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fig_03_timing_diffs.py
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fig_03_timing_diffs.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import joblib as jl
from matplotlib.patches import ConnectionPatch
plt.style.use('figstyle.mplstyle')
def style_plot(fig, axes):
fig.subplots_adjust(hspace=0.1)
for i, ax in enumerate(axes):
ax.patch.set_alpha(0.0)
side = ['left', 'right'][i % 2]
for _, spine in ax.spines.items():
spine.set_visible(False)
ax.spines[side].set_visible(True)
ax.yaxis.set_label_position(side)
ax.yaxis.set_ticks_position(side)
ax.xaxis.set_ticks_position('none')
axes[0].spines['top'].set_visible(True)
axes[0].xaxis.set_label_position('top')
axes[-1].spines['bottom'].set_visible(True)
axes[-1].xaxis.set_ticks_position('bottom')
axes[0].xaxis.set_ticks_position('top')
return fig, axes
def vmarker(x0, x1, ax0, ax1, **kwargs):
xy0 = (x0, ax0.get_ylim()[0])
xy1 = (x1, ax1.get_ylim()[1])
ax0.axvline(x0, **kwargs)
ax1.axvline(x1, **kwargs)
con = ConnectionPatch(xy0, xy1, 'data', 'data', ax0, ax1, **kwargs)
ax0.add_artist(con)
def calc_med_iqr(y, q=[5, 95], axis=None):
qs = np.percentile(y, [q[0], 50, q[-1]], axis=axis)
yerr = np.diff(qs, axis=0)
y = qs[1]
return y, yerr
def calc_point_lags(t0_1, dt_1, t0_2=0, dt_2=0):
dt0 = t0_1 - t0_2
dtm = t0_1 + 0.5 * dt_1 - (t0_2 + 0.5 * dt_2)
dt1 = t0_1 + dt_1 - (t0_2 + dt_2)
return dt0, dtm, dt1
iso_data = iso_data = pd.read_csv('data/NGRIP_10yr.csv', usecols=(0, 4), comment='#')
iso_data = iso_data.loc[(iso_data['Age_BP'] > 10000) &
(iso_data['Age_BP'] <= 60000), :]
gi_table = pd.read_table('data/GIS_table.txt', comment='#')
gi_table = gi_table[['GI' in s or 'Holocene' in s for s in gi_table['Event']]]
gi_table = gi_table[gi_table['Age'] < 60000]
gi_table = gi_table[np.any(gi_table[['NGRIP_Ca', 'NGRIP_Na', 'NGRIP_lt',
'NEEM_Ca', 'NEEM_Na']] == 1.0, axis=1)]
fig, axes = plt.subplots(nrows=4, sharex=False, figsize=(1.414 * 7, 7))
# Isotope data
axes[0].plot(iso_data['Age_BP'] / 1000, iso_data['d18O'], color='k', lw=0.25)
ref_par = 'Na'
x_plt = pd.Series(np.arange(len(gi_table)), gi_table['Event'])
dx = -0.225
# Calcium
for fmt, CORE in zip(('^', 'o'), ['NEEM', 'NGRIP']):
traces = jl.load('ramp_fits/traces/%s.gz' % CORE) \
.sel(param=['Ca', ref_par]).dropna(dim='event')
x = x_plt[np.array(traces.coords['event'])]
dt0, dtm, dt1 = calc_point_lags(traces.sel(param='Ca', model='t0'),
traces.sel(param='Ca', model='dt'),
traces.sel(param=ref_par, model='t0'),
traces.sel(param=ref_par, model='dt'),)
for ax, tr in zip(axes[1:], (dt0, dtm, dt1)):
y, yerr = calc_med_iqr(tr, axis=0)
ax.errorbar(x + dx, y, yerr, fmt=fmt, ms=2.0, capsize=0,
elinewidth=0.75, color='#984ea3',
label='Ca$^{2+}$ (%s)' % CORE)
dx += 0.15
# layer thickness
traces = jl.load('ramp_fits/traces/NGRIP.gz') \
.sel(param=['lt', ref_par]).dropna(dim='event')
x = x_plt[np.array(traces.coords['event'])]
dt0, dtm, dt1 = calc_point_lags(traces.sel(param='lt', model='t0'),
traces.sel(param='lt', model='dt'),
traces.sel(param=ref_par, model='t0'),
traces.sel(param=ref_par, model='dt'),)
for ax, tr in zip(axes[1:], (dt0, dtm, dt1)):
y, yerr = calc_med_iqr(tr, axis=0)
ax.errorbar(x + dx, y, yerr, fmt=fmt, ms=2.0, capsize=0,
elinewidth=0.75, color='#4daf4a',
label='$\lambda$ (NGRIP)')
dx += 0.15
traces = jl.load('ramp_fits/traces/NGRIP.gz') \
.sel(param=['d18O', ref_par]).dropna(dim='event')
x = x_plt[np.array(traces.coords['event'])]
dt0, dtm, dt1 = calc_point_lags(traces.sel(param='d18O', model='t0'),
traces.sel(param='d18O', model='dt'),
traces.sel(param=ref_par, model='t0'),
traces.sel(param=ref_par, model='dt'),)
for ax, tr in zip(axes[1:], (dt0, dtm, dt1)):
y, yerr = calc_med_iqr(tr, axis=0)
ax.errorbar(x + dx, y, yerr, fmt=fmt, ms=2.0, capsize=0,
elinewidth=0.75, color='0.5', label='$\delta^{18}$O (NGRIP)')
dx += 0.15
for i, a in enumerate(gi_table['Age'] / 1000):
vmarker(a, i, axes[0], axes[1], lw=0.2, ls='solid', zorder=-100)
vmarker(i, i, axes[1], axes[2], lw=0.2, ls='solid', zorder=-100)
vmarker(i, i, axes[2], axes[3], lw=0.2, ls='solid', zorder=-100)
for ax in axes[1:]:
ax.set_xlim(-1.0, len(x_plt))
ax.axhline(0.0, zorder=-100, lw=0.25, ls='solid')
axes[0].set_ylabel('$\delta^{18}\mathrm{O}$ (‰)')
axes[1].set_ylabel('Onset lag (yr)')
axes[2].set_ylabel('Midpoint lag (yr)')
axes[3].set_ylabel('Endpoint lag (yr)')
axes[0].set_xlabel('GICC05 Age (kyr before 1950)')
axes[0].xaxis.set_major_locator(plt.MultipleLocator(10))
axes[0].xaxis.set_minor_locator(plt.MultipleLocator(2))
axes[-1].set_xlabel('Onset of…')
fig, axes = style_plot(fig, axes)
axes[1].xaxis.set_ticks(())
axes[2].xaxis.set_ticks(())
axes[-1].xaxis.set_ticks(np.arange(len(gi_table)))
axes[-1].xaxis.set_ticklabels(gi_table['Event'],
rotation=30, va='top', ha='right')
legend_ax = axes[-1]
legend_ax.legend(loc='lower right', fontsize=5, ncol=4,
frameon=True, framealpha=1.0,
edgecolor='white', facecolor='white',
columnspacing=0.2, borderpad=0.2)
for l, ax in zip(('a', 'b', 'c', 'd'), axes):
ax.text(0.01, 0.95, '(%s)'% l, ha='left', va='top', transform=ax.transAxes,
weight='bold', fontsize=7)
fig.savefig('figures/fig_03_timing_diffs.pdf')