Getting Started

Installation

LePHARE is distributed with the Python Package Index (PyPI), and thus the simplest way to install it is with pip:

pip install lephare

We also reccommend using a conda environment to control Python version and isolate your installation:

# We recommend using Python 3.12
conda create -n <env_name> python=3.12
conda activate <env_name>
pip install lephare
# We have prepared a number of introductory notebooks. In order to run them
# you must install jupyterlab with the following commands.
conda install -c conda-forge jupyterlab
# And create a kernel which has access to this environment
python -m ipykernel install --user --name <kernel_name>

A this stage, the following Python snippet should work:

import lephare as lp

Warning

For previous users of who have set LEPHAREDIR and LEPHAREWORK in their environment these should be unset to avoid clashing versions.

Be aware that the structure of the repository has changed with respect to all previous versions.

In order to run LePHARE we need to download auxiliary data such as filters, SEDs, and attenuation curves which are not shipped with the code. These are explained in more detail below but for simplicity you can download everything via Python (~1.3Gb):

import lephare as lp
lp.data_retrieval.get_auxiliary_data(clone=True)

The following Python snippet is the most basic example to test the installation has worked. This will generate intermediate files and outputs stored in the cache or to the user defined storage locations discussed later. You can also get an example notebook running this code here.

import lephare as lp
from astropy.table import Table
# The following config is highly dependent on your input data and science goals
# You can change it for your own needs
config=lp.default_cosmos_config.copy()
lp.prepare(config)
# The following example table is in the lephare input format.
input_table=Table.read(f"{lp.LEPHAREDIR}/examples/COSMOS.in",format="ascii")
# In the next command output is an astropy.table.Table object with the results
output,_ = lp.process(config, input_table)
# One can then inspect, for instance, the first 5 lines of output
output[:5]

This will take over ten minutes to run. To check that everything was successful, this workflow should produce a 1 to 1 relationship between the spectroscopic redshift output[‘ZSPEC’] and predicted redshift output[‘Z_BEST’].

The Configuration Keywords

Most users will want to run lephare on their own data set so will have to change the config, set the filters and modify the input tables.

Taking advantage of the full capabilities of LePHARE will depend on a detailed understanding of the configurations which can be specified by text file or via a dictionary in Python. In the later stages of the documentation we cover the various options that can be specified via keywords.

For an example text file see the COSMOS example here.

One way to set config values is to start with the default cosmos config dictionary which is shipped with the Python by default and to update those elements you want to change. In the following Python snippet we start with the default COSMOS config and update the redshift grid using the Z_STEP keyword to a finer grid which would increase accuracy but take longer to execute:

import lephare as lp
config=lp.default_cosmos_config.copy()
config.update({
    'Z_STEP': '0.01,0.,7.', # A very fine redshift grid
})