[1]:

%matplotlib widget

Plots and interfaces

In this section, we review the visual aids provided by $LiMe$ , as well as, some tips on how to adjust them to your workflow. If you have any issues with visualizing your data with $LiMe$ , please contact the author or open an issue.

LiMe features a composite software design, utilizing instances of other classes to implement its desired functionality. This approach is akin to that of IRAF: Functions are organized into multi-level packages, which users access to perform the desired operations. The diagram in Fig.1 outlines this workflow.

09cdc5118be34cedbb47709d1fd8b705

As you can see there are two kind of plots * The functions under the .plot attribute are standard matplotlib plots. By default the plots will be displayed unless the user provides an output_address in which case the plot will be saved. * The functions under the .check attribute are interactive matplotlib plots. The user usually needs to provide an output_address where the new data selection is stored.

Adjusting the figures format

1) Plotting functions configuration

Let’s start by getting the data from the third tutorial:

[3]:

import lime

lime.theme.set_style(fig_cfg={"figure.dpi": 100})

# State the data files
obsFitsFile = '../sample_data/spectra/gp121903_osiris.fits'
lineBandsFile = '../sample_data/osiris_bands.txt'
cfgFile = '../sample_data/osiris.toml'

# Load configuration
obs_cfg = lime.load_cfg(cfgFile)
z_obj = obs_cfg['sample_data']['z_array'][2]
norm_flux = obs_cfg['sample_data']['norm_flux']

# Declare LiMe spectrum
gp_spec = lime.Spectrum.from_file(obsFitsFile, instrument='osiris', redshift=z_obj, norm_flux=norm_flux)
gp_spec.plot.spectrum(rest_frame=True)

In the .plot and .check functions you can use the fig_cfg and ax_cfg arguments to adjust the figure labels and format:

[8]:

ax_cfg = {'title': 'GP121903 osiris observation', 'ylabel':'Flux (FLAM)'}
fig_cfg = {"axes.titlesize" : 30, "figure.facecolor": "red", "axes.facecolor": "red"}
gp_spec.plot.spectrum(rest_frame=True, ax_cfg=ax_cfg, fig_cfg=fig_cfg)

2) Global configuration functions configuration

Additionally, you can adjust the overall format calling the lime.themer funtion:

[12]:

lime.theme.set_style(style='dark', fig_cfg={"axes.titlesize" : 30})

gp_spec.plot.spectrum(rest_frame=True, ax_cfg=ax_cfg)

3) Changing the default values

If you have access to the $LiMe$ installation folder, you can change the default values for the figures format. This file is called config.toml and it should be located at:

[21]:

from lime.io import _LIME_FOLDER

print(_LIME_FOLDER/'config.toml')

/home/vital/PycharmProjects/lime/src/lime/config.toml

This file has a .toml format where the plotting parameters can be found on the matplotlib and colors sections:

[20]:

lime_cfg = lime.load_cfg(_LIME_FOLDER/'config.toml')
lime_cfg

[20]:

{'metadata': {'name': 'lime-stable', 'version': '0.9.99.9'},
 'lime_colors': {'default': {'bg': '#FFFFFF',
   'fg': '#000000',
   'cont_band': '#8c564b',
   'line_band': '#b5bd61',
   'color_cycle': ['#279e68',
    '#d62728',
    '#aa40fc',
    '#8c564b',
    '#e377c2',
    '#7f7f7f',
    '#b5bd61',
    '#17becf',
    '#1f77b4',
    '#ff7f0e'],
   'match_line': '#b5bd61',
   'peak': '#aa40fc',
   'trough': '#7f7f7f',
   'profile': '#1f77b4',
   'cont': '#ff7f0e',
   'error': '#FF0000',
   'mask_map': 'viridis',
   'comps_map': 'Dark2',
   'mask_marker': '#FF0000',
   'inspection_positive': '#FFFFFF',
   'inspection_negative': '#ff796c',
   'fade_fg': '#CCCCCC'},
  'dark': {'bg': '#2B2B2B',
   'fg': '#CCCCCC',
   'cont_band': '#e7298a',
   'line_band': '#8fff9f',
   'color_cycle': ['#66c2a5',
    '#fc8d62',
    '#e68ac3',
    '#a6d854',
    '#ffd92f',
    '#e5c494',
    '#8dcbe2',
    '#e7298a',
    '#332288',
    '#88ccee'],
   'match_line': '#8fff9f',
   'peak': '#aa40fc',
   'trough': '#7f7f7f',
   'profile': '#ddcc77',
   'cont': '#CCCCCC',
   'error': '#FF0000',
   'mask_map': 'viridis',
   'comps_map': 'PuRd',
   'mask_marker': '#FF0000',
   'inspection_positive': '#2B2B2B',
   'inspection_negative': '#840000',
   'fade_fg': '#73879B'}},
 'matplotlib': {'default': {'figure.dpi': 200,
   'figure.figsize': [8, 5],
   'axes.titlesize': 14,
   'axes.labelsize': 14,
   'legend.fontsize': 12,
   'xtick.labelsize': 13,
   'ytick.labelsize': 13},
  'high_res': {'figure.dpi': 300,
   'figure.figsize': [11, 6],
   'axes.titlesize': 14,
   'axes.labelsize': 14,
   'legend.fontsize': 12,
   'xtick.labelsize': 12,
   'ytick.labelsize': 12},
  'dark': {'figure.facecolor': '#2B2B2B',
   'axes.facecolor': '#2B2B2B',
   'axes.edgecolor': '#CCCCCC',
   'axes.labelcolor': '#CCCCCC',
   'xtick.labelcolor': '#CCCCCC',
   'ytick.labelcolor': '#CCCCCC',
   'xtick.color': '#CCCCCC',
   'ytick.color': '#CCCCCC',
   'text.color': '#CCCCCC',
   'legend.edgecolor': 'inherit',
   'legend.facecolor': 'inherit'},
  'bands': {'axes.labelsize': 14},
  'grid': {'axes.titlesize': 12},
  'cube_interactive': {'figure.figsize': [16, 8],
   'axes.titlesize': 12,
   'legend.fontsize': 12,
   'axes.labelsize': 12,
   'xtick.labelsize': 10,
   'ytick.labelsize': 10}}}

If you need to restore the default values from this file, you can find it in the github.

Jupyter notebooks and interactive shells

$LiMe$ functions should work in any IDE (Integrated Developement Environment) including Jupyter Notebooks. This tutorial can be found as a notebook in the Github examples/outputs folder.

However, many of $LiMe$ interactive plots cannot be easily used within a notebook even with the %matplotlib widgetcommand.

To get access to the matplotlib interactive tools you should include the magic command %matplotlib widgetat the begining of your notebook.

It is recommended to install the Pyqt library and use the %matplotlib qt. This will display the matplotlib plots in a new window making it easier to interact.