def events_per_second_plot(flist, savefig=None, ax=None):
log.info("Generating EXP per s plot")
d = {}
for f in tqdm(flist):
tab = Table.read(f, hdu=1)
date = tab.meta['DATE-OBS']
exposure = tab.meta['EXPOSURE']
table_length = len(tab)
if exposure == 0:
continue
if date in d.keys():
d[date] += table_length / exposure
else:
d[date] = table_length / exposure
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
plt.subplots_adjust(top=.98, right=.98)
ax = niutils.plotparams(ax)
created_fig = True
else:
created_fig = False
dates = list(d.keys())
format_specifier = '%Y-%m-%dT%H:%M:%S'
dates_formatted = [
datetime.datetime.strptime(s, format_specifier) for s in dates
]
temp = []
for i in range(len(dates_formatted)):
dtemp = datetime.datetime(year=2017, month=11, day=1)
if dates_formatted[i] < dtemp:
temp.append(d[dates[i]])
print(len(temp))
print(np.median(temp))
print(np.mean(temp))
#Need to fix this
df = pd.DataFrame({'dates': dates_formatted, 'vals': list(d.values())})
df.sort_values('dates', inplace=True)
print(df)
color = niutils.get_colors()['br_colors'][-1]
ax.plot_date(df['dates'], df['vals'], color=color, ls='-')
ax.xaxis.set_minor_locator(MonthLocator())
ax.set_xlabel("Observation Start Date", fontsize=20)
ax.set_ylabel(r'Count Rate (s$^{-1}$)', fontsize=20)
if (savefig is None) and (created_fig):
plt.show()
elif savefig is not None:
fig.savefig(savefig)
else:
return ax
def plot_data(fnames, column, savefig=None):
if niutils.check_iter(fnames):
df = merge_tables(fnames)
tab_for_format = fnames[0]
ntab = len(fnames)
else:
df = read_table(fnames)
tab_for_format = fname
ntab = 1
if column is None:
raise ValueError("Column can not be None")
df = add_datetime_col(df)
fig, ax = plt.subplots(1, 1, figsize=(12*ntab, 6))
ax = niutils.plotparams(ax)
if not niutils.check_iter(column):
column = [column]
mask = [ np.where(df[column[i]] != -9999.9)[0]
for i in range(len(column)) ]
colors = niutils.get_colors()['br_colors']
colors = [colors[0], colors[2]]
ax.plot(df['datetime'][mask[0]], df[column[0]][mask[0]],
color=colors[0], label=column[0])
ax.set_xlabel("Date", fontsize=20)
ax.set_ylabel(format_ace_column(column[0], table=tab_for_format),
fontsize=20)
if len(column) > 1:
ax, ax1 = niutils.DoubleY(ax, colors=(colors[0], colors[1]))
ax1.plot(df['datetime'][mask[1]], df[column[1]][mask[1]],
color=colors[1], label=column[1])
ax1.set_ylabel(format_ace_column(column[1], table=tab_for_format),
fontsize=20)
if max(df['day']) > 180:
ax.xaxis.set_minor_locator(MonthLocator())
else:
ax.xaxis.set_major_locator(MonthLocator())
ax.xaxis.set_minor_locator(MonthLocator(bymonthday=15))
myfmt = DateFormatter('%Y-%m')
ax.xaxis.set_major_formatter(myfmt)
if savefig is None:
plt.show()
else:
fig.savefig(savefig)
def plot_data_split(fnames, column, savefig=None):
if niutils.check_iter(fnames):
df_list = [read_table(f) for f in fnames]
else:
raise ValueError("Must input multiple filenames")
if column is None:
raise ValueError("Column can not be None")
df_list = [add_datetime_col(df) for df in df_list]
fig, ax = plt.subplots(len(df_list), 1, figsize=(12, 5.2*len(df_list)))
plt.subplots_adjust(top=.98, bottom=.07)
for a in ax: a = niutils.plotparams(a)
if not niutils.check_iter(column):
column = [column]
colors = niutils.get_colors()['br_colors']
colors = [colors[0], colors[2]]
for i in range(len(df_list)):
df = df_list[i]
mask = [ np.where(df[column[i]] != -9999.9)[0]
for i in range(len(column)) ]
ax[i].plot(df['datetime'][mask[0]], df[column[0]][mask[0]],
color=colors[0], label=column[0])
ax[i].set_xlabel("Date", fontsize=20)
ax[i].set_ylabel(format_ace_column(column[0], table=fnames[0]),
fontsize=20)
if len(column) > 1:
ax[i], ax1 = niutils.DoubleY(ax[i], colors=(colors[0], colors[1]))
ax1.plot(df['datetime'][mask[1]], df[column[1]][mask[1]],
color=colors[1], label=column[1])
ax1.set_ylabel(format_ace_column(column[1],
table=fnames[0]), fontsize=20)
if max(df['day']) > 180:
ax[i].xaxis.set_minor_locator(MonthLocator())
else:
ax[i].xaxis.set_major_locator(MonthLocator())
ax[i].xaxis.set_minor_locator(MonthLocator(bymonthday=15))
myfmt = DateFormatter('%Y-%m')
ax[i].xaxis.set_major_formatter(myfmt)
if savefig is None:
plt.show()
else:
fig.savefig(savefig)
def br_earth_hist(table, bins=50, save=None):
tab = Table.read(table, hdu=1)
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
ax = niutils.plotparams(ax)
plt.subplots_adjust(top=.98, right=.98)
br_earth = [b for b in tab['BR_EARTH'] if b < 180]
ax.hist(br_earth,
bins=bins,
edgecolor='black',
color=niutils.get_colors()['br_colors'][-1])
ax.set_ylabel("N Rows", fontsize=20)
ax.set_xlabel('Bright Earth Angle', fontsize=20)
plt.setp(ax.get_yticklabels()[0], visible=False)
if save is not None:
fig.savefig(save)
else:
plt.show()
def mkf_hist(table, key, hdu=1, bins=50, pickle_file=None, save=None):
if pickle_file is None:
tab = Table.read(table, hdu=hdu)
else:
tab = pickle.load(open(pickle_file, 'rb'))
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
ax = niutils.plotparams(ax)
assert (key in tab.colnames)
ax.hist(tab[key],
bins=bins,
edgecolor='black',
color=niutils.get_colors()['br_colors'][-1])
ax.set_ylabel("N Rows", fontsize=20)
ax.set_xlabel(key, fontsize=20)
if save is not None:
fig.savefig(save)
else:
plt.show()
def paper_plot(mkf_list):
"""
Go through all mkfs
find out which split tables are longest in each of the four categories
Plot the earth elevation and bright earth angles
"""
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
d = {
'increasing': ['', 0],
'decreasing': ['', 0],
'touches200': ['', 0],
'localextrema': ['', 0]
}
for mkf in tqdm(mkf_list):
tab = Table.read(mkf, hdu=1)
tab_list = dfsplit(tab, 2)
for t in tab_list:
if len(t) <= 5:
continue
if touches_200(t, 'BR_EARTH'):
if len(t) > d['touches200'][1]:
d['touches200'][0] = t
d['touches200'][1] = len(t)
elif always_increasing(t, 'BR_EARTH'):
if len(t) > d['increasing'][1]:
d['increasing'][0] = t
d['increasing'][1] = len(t)
elif always_decreasing(t, 'BR_EARTH'):
if len(t) > d['decreasing'][1]:
d['decreasing'][0] = t
d['decreasing'][1] = len(t)
elif local_extrema(t, 'BR_EARTH'):
if len(t) > d['localextrema'][1]:
d['localextrema'][0] = t
d['localextrema'][1] = len(t)
else:
if len(groups) == 4:
print(f'Problem with groups for {mkf}')
ax = niutils.plotparams(ax)
colors = niutils.get_colors()['br_colors']
darkcolors = niutils.get_colors()['br_colors_dark']
for i in range(len(d.keys())):
t = d[list(d.keys())[i]][0]
ax.plot(t['TIME'] - t['TIME'].min(),
t['BR_EARTH'],
color=colors[i],
lw=2)
ax.plot(t['TIME'] - t['TIME'].min(),
t['ELV'],
color=darkcolors[i],
lw=2)
plt.show()
def cumulative_exposure_plot(flist, savefig=None, ax=None):
log.info("Generating cumulative exposure plot")
d = {}
format_specifier = '%Y-%m-%dT%H:%M:%S'
for f in tqdm(flist):
tab = Table.read(f, hdu=1)
date = tab.meta['DATE-OBS']
exposure = tab.meta['EXPOSURE']
if exposure == 0:
continue
date_formatted = datetime.datetime.strptime(date, format_specifier)
date_formatted = date_formatted.replace(hour=0, minute=0, second=0)
if date_formatted in d.keys():
d[date_formatted] += exposure
else:
d[date_formatted] = exposure
sorted_dates = list(d.keys())
sorted_dates.sort()
mindate = min(list(d.keys()))
maxdate = max(list(d.keys()))
iterdate = mindate - datetime.timedelta(1)
date_list = []
exposure_cumulative = []
while iterdate <= maxdate + datetime.timedelta(1):
date_list.append(iterdate)
if len(exposure_cumulative) == 0:
exposure_next = 0
elif iterdate in d.keys():
exposure_next = exposure_cumulative[-1] + d[iterdate]
else:
exposure_next = exposure_cumulative[-1]
exposure_cumulative.append(exposure_next)
iterdate = iterdate + datetime.timedelta(1)
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(12, 6))
plt.subplots_adjust(top=.98, right=.98, bottom=.16)
ax = niutils.plotparams(ax)
created_fig = True
else:
created_fig = False
exposure_cumulative = [e / (10e10) for e in exposure_cumulative]
ax.plot(date_list,
exposure_cumulative,
color=niutils.get_colors()['3c50_color'],
lw=3)
ax.xaxis.set_minor_locator(MonthLocator())
ax.set_xlabel("Observation Start Date", fontsize=20)
ax.set_ylabel(r'Cumulative Expsoure ($\times10^{10}$ s)', fontsize=20)
if (savefig is None) and (created_fig):
plt.show()
elif savefig is not None:
fig.savefig(savefig)
else:
return ax
def plot_bkgd_spectra(br_data_list, br_ranges,
sa_ranges=None,
environ=None, bkg3c50=None,
two_panel=False, savefig=None,
zoom_top=0.9, zoom_bottom=0.3,
zoom_left=0.28, zoom_right=0.92):
assert(len(br_data_list) == len(br_ranges))
br_data_list = reformat_input(br_data_list)
if any( [ l > 1 for l in check_length(br_data_list)[1] ] ):
assert(sa_ranges is not None)
sa_ranges = reformat_input(sa_ranges)
assert(check_length(br_data_list) == check_length(sa_ranges))
linestyles = ['-', '--', '-.', ':']
data_flat = []
br_ranges_flat = []
sa_ranges_flat = []
ls_flat = []
for i in range(len(br_data_list)):
for j in range(len(br_data_list[i])):
data_flat.append(br_data_list[i][j])
br_ranges_flat.append(br_ranges[i])
if sa_ranges is not None:
sa_ranges_flat.append(sa_ranges[i][j])
ls_flat.append(linestyles[j])
colors_flat = [ niutils.map_colors()[r] for r in br_ranges_flat ]
if sa_ranges is None:
labels_flat = [ niutils.br_label(r) for r in br_ranges_flat ]
else:
labels_flat = [ f'{niutils.br_label(br_ranges_flat[i])}; {niutils.sa_label(sa_ranges_flat[i])}' for i in range(len(br_ranges_flat)) ]
if environ is not None:
data_flat.append(environ)
colors_flat.append(niutils.get_colors()['environ_color'])
labels_flat.append("Environmental Model")
ls_flat.append('-')
if bkg3c50 is not None:
data_flat.append(bkg3c50)
colors_flat.append(niutils.get_colors()['3c50_color'])
labels_flat.append('3C50 Model')
ls_flat.append('-')
if two_panel:
fig, (ax, ax1) = plt.subplots(2, 1, figsize=(16, 14))
plt.subplots_adjust(hspace=.1, top=.98, right=.98,
bottom=.08, left=.08)
ax_list = [ax, ax1]
else:
fig, ax = plt.subplots(1, 1, figsize=(15, 8))
ax_list = [ax]
for a in ax_list: a = niutils.plotparams(a)
markers = ['.', 'o']
for i in range(len(data_flat)):
df = pd.read_csv(data_flat[i], skiprows=3, delimiter=" ", header=None)
df.columns = ['energy', 'energy_err', 'counts', 'counts_err']
mi = 0
for a in ax_list:
a.errorbar(df['energy'], df['counts'],
xerr=df['energy_err'], yerr=df['counts_err'],
ls=ls_flat[i], marker=markers[mi],
color=colors_flat[i],
label=labels_flat[i])
mi += 1
ax.legend(edgecolor='black', fontsize=20)
for a in ax_list:
a.set_ylabel(r'Normalized Counts (s$^{-1}$ keV$^{-1}$)', fontsize=20)
a.set_xscale('log')
a.set_yscale('log')
a.set_xlabel('Energy (keV)', fontsize=20)
ax.xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f'))
if two_panel:
ax1.xaxis.set_minor_formatter(mticker.FormatStrFormatter('%.01f'))
ax1.set_xlim(left=zoom_left, right=zoom_right)
ax1.set_ylim(bottom=zoom_bottom, top=zoom_top)
con1 = ConnectionPatch(xyA=(ax1.get_xlim()[0], ax1.get_ylim()[1]),
xyB=(ax1.get_xlim()[0], ax1.get_ylim()[0]),
coordsA='data', coordsB='data',
axesA=ax1, axesB=ax,
color='black', ls='-')
con2 = ConnectionPatch(xyA=(ax1.get_xlim()[1], ax1.get_ylim()[1]),
xyB=(ax1.get_xlim()[1], ax1.get_ylim()[0]),
coordsA='data', coordsB='data',
axesA=ax1, axesB=ax,
color='black', ls='-')
rect = Rectangle((ax1.get_xlim()[0], ax1.get_ylim()[0]),
(ax1.get_xlim()[1]-ax1.get_xlim()[0]),
(ax1.get_ylim()[1]-ax1.get_ylim()[0]),
facecolor='none', edgecolor='black',
alpha=.5)
ax.add_patch(rect)
ax1.add_patch(con1)
ax1.add_patch(con2)
if savefig is not None:
fig.savefig(savefig)
if savefig.endswith('pdf'):
fig.savefig(savefig.replace('pdf', 'png'))
else:
plt.show()