In [7]:
from gaiaxpy import plot_spectra
# multi allows the spectra to appear in the same canvas
plot_spectra(converted_spectra, sampling=custom_sampling, multi=True)
This tool converts the input internally calibrated mean spectra from the continuous representation to a sampled form.
Information about the units of the variables used in this tutorial can be found here.
# Import the tool
from gaiaxpy import convert
# Path to file with XP CONTINUOUS RAW data (csv, ecsv, fits, or xml)
f = '/path/to/XP_CONTINUOUS_RAW.fits'
converted_spectra, sampling = convert(f)
converted_spectra
| source_id | xp | flux | flux_error | |
|---|---|---|---|---|
| 0 | 6 | BP | [790.6652760273903, 816.0488321107772, 838.088... | [51.12808805755064, 52.46049740480984, 53.5705... |
| 1 | 6 | RP | [908.9968995861024, 988.5280913043142, 1071.87... | [114.90126995378492, 116.22868541113809, 116.8... |
| 2 | 4 | BP | [15.117857072361456, 17.545174204016977, 20.14... | [6.55554367553648, 6.695615615689135, 6.804558... |
| 3 | 4 | RP | [6.5153802457522785, 7.081726114227435, 7.6757... | [0.9436286507422352, 0.9450255485429504, 0.940... |
The input does not have to be a path to a file. There are other options: a pandas DataFrame, an ADQL query or a list of sourceIds.
import pandas as pd
f = '/path/to/XP_CONTINUOUS_RAW.csv'
df = pd.read_csv(f) # The values in the DataFrame can be edited if the user wishes to do so.
converted_spectra, sampling = convert(df)
converted_spectra
| source_id | xp | flux | flux_error | |
|---|---|---|---|---|
| 0 | 6 | BP | [790.6652760273903, 816.0488321107772, 838.088... | [51.128087665439566, 52.46049701409262, 53.570... |
| 1 | 6 | RP | [908.9968995861024, 988.5280913043142, 1071.87... | [114.90126806544902, 116.22868348669782, 116.8... |
| 2 | 4 | BP | [15.117857072361456, 17.545174204016977, 20.14... | [6.555543736072461, 6.695615678678378, 6.80455... |
| 3 | 4 | RP | [6.5153802457522785, 7.081726114227435, 7.6757... | [0.9436286353147278, 0.9450255340198569, 0.940... |
The converter can also take the parameters 'username' and 'password' for Cosmos credentials, instead of using the interactive login (like in the example below). See that information here.
query_input = "select TOP 2 source_id from gaiadr3.gaia_source where has_xp_continuous = 'True'"
converted_spectra, sampling = convert(query_input)
converted_spectra
INFO: Login to gaia TAP server [astroquery.gaia.core] User: user Password: ········ OK INFO: Login to gaia data server [astroquery.gaia.core] OK INFO: Query finished. [astroquery.utils.tap.core]
| source_id | xp | flux | flux_error | |
|---|---|---|---|---|
| 0 | 48 | BP | [9.25655942031924, 9.704017573605816, 10.13033... | [2.467990582349073, 2.5096246553216672, 2.5395... |
| 1 | 48 | RP | [7.486068042729214, 8.016885302844601, 8.56322... | [1.538632626158625, 1.5589587619418803, 1.5712... |
| 2 | 44 | BP | [1.643567813219948, 1.7677135472274004, 1.8932... | [1.48517091514435, 1.508713194396786, 1.525715... |
| 3 | 44 | RP | [2.5606551733926333, 2.7662244174915362, 2.978... | [1.3121525986663758, 1.34090699773081, 1.36418... |
A list of sourceIds can be passed to the converter as the first argument. The converter will then query the Archive for these objects.
The converter can also take the parameters 'username' and 'password' for Cosmos credentials, instead of using the interactive login (like in the example below). See that information here.
sources_list = ['48', 44] # The sourceIds can be string or long.
converted_spectra, sampling = convert(sources_list)
converted_spectra
INFO: Login to gaia TAP server [astroquery.gaia.core] User: user Password: ········ OK INFO: Login to gaia data server [astroquery.gaia.core] OK
| source_id | xp | flux | flux_error | |
|---|---|---|---|---|
| 0 | 48 | BP | [9.25655942031924, 9.704017573605816, 10.13033... | [2.467990582349073, 2.5096246553216672, 2.5395... |
| 1 | 48 | RP | [7.486068042729214, 8.016885302844601, 8.56322... | [1.538632626158625, 1.5589587619418803, 1.5712... |
| 2 | 44 | BP | [1.643567813219948, 1.7677135472274004, 1.8932... | [1.48517091514435, 1.508713194396786, 1.525715... |
| 3 | 44 | RP | [2.5606551733926333, 2.7662244174915362, 2.978... | [1.3121525986663758, 1.34090699773081, 1.36418... |
Additional arguments can be passed to the converter.
These are:
A custom sampling can be passed to the function. This sampling should be an iterable (list, tuple, generator, or preferably a NumPy array). If no sampling is given, the default sampling is returned.
The default sampling of the converter corresponds to numpy.linspace(0., 60., 600.) in pseudo-wavelength.
The minimum value allowed in the sampling is -10, and the maximum is 70. The program will raise an error is the sampling does not comply.
import numpy as np
converted_spectra_default, default_sampling = convert(df.iloc[[0]])
converted_spectra, custom_sampling = convert(f, sampling=np.arange(-10, 70, 0.5))
converted_spectra
| source_id | xp | flux | flux_error | |
|---|---|---|---|---|
| 0 | 6 | BP | [1.3082126778732341e-05, 5.4669320529179805e-0... | [3.825912328340359e-06, 1.4022303133483225e-05... |
| 1 | 6 | RP | [2.103162458167948e-06, 8.936766026982058e-06,... | [5.078924195073175e-06, 1.9682498669323115e-05... |
| 2 | 4 | BP | [-2.711554384665641e-06, -9.74899522327931e-06... | [4.941224651787589e-07, 1.8211816533255976e-06... |
| 3 | 4 | RP | [-3.125339376302649e-10, 8.193829976216151e-09... | [5.086054060015767e-08, 1.9680526890289216e-07... |
The results can be plotted using the plotter utility.
from gaiaxpy import plot_spectra
# multi allows the spectra to appear in the same canvas
plot_spectra(converted_spectra, sampling=custom_sampling, multi=True)
The figure can be saved too.
plot_spectra(converted_spectra, sampling=custom_sampling, multi=True, output_path='/path/to', show_plot=False)
The source mean BP/RP spectrum is described as a combination of basis functions. Particularly for faint sources or sources with a low number of observations, it is useful to represent the spectrum using a smaller set of basis functions to avoid higher-order bases fitting the noise in the observed data.
The truncation parameter is a boolean which toggles the truncation of the set of bases.
first_source = df.drop([0]) # We'll use only the first source (sourceId 4) in the demonstration
non_truncated_spectra, sampling = convert(first_source) # truncation is False by default
truncated_spectra, _ = convert(first_source, truncation=True)
We can use GaiaXPy's plot spectra utility to see the differences in the results.
from gaiaxpy import plot_spectra
# Construct a DataFrame so we can plot both sources using just one function call
data = pd.concat([non_truncated_spectra, truncated_spectra]).reset_index(drop=True)
# As both spectra we want to plot have the same sourceId, we'll rename them to get descriptive labels
data['source_id'][0:2] = 'Non truncated'
data['source_id'][2:4] = 'Truncated'
plot_spectra(data, sampling=sampling, multi=True)
The differences between the truncated and not truncated versions are too small to be seen in the previous plot. But we can compare the values.
# We'll get the flux values of the BP band of the first spectrum in the data
non_truncated_bp_flux = data[(data['source_id'] == 'Non truncated') & (data['xp'] == 'BP')]['flux'].iloc[0]
truncated_bp_flux = data[(data['source_id'] == 'Truncated') & (data['xp'] == 'BP')]['flux'].iloc[0]
# We can then compute the absolute differences
abs_differences = np.abs(non_truncated_bp_flux - truncated_bp_flux)
# And find the values and indices of those differences
min_diff = np.min(abs_differences)
max_diff = np.max(abs_differences)
print(f'The length of the sampling is {len(non_truncated_bp_flux)}.')
print(f'Minimum difference: {min_diff}. Index of minimum difference: {np.where(abs_differences == min_diff)[0][0]}.')
print(f'Maximum difference: {max_diff}. Index of maximum difference: {np.where(abs_differences == max_diff)[0][0]}.')
The length of the sampling is 600. Minimum difference: 1.736381318551139e-05. Index of minimum difference: 223. Maximum difference: 1.7060870650311983. Index of maximum difference: 64.
Three parameters: output_path, output_file, and output_format define the entire path of the resulting file.
The default output path is the current path. If the given output path does not exist, it will be created.
The default output file name is 'output_spectra'.
The default output format is the format of the input file (i.e. if the input file is a 'fits', then the output file will be a FITS file by default.), or CSV in any other case (DataFrame, ADQL query or list).
NOTE: If a file with the same path and name already exists, it will be AUTOMATICALLY OVERWRITTEN.
converted_spectra, _ = convert(f, output_path='/path/to', output_file='my_file', output_format='ecsv')
The additional parameter save_file is a boolean that tells the program whether to save the results or not. If 'output_file' is given but 'save_file' is set to False, a warning will be raised.
converted_spectra, _ = convert(f, output_file='my_file', output_format='.csv', save_file=False)
UserWarning: Argument output_file was given, but save_file is set to False. Set save_file to True to store the output of the function.