def plotsummary(ax, lc, trace, **kwargs):
summary = pm.summary(trace)
summary['mode'] = list(utils.modes(trace).values())
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
tbl = table(ax, summary.round(3), **kwargs)
tbl.set_fontsize(30)
return tbl
def acfplot_withsummary(axs, lc, trace, summary_kwargs={}, acf_kwargs={}):
summary = pm.summary(trace)
summary['mode'] = list(utils.modes(trace).values())
plotacf(axs[0], lc, **acf_kwargs)
axs[1].xaxis.set_visible(False)
axs[1].yaxis.set_visible(False)
table(axs[1], summary.round(3))
pl.tight_layout()
return axs
def lcplot(ax,
lc,
trace=None,
detrend_order=2,
plot_outliers=False,
plot_trend=False,
uncertainties=False,
highlight_color='r',
vline_color='b',
sigmaclip_kernel_size=5,
sigmaclip_sigma=5,
**kwargs):
ax.plot(lc.t, lc.flux, '.', color='k')
trend = utils.trend(lc.t, lc.flux, detrend_order)
mask = utils.clipmask(lc.t,
lc.flux,
kernel_size=sigmaclip_kernel_size,
sigma=sigmaclip_sigma)
if plot_outliers:
ax.plot(lc.t[mask],
lc.flux[mask],
'.',
color=highlight_color,
label='masked outliers')
if plot_trend:
ax.plot(lc.t,
trend,
color=highlight_color,
label='order {0} polynomial fit'.format(detrend_order))
if trace is not None:
period = utils.modes(trace)['P']
low, high = pm.stats.hpd(trace['P'], credible_interval=0.67)
for i in np.arange(np.int((lc.t[-1] - lc.t[0]) / period) + 2):
if not uncertainties:
ax.axvline(min(lc.t) + i * period,
alpha=0.3,
linewidth=2,
color=vline_color)
if uncertainties:
vlines_min = min(lc.t) + i * low
vlines_max = min(lc.t) + i * high
ax.axvspan(vlines_min,
vlines_max,
alpha=0.3,
color=vline_color)
ax.set_xlim((min(lc.t), max(lc.t)))
return ax
def writesummary(lc, trace, file):
summary = pm.summary(trace)
summary['mode'] = list(utils.modes(trace).values())
columns = [
c + "_" + n for c, n in itertools.product(list(summary.index),
list(summary.columns))
]
columnstring = '\t'.join(columns)
columnstring += '\tepic\n'
if not os.path.isfile(file):
with open(file, "w") as f:
f.write("Generated with round.py version 0.1 on " + "{0}\n".format(
datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))
f.write("Campaign: {0}\n".format(campaign))
f.write(columnstring)
summarystring = summary.to_numpy().flatten()
summarystring = np.append(summarystring, lc.id)
summarystring = summarystring[None, :]
fmtstring = ["%0.3f"] * (summarystring.size - 1) + ["%d"]
with open(file, "a+") as f:
np.savetxt(f, summarystring, fmt=fmtstring, delimiter='\t')
return summarystring
def detect_mode(cf):
'''Given a cantus firmus as a list of ints, attempts to detect the mode
and returns a tuple containing a string for the name of the mode and ints
representing the notes present in the mode, assuming that the first note provided
is the tonic.'''
modes = u.modes()
tonic = cf[0]
transposed = [(note - tonic) % 12 for note in cf]
sums = [(sum(1 for note in transposed if note in modes[mode]), mode)
for mode in modes]
max_freq = max(sums, key=lambda i: i[0])[0]
filtered = [i[1] for i in sums if i[0] == max_freq]
if len(filtered) == 1:
notes = [(note + tonic) % 12 for note in modes[filtered[0]]]
return (filtered[0], notes)
else:
while True:
stout = "Which mode is this in: " + ", ".join(filtered) + "?\n"
resp = raw_input(stout).lower()
if resp in modes:
notes = [(note + tonic) % 12 for note in modes[resp]]
return (resp, notes)
print "I didn't recognize that. Type the name of a mode.\n"
