### Import Generator and PhotometricSystem

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Import the necessary functions and classes from the gaiaxpy library to start generating photometry.

```python
from gaiaxpy import generate, PhotometricSystem
```

--------------------------------

### Get available photometric systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Retrieve a list of all available photometric systems.

```python
PhotometricSystem.get_available_systems()
```

--------------------------------

### Import find_lines Tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Import the `find_lines` function from the gaiaxpy library. This is a necessary setup step before using the function.

```python
# Import the tool
from gaiaxpy import find_lines
```

--------------------------------

### Get Available Photometric Systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Retrieve a list of all currently available photometric systems, including built-in and loaded additional systems.

```python
PhotometricSystem.get_available_systems()

```

--------------------------------

### Get Version of a Built-in Photometric System

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Retrieve the version of a built-in photometric system. This will return the current version of the GaiaXPy library.

```python
PhotometricSystem.SDSS.get_version()

```

--------------------------------

### Get Version of an Additional Photometric System

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Consult the version of a specific additional photometric system. This is crucial for reproducibility when publishing results.

```python
PhotometricSystem.USER_MSSSO.get_version()

```

--------------------------------

### Calibrate Spectra from DataFrame

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Pass a pandas DataFrame containing raw XP continuous data to the calibrate function. Ensure the DataFrame is loaded correctly, for example, using pandas.read_csv.

```python
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.

calibrated_spectra, sampling = calibrate(df)
calibrated_spectra
```

--------------------------------

### Set Output Path and Format

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Demonstrates how to specify the output path, file name, and format for the calibrated spectra. The file will be automatically overwritten if it exists.

```python
calibrated_spectra, _ = calibrate(f, output_path='/path/to', output_file='my_file', output_format='fits')
```

--------------------------------

### Use Built-in and Additional Systems for Generation

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Import the generate function and create a list of photometric systems, including both built-in systems (e.g., PhotometricSystem.Pristine) and loaded additional systems (e.g., PhotometricSystem.USER_PS1), to be used with the generate function.

```python
from gaiaxpy import generate
phot_systems_list = [PhotometricSystem.Pristine, PhotometricSystem.USER_PS1]
```

--------------------------------

### Import photometric systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Import the class for handling photometric systems.

```python
from gaiaxpy import PhotometricSystem
```

--------------------------------

### Import the find_fast tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Import the necessary tool for finding extrema. This is the first step before using the line finder functionality.

```python
# Import the tool
from gaiaxpy import find_fast
```

--------------------------------

### Specify Output Path, File, and Format

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Use output_path, output_file, and output_format to control the destination and naming of the converted file. The output path will be created if it doesn't exist. Existing files will be overwritten.

```python
converted_spectra, _ = convert(f, output_path='/path/to', output_file='my_file', output_format='ecsv')
```

--------------------------------

### Load Additional Photometric Systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Load custom photometric systems from a specified directory. This replaces any previously loaded additional systems.

```python
f = '/path/to/XP_CONTINUOUS_RAW.ecsv'
synthetic_photometry = generate(f, photometric_system=phot_systems_list)
synthetic_photometry
```

```python
PhotometricSystem = load_additional_systems()

```

--------------------------------

### Import Calibrator Tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Import the calibrate function from the gaiaxpy library and specify the path to your raw XP continuous data file.

```python
# Import the tool
from gaiaxpy import calibrate
# Path to file with XP CONTINUOUS RAW data (csv, ecsv, fits, or xml)
f = '/path/to/XP_CONTINUOUS_RAW.xml'
```

--------------------------------

### Import Necessary Modules for GaiaXPy

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Import the PhotometricSystem class and the load_additional_systems function from the gaiaxpy library. This is the first step before working with additional photometric systems.

```python
from gaiaxpy import PhotometricSystem, load_additional_systems
```

--------------------------------

### Import generator tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Import the tool used to generate synthetic photometry.

```python
from gaiaxpy import generate
```

--------------------------------

### Load Additional Photometric Systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Load custom photometric systems by providing the path to the directory containing the system files to the load_additional_systems function. GaiaXPy will recursively search for system files within the specified directory. Ensure that the directory contains valid system files; otherwise, warnings will be issued, and invalid files will be ignored. If duplicate system names are found, an error will be raised, and no systems will be loaded.

```python
PhotometricSystem = load_additional_systems('/path/to/additional_filters_directory')
```

--------------------------------

### Convert Spectra with Custom Sampling

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Demonstrates converting spectra using a custom sampling array. The sampling array must be within the range of -10 to 70.

```python
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
```

--------------------------------

### Customize Output Path and Filename

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Specify the output path, filename, and format for the generated synthetic photometry. The directory will be created if it doesn't exist. Existing files will be overwritten.

```python
synthetic_photometry = generate(f, photometric_system=phot_system, output_path='/path/to', output_file='my_file', output_format='xml')

```

--------------------------------

### Import the find_extrema tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Import the find_extrema tool, which is used to find extrema and their properties in internally calibrated mean spectra.

```python
# Import the tool
from gaiaxpy import find_extrema
```

--------------------------------

### Control File Saving

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Illustrates how to control whether the calibrated spectra are saved to a file using the `save_file` parameter. A warning is raised if `output_file` is provided but `save_file` is False.

```python
calibrated_spectra, _ = calibrate(f, output_file='my_file', output_format='.xml', save_file=False)
```

--------------------------------

### Select photometric systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Select a single photometric system or a list of systems for use.

```python
phot_system = PhotometricSystem.Gaia_DR3_Vega
phot_systems_list = [PhotometricSystem.Pristine, PhotometricSystem.JWST_NIRCAM, PhotometricSystem.Euclid_VIS]
```

--------------------------------

### Calibrate Spectra from ADQL Query

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Calibrate spectra by providing an ADQL query string. The calibrator can use provided username and password for Cosmos credentials.

```python
query_input = "select TOP 2 source_id from gaiadr3.gaia_source where has_xp_continuous = 'True'"
calibrated_spectra, sampling = calibrate(query_input)
calibrated_spectra
```

--------------------------------

### Generate Synthetic Photometry from Query

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Generate synthetic photometry by providing a SQL query to fetch data from the Archive. This is useful for selecting specific data subsets. The query must be a valid SQL string.

```python
query_input = "select TOP 2 source_id from gaiadr3.gaia_source where has_xp_continuous = 'True'"
phot_system_list = [PhotometricSystem.Stromgren, PhotometricSystem.Stromgren_Std]
synthetic_photometry = generate(query_input, photometric_system=phot_system_list)
synthetic_photometry
```

--------------------------------

### Plot Flux Spectra with STILTS

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Use STILTS to generate a 2D plot of flux spectra from a FITS file. Ensure the 'sampling' parameter is included in the file for wavelength information.

```bash
stilts plot2plane in=calibrator.fits xs='param$sampling' ys=flux layer1=lines
```

--------------------------------

### Find Extrema with Truncation

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Utilize the find_fast tool with the truncation=True option to simplify spectral representations, which is beneficial for faint sources or those with limited observations. This helps prevent higher-order bases from fitting noise.

```python
sources_list = [5853498713190525696, 5762406957886626816]

extrema_pwl_tr = find_fast(sources_list, truncation=True)
extrema_pwl_tr.head()
```

--------------------------------

### Calibrate with Truncation

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Shows how to calibrate spectra with and without truncation. Truncation is useful for faint sources to avoid fitting noise.

```python
second_source = df.drop([0]) # We'll use only the second source (sourceId 4) in the demonstration
non_truncated_spectra, sampling = calibrate(second_source) # truncation is False by default
truncated_spectra, _ = calibrate(second_source, truncation=True)
```

--------------------------------

### Customizing Output Path and File with Linefinder

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Specify `output_path`, `output_file`, and `output_format` to control where and how the results are saved. The output path will be created if it doesn't exist.

```python
lines = find_lines([5853498713190525696], output_path='.', output_file='my_file', output_format='ecsv')

```

--------------------------------

### Basic Conversion with File Path

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Convert spectra from a specified file path. The converter returns sampled spectra as a pandas DataFrame and the sampling used.

```python
converted_spectra, sampling = convert(f)
converted_spectra
```

--------------------------------

### Generate Synthetic Photometry for Multiple Systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Generate synthetic photometry for a specified input file using a list of photometric systems. The output is saved to a file.

```python
# Create a list of photometric systems
phot_system_list = [PhotometricSystem.Gaia_DR3_Vega, PhotometricSystem.JKC, PhotometricSystem.Pristine]
synthetic_photometry = generate(f, photometric_system=phot_system_list)
synthetic_photometry
```

--------------------------------

### Calibrate with Geometric Sampling

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Demonstrates calibrating spectra using a geometric progression sampling for improved resolution at the blue end. Ensure numpy is imported.

```python
import numpy as np

sampling = np.geomspace(330,1049.9999999999, 361)
calibrated_spectra_geom, sampling = calibrate(f, sampling=sampling)
calibrated_spectra_geom
```

--------------------------------

### Generate Synthetic Photometry from List of Source IDs

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Generate synthetic photometry by passing a list of source IDs. Source IDs can be strings or integers. The generator will query the Archive for these objects.

```python
sources_list = ['6030020833890693248', 6030064028385961344] # The source IDs can be string or long.
synthetic_photometry = generate(sources_list, photometric_system=PhotometricSystem.JKC_Std)
synthetic_photometry
```

--------------------------------

### Generate Synthetic Photometry for a Single System

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Generate synthetic photometry for a specified input file and a single photometric system. The output is saved to a file.

```python
# Path to file with XP CONTINUOUS RAW data (csv, ecsv, fits, or xml)
f = '/path/to/XP_CONTINUOUS_RAW.xml'
# Select a photometric system
phot_system = PhotometricSystem.Gaia_DR3_Vega
synthetic_photometry = generate(f, photometric_system=phot_system)
synthetic_photometry
```

--------------------------------

### Convert Spectra using List of Source IDs

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Convert spectra by passing a list of source IDs. The converter will query the Archive for these specific objects. Interactive login credentials may be prompted.

```python
sources_list = ['48', 44] # The sourceIds can be string or long.
converted_spectra, sampling = convert(sources_list)
converted_spectra
```

--------------------------------

### Import Converter Tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Import the necessary convert function from the gaiaxpy library and specify the path to your input data file.

```python
# 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'
```

--------------------------------

### Calibrate Spectra from List of Source IDs

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Calibrate spectra by passing a list of source IDs. The calibrator will query the Archive for these objects. Source IDs can be strings or integers. Username and password can be provided for authentication.

```python
sources_list = ['48', 44] # The sourceIds can be string or long.
calibrated_spectra, sampling = calibrate(sources_list)
calibrated_spectra
```

--------------------------------

### Convert Spectra using ADQL Query

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Convert spectra by providing an ADQL query string. This method allows for dynamic selection of data from the Gaia Archive. Interactive login credentials may be prompted.

```python
query_input = "select TOP 2 source_id from gaiadr3.gaia_source where has_xp_continuous = 'True'"
converted_spectra, sampling = convert(query_input)
converted_spectra
```

--------------------------------

### Find extrema using an ADQL query

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Use an ADQL query to select source IDs from the Gaia Archive. The find_fast function will process the query to find extrema.

```python
query_input = "select TOP 2 source_id from gaiadr3.gaia_source where has_xp_continuous = 'True'"

extrema_pwl = find_fast(query_input)
extrema_pwl.head()
```

--------------------------------

### Find Lines in QSO Spectra with Redshift

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

When working with QSO spectra, you can specify the source type as 'QSO' and provide redshift(s). By default, a short list of common lines is searched.

```python
# A list of sourceIds
sources_list = [858200268935710208, 3937755935439564672]
```

--------------------------------

### Calibrate Spectra from File

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Use the calibrate function with a file path to obtain calibrated spectra and the corresponding sampling. The calibrated spectra is a pandas DataFrame, and the sampling is a NumPy array.

```python
calibrated_spectra, sampling = calibrate(f)
calibrated_spectra
```

--------------------------------

### Compare Default and Geometric Sampling

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Compares the results of default sampling versus geometric progression sampling for a single source. Requires the gaiaxpy plotting utility.

```python
first_source = df.iloc[[0]] # Select row with index 0
first_spectra, sampling = calibrate(first_source)
first_spectra_geom, geom_sampling = calibrate(first_source, sampling=np.geomspace(330,1049.9999999999, 361))
```

```python
from gaiaxpy import plot_spectra

print('Default sampling')
# Do not show the legend as there's only one source in the data
plot_spectra(first_spectra, sampling=sampling, legend=False)
print('Geometric progression (log) sampling')
plot_spectra(first_spectra_geom, sampling=geom_sampling, legend=False)
```

--------------------------------

### Download Filter File using wget

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/SVO%20download.html

Use the `wget` command to download a filter file programmatically. The `--content-disposition` option is crucial for preserving the original filename, which contains essential information. Ensure the output directory is specified.

```bash
wget --content-disposition -P outputdir/ http://svo2.cab.inta-csic.es/theory/fps/getgaiaxpy.php/?gxpyid=Filter/Filter
```

--------------------------------

### Find Spectral Lines with Redshift

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Use the find_lines tool to detect spectral lines for quasars (source_type='qso') by providing a list of redshifts. The output includes detailed line properties.

```python
zets = [(858200268935710208, 0.06107), (3937755935439564672, 2.698)]

lines = find_lines(sources_list, source_type='qso', redshift=zets)
lines
```

--------------------------------

### Find Lines with User-Defined Lines from File

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Specify spectral lines for analysis by providing a file path to the user_lines parameter in the find_lines tool. The file should contain wavelengths and line names, separated by spaces, without a header.

```python
sources_list = [5853498713190525696, 5762406957886626816]
f = '/path/to/lines_example.txt'

lines = find_lines(sources_list, user_lines=f)
lines
```

```text
656.461 H_alpha
486.268 H_beta
```

--------------------------------

### Find Extrema from FITS File

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Use `find_extrema` to process a FITS file and display the first few rows of the resulting DataFrame. Requires the path to the FITS file.

```python
f = '/path/to/XP_CONTINUOUS_RAW.fits'

extrema = find_extrema(f)
extrema.head()
```

--------------------------------

### Find extrema using a list of source IDs

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Provide a list of source IDs (as strings or integers) to the find_fast function. The tool will query the Archive for these specific sources.

```python
sources_list = ['5853498713190525696', 5762406957886626816] # The sourceIds can be string or long.

extrema_pwl = find_fast(sources_list)
extrema_pwl.head()
```

--------------------------------

### Generate synthetic photometry

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Generate synthetic photometry from an input file using specified photometric systems. Ensure that the 'Gaia_DR3_Vega' system is included if you plan to apply error corrections based on Gaia photometry.

```python
# File with input data
f = '/path/to/XP_CONTINUOUS_ECSV'
synthetic_photometry = generate(f, photometric_system=phot_systems_list)
```

--------------------------------

### Apply Error Correction with Custom Output Path and Filename

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Use this snippet to apply error correction and specify a custom output path, filename, and format. The output path will be created if it doesn't exist. Be aware that existing files with the same path and name will be overwritten.

```python
synth_phot_corrected = apply_error_correction(synthetic_photometry, output_path='/output/path', output_file='my_file', output_format='csv')
```

--------------------------------

### Convert Spectra with Truncation

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Compares spectra conversion with and without truncation. Truncation is useful for faint sources to avoid fitting noise with higher-order bases. It is False by default.

```python
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)
```

--------------------------------

### Find Extrema from ADQL Query

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Use an ADQL query to select source IDs from the Archive. The find_extrema function will process the query results.

```python
query_input = "select TOP 2 source_id from gaiadr3.gaia_source where has_xp_continuous = 'True'"

extrema = find_extrema(query_input)
extrema.head()
```

--------------------------------

### Find extrema using a file path

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Specify the path to a file containing XP CONTINUOUS RAW data (e.g., .fits, .csv, .ecsv, .xml). The find_fast function will read and process the data from the file.

```python
f = '/path/to/XP_CONTINUOUS_RAW.fits'

extrema_pwl = find_fast(f)
extrema_pwl.head() # Only the first few rows of the output are displayed when head() is used.
```

--------------------------------

### Generate Synthetic Photometry from DataFrame

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Use this to generate synthetic photometry from a pandas DataFrame. The DataFrame can be modified before generation. Ensure the DataFrame contains necessary columns for photometry.

```python
import pandas as pd

f = '/path/to/XP_CONTINUOUS_RAW.csv'
df = pd.read_csv(f) # At this point, the values in the DataFrame can be edited if the user wishes to do so.

synthetic_photometry = generate(df, photometric_system=PhotometricSystem.Els_Custom_W09_S2)
synthetic_photometry
```

--------------------------------

### Apply Truncation to Synthetic Photometry

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Enable truncation of the basis set for synthetic photometry generation. This is useful for faint sources or those with few observations to avoid fitting noise.

```python
synthetic_photometry = generate(f, photometric_system=phot_system, truncation=True)

```

--------------------------------

### Find Lines from CSV File using DataFrame

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Load data from a CSV file into a pandas DataFrame and then use `find_lines` to detect spectral lines. Requires pandas and the path to the CSV file.

```python
import pandas as pd

f = '/path/to/XP_CONTINUOUS_RAW.csv'
df = pd.read_csv(f)

lines = find_lines(df)
lines
```

--------------------------------

### Find Lines from a File Path

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Provide a file path to the find_lines function to process a specific data file and identify spectral lines.

```python
f = '/path/to/XP_CONTINUOUS_RAW.fits'

lines = find_lines(f)
lines
```

--------------------------------

### Find Extrema from List of Source IDs

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Provide a list of source IDs (as strings or longs) to the find_extrema function. The tool will query the Archive for the specified objects.

```python
sources_list = ['5853498713190525696', 5762406957886626816] # The sourceIds can be string or long.

extrema = find_extrema(sources_list)
extrema.head()
```

--------------------------------

### Redefine Additional Systems Path

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

When a path for additional filters is already defined, you can choose to redefine it. This prompt asks for user confirmation before setting a new path.

```python
PhotometricSystem = load_additional_systems()

```

--------------------------------

### Find Lines with User-Defined Wavelengths

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Provide a custom list of wavelengths and line names to the user_lines parameter in the find_lines tool for targeted spectral line detection. This allows for analysis of specific, user-specified spectral features.

```python
sources_list = [5853498713190525696, 5762406957886626816]
new_lines = [(434.0472, 410.1734), ('H_gamma', 'H_delta')]

lines = find_lines(sources_list, user_lines=new_lines)
lines
```

--------------------------------

### Analyze Flux Differences Between Truncated Spectra

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Calculates and prints the minimum and maximum absolute differences in flux values between truncated and non-truncated BP spectra, along with their indices.

```python
# 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]}.')
```

--------------------------------

### Import error correction tool

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Import the necessary function for applying error correction.

```python
from gaiaxpy import apply_error_correction
```

--------------------------------

### Control Saving of Output File

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Use the `save_file` parameter to control whether the generated output is saved to disk. If `output_file` is provided but `save_file` is `False`, a warning is issued.

```python
synthetic_photometry = generate(f, photometric_system=phot_system, output_file='my_file', save_file=False)

```

--------------------------------

### Saving Spectra Plots with Linefinder

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Set `save_plots=True` in addition to `plot_spectra=True` to save the generated spectra plots to disk. This allows for later review or inclusion in reports.

```python
lines = find_lines([5853498713190525696], plot_spectra=True, save_plots=True)

```

--------------------------------

### Control Saving Output with save_file Parameter

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

The save_file parameter determines if the converted data is written to disk. If output_file is specified but save_file is False, a warning is issued.

```python
converted_spectra, _ = convert(f, output_file='my_file', output_format='.csv', save_file=False)
```

```text
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.
```

--------------------------------

### Plotting Spectra with Linefinder

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Use `plot_spectra=True` to visualize detected lines directly within the linefinder tool. This option is only available for linefinder and extremafinder.

```python
lines = find_lines([5762406957886626816], plot_spectra=True)

```

--------------------------------

### Remove Additional Photometric Systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Additional%20systems%20tutorial.html

Manually remove all loaded additional photometric systems. This action is also performed automatically upon program exit.

```python
from gaiaxpy import remove_additional_systems
PhotometricSystem = remove_additional_systems()

```

--------------------------------

### Convert Wavelength to Frequency

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Convert pseudo-wavelength values from nm to pseudo-frequency in Hz using the TOPCAT expression language. This calculation involves the reciprocal of the sampling parameter and a constant C_KMS multiplied by 1e12.

```expression
multiply(reciprocal(param$sampling), C_KMS*1e12)
```

--------------------------------

### Plot Spectra with Errors using STILTS

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Generate a plot of flux spectra with associated error bars using STILTS. This command specifies the X and Y data, and additionally defines the error bar data using 'flux_error'.

```bash
stilts plot2plane in=calibrator.fits xs='param$sampling' ys=flux \
                  layer1=lines \
                  layer2=yerrors yerrhis=flux_error
```

--------------------------------

### Plot Truncated vs. Non-Truncated Spectra

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Calibrator%20tutorial.html

Visualizes the difference between truncated and non-truncated spectra using the plot_spectra utility. Requires pandas for data manipulation.

```python
import pandas as pd
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] = 'Non truncated'
data['source_id'][1] = 'Truncated'
plot_spectra(data, sampling=sampling, multi=True)
```

--------------------------------

### Take Logarithm of Flux Array

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Apply a base-10 logarithm to each element of the flux array using the TOPCAT expression language. This can help in visualizing data with a large dynamic range.

```expression
arrayFunc("log10(x)", flux)
```

--------------------------------

### Verify equivalence of corrected dataframes

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Compare two DataFrames containing corrected synthetic photometry to ensure they are identical.

```python
print(f'Are both frames equal? {synth_phot_corrected.equals(synth_phot_corrected_infer)}')
```

--------------------------------

### Controlling File Saving with Linefinder

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Use the `save_file` parameter to explicitly enable or disable saving the results. If `output_file` is specified but `save_file` is `False`, a warning will be issued.

```python
lines = find_lines([5853498713190525696], output_path='.', output_file='my_file', output_format='ecsv', save_file=False)

```

--------------------------------

### Find Lines from a List of Source IDs

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Pass a list of source IDs (strings or integers) to the find_lines function to query the Archive for these objects and identify spectral lines.

```python
sources_list = ['5853498713190525696', 5762406957886626816] # The source IDs can be either strings or long integers.

lines = find_lines(sources_list)
lines
```

--------------------------------

### Convert Wavelength from nm to Angstrom

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Use the TOPCAT expression language to convert pseudo-wavelength values from nanometers (nm) to Angstroms (Å). This involves multiplying the 'param$sampling' values by 10.0.

```expression
multiply(param$sampling, 10.0)
```

--------------------------------

### Apply error correction by inferring systems

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Apply error correction to synthetic photometry, allowing the function to infer the photometric systems directly from the data. This may result in warnings for systems lacking correction tables.

```python
synth_phot_corrected_infer = apply_error_correction(synthetic_photometry)
```

--------------------------------

### Find extrema using a pandas DataFrame

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Pass a pandas DataFrame containing XP CONTINUOUS RAW data to the find_fast function. Ensure the DataFrame is properly loaded.

```python
import pandas as pd

f = '/path/to/XP_CONTINUOUS_RAW.csv'
df = pd.read_csv(f)

extrema_pwl = find_fast(df)
extrema_pwl.head()
```

--------------------------------

### Apply error correction to synthetic photometry

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Apply error correction to the generated synthetic photometry. The photometric systems must be explicitly passed if they include 'Gaia_DR3_Vega' and are not inferred from the data. Warnings will be raised for systems without correction tables.

```python
# Add the system
phot_systems_list = [PhotometricSystem.Pristine, PhotometricSystem.JWST_NIRCAM, PhotometricSystem.Euclid_VIS, PhotometricSystem.Gaia_DR3_Vega]
# Recompute
synthetic_photometry = generate(f, photometric_system=phot_systems_list)
# Now we can apply the error correction
synth_phot_corrected = apply_error_correction(synthetic_photometry, photometric_system=phot_systems_list)
```

--------------------------------

### Convert Spectra from Pandas DataFrame

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Load spectra from a CSV file into a pandas DataFrame and then use the convert function. The DataFrame values can be edited before conversion.

```python
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
```

--------------------------------

### Apply Error Correction to Synthetic Photometry

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Generator%20tutorial.html

Activate empirical corrections to uncertainties when generating synthetic photometry. A warning is raised for systems that cannot be corrected.

```python
# To apply the error correction, first create a list of systems.
phot_system_list = [PhotometricSystem.Euclid_VIS, PhotometricSystem.Els_Custom_W09_S2]
synthetic_phot_corrected = generate(df, photometric_system=phot_system_list, error_correction=True)
synthetic_phot_corrected
# A warning is raised for each of the systems that cannot be corrected. No changes are made to these systems.

```

--------------------------------

### Plot Truncated vs. Non-Truncated Spectra

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Plots both truncated and non-truncated spectra for comparison. Source IDs are renamed for clarity in the plot labels.

```python
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)
```

--------------------------------

### Plot Converted Spectra

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Plots the converted spectra using the gaiaxpy plot_spectra utility. The 'multi=True' argument allows multiple spectra to be displayed on the same canvas.

```python
from gaiaxpy import plot_spectra
# multi allows the spectra to appear in the same canvas
plot_spectra(converted_spectra, sampling=custom_sampling, multi=True)
```

--------------------------------

### Calculate Upper Bound of Spectrum

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Calculate the upper bound of a spectrum by adding the error values to the flux values using the TOPCAT expression language. This is useful for visualizing error ranges.

```expression
add(flux, flux_error)
```

--------------------------------

### Find Extrema from Pandas DataFrame

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Linefinder%20tutorial.html

Pass a pandas DataFrame containing XP CONTINUOUS RAW data to the find_extrema function. The DataFrame values can be edited before processing.

```python
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.

extrema = find_extrema(df)
extrema.head()
```

--------------------------------

### Apply Error Correction Without Saving Output

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Error%20correction%20tutorial.html

Use this snippet to apply error correction but prevent the results from being saved to a file. A warning will be raised if an output file is specified but save_file is set to False.

```python
synth_phot_corrected = apply_error_correction(synthetic_photometry, output_file='my_file', save_file=False)
```

--------------------------------

### Save Converted Spectra Plot

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/Converter%20tutorial.html

Saves the plot of converted spectra to a specified output path without displaying it. Use 'show_plot=False' to prevent the plot from appearing.

```python
plot_spectra(converted_spectra, sampling=custom_sampling, multi=True, output_path='/path/to', show_plot=False)
```

--------------------------------

### Normalize Spectrum by Mean Value

Source: https://gaia-dpci.github.io/GaiaXPy-website/tutorials/TOPCAT%20tutorial.html

Normalize a spectrum by its mean value using the TOPCAT expression language. This scales the flux values so that the mean flux is 1, which is useful for comparing spectral shapes.

```expression
multiply(flux, 1./mean(flux))
```

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