# Licensed under a 3-clause BSD style license - see LICENSE.rst
# -*- coding: utf-8 -*-
"""
====================
prospect.grid_thumbs
====================
Grid of thumbnail plots displaying a set of spectra.
To Do Items
~~~~~~~~~~~
* Structure: class?
* Improve thumbnails with additional information, plot quality.
"""
import numpy as np
import astropy.convolution
import bokeh.plotting as bk
import bokeh.layouts as bl
from .coaddcam import coaddcam_prospect
[docs]
def grid_thumbs(spectra, thumb_width, x_range=(3400, 10000), thumb_height=None,
resamp_factor=15, ncols_grid=5, titles=None):
'''
Create a bokeh gridplot of thumbnail pictures from spectra
- coadd arms
- smooth+resample to reduce size of embedded CDS, according to resamp_factor
- titles : optional list of titles for each thumb
TODO: Not tested on :class:`~specutils.Spectrum` objects.
'''
if thumb_height is None:
thumb_height = thumb_width // 2
if titles is not None:
assert len(titles) == spectra.num_spectra()
thumb_wave, thumb_flux, dummy = coaddcam_prospect(spectra)
kernel = astropy.convolution.Gaussian1DKernel(stddev=resamp_factor)
thumb_plots = []
for i_spec in range(spectra.num_spectra()):
x_vals = (thumb_wave[::resamp_factor])[resamp_factor:-resamp_factor]
# Use astropy convolution : handles NaNs
y_vals = astropy.convolution.convolve(thumb_flux[i_spec,:], kernel, nan_treatment='fill')
y_vals = (y_vals[::resamp_factor])[resamp_factor:-resamp_factor]
x_vals = x_vals[~np.isnan(y_vals)] # Needed to avoid 'ValueError: Out of range float values are not JSON compliant'
y_vals = y_vals[~np.isnan(y_vals)]
if len(x_vals) == 0: # All NaN ... this should not happen ...
ymin, ymax = -1, 1
else:
yampl = np.max(y_vals) - np.min(y_vals)
ymin = np.min(y_vals) - 0.1*yampl
ymax = np.max(y_vals) + 0.1*yampl
plot_title = None
if titles is not None:
plot_title = titles[i_spec]
# Bokeh 3, plot_width and plot_height are now just width and height.
mini_plot = bk.figure(width=thumb_width, height=thumb_height, x_range=x_range, y_range=(ymin,ymax), title=plot_title)
if len(x_vals) != 0:
mini_plot.line(x_vals, y_vals, line_color='red')
mini_plot.xaxis.visible = False
mini_plot.yaxis.visible = False
mini_plot.min_border_left = 0
mini_plot.min_border_right = 0
mini_plot.min_border_top = 0
mini_plot.min_border_bottom = 0
thumb_plots.append(mini_plot)
return bl.gridplot(thumb_plots, ncols=ncols_grid, toolbar_location=None, sizing_mode='stretch_width')