def main():
path = "."
pattern = ""
if len(sys.argv) == 3:
path = sys.argv[1]
pattern = sys.argv[2]
else:
pattern = sys.argv[1]
global ts
ts = mark_time("start loop", ts)
for file in os.listdir(path):
if fnmatch.fnmatch(file, pattern):
data = Table(Table.read(os.path.join(path, file), format="ascii"), masked=True)
orig_mask = (data['ivar'] == 0)
data.mask = [(data['ivar'] == 0)]*len(data.columns)
idstr = file[:file.rfind('.')]
ts = mark_time("getting match", ts)
peaks = measure_peaks.find_and_measure_peaks(data, use_flux_con=False, ignore_defects=False)
ts = mark_time("measure_peaks.find_and_measure_peaks", ts)
peaks_mask = measure_peaks.mask_known_peaks(data, peaks)
'''
test_data = data.copy()
test_data.mask = np.ma.nomask
test_data.mask = [peaks_mask]*len(data.columns)
peaks = measure_peaks.find_and_measure_peaks(test_data, use_flux_con=False, ignore_defects=False)
'''
#peaks_mask = np.zeros((7080,), dtype=bool)
ts = mark_time("measure_peaks.mask_known_peaks", ts)
data.mask = [orig_mask]*len(data.columns)
start_continuum, start_wo_continuum = split_spectrum(data['wavelength'][:split_noisy_app], data['flux'][:split_noisy_app],
peaks_mask[:split_noisy_app], orig_mask[:split_noisy_app],
idstr=idstr, block_sizes=block_sizes)
end_continuum, end_wo_continuum = split_spectrum(data['wavelength'][split_noisy_app:], data['flux'][split_noisy_app:],
peaks_mask[split_noisy_app:], orig_mask[split_noisy_app:],
idstr=idstr, block_sizes=block_sizes, mult=6)
ts = mark_time("smoothing", ts)
wo_continuum = np.ma.concatenate([start_wo_continuum, end_wo_continuum])
continuum = np.ma.concatenate([start_continuum, end_continuum])
#continuum, wo_continuum = tamp_down(continuum, wo_continuum, span=51)
continuum, wo_continuum = tamp_down(continuum, wo_continuum)
continuum, wo_continuum = tamp_down(continuum, wo_continuum, span=31)
#continuum, wo_continuum = tamp_down(continuum, wo_continuum, span=31)
continuum, wo_continuum = tamp_down(continuum, wo_continuum, span=21)
#continuum, wo_continuum = tamp_down(continuum, wo_continuum, span=11)
# Do not smooth
#continuum, wo_continuum = smooth(continuum, wo_continuum)
save_data(data['wavelength'], wo_continuum, continuum, data['ivar'], orig_mask, idstr)
ts = mark_time("save_data", ts)
def main():
path = "."
pattern = ""
if len(sys.argv) == 3:
path = sys.argv[1]
pattern = sys.argv[2]
else:
pattern = sys.argv[1]
for file in os.listdir(path):
if fnmatch.fnmatch(file, pattern):
data = Table(Table.read(os.path.join(path, file), format="ascii"), masked=True)
data.mask = [(data['ivar'] == 0)]*len(data.columns)
if np.count_nonzero(data['con_flux'].data < 0):
print file, "BAD: continuum has zub-zero con_flux!!!!"
print data['con_flux'][data['con_flux'].data < 0]
total = data['con_flux']+data['flux']
if np.count_nonzero(total < 0):
print file, "WORSE: total has zub-zero con_flux!!!!"
print total[total < 0]
ivar_cutoff = 0.005
ivar_cutoff_mask = (data['ivar'].data < ivar_cutoff) & (data['ivar'].data > 0)
if np.any(ivar_cutoff_mask):
print file, "QUESTIONABLE: ivar less than", ivar_cutoff
print data["ivar"][ivar_cutoff_mask]
def main():
path = "."
pattern = ""
if len(sys.argv) == 3:
path = sys.argv[1]
pattern = sys.argv[2]
else:
pattern = sys.argv[1]
for file in os.listdir(path):
if fnmatch.fnmatch(file, pattern):
data = Table(Table.read(os.path.join(path, file), format="ascii"), masked=True)
data.mask = [(data['ivar'] == 0)]*len(data.columns)
data['wavelength'].mask = False
idstr = file[:file.rfind('.')]
peak_flux_list = []
peak_flux = find_and_measure_peaks(data, peak_flux_list)
#Let's just forget this for now; spans are maybe something to come back to
'''
for key, vals in wlen_spans.items():
target_flux_totals = get_total_flux(key, data['wavelength'], data['flux'], data['con_flux'], target_wlens=None, wlen_spans=vals)
peak_flux_list.append(target_flux_totals)
'''
save_data(peak_flux, idstr)
def main():
path = "."
pattern = ""
if len(sys.argv) == 3:
path = sys.argv[1]
pattern = sys.argv[2]
else:
pattern = sys.argv[1]
for file in os.listdir(path):
if fnmatch.fnmatch(file, pattern):
data = Table(Table.read(os.path.join(path, file), format="ascii"), masked=True)
orig_mask = (data['ivar'] == 0)
data.mask = [(data['ivar'] == 0)]*len(data.columns)
idstr = file[:file.rfind('.')]
test_data = data.copy()
test_data['flux'] = minimize(test_data['wavelength'], test_data['flux'], [100, 200, 300], 0, start_ind=split_noisy_app)
filtered = filter_and_subtract(test_data['flux'], test_data['wavelength'], 201, 24)
test_data['flux'] = np.ma.min(np.ma.vstack([test_data['flux'], filtered]), axis=0)
filtered = filter_and_subtract(test_data['flux'], test_data['wavelength'], 161, 18)
test_data['flux'] = np.ma.min(np.ma.vstack([test_data['flux'], filtered]), axis=0)
continuum = split_spectrum(test_data['wavelength'], test_data['flux'])
wo_continuum = data['flux'] - continuum
save_data(data['wavelength'], wo_continuum, continuum, data['ivar'], orig_mask, idstr)
def plot_it(data, key, peaks_table=None, no_con_flux=False, unmask=False, data_col=None, title=None, stack=False):
data = Table(data, masked=True)
if data_col is None:
data_col = 'flux'
if not unmask:
if 'ivar' in data.colnames:
data.mask = [(data['ivar'] == 0) | (np.abs(data['ivar']) <= 0.0001)]*len(data.columns)
else:
data.mask = [(data[data_col] == 0)]*len(data.columns)
val = data[data_col]
if 'sky' in data.colnames:
val += data['sky']
if 'ivar' in data.colnames:
sigma = np.power(data['ivar'], -0.5)
else:
sigma = 0
if 'con_flux' in data.colnames and not no_con_flux:
val += data['con_flux']
plt.plot(data[key], [0]*len(data[key]), color='red')
plt.plot(data[key], data['con_flux'], color='green')
plt.plot(data[key], val, color='orange', alpha=0.7)
else:
if stack:
plt.plot(data[key], val, alpha=0.7)
else:
plt.plot(data[key], [0]*len(data[key]), color='red')
plt.plot(data[key], val, color='orange', alpha=0.7)
if not stack and np.any(sigma > 0) and 'con_flux' not in data.colnames:
plt.fill_between(data[key], val-sigma, val+sigma, color='red')
if peaks_table is not None:
for row in peaks_table:
if row['total_type'] == 'line' and row['wavelength_lower_bound'] != 0:
plt.fill_between([row['wavelength_lower_bound'], row['wavelength_upper_bound']],
[0, np.max(val)], color='gray')
if title is not None:
plt.suptitle(title)
plt.tight_layout()
#plt.ylim((np.percentile(val-sigma,0.1),np.percentile(val+sigma,99.9)))
if not stack:
plt.show()
plt.close()
