------------------------------

### Uninstall SPXQuery with pip

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Remove the SPXQuery package from your environment if it was installed using pip.

```bash
pip uninstall spxquery
```

--------------------------------

### Re-lock Poetry Dependencies

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

Generate or update the `poetry.lock` file without updating existing dependencies, and then install the project dependencies.

```bash
poetry lock --no-update
poetry install
```

--------------------------------

### Configure Advanced Window Background

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Use this to set up advanced window background estimation. Load from a YAML file and update parameters like aperture method, diameter, and background method.

```python
advanced_window = AdvancedConfig.from_yaml_file(output_dir / "cloverleaf.yaml")
advanced_window.update(aperture_method="fixed", aperture_diameter=2.0, background_method="window", window_size=50)
```

--------------------------------

### Configure Advanced Photometry Parameters

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Imports necessary configuration classes for customizing SPXQuery photometry, including adaptive aperture sizing and background estimation methods.

```python
# FWHM-based adaptive apertures
from spxquery.core.config import (
    PhotometryConfig,
    QueryConfig,
    Source,
    AdvancedConfig,
    DownloadConfig,
    VisualizationConfig,
)
```

--------------------------------

### SPXQuery Pipeline Execution Log

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Example log output from the SPXQuery pipeline execution, showing stages like query, download, and processing.

```log
2025-11-13 00:33:48 - spxquery.core.pipeline - INFO - Initialized pipeline for source at RA=213.942708, Dec=11.495389
2025-11-13 00:33:48 - spxquery.core.pipeline - INFO - Pipeline stages: ['query', 'download', 'processing', 'visualization']
2025-11-13 00:33:48 - spxquery.core.pipeline - INFO - State file: cloverleaf.yaml
2025-11-13 00:33:48 - spxquery.core.pipeline - INFO - Starting full pipeline execution
2025-11-13 00:33:48 - spxquery.core.pipeline - INFO - Pipeline stages: ['query', 'download', 'processing', 'visualization']
2025-11-13 00:33:48 - spxquery.core.pipeline - INFO - Running query stage
2025-11-13 00:33:48 - spxquery.core.query - INFO - Querying SPHEREx observations for source at RA=213.942708, Dec=11.495389
2025-11-13 00:33:52 - spxquery.core.query - INFO - Found 108 observations spanning 33.1 days
2025-11-13 00:33:52 - spxquery.core.pipeline - INFO - Marked stage 'query' as complete
2025-11-13 00:33:52 - spxquery.core.pipeline - INFO - Saved pipeline state: stage=download, completed=['query']
2025-11-13 00:33:52 - spxquery.core.pipeline - INFO - Running download stage (skip_existing=True)
2025-11-13 00:33:52 - spxquery.core.download - INFO - Starting parallel download of 108 files
2025-11-13 00:33:52 - spxquery.core.download - INFO - Download settings: timeout=300s, retries=3, workers=4
```

--------------------------------

### Pipeline Initialization and Query Stage Log

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Logs indicating successful loading of advanced parameters and the initialization of the pipeline for a specific source. It shows the query stage beginning.

```log
2025-11-13 00:33:11 - spxquery.core.config - INFO - Loaded advanced parameters from demo_data/cloverleaf/config_params.yaml
2025-11-13 00:33:11 - spxquery.utils.params - INFO - Loaded advanced parameters from demo_data/cloverleaf/config_params.yaml
2025-11-13 00:33:11 - spxquery.core.pipeline - INFO - Initialized pipeline for source at RA=213.942708, Dec=11.495389
2025-11-13 00:33:11 - spxquery.core.pipeline - INFO - Pipeline stages: ['query', 'download', 'processing', 'visualization']
2025-11-13 00:33:11 - spxquery.core.pipeline - INFO - State file: cloverleaf.yaml
2025-11-13 00:33:11 - spxquery.core.pipeline - INFO - Starting full pipeline execution
2025-11-13 00:33:11 - spxquery.core.pipeline - INFO - Pipeline stages: ['query', 'download', 'processing', 'visualization']
2025-11-13 00:33:11 - spxquery.core.pipeline - INFO - Running query stage
2025-11-13 00:33:11 - spxquery.core.query - INFO - Querying SPHEREx observations for source at RA=213.942708, Dec=11.495389
```

--------------------------------

### Log Messages for Variance Repair

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Example log messages indicating that variance repair was applied due to NaN variance at the source position.

```text
WARNING: Variance at source position is NaN for file_D3_20250325_062.fits
INFO: Median variance from valid pixels: 2.34e-05
INFO: Using median variance as fallback for flux uncertainty calculation
```

--------------------------------

### Run SPxQuery Pipeline (Basic)

Source: https://github.com/wenkeren/spxquery/blob/main/docs/api/core.md

Use this function for basic pipeline execution with essential parameters like RA, Dec, and output directory. Ensure the output directory exists or is specified.

```python
>>> # Basic usage
>>> run_pipeline(ra=304.69, dec=42.44, output_dir="output")
```

--------------------------------

### Troubleshoot ImportError with pip

Source: https://github.com/wenkeren/spxquery/blob/main/INSTALL.md

If you encounter an ImportError after installing with pip, ensure you are not running Python from within the source directory. Navigate to a different directory and re-import.

```python
cd ~
python -c "import spxquery; print(spxquery.__version__)"
```

--------------------------------

### Crowded Field Filtering Parameters

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Example of quality control parameters for analyzing sources in crowded fields, including the PHANTOM flag in the rejection criteria.

```python
sigma_threshold=5.0
bad_flags=[0, 1, 2, 4, 6, 7, 9, 10, 11, 15]  # Add PHANTOM flag
```

--------------------------------

### Run SPXQuery Pipeline with Window Background

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Initialize and run the full SPXQuery pipeline with window background processing. Ensure pandas is imported as pd and the output directory is defined.

```python
config_window = SPXQueryPipeline(advanced_window)
config_window.run_full_pipeline(skip_existing_downloads=True)
df_window = pd.read_csv(output_dir / "results/lightcurve.csv", comment="#")
print("Window method configuration ready.")
print("Uncomment the code above to run the pipeline.")
```

--------------------------------

### Pipeline Download Stage Log

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Logs detailing the completion of the query stage and the start of the download stage, including download settings and the number of files to be downloaded.

```log
2025-11-13 00:33:37 - spxquery.core.query - INFO - Found 108 observations spanning 33.1 days
2025-11-13 00:33:37 - spxquery.core.pipeline - INFO - Marked stage 'query' as complete
2025-11-13 00:33:37 - spxquery.core.pipeline - INFO - Saved pipeline state: stage=download, completed=['query']
2025-11-13 00:33:37 - spxquery.core.pipeline - INFO - Running download stage (skip_existing=True)
2025-11-13 00:33:37 - spxquery.core.download - INFO - Starting parallel download of 108 files
2025-11-13 00:33:37 - spxquery.core.download - INFO - Download settings: timeout=300s, retries=3, workers=4
```

--------------------------------

### Print Configuration Details

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Print the current aperture method and diameter/multiplier for both fixed and FWHM configurations. This helps verify the applied settings.

```python
print("✓ Configurations created:")
print(
    f"  Fixed: method={advanced_fixed.photometry.aperture_method}, diameter={advanced_fixed.photometry.aperture_diameter}"
)
print(
    f"  FWHM: method={advanced_fwhm.photometry.aperture_method}, multiplier={advanced_fwhm.photometry.fwhm_multiplier}"
)
```

--------------------------------

### Log Message Example for Variance Repair

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

This log message indicates that pixels with NaN variance have been automatically repaired using the median variance, and that these pixels had non-zero flags.

```log
INFO - Repaired 23 pixels with NaN variance using median variance (1.234e-06). 
       All repaired pixels have non-zero flags.
```

--------------------------------

### Faint Variable Source Filtering Parameters

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Example of quality control parameters for analyzing faint variable sources, accepting marginal detections and removing specific pixel flags.

```python
sigma_threshold=3.0  # Accept marginal detections
bad_flags=[0, 1, 2, 6, 7, 9]  # Remove hot/cold pixel flags
```

--------------------------------

### Check Total Storage Usage

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/cutouts.md

Load the `query_summary.json` file to check the total downloaded size in GB and the sizes per band. This requires the `json` library to be imported.

```python
import json

with open("output/results/query_summary.json") as f:
    summary = json.load(f)
    print(f"Total size: {summary['total_size_gb']:.2f} GB")
    print(f"Band sizes: {summary['band_sizes']}")
```

--------------------------------

### Run Full SPXQuery Pipeline

Source: https://github.com/wenkeren/spxquery/blob/main/example/quickstart_demo.ipynb

Uncomment these lines to execute the complete SPXQuery pipeline. Ensure the pipeline_custom object is properly initialized.

```python
# Uncomment to run:
pipeline_custom.run_full_pipeline()

print("\nTo run pipeline, uncomment the last two lines above.")
```

--------------------------------

### Load and Filter CSV Data with Pandas

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Loads a CSV file into a Pandas DataFrame and demonstrates custom filtering based on SNR and quality flags. Ensure the pandas library is installed.

```python
import pandas as pd

df = pd.read_csv("output/results/lightcurve.csv", comment="#")

# Custom filtering
good = df[(df['snr'] >= 5.0) & (df['flag'] == 0)]

# Or more complex criteria
def has_bad_flags(flag_value, bad_flags=[0, 1, 2]):
    return any(flag_value & (1 << bit) for bit in bad_flags)

df['is_good'] = (df['snr'] >= 5.0) & ~df['flag'].apply(has_bad_flags)
good = df[df['is_good']]
```

--------------------------------

### Bright Stable Source Filtering Parameters

Source: https://github.com/wenkeren/spxquery/blob/main/docs/user_guide/quality_control.md

Example of quality control parameters for analyzing bright stable sources, requiring high confidence detections and strict rejection of various flags.

```python
sigma_threshold=10.0  # High-confidence only
bad_flags=[0, 1, 2, 6, 7, 9, 10, 11, 15, 17]  # Strict rejection
```