| Name | pz-rail-dnf JSON |
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1.1.6
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| upload_time | 2025-07-11 21:24:41 |
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| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2023 LSST Dark Energy Science Collaboration (DESC)
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# rail_dnf
[](https://lincc-ppt.readthedocs.io/en/latest/)
[](https://codecov.io/gh/LSSTDESC/rail_dnf)
[](https://pypi.org/project/pz-rail-dnf/)
## DNF: Directional Neighbourhood Fitting
DNF is a nearest-neighbor approach for photometric redshift estimation developed at the CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas). DNF computes the photo-z hyperplane that best fits the directional neighbourhood of a photometric galaxy in the training sample. A detailed description of DNF is available [here](https://arxiv.org/abs/1511.07623).
If you have any questions or suggestions, please don't hesitate to contact us at laura.toribio@ciemat.es and/or juan.vicente@ciemat.es.
The current version of the code for `RAIL`consists of a training stage, `DNFInformer` and a estimation stage `DNFEstimator`. `DNFInformer` is a class that preprocesses the protometric data, handles missing or non-detected values, and trains a firts basic k-Nearest Neighbors regressor for redshift prediction. The `DNFEstimator` calculates photometric redshifts based on an enhancement of Nearest Neighbor techniques. The class supports three main metrics for redshift estimation: ENF, ANF or DNF.
- **ENF**: Euclidean neighbourhood. It's a common distance metric used in kNN (k-Nearest Neighbors) for photometric redshift prediction.
- **ANF**: uses normalized inner product for more accurate photo-z predictions. It is particularly **recommended** when working with datasets containing more than four filters. Use normalized inner product for more accurate photo-z predictions when signal/noise is good enough.
- **DNF**: combines Euclidean and angular metrics, improving accuracy, especially for larger neighborhoods, and maintaining proportionality in observable content.
### `DNFInformer`
The `DNFInformer` class processes a training dataset and produces a model file containing the computed magnitudes, colors, and their associated errors for the dataset. This model is then utilized in the `DNFEstimator` stage for photometric redshift estimation. Missing photometric detections (non-detections) are handled by replacing them with a configurable placeholder value, or optionally ignoring them during model training.
The configurable parameters for `DNFInformer` include:
- `bands`: List of band names expected in the input dataset.
- `err_bands`: List of magnitude error column names corresponding to the bands.
- `redshift_col`: String indicating the name of the redshift column in the input data.
- `mag_limits`: Dictionary with band names as keys and floats representing the acceptable magnitude range for each band.
- `nondetect_val`: Float or np.nan, the value indicating a non-detection, which will be replaced by the values in mag_limits.
- `replace_nondetect`: Boolean; if True, non-detections are replaced with the specified nondetect_val. If False, non-detections are ignored during the neighbor-finding process.
### `DNFEstimator`
The `DNFEstimator` class uses the model generated by DNFInformer to compute photometric redshifts for new datasets and the PDFs. It identifies the nearest neighbors from the training data using various distance metrics and estimates redshifts based on these neighbors.
The configurable parameters for `DNFEstimator` include:
- `bands`, `err_bands`, `redshift_col`, `nondetect_val`, `mag_limits`: As described for `DNFInformer`.
- `selection_mode`: Integer indicating the method for neighbor selection:
* `0`: Euclidean Neighbourhood Fitting (ENF).
* `1`: Angular Neighbourhood Fitting (ANF).
* `2`: Directional Neighbourhood Fitting (DNF).
- `zmin`, `zmax`, `nzbins`: Float values defining the minimum and maximum redshift range and the number of bins for estimation of the PDFs.
- `pdf_estimation`: Boolean; if True, computes a probability density function (PDF) for the redshift of each object.
DNF calculates its own point estimate, `DNF_Z`, which is stored in the qp Ensemble `ancil` data. Also, DNF calculates other photo-zs called `DNF_ZN`.
- `DNF_Z` represents the photometric redshift for each galaxy computed as the weighted average or hyperplane fit (depending on the option selected) for a set of neighbors determined by a specific metric (ENF, ANF, DNF) where the outliers are removed
- `DNF_ZN` represents the photometric redshift using only the closest neighbor. It is mainly used for computing the redshift distributions.
## RAIL: Redshift Assessment Infrastructure Layers
This package is part of the larger ecosystem of Photometric Redshifts
in [RAIL](https://github.com/LSSTDESC/RAIL).
### Citing RAIL
This code, while public on GitHub, has not yet been released by DESC and is
still under active development. Our release of v1.0 will be accompanied by a
journal paper describing the development and validation of RAIL.
If you make use of the ideas or software in RAIL, please cite the repository
<https://github.com/LSSTDESC/RAIL>. You are welcome to re-use the code, which
is open source and available under terms consistent with the MIT license.
External contributors and DESC members wishing to use RAIL for non-DESC projects
should consult with the Photometric Redshifts (PZ) Working Group conveners,
ideally before the work has started, but definitely before any publication or
posting of the work to the arXiv.
### Citing this package
Users of rail_dnf can cite [De Vicente, Sanchez, & Sevilla-Noarbe](https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.3078D/abstract)
If you use this package, you should also cite the appropriate papers for each
code used. A list of such codes is included in the
[Citing RAIL](https://lsstdescrail.readthedocs.io/en/stable/source/citing.html)
section of the main RAIL Read The Docs page.
Raw data
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"description": "# rail_dnf\n\n[](https://lincc-ppt.readthedocs.io/en/latest/)\n[](https://codecov.io/gh/LSSTDESC/rail_dnf)\n[](https://pypi.org/project/pz-rail-dnf/)\n\n## DNF: Directional Neighbourhood Fitting\n\nDNF is a nearest-neighbor approach for photometric redshift estimation developed at the CIEMAT (Centro de Investigaciones Energ\u00e9ticas, Medioambientales y Tecnol\u00f3gicas). DNF computes the photo-z hyperplane that best fits the directional neighbourhood of a photometric galaxy in the training sample. A detailed description of DNF is available [here](https://arxiv.org/abs/1511.07623).\n\nIf you have any questions or suggestions, please don't hesitate to contact us at laura.toribio@ciemat.es and/or juan.vicente@ciemat.es.\n\nThe current version of the code for `RAIL`consists of a training stage, `DNFInformer` and a estimation stage `DNFEstimator`. `DNFInformer` is a class that preprocesses the protometric data, handles missing or non-detected values, and trains a firts basic k-Nearest Neighbors regressor for redshift prediction. The `DNFEstimator` calculates photometric redshifts based on an enhancement of Nearest Neighbor techniques. The class supports three main metrics for redshift estimation: ENF, ANF or DNF.\n\n- **ENF**: Euclidean neighbourhood. It's a common distance metric used in kNN (k-Nearest Neighbors) for photometric redshift prediction.\n- **ANF**: uses normalized inner product for more accurate photo-z predictions. It is particularly **recommended** when working with datasets containing more than four filters. Use normalized inner product for more accurate photo-z predictions when signal/noise is good enough.\n- **DNF**: combines Euclidean and angular metrics, improving accuracy, especially for larger neighborhoods, and maintaining proportionality in observable content.\n\n\n### `DNFInformer`\n\nThe `DNFInformer` class processes a training dataset and produces a model file containing the computed magnitudes, colors, and their associated errors for the dataset. This model is then utilized in the `DNFEstimator` stage for photometric redshift estimation. Missing photometric detections (non-detections) are handled by replacing them with a configurable placeholder value, or optionally ignoring them during model training.\n\nThe configurable parameters for `DNFInformer` include:\n\n- `bands`: List of band names expected in the input dataset.\n- `err_bands`: List of magnitude error column names corresponding to the bands.\n- `redshift_col`: String indicating the name of the redshift column in the input data.\n- `mag_limits`: Dictionary with band names as keys and floats representing the acceptable magnitude range for each band.\n- `nondetect_val`: Float or np.nan, the value indicating a non-detection, which will be replaced by the values in mag_limits.\n- `replace_nondetect`: Boolean; if True, non-detections are replaced with the specified nondetect_val. 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