Source code for species.plot.plot_color

"""
Module with functions for creating color-magnitude and color-color plots.
"""

import os
import math

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

from scipy.interpolate import interp1d
from matplotlib.colorbar import Colorbar

from species.core import box
from species.read import read_object
from species.util import plot_util


mpl.rcParams['font.serif'] = ['Bitstream Vera Serif']
mpl.rcParams['font.family'] = 'serif'

plt.rc('axes', edgecolor='black', linewidth=2.5)


[docs]def plot_color_magnitude(boxes, objects=None, mass_labels=None, companion_labels=False, field_range=None, label_x='Color [mag]', label_y='Magnitude [mag]', xlim=None, ylim=None, offset=None, legend='upper left', output='color-magnitude.pdf'): """ Parameters ---------- boxes : list(species.core.box.ColorMagBox, species.core.box.IsochroneBox, ) Boxes with the color-magnitude and isochrone data from photometric libraries, spectral libraries, and/or atmospheric models. The synthetic data have to be created with :func:`~species.read.read_isochrone.ReadIsochrone.get_color_magnitude`. These boxes contain synthetic colors and magnitudes for a given age and a range of masses. objects : tuple(tuple(str, str, str, str), ), tuple(tuple(str, str, str, str, str, str, float, float), ), None Tuple with individual objects. The objects require a tuple with their database tag, the two filter IDs for the color, and the filter ID for the absolute magnitude. Optionally, the horizontal and vertical alignment and fractional offset values can be provided for the label can be provided (default: 'left', 'bottom', 8e-3, 8e-3). Not used if set to None. mass_labels : list(float, ), None Plot labels with masses next to the isochrone data of `models`. The list with masses has to be provided in Jupiter mass. No labels are shown if set to None. companion_labels : bool Plot labels with the names of the directly imaged companions. field_range : tuple(str, str), None Range of the discrete colorbar for the field dwarfs. The tuple should contain the lower and upper value ('early M', 'late M', 'early L', 'late L', 'early T', 'late T', 'early Y). The full range is used if set to None. label_x : str Label for the x-axis. label_y : str Label for the y-axis. xlim : tuple(float, float), None Limits for the x-axis. ylim : tuple(float, float), None Limits for the y-axis. offset : tuple(float, float), None Offset of the x- and y-axis label. legend : str, None Legend position. Not shown if set to None. output : str Output filename. Returns ------- NoneType None """ print(f'Plotting color-magnitude diagram: {output}... ', end='', flush=True) model_color = ('#234398', '#f6a432') model_linestyle = ('-', '--', ':', '-.') isochrones = [] models = [] empirical = [] for item in boxes: if isinstance(item, box.IsochroneBox): isochrones.append(item) elif isinstance(item, box.ColorMagBox): if item.object_type == 'model': models.append(item) else: empirical.append(item) else: raise ValueError(f'Found a {type(item)} while only ColorMagBox and IsochroneBox ' f'objects can be provided to \'boxes\'.') if empirical: plt.figure(1, figsize=(4., 4.8)) gridsp = mpl.gridspec.GridSpec(3, 1, height_ratios=[0.2, 0.1, 4.5]) gridsp.update(wspace=0., hspace=0., left=0, right=1, bottom=0, top=1) ax1 = plt.subplot(gridsp[2, 0]) ax2 = plt.subplot(gridsp[0, 0]) else: plt.figure(1, figsize=(4., 4.5)) gridsp = mpl.gridspec.GridSpec(1, 1) gridsp.update(wspace=0., hspace=0., left=0, right=1, bottom=0, top=1) ax1 = plt.subplot(gridsp[0, 0]) ax1.tick_params(axis='both', which='major', colors='black', labelcolor='black', direction='in', width=1, length=5, labelsize=12, top=True, bottom=True, left=True, right=True) ax1.tick_params(axis='both', which='minor', colors='black', labelcolor='black', direction='in', width=1, length=3, labelsize=12, top=True, bottom=True, left=True, right=True) ax1.set_xlabel(label_x, fontsize=14) ax1.set_ylabel(label_y, fontsize=14) ax1.invert_yaxis() if offset is not None: ax1.get_xaxis().set_label_coords(0.5, offset[0]) ax1.get_yaxis().set_label_coords(offset[1], 0.5) else: ax1.get_xaxis().set_label_coords(0.5, -0.08) ax1.get_yaxis().set_label_coords(-0.12, 0.5) if xlim is not None: ax1.set_xlim(xlim[0], xlim[1]) if ylim is not None: ax1.set_ylim(ylim[0], ylim[1]) if models is not None: cmap_teff = plt.cm.afmhot teff_min = np.inf teff_max = -np.inf for item in models: if np.amin(item.sptype) < teff_min: teff_min = np.amin(item.sptype) if np.amax(item.sptype) > teff_max: teff_max = np.amax(item.sptype) norm_teff = mpl.colors.Normalize(vmin=teff_min, vmax=teff_max) count = 0 model_dict = {} for j, item in enumerate(models): if item.library not in model_dict: model_dict[item.library] = [count, 0] count += 1 else: model_dict[item.library] = [model_dict[item.library][0], model_dict[item.library][1]+1] model_count = model_dict[item.library] if model_count[1] == 0: label = plot_util.model_name(item.library) ax1.plot(item.color, item.magnitude, linestyle=model_linestyle[model_count[1]], linewidth=1.2, color=model_color[model_count[0]], label=label, zorder=0) if mass_labels is not None: interp_magnitude = interp1d(item.sptype, item.magnitude) interp_color = interp1d(item.sptype, item.color) for i, mass_item in enumerate(mass_labels): if j == 0 or (j > 0 and mass_item < 20.): pos_color = interp_color(mass_item) pos_mag = interp_magnitude(mass_item) mass_label = str(int(mass_item))+r' M$_\mathregular{J}$' xlim = ax1.get_xlim() ylim = ax1.get_ylim() if xlim[0]+0.2 < pos_color < xlim[1]-0.2 and \ ylim[1]+0.2 < pos_mag < ylim[0]-0.2: ax1.scatter(pos_color, pos_mag, c=model_color[model_count[0]], s=15, edgecolor='none', zorder=0) ax1.annotate(mass_label, (pos_color, pos_mag), color=model_color[model_count[0]], fontsize=9, xytext=(pos_color+0.05, pos_mag-0.1), zorder=1) else: ax1.plot(item.color, item.magnitude, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color=model_color[model_count[0]], zorder=0) if empirical: cmap = plt.cm.viridis bounds, ticks, ticklabels = plot_util.field_bounds_ticks(field_range) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) for item in empirical: sptype = item.sptype color = item.color magnitude = item.magnitude indices = np.where(sptype != b'None')[0] sptype = sptype[indices] color = color[indices] magnitude = magnitude[indices] spt_disc = plot_util.sptype_substellar(sptype, color.shape) _, unique = np.unique(color, return_index=True) sptype = sptype[unique] color = color[unique] magnitude = magnitude[unique] spt_disc = spt_disc[unique] if item.object_type == 'field' or item.object_type is None: scat = ax1.scatter(color, magnitude, c=spt_disc, cmap=cmap, norm=norm, s=50, alpha=0.7, edgecolor='none', zorder=2) cb = Colorbar(ax=ax2, mappable=scat, orientation='horizontal', ticklocation='top', format='%.2f') cb.ax.tick_params(width=1, length=5, labelsize=10, direction='in', color='black') cb.set_ticks(ticks) cb.set_ticklabels(ticklabels) elif item.object_type == 'young': ax1.plot(color, magnitude, marker='s', ms=4, linestyle='none', alpha=0.7, color='gray', markeredgecolor='black', label='Young/low-gravity', zorder=2) if isochrones: for item in isochrones: ax1.plot(item.color, item.magnitude, linestyle='-', linewidth=1.2, color='black') if objects is not None: for i, item in enumerate(objects): objdata = read_object.ReadObject(item[0]) objcolor1 = objdata.get_photometry(item[1]) objcolor2 = objdata.get_photometry(item[2]) abs_mag = objdata.get_absmag(item[3]) colorerr = math.sqrt(objcolor1[1]**2+objcolor2[1]**2) x_color = objcolor1[0]-objcolor2[0] y_mag = abs_mag[0] # if item[0] in ('PDS 70 b'): # if item[0] in ('beta Pic b', 'HIP 65426 b', 'PZ Tel B', 'HD 206893 B'): # marker = '*' # markersize = 12 # color = '#eb4242' # markerfacecolor = '#eb4242' # markeredgecolor = 'black' # # else: # marker = '>' # markersize = 6 # color = 'black' # markerfacecolor = 'white' # markeredgecolor = 'black' # if item[0] == 'HR 8799 b': # label = 'Directly imaged' # elif item[0] == 'beta Pic b': # label = 'This work' # elif item[0] == 'PDS 70 b': # label = 'This work' # else: # label = None marker = '>' markersize = 6 color = 'black' markerfacecolor = 'white' markeredgecolor = 'black' if i == 0: label = 'Directly imaged' else: label = None ax1.errorbar(x_color, y_mag, yerr=abs_mag[1], xerr=colorerr, marker=marker, ms=markersize, color=color, markerfacecolor=markerfacecolor, zorder=3, markeredgecolor=markeredgecolor, label=label) if companion_labels: x_range = ax1.get_xlim() y_range = ax1.get_ylim() if len(item) == 8: ha = item[4] va = item[5] x_scaling = item[6] y_scaling = item[7] else: ha = 'left' va = 'bottom' x_scaling = 1e-2 y_scaling = 5e-3 x_offset = x_scaling*abs(x_range[1]-x_range[0]) y_offset = y_scaling*abs(y_range[1]-y_range[0]) ax1.text(x_color+x_offset, y_mag-y_offset, objdata.object_name, ha=ha, va=va, fontsize=8, color=color, zorder=3) if legend is not None: handles, labels = ax1.get_legend_handles_labels() if handles: ax1.legend(loc=legend, prop={'size': 8.5}, frameon=False, numpoints=1) plt.savefig(os.getcwd()+'/'+output, bbox_inches='tight') plt.clf() plt.close() print('[DONE]')
[docs]def plot_color_color(boxes, objects=None, mass_labels=None, companion_labels=False, field_range=None, label_x='color [mag]', label_y='color [mag]', xlim=None, ylim=None, offset=None, legend='upper left', output='color-color.pdf'): """ Parameters ---------- boxes : species.core.box.ColorColorBox, None Box with the colors and magnitudes. objects : tuple(tuple(str, str, str, str), ), tuple(tuple(str, str, str, str, str, str, float, float), ), None Tuple with individual objects. The objects require a tuple with their database tag, the two filter IDs for the first color, and the two filter IDs for the second color. Optionally, the horizontal and vertical alignment and fractional offset values can be provided for the label can be provided (default: 'left', 'bottom', 8e-3, 8e-3). Not used if set to None. models : tuple(species.core.box.ColorMagBox, ), None Tuple with :class:`~species.core.box.ColorColorBox` objects which have been created from the isochrone data with :func:`~species.read.read_isochrone.ReadIsochrone.get_color_color`. These boxes contain synthetic photometry for a given age and a range of masses. Not used if set to None. mass_labels : list(float, ), None Plot labels with masses next to the isochrone data of `models`. The list with masses has to be provided in Jupiter mass. No labels are shown if set to None. companion_labels : bool Plot labels with the names of the directly imaged companions. field_range : tuple(str, str), None Range of the discrete colorbar for the field dwarfs. The tuple should contain the lower and upper value ('early M', 'late M', 'early L', 'late L', 'early T', 'late T', 'early Y). The full range is used if set to None. label_x : str Label for the x-axis. label_y : str Label for the y-axis. output : str Output filename. xlim : tuple(float, float) Limits for the x-axis. ylim : tuple(float, float) Limits for the y-axis. offset : tuple(float, float), None Offset of the x- and y-axis label. legend : str Legend position. Returns ------- NoneType None """ print(f'Plotting color-color diagram: {output}... ', end='', flush=True) model_color = ('#234398', '#f6a432') model_linestyle = ('-', '--', ':', '-.') isochrones = [] models = [] empirical = [] for item in boxes: if isinstance(item, box.IsochroneBox): isochrones.append(item) elif isinstance(item, box.ColorMagBox): if item.object_type == 'model': models.append(item) else: empirical.append(item) else: raise ValueError(f'Found a {type(item)} while only ColorMagBox and IsochroneBox ' f'objects can be provided to \'boxes\'.') plt.figure(1, figsize=(4, 4.3)) gridsp = mpl.gridspec.GridSpec(3, 1, height_ratios=[0.2, 0.1, 4.]) gridsp.update(wspace=0., hspace=0., left=0, right=1, bottom=0, top=1) ax1 = plt.subplot(gridsp[2, 0]) ax2 = plt.subplot(gridsp[0, 0]) ax1.tick_params(axis='both', which='major', colors='black', labelcolor='black', direction='in', width=1, length=5, labelsize=12, top=True, bottom=True, left=True, right=True) ax1.tick_params(axis='both', which='minor', colors='black', labelcolor='black', direction='in', width=1, length=3, labelsize=12, top=True, bottom=True, left=True, right=True) ax1.set_xlabel(label_x, fontsize=14) ax1.set_ylabel(label_y, fontsize=14) ax1.invert_yaxis() if offset: ax1.get_xaxis().set_label_coords(0.5, offset[0]) ax1.get_yaxis().set_label_coords(offset[1], 0.5) else: ax1.get_xaxis().set_label_coords(0.5, -0.08) ax1.get_yaxis().set_label_coords(-0.12, 0.5) if xlim: ax1.set_xlim(xlim[0], xlim[1]) if ylim: ax1.set_ylim(ylim[0], ylim[1]) if models is not None: cmap_teff = plt.cm.afmhot teff_min = np.inf teff_max = -np.inf for item in models: if np.amin(item.sptype) < teff_min: teff_min = np.amin(item.sptype) if np.amax(item.sptype) > teff_max: teff_max = np.amax(item.sptype) norm_teff = mpl.colors.Normalize(vmin=teff_min, vmax=teff_max) count = 0 model_dict = {} for j, item in enumerate(models): if item.library not in model_dict: model_dict[item.library] = [count, 0] count += 1 else: model_dict[item.library] = [model_dict[item.library][0], model_dict[item.library][1]+1] model_count = model_dict[item.library] if model_count[1] == 0: label = plot_util.model_name(item.library) ax1.plot(item.color1, item.color2, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color=model_color[model_count[0]], label=label, zorder=0) if mass_labels is not None: interp_color1 = interp1d(item.sptype, item.color1) interp_color2 = interp1d(item.sptype, item.color2) for i, mass_item in enumerate(mass_labels): if j == 0 or (j > 0 and mass_item < 20.): pos_color1 = interp_color1(mass_item) pos_color2 = interp_color2(mass_item) mass_label = str(int(mass_item))+r' M$_\mathregular{J}$' xlim = ax1.get_xlim() ylim = ax1.get_ylim() if xlim[0]+0.2 < pos_color1 < xlim[1]-0.2 and \ ylim[0]+0.2 < pos_color2 < ylim[1]-0.2: ax1.scatter(pos_color1, pos_color2, c=model_color[model_count[0]], s=15, edgecolor='none', zorder=0) ax1.annotate(mass_label, (pos_color1, pos_color2), color=model_color[model_count[0]], fontsize=9, xytext=(pos_color1+0.05, pos_color2-0.1), zorder=1) else: ax1.plot(item.color1, item.color2, linestyle=model_linestyle[model_count[1]], linewidth=0.6, color=model_color[model_count[0]], zorder=0) if empirical: cmap = plt.cm.viridis bounds, ticks, ticklabels = plot_util.field_bounds_ticks(field_range) norm = mpl.colors.BoundaryNorm(bounds, cmap.N) for item in empirical: sptype = item.sptype color1 = item.color1 color2 = item.color2 indices = np.where(sptype != 'None')[0] sptype = sptype[indices] color1 = color1[indices] color2 = color2[indices] spt_disc = plot_util.sptype_substellar(sptype, color1.shape) _, unique = np.unique(color1, return_index=True) sptype = sptype[unique] color1 = color1[unique] color2 = color2[unique] spt_disc = spt_disc[unique] if item.object_type == 'field': scat = ax1.scatter(color1, color2, c=spt_disc, cmap=cmap, norm=norm, s=50, alpha=0.7, edgecolor='none', zorder=2) cb = Colorbar(ax=ax2, mappable=scat, orientation='horizontal', ticklocation='top', format='%.2f') cb.ax.tick_params(width=1, length=5, labelsize=10, direction='in', color='black') cb.set_ticks(ticks) cb.set_ticklabels(ticklabels) elif item.object_type == 'young': ax1.plot(color1, color2, marker='s', ms=4, linestyle='none', alpha=0.7, color='gray', markeredgecolor='black', label='Young/low-gravity', zorder=2) if isochrones: for item in isochrones: ax1.plot(item.colors[0], item.colors[1], linestyle='-', linewidth=1.2, color='black') if objects is not None: for i, item in enumerate(objects): objdata = read_object.ReadObject(item[0]) mag1 = objdata.get_photometry(item[1][0])[0] mag2 = objdata.get_photometry(item[1][1])[0] mag3 = objdata.get_photometry(item[2][0])[0] mag4 = objdata.get_photometry(item[2][1])[0] err1 = objdata.get_photometry(item[1][0])[1] err2 = objdata.get_photometry(item[1][1])[1] err3 = objdata.get_photometry(item[2][0])[1] err4 = objdata.get_photometry(item[2][1])[1] color1 = mag1 - mag2 color2 = mag3 - mag4 error1 = math.sqrt(err1**2+err2**2) error2 = math.sqrt(err3**2+err4**2) # if item[0] in ('beta Pic b', 'HIP 65426 b', 'PZ Tel B', 'HD 206893 B'): # marker = '*' # markersize = 12 # color = '#eb4242' # markerfacecolor = '#eb4242' # markeredgecolor = 'black' # # else: # marker = '>' # markersize = 6 # color = 'black' # markerfacecolor = 'white' # markeredgecolor = 'black' # # if item[0] == 'HR 8799 b': # label = 'Directly imaged' # elif item[0] == 'beta Pic b': # label = 'This work' # else: # label = None marker = '>' markersize = 6 color = 'black' markerfacecolor = 'white' markeredgecolor = 'black' if not companion_labels and i == 0: label = 'Directly imaged' else: label = None ax1.errorbar(color1, color2, xerr=error1, yerr=error2, marker=marker, ms=markersize, color=color, markerfacecolor=markerfacecolor, markeredgecolor=markeredgecolor, label=label, zorder=3) if companion_labels: x_range = ax1.get_xlim() y_range = ax1.get_ylim() if len(item) == 7: ha = item[3] va = item[4] x_scaling = item[5] y_scaling = item[6] else: ha = 'left' va = 'bottom' x_scaling = 8e-3 y_scaling = 8e-3 x_offset = x_scaling*abs(x_range[1]-x_range[0]) y_offset = y_scaling*abs(y_range[1]-y_range[0]) ax1.text(color1+x_offset, color2+y_offset, objdata.object_name, ha=ha, va=va, fontsize=8, color=color, zorder=3) handles, labels = ax1.get_legend_handles_labels() if handles: ax1.legend(loc=legend, prop={'size': 9}, frameon=False, numpoints=1) plt.savefig(os.getcwd()+'/'+output, bbox_inches='tight') plt.clf() plt.close() print('[DONE]')