def plot_curve(tfe,period=0,plot_folded=True,
plot_unfolded=True,smooth_folded=True,
classification='Unknown Class',
survey='Unknown Survey',
show_plot=True,
save_figure=False,
save_figure_name='figure.pdf'):
## check to make sure arguments are consistent
if period == 0 and plot_folded == True:
print "Need a period to fold on. Add period as an argument to the function call."
return
if not plot_folded and not plot_unfolded:
print "No plots requested!"
return
## don't want to overwrite plots
plt.close()
## make title and phase times
title = classification + ' from ' + survey
## both folded and unfolded
if plot_unfolded and plot_folded:
plt.figure(1)
plt.subplot(211)
plt.title(title)
plt.plot(tfe[:,0],tfe[:,1],'r.')
plt.xlabel("Time")
plt.ylabel("Flux")
## smooth on
plt.subplot(212)
smo = smoothers.supersmooth(tfe,period)
plt.plot(tfe[:,0],smo,tfe[:,0],tfe[:,1],'x')
plt.xlabel("Phased using period: " + repr(period))
plt.ylabel("Flux")
## smooth on estimated period
## plt.subplot(213)
## smo = smoothers.supersmooth(tfe,1/2)
## plt.plot(tfe[:,0],smo,'o',tfe[:,0],tfe[:,1],'x')
## only unfolded plot
if plot_unfolded and not plot_folded:
plt.plot(tfe[:,0],tfe[:,1],'r.')
plt.title(title)
plt.xlabel("Time")
plt.ylabel("Flux")
## only folded plot
if not plot_unfolded and plot_folded:
plt.plot(folded,tfe[:,1],'r.')
plt.title(title)
plt.ylabel("Flux")
plt.xlabel("Phase")
## either save figure or show figure
if save_figure:
plt.savefig(save_figure_name)
if show_plot:
plt.show()
######### STORE SMOOTHED CURVES
## smooth curves and store smoothed values in measurements_smoothed
cursor.execute(sql_cmd)
connection.commit()
sql_cmd = """SELECT S.source_id, true_period, classification, survey, freq1_harmonics_freq_0 FROM sources AS S JOIN features AS F ON S.source_id=F.source_id"""
cursor.execute(sql_cmd)
db_info = cursor.fetchall()
for i in db_info:
print i
for i in db_info:
tfe = create_database.get_measurements(i[0],cursor)
smo = smoothers.supersmooth(tfe,1/i[4])
tfe[:,1] = smo
tfe[:,2] = 1
create_database.insert_measurements(cursor,i[0],tfe,table='measurements_smoothed')
connection.commit()
########### VISUALIZE SMOOTHED CURVES ###########
## does this work out okay?
## run several times and use evince to view both at once
which = np.random.randint(low=0,high=len(db_info))
delete_existing=True)
connection.commit()
######### STORE SMOOTHED CURVES
## smooth curves and store smoothed values in measurements_smoothed
cursor.execute(sql_cmd)
connection.commit()
sql_cmd = """SELECT S.source_id, true_period, classification, survey, freq1_harmonics_freq_0 FROM sources AS S JOIN features AS F ON S.source_id=F.source_id"""
cursor.execute(sql_cmd)
db_info = cursor.fetchall()
for i in db_info:
print i
for i in db_info:
tfe = create_database.get_measurements(i[0], cursor)
smo = smoothers.supersmooth(tfe, 1 / i[4])
tfe[:, 1] = smo
tfe[:, 2] = 1
create_database.insert_measurements(cursor,
i[0],
tfe,
table='measurements_smoothed')
connection.commit()
########### VISUALIZE SMOOTHED CURVES ###########
## does this work out okay?
## run several times and use evince to view both at once
which = np.random.randint(low=0, high=len(db_info))
reload(visualize)
def compute_residuals(tfe,period):
smo = smoothers.supersmooth(tfe,period,normalize_times=False)
tfe[:,1] = np.mean(tfe[:,1]) + tfe[:,1] - smo
def compute_residuals(tfe, period):
smo = smoothers.supersmooth(tfe, period, normalize_times=False)
tfe[:, 1] = np.mean(tfe[:, 1]) + tfe[:, 1] - smo
sql_cmd = """SELECT S.source_id, survey, freq1_harmonics_freq_0, classification, xml_filename FROM sources AS S JOIN features AS F ON S.source_id=F.source_id WHERE classification = 'rr'"""
cursor.execute(sql_cmd)
db_info = cursor.fetchall()
for i in db_info:
print i
curve_info_names = ["classification","survey","original_source_id","number_points","xml_filename"]
for i in db_info:
tfe = create_database.get_measurements(i[0],cursor)
period = 1 / i[2]
smo = smoothers.supersmooth(tfe,period,normalize_times=False)
tfe_smoothed = np.concatenate((tfe[:,0].reshape((tfe[:,0].size,1)),
smo.reshape(smo.size,1),
tfe[:,2].reshape((tfe[:,2].size,1))),
axis=1)
residuals = np.mean(tfe[:,1]) + tfe[:,1] - smo
tfe_residual = np.concatenate((tfe[:,0].reshape((tfe[:,0].size,1)),
residuals.reshape(residuals.size,1),
tfe[:,2].reshape((tfe[:,2].size,1))),
axis=1)
curve_info = ['smoothed',i[1],i[0],tfe.shape[0],i[4]]
create_database.enter_record(curve_info,
curve_info_names,
tfe_smoothed,
cursor=cursor)
curve_info[0] = 'residual'
