DataVector class#

FLIP includes a DataVector abstract class that is used to build different classes to easily handle the data you want to use and to pass them to one of the different likelihood implemented in the flip.likelihood module.

Using the DataVector class you can obtain data and variance / covariance:

data, var_data = DataVector(parameter_dic)

You also can compute covariance:

Cov = DataVector.compute_covariance(
    # model and pw used
    model_name,
    power_spectrum_dict,
    # parallelization
    size_batch=size_batch,
    number_worker=number_worker,
    # additional parameters
    additional_parameters_values=()
    )

Density#

Direct Density#

The Dens class is used on example data as:

import pandas as pd
from flip import data_vector

grid = pd.read_parquet("flip/flip/data/density_data.parquet")
grid.rename(columns={'density_err': 'density_error',
                    'rcom': 'rcom_zobs'}, inplace=True)
DataDensity = data_vector.Dens(grid.to_dict(orient='list'))

Velocity#

Direct Velocity#

The DirectVel class is used on example data as:

import pandas as pd
import numpy as np
from flip import data_vector

data_velocity = pd.read_parquet("flip/flip/data/velocity_data.parquet"))
data_velocity.rename(columns={'vpec': 'velocity'}, inplace=True)
data_velocity["velocity_error"] = np.zeros(len(data_velocity["vpec"])

DataTrueVel = data_vector.DirectVel(data_velocity_true)

Velocity from Hubble diagram residuals#

When using the VelFromHDres class different estimator of velocities can be used from HD residuals. They are described in Velocity estimators.

The DataVector is initialised as:

from flip import data_vector

DataVel = data_vector.VelFromHDres(data, velocity_estimator=estimator_name, **kwargs)

Velocity from SNe Ia SALT2 parameters#

The VelFromSALTfit class use the same estimators as VelFromHDres, but the input data are the SALT2 fit parameters mb, x1 and c alongs with their errors and covariance. Also this class always require the rcom_zobs field in the data.

The HD residuals are computed using the Tripp relation:

\[\Delta\mu = m_b + \alpha x_1 - \beta c - M_0 - 5\log_{10}\left[(1+z)r(z)\right] - 25\]

The DataVector is initialised as:

import pandas as pd
from flip import data_vector

data_velocity = pd.read_parquet("flip/flip/data/velocity_data.parquet"))
DataVel = data_vector.snia_vectors.VelFromSALTfit(
    data_velocity.to_dict(orient='list'),
    velocity_estimator='full'
    )

mu = DataVel.compute_observed_distance_modulus(test_parameters)
variance_mu = DataVel.compute_observed_distance_modulus_error(test_parameters)

When using the __call__ method the SNe Ia HD parameters need to be passed:

test_parameters = {
"alpha":0.14,
"beta": 3.1,
"M_0": -19.133,
"sigma_M": 0.12
}

velocity, velocity_error = DataVel(test_parameters)

Density X Velocity#

The DensVel class allows to init a DataVector with density and velocity. It is initialised as:

import pandas as pd
from flip import data_vector

grid = pd.read_parquet("flip/flip/data/density_data.parquet")
grid.rename(columns={'density_err': 'density_error',
                    'rcom': 'rcom_zobs'}, inplace=True)

DataDensity = data_vector.Dens(grid.to_dict(orient='list'))

data_velocity = pd.read_parquet("flip/flip/data/velocity_data.parquet"))
DataVel = data_vector.snia_vectors.VelFromSALTfit(
    data_velocity.to_dict(orient='list'),
    velocity_estimator='full'
    )

DensCrossVel = data_vector.DensVel(DataDensity, DataTrueVel)

DataVector class

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