semvec/pypi-packages huggingface dataset:
kilonovanet 0.1.1
Kilonova surrogate modelling via cVAE
Requires Python: >=3.6

# KilonovaNet: Kilonova Surrogate Modelling

A conditional variational autoencoder (cVAE) framework for producing continuous
surrogate spectra for kilonova models.

This package provides the interface to predict spectra. It does not provide an
interface to do the data prep for training and training itself. The currently
trained and provided models are:

- [M. Bulla BNS Models](https://github.com/mbulla/kilonova_models/tree/master/bns_m3_3comp)
- [M. Bulla BHNS Models](https://github.com/mbulla/kilonova_models/tree/master/bhns_m1_2comp)
- [D.Kasen BNS Models](https://github.com/dnkasen/Kasen_Kilonova_Models_2017)

This work requires the use of [pyphot](https://github.com/mfouesneau/pyphot); pyphot requires hdf5. 
See their installation intructions to see how to install that for your system. Then, if you install 
KilonovaNet via pip, dependencies (including pyphot) should install properly. 

## Installation
Install via pip: 

`pip install kilonovanet`

## Usage
In order to produce surrogate spectra, you will need to specify the model and torch files. 
*These are not included in this package, you must download them separately from 
[the KilonovaNet github](https://github.com/klukosiute/kilonovanet) from data and model folders.*

After you have the files in your system, you can produce spectra with the following:
```python
import kilonovanet
import numpy as np

metadata_file = "data/metadata_bulla_bns.json"
torch_file = "models/bulla-bns-latent-20-hidden-1000-CV-4-2021-04-21-epoch-200.pt"
times = np.array([1.2, 2.2])
physical_parameters = np.array([1.0e-2, 9.0e-2, 3.0e1, 3.0e-1])

model = kilonovanet.Model(metadata_file, torch_file)
spectra = model.predict_spectra(physical_parameters, times)
```

In order to produce some photometric observations, the following have to be specified:
- the model
- the corresponding parameters of the model (see their papers, repositories, etc.)
- the times post-merger to produce the observations
- the filters in which to produce the observations

I have specified some filters in the github folder filter_data, but any filter transmission curves should work properly.

After you have filter profiles, use the following to produce synthetic photometric observations:

```python
import kilonovanet
import numpy as np
 
metadata_file = "data/metadata_bulla_bns.json"
torch_file = "models/bulla-bns-latent-20-hidden-1000-CV-4-2021-04-21-epoch-200.pt"
filter_lib = "data/filter_data"

times = np.array([1.2, 1.2, 1.2, 2.2, 2.2, 2.2, 2.2])
filters = np.array(["LSST_u", "LSST_z", "LSST_y", "LSST_u", "LSST_z", "LSST_y"])
distance = 40.0 * 10 ** 6 * 3.086e18 # 40 Mpc in cm
physical_parameters = np.array([1.0e-2, 9.0e-2, 3.0e1, 3.0e-1])

model = kilonovanet.Model(metadata_file, torch_file, filter_library_path=filter_lib)
mags = model.predict_magnitudes(physical_parameters, times=times, filters=filters,
distance=distance)
```

If you intend to use the same set of observations often, e.g. when doing an
MCMC-based fit, you can specify all of them in an `Observations` object and
then simply call `model.predict_magnitudes(physical_parameters)`.
conveyor-mentalblood 0.4.2
Library for creating cold-pipeline-oriented systems

# Conveyor

Library for creating cold-pipeline-oriented systems

## Workers classes

* `Creator`
* `Transformer`
* `Mover`
* `Destroyer`

## Item repositories classes

* `DefaultItemRepository` -- uses PostgreSQL database for storing metadata and dir tree for storing files

## Example

See `tests/example_workers.py` and `tests/test_pipeline.py`