def plot_degen_vs_time(df, filename=None, show=False, limits=None,
scale=['linear','linear']):
# Import external modules
import numpy as np
import math
import pyfits as pf
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from astroML.plotting import scatter_contour
from itertools import cycle
from matplotlib.ticker import FormatStrFormatter
from matplotlib.ticker import ScalarFormatter
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
plt.close;plt.clf()
plt.rcdefaults()
# Color map
cmap = plt.cm.gnuplot
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 5)]
font_scale = 13
line_weight = 600
font_weight = 600
params = {
'axes.color_cycle': color_cycle, # colors of different plots
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
#'axes.weight': line_weight,
'axes.linewidth': 1.2,
'axes.labelweight': font_weight,
'legend.fontsize': font_scale*3/4,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': font_weight,
'font.serif': 'computer modern roman',
'text.fontsize': font_scale,
'text.usetex': True,
#'text.latex.preamble': r'\usepackage[T1]{fontenc}',
#'font.family': 'sans-serif',
'figure.figsize': (5, 5),
'figure.dpi': 600,
'backend' : 'pdf',
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
ax = fig.add_subplot(111)
lines = ["-","--","-.",":"]
linecycler = cycle(lines)
for i, Z in enumerate([0.0001, 0.001, 0.01, 0.03]):
Z = str(Z)
ax.plot(df[Z]['Time [years]'] / 10**10,
df[Z]['Degeneracy'],
#linestyle=next(linecycler),
label=r'Z = {0:s}'.format(Z)
)
if limits is not None:
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# set scales, linear or log
ax.set_xscale(scale[0])
ax.set_yscale(scale[1])
# change labels to be integer scalars
if 0:
fig.canvas.draw()
ax.xaxis.set_major_formatter(ScalarFormatter())
ax.xaxis.set_minor_formatter(ScalarFormatter())
if 1:
ax.xaxis.set_major_formatter(ScalarFormatter())
ax.xaxis.set_minor_formatter(ScalarFormatter())
#ax.xaxis.set_minor_formatter(FormatStrFormatter('%.1f'))
# Make sure no tick labels are overplotting one another
ax = myplt.delete_overlapping_xlabels(fig, ax)
#ax.xaxis.set_minor_formatter(FormatStrFormatter('%.1f'))
# Make sure no tick labels are overplotting one another
#ax = myplt.delete_overlapping_xlabels(fig, ax)
# Adjust asthetics
ax.set_xlabel(r'Time [10 Gyr]')
ax.set_ylabel(r'Central Electron Degeneracy Parameter')
ax.legend(loc='best')
#ax.locator_params(nbins=5, axis='x')
if filename is not None:
plt.tight_layout()
plt.savefig(filename, bbox_inches='tight')
if show:
fig.show()
def plot_L_vs_T(df,
filename=None,
show=False,
limits=None,
scale=['linear', 'linear']):
# Import external modules
import numpy as np
import math
import pyfits as pf
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from astroML.plotting import scatter_contour
from itertools import cycle
from matplotlib.ticker import FormatStrFormatter
from matplotlib.ticker import ScalarFormatter
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
plt.close
plt.clf()
plt.rcdefaults()
# Color map
cmap = plt.cm.gnuplot
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 5)]
font_scale = 13
line_weight = 600
font_weight = 600
params = {
'axes.color_cycle': color_cycle, # colors of different plots
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
#'axes.weight': line_weight,
'axes.linewidth': 1.2,
'axes.labelweight': font_weight,
'legend.fontsize': font_scale * 3 / 4,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': font_weight,
'font.serif': 'computer modern roman',
'text.fontsize': font_scale,
'text.usetex': True,
#'text.latex.preamble': r'\usepackage[T1]{fontenc}',
#'font.family': 'sans-serif',
'figure.figsize': (5, 5),
'figure.dpi': 600,
'backend': 'pdf',
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
ax = fig.add_subplot(111)
lines = ["-", "--", "-.", ":"]
linecycler = cycle(lines)
#for i, Z in enumerate(df):
for i, Z in enumerate([0.0001, 0.001, 0.01, 0.03]):
Z = str(Z)
ax.plot(
df[Z]['Teff [K]'],
df[Z]['Luminosity [Lsun]'],
#linestyle=next(linecycler),
label=r'Z = {0:s}'.format(Z))
if limits is not None:
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# set scales, linear or log
ax.set_xscale(scale[0])
ax.set_yscale(scale[1])
# change labels to be integer scalars
fig.canvas.draw()
if 1:
ax.xaxis.set_major_formatter(ScalarFormatter())
ax.xaxis.set_minor_formatter(ScalarFormatter())
#ax.xaxis.set_minor_formatter(FormatStrFormatter('%.1f'))
# Make sure no tick labels are overplotting one another
ax = myplt.delete_overlapping_xlabels(fig, ax)
# Adjust asthetics
ax.set_xlabel(r'$T_{\rm eff}$ [K]')
ax.set_ylabel(r'L [L$_\odot$]')
ax.legend(loc='best')
#ax.locator_params(nbins=5, axis='x')
if filename is not None:
plt.tight_layout()
plt.savefig(filename, bbox_inches='tight')
if show:
fig.show()