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plot.py
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plot.py
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import datetime
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator, MaxNLocator
from matplotlib.offsetbox import AnchoredText
from sd import LightCurve, Region, Flare
from fit import lmfit_exp_gaus_single
plt.rcParams.update({'font.size': 22, 'lines.linewidth': 3})
"""
Old plot for plotting light curve
Inputs:
1. LightCurve object
2. Log or linear yscale
Input (optional):
1. Filename if plot needs to be saved
"""
def plot_lc(lc: LightCurve, filename: str | None = None, yscale: str = 'log'):
plt.figure(dpi=100, figsize=(19.2, 7.2))
plt.tick_params(which='major', width=2)
plt.tick_params(which='major', length=7)
plt.tick_params(which='minor', length=4)
plt.gca().xaxis.set_minor_locator(AutoMinorLocator())
plt.title(f'Light Curve {lc.config.date}')
plt.ylabel('Flux (nW/m$^{2}$)')
plt.xlabel('Time (s)')
plt.plot(lc.pptime, lc.ppcounts, c='c', label='data')
plt.scatter(lc.pptime[lc.scpeaks], lc.ppcounts[lc.scpeaks], c='r', s=30, zorder=11)
plt.plot(lc.bgtime, lc.bgcounts, c='g', alpha=0.4, label='ss bg')
plt.margins(x=0)
if yscale == 'linear' or yscale == 'log':
plt.yscale(yscale)
else:
print('yscale must be either linear or log. Default: linear')
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=150)
plt.close()
else:
plt.show()
"""
Old plot for plotting individual regions in light curve
Inputs:
1. LightCurve object
2. Log or linear yscale
Input (optional):
1. Filename if plot needs to be saved
"""
def plot_lc_regions(lc: LightCurve, filename: str | None = None, yscale: str = 'log'):
plt.figure(dpi=100, figsize=(19.2, 7.2))
plt.tick_params(which='major', width=2)
plt.tick_params(which='major', length=7)
plt.tick_params(which='minor', length=4)
plt.gca().xaxis.set_minor_locator(AutoMinorLocator())
plt.title(f'Lightcurve {lc.config.date} Identified Regions')
plt.ylabel('Flux (nW/m$^{2}$)')
plt.xlabel('Time (s)')
mu = np.mean(lc.bgcounts)
sigma = np.std(lc.bgcounts)
x = lc.pptime
y = lc.ppcounts
plt.plot(x, y, alpha=0.3)
for i in range(len(lc.llist)):
index = np.sort(np.array(lc.llist[i]))
x = (lc.pptime[index])
y = lc.ppcounts[index]
plt.plot(x, y, c='b')
plt.axvspan(x[0], x[-1], alpha=0.1, color='orange')
plt.axvline(x[0], alpha=0.1, color='orange')
plt.axvline(x[-1], alpha=0.1, color='orange')
# plt.text(x[-1], np.nanmax(lc.ppcounts) * 0.8, f'Region {i+1}', rotation=-90, va='center', fontsize=14, ha='left')
plt.text(x[-1], np.nanmax(lc.ppcounts) * 0.7, f'Region {i+1} ({lc.regions[i].numflares})', rotation=-90, va='center', fontsize=14, ha='left')
plt.text(2000, np.nanmax(lc.ppcounts) * 0.95, f'#Flares: {lc.numflares}', va='center', fontsize=14, ha='left')
plt.margins(x=0)
if yscale == 'linear' or yscale == 'log':
plt.yscale(yscale)
else:
print('yscale must be either linear or log. Default: linear')
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=150)
plt.close()
else:
plt.show()
"""
New plot to encapsulate LightCurve, Background subtracted light curve, and Region decomposition
Input:
1. LightCurve object
Inputs (optional):
1. Filename if plots need to be saved
2. Log or linear yscale
"""
def plot_all(lc: LightCurve, filename: str | None = None, yscale: str = 'log'):
fig, axs = plt.subplots(nrows=3, dpi=100, figsize=(19.2, 12.0))
plt.subplots_adjust(hspace=0.05)
for ax in axs:
ax.tick_params(which='major', width=2)
ax.tick_params(which='major', length=7)
ax.tick_params(which='minor', length=4)
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
ax.set_ylabel('Flux (nW/m$^{2}$)')
ax.set_xmargin(0)
axs[0].set_title(f'Lightcurve {lc.config.date}')
axs[0].set_xticklabels([])
axs[1].set_xticklabels([])
axs[2].set_xlabel('Time (s)')
# ---------------------------------------------------
axs[0].plot(lc.pptime, lc.ppcounts, c='c', label='Light curve')
axs[0].scatter(lc.pptime[lc.scpeaks], lc.ppcounts[lc.scpeaks], c='r', s=30, zorder=11, label='Scipy peaks')
allowed_indices_pp = np.where((lc.pptime>0) & (lc.pptime<86400))
axs[0].plot(lc.bgtime, lc.tmp_bgcounts[allowed_indices_pp], c='coral', alpha=0.4, label='BG 1')
axs[0].plot(lc.bgtime, lc.bgcounts, c='deeppink', alpha=0.4, label='BG 2')
axs[0].legend(fontsize=16, loc='upper right')
axs[0].set_yscale('log')
# ---------------------------------------------------
mu = np.mean(lc.bgcounts)
sigma = np.std(lc.bgcounts)
x = lc.pptime
y = np.array(lc.nobgcounts)
axs[1].plot(x, lc.nobgcounts, c='k', alpha=0.3)
axs[1].plot(x, np.ones(len(x)) * lc.config.nsigma * sigma, '--', c='limegreen', alpha=0.7)
z = np.where(y > lc.config.nsigma * sigma)[0]
z2 = np.where(y <= lc.config.nsigma * sigma)[0]
y[z2] = np.nan
axs[1].plot(x, y, c='k')
axs[1].text(x[-1], lc.config.nsigma * sigma, 'n$\sigma_{BG}$', va='bottom', fontsize=14, ha='right')
axs[1].set_yscale('log')
# ---------------------------------------------------
x = lc.pptime
y = lc.ppcounts
axs[2].plot(x, y, alpha=0.3)
axs[2].set_xlim(left=x[0], right=x[-1])
for i in range(len(lc.llist)):
index = np.sort(np.array(lc.llist[i]))
x = (lc.pptime[index])
y = lc.ppcounts[index]
axs[2].plot(x, y, c='b')
axs[2].axvspan(x[0], x[-1], alpha=0.1, color='orange')
axs[2].axvline(x[0], alpha=0.1, color='orange')
axs[2].axvline(x[-1], alpha=0.1, color='orange')
axs[2].text(x[-1], np.nanmax(lc.ppcounts) * 0.95, f'Group {i+1}', rotation=-90, va='top', fontsize=14, ha='left') # Commentable
# axs[2].text(x[-1], np.nanmax(lc.ppcounts) * 0.95, f'Group {i+1} ({lc.regions[i].numflares})', rotation=-90, va='top', fontsize=14, ha='left') # Commentable
axs[2].set_yscale('log')
# plt.text(2000, np.nanmax(lc.ppcounts) * 0.95, f'#Flares: {lc.numflares}', va='center', fontsize=14, ha='left') # Commentable
# --------------------------------------------------
if yscale == 'linear' or yscale == 'log':
for ax in axs:
ax.set_yscale(yscale)
else:
print('yscale must be either linear or log. Default: linear')
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=100)
plt.close()
else:
plt.show()
"""
Individual flares decomposed in a given region
Input:
1. Region object
Inputs (optional):
1. Filename if plots need to be saved
2. Linear or log yscale
"""
def plot_region(region: Region, filename: str | None = None, yscale: str = 'linear'):
plt.style.use('seaborn-bright')
fig, axs = plt.subplots(nrows=2, figsize=(9.6, 9.6), gridspec_kw={'height_ratios': [3, 1]})
axs[0].set_title(f'Light Curve {region.date}')
plt.subplots_adjust(hspace=.0)
for ax in axs:
ax.tick_params(which='major', width=1)
ax.tick_params(which='major', length=4)
ax.set_xmargin(0)
axs[0].minorticks_on()
axs[0].set_xticklabels([])
axs[0].tick_params(axis='x', direction='in')
axs[1].set_xlabel('Time (s)')
linestyle = {"elinewidth":1, "capsize":0, "ecolor":"grey"}
axs[0].errorbar(region.time, region.counts, yerr=region.cnterror,
fmt='D', ms=3, c='navy', label='data', **linestyle)
axs[0].plot(region.time, region.fit_result.best_fit, lw=3, c='deeppink', label='fit')
comps = region.fit_result.eval_components()
# axs[0].plot([],[]) # To cycle through first colour (blue)
for name, comp in comps.items():
axs[0].plot(region.time, comp, '--', label=name, lw=2)
axs[0].set_ylabel('Flux (nW/m$^2$)')
axs[0].legend(fontsize=16, ncol=2)
# TODO: Add more attribute information about region to plot
xlen = region.time[-1] - region.time[0]
ylen = np.max(region.counts) - np.min(region.counts)
axs[0].text(region.time[0] + 0.05 * xlen, 0.9 * np.nanmax(region.counts + region.cnterror),
f'$R^2$: {region.fit_result.rsquared.round(2)}\n$\chi^2_r$: {region.fit_result.redchi.round(2)}',
fontsize=16, ha='left')
# -------------------------------------
ydata = (region.counts - region.fit_result.best_fit)
# axs[1].errorbar(region.time, (ydata)/np.std(ydata), yerr=region.cnterror/np.std(ydata), fmt='D', ms=3, c='blue', **linestyle)
axs[1].scatter(region.time, (ydata)/np.nanstd(ydata), c='navy', s=3, marker='D')
axs[1].axhline(np.nanmean((ydata)/np.nanstd(ydata)), linestyle='--', lw=1, c='k')
axs[1].set_xlim(left=region.time[0], right=region.time[-1])
axs[1].set_ylabel('(data-fit)/$\sigma$')
axs[1].set_yticks([-3,0,3])
axs[1].set_yticklabels([-3,0,3])
axs[1].set_ylim(bottom=-5,top=5) # To set ylim on error plot
# -------------------------------------
if yscale == 'linear' or yscale == 'log':
axs[0].set_yscale(yscale)
else:
print('yscale must be either linear or log. Default: linear')
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=100)
plt.close()
else:
plt.show()
"""
Plotting a flare with all relevant information
Note that the counts associated with the flare are
an estimate from the flare decomposition step under
the fast-rise, slow-decay model
Input:
1. Flare object
Input (optional):
1. Filename if plot needs to be saved
"""
def plot_flare(flare: Flare, filename: str | None = None):
fig, axs = plt.subplots(2, figsize=(9.6, 9.6), gridspec_kw={'height_ratios': [3, 1]})
# axs[0].set_title(f'Flare at t = {flare.fit.post_fit_peak_time}s')
plt.xlabel('Time (s)')
plt.subplots_adjust(hspace=.0)
linestyle = {"elinewidth":1, "capsize":0, "ecolor":"grey"}
plt.setp(axs[0].get_xticklabels(), visible=False)
f = flare.fit
xt = flare.time - flare.fit.post_fit_peak_time
yt = flare.counts
# axs[0].set_title(f'Flare at {f.post_fit_peak_time}s in light curve {flare.date}')
axs[0].errorbar(xt, yt, yerr=flare.cnterror, fmt='D', ms=3, c='blue', **linestyle)
at = AnchoredText(f'Class: {flare.flare_class_without_bg}\nSNR: {flare.snr:.2f}', \
prop=dict(size=18, color='black'), frameon=False, loc='upper left')
at.patch.set_boxstyle('round,pad=0.,rounding_size=0.2')
axs[0].add_artist(at)
at = AnchoredText(f'{flare.date[:4]}-{flare.date[4:6]}-{flare.date[-2:]} UT{str(datetime.timedelta(seconds=int(f.post_fit_peak_time)))[:-3]:0>5}', \
prop=dict(size=18, color='black'), frameon=False, loc='upper right')
at.patch.set_boxstyle('round,pad=0.,rounding_size=0.2')
axs[0].add_artist(at)
for ax in axs:
ax.tick_params(which='major', width=1)
ax.tick_params(which='major', length=4)
ax.margins(x=0)
axs[0].set_ylabel('Flux (nW/m$^2$)')
axs[1].set_ylabel('Residual ((o-f)/$\sigma$)')
#axs[1].set_ylabel('Relative\nError (%)')
axs[1].set_ylim(bottom=-4,top=4)
range = (np.max(yt) - np.min(yt))/10
axs[0].scatter(f.post_fit_start_time - f.post_fit_peak_time, \
f.post_fit_start_count, c='k', s=30, zorder=15)
axs[0].vlines(x=f.post_fit_start_time - f.post_fit_peak_time, \
ymin=f.post_fit_start_count - range, \
ymax=f.post_fit_start_count + range, color='k', linestyle='dotted', linewidth=2)
axs[0].scatter(f.post_fit_end_time - f.post_fit_peak_time, \
f.post_fit_end_count, c='k', s=30, zorder=15)
axs[0].vlines(x=f.post_fit_end_time - f.post_fit_peak_time, \
ymin=f.post_fit_end_count - range, \
ymax=f.post_fit_end_count + range, color='k', linestyle='dotted', linewidth=2)
yfit = f.fit
axs[0].plot(xt, yfit, c='r', zorder=10, lw=3)
axs[0].tick_params(axis='x', direction='in')
axs[1].errorbar(xt, (yt-yfit)/np.std(yt-yfit), yerr=flare.cnterror/np.std(yt-yfit), fmt='D', ms=3, c='blue', **linestyle)
axs[1].axhline(np.mean((yt-yfit)/np.std(yt-yfit)), linestyle='--', lw=1, c='navy')
plt.show()
###
#
###
def plot_threes(flares):
if len(flares) != 4:
print("Need to call function with three flare objects as argument")
return
fig, axs = plt.subplots(ncols=4, figsize=(21.6, 5.4))
axs[0].set_ylabel('Flux (nW/m$^2$)')
# axs[1].set_xlabel('Time (s)')
# fig.subxlabel('Time (s)')
plt.subplots_adjust(wspace=0.15)
linestyle = {"elinewidth":0.5, "capsize":0, "ecolor":"grey"}
for i, flare in enumerate(flares):
# if np.max(flare.counts) > 1000:
axs[i].set_xlabel('Time (s)')
f = flare.fit
xt = flare.time - flare.fit.post_fit_peak_time
yt = flare.counts
axs[i].errorbar(xt, yt, yerr=flare.cnterror, fmt='D', ms=3, c='blue', **linestyle)
at = AnchoredText(f'{flare.flare_class_without_bg}\n{flare.snr:.2f}', \
prop=dict(size=14, color='black'), frameon=False, loc='upper left')
at.patch.set_boxstyle('round,pad=0.,rounding_size=0.2')
axs[i].add_artist(at)
at = AnchoredText(f'{flare.date[:4]}-{flare.date[4:6]}-{flare.date[-2:]} UT{str(datetime.timedelta(seconds=int(f.post_fit_peak_time)))[:-3]:0>5}', \
prop=dict(size=14, color='black'), frameon=False, loc='upper right')
at.patch.set_boxstyle('round,pad=0.,rounding_size=0.2')
axs[i].add_artist(at)
for ax in axs:
ax.tick_params(which='major', width=1)
ax.tick_params(which='major', length=4)
ax.margins(x=0)
range = (np.max(yt) - np.min(yt))/10
axs[i].scatter(f.post_fit_start_time - f.post_fit_peak_time, \
f.post_fit_start_count, c='k', s=30, zorder=15)
axs[i].vlines(x=f.post_fit_start_time - f.post_fit_peak_time, \
ymin=f.post_fit_start_count - range, \
ymax=f.post_fit_start_count + range, color='k', linestyle='--', linewidth=3, zorder=15)
axs[i].scatter(f.post_fit_end_time - f.post_fit_peak_time, \
f.post_fit_end_count, c='k', s=30, zorder=15)
axs[i].vlines(x=f.post_fit_end_time - f.post_fit_peak_time, \
ymin=f.post_fit_end_count - range, \
ymax=f.post_fit_end_count + range, color='k', linestyle='--', linewidth=3, zorder=15)
# axs[i].set_xlim([f.post_fit_start_time, f.post_fit_end_time] - f.post_fit_peak_time)
yfit = f.fit
axs[i].plot(xt, yfit, c='r', zorder=10, lw=3)
axs[i].tick_params(axis='x', direction='in')
xlim = min(f.post_fit_peak_time - flare.time[0], flare.time[-1] - f.post_fit_peak_time)
axs[i].set_xlim([-xlim, xlim])
# axs[i].yaxis.set_major_locator(MaxNLocator(integer=True, nbins=8))
if (flare.multi_flare_region == True):
axs[i].set_facecolor('ivory')
plt.show()
###
def plot_three_regions(regions):
if len(regions) != 4:
print("Need to call function with three region objects as argument")
return
fig, axsarr = plt.subplots(nrows=2, ncols=4, figsize=(21.6, 6.2), gridspec_kw={'height_ratios': [3, 1]})
axsarr[0, 0].set_ylabel('Flux (nW/m$^2$)')
axsarr[1, 0].set_ylabel('(data-fit)/$\sigma$')
# fig.supxlabel('Time (s)')
plt.subplots_adjust(wspace=0.15)
plt.subplots_adjust(hspace=.0)
linestyle = {"elinewidth":0.5, "capsize":0, "ecolor":"grey"}
plt.style.use('seaborn-bright')
# axs[0].set_title(f'Flaring duration in light curve {region.date}')
for i, region in enumerate(regions):
axs = axsarr[:,i]
for ax in axs:
ax.tick_params(which='major', width=1)
ax.tick_params(which='major', length=4)
ax.set_xmargin(0)
axs[0].minorticks_on()
axs[0].set_xticklabels([])
axs[0].tick_params(axis='x', direction='in')
if np.nanmax(region.counts) > 1200:
axs[1].set_xlabel('Time (s)')
axs[0].errorbar(region.time, region.counts, yerr=region.cnterror,
fmt='D', ms=2, c='navy', label='data', **linestyle)
axs[0].plot(region.time, region.fit_result.best_fit, lw=3, c='deeppink', label='fit')
comps = region.fit_result.eval_components()
# axs[0].plot([],[]) # To cycle through first colour (blue)
for name, comp in comps.items():
axs[0].plot(region.time, comp, '--', label=name, lw=2)
# axs[0].set_ylabel('Flux (nW/m$^2$)')
# axs[0].legend(fontsize=16, ncol=2)
# TODO: Add more attribute information about region to plot
xlen = region.time[-1] - region.time[0]
ylen = np.max(region.counts) - np.min(region.counts)
axs[0].text(region.time[0] + 0.05 * xlen, 0.84 * np.nanmax(region.counts + region.cnterror),
f'$R^2$: {region.fit_result.rsquared.round(2)}\n$\chi^2_r$: {region.fit_result.redchi.round(2)}',
fontsize=14, ha='left')
axs[0].text(region.time[-1] - 0.05 * xlen, 0.89 * np.nanmax(region.counts + region.cnterror),
f'{region.date[:4]}-{region.date[4:6]}-{region.date[6:]}',
fontsize=14, ha='right')
# -------------------------------------
ydata = (region.counts - region.fit_result.best_fit)
# axs[1].errorbar(region.time, (ydata)/np.std(ydata), yerr=region.cnterror/np.std(ydata), fmt='D', ms=3, c='blue', **linestyle)
axs[1].scatter(region.time, (ydata)/np.nanstd(ydata), c='navy', s=2, marker='D')
axs[1].axhline(np.nanmean((ydata)/np.nanstd(ydata)), linestyle='--', lw=2, c='k')
axs[1].set_xlim(left=region.time[0], right=region.time[-1])
# axs[1].set_ylabel('(data-fit)/$\sigma$')
axs[1].set_yticks([-3,0,3])
axs[1].set_yticklabels([-3,0,3])
axs[1].set_ylim(bottom=-5,top=5) # To set ylim on error plot
plt.show()