def main(spectraDir, atelTextFile, saveMatchesFilename):
filenames, knownRedshifts, wikiClassifications = [], [], []
# Store all the file paths to the objects in this directory
allFilePaths = glob.glob('%s/*.dat' % spectraDir)
# Get filenames and corresponding redshifts
atels = read_all_atels(atelTextFile)
for i, row in atels.iterrows():
count = 0
for filePath in allFilePaths:
name = os.path.basename(filePath).replace('.dat', '').split('_')[0]
if row.Name == name:
filenames.append(filePath)
knownRedshifts.append(row.Redshift)
wikiClassifications.append("{} {}".format(row.Type, row.Phase))
count += 1
# break # Uncomment the break to only classify the last dated spectrum for each object instead of classifying all dates.
print(count)
if count == 0:
print(row.Name)
# Classify and print the best matches
classification = astrodash.Classify(filenames, knownRedshifts, classifyHost=False, rlapScores=True, smooth=6, knownZ=True, data_files='models_v04')
bestFits, redshifts, bestTypes, rlapFlag, matchesFlag = classification.list_best_matches(n=5, saveFilename=saveMatchesFilename)
print("{0:17} | {1:5} | {2:8} | {3:10} | {4:6} | {5:10} | {6:10}".format("Name", " z ", "DASH_Fit", " Age ", "Prob.", "Flag", "Wiki Fit"))
for i in range(len(filenames)):
print("{0:17} | {1:5} | {2:8} | {3:10} | {4:6} | {5:10} | {6:10}".format('_'.join([filenames[i].split('/')[-1].split('_')[0], filenames[i].split('/')[-1].split('_')[3]]) , redshifts[i], bestTypes[i][0], bestTypes[i][1], bestTypes[i][2], matchesFlag[i].replace(' matches',''), wikiClassifications[i]))
# Plot one of the matches
classification.plot_with_gui(indexToPlot=7)
def main(runDirectory, atelTextFile, saveMatchesFilename):
# Store all the file paths to the objects in this run
directoryPath = os.path.join(os.path.dirname(os.path.abspath(__file__)),
runDirectory)
allFilePaths = []
for dirpath, dirnames, filenames in os.walk(directoryPath):
for filename in [f for f in filenames if f.endswith(".dat")]:
allFilePaths.append(os.path.join(dirpath, filename))
allFilePaths.reverse()
# Get filenames and corresponding redshifts
names, knownRedshifts, wikiClassifications = read_ozdes_wiki_atel(
atelTextFile)
run = []
for i in range(len(names)):
for filePath in allFilePaths:
if names[i] == filePath.split('/')[-1].split('_')[0]:
run.append(
(filePath, knownRedshifts[i], wikiClassifications[i]))
#break # Uncomment the break to only classify the last dated spectrum for each object instead of classifying all dates.
filenames = [i[0] for i in run]
knownRedshifts = [i[1] for i in run]
wikiClassifications = [i[2] for i in run]
# Classify and print the best matches
classification = astrodash.Classify(filenames,
knownRedshifts,
classifyHost=False,
rlapScores=True,
smooth=6)
bestFits, redshifts, bestTypes, rlapFlag, matchesFlag, redshiftErrs = classification.list_best_matches(
n=5, saveFilename=saveMatchesFilename)
print("{0:17} | {1:5} | {2:8} | {3:10} | {4:6} | {5:10} | {6:10}".format(
"Name", " z ", "DASH_Fit", " Age ", "Prob.", "Flag", "Wiki Fit"))
for i in range(len(filenames)):
print(
"{0:17} | {1:5} | {2:8} | {3:10} | {4:6} | {5:10} | {6:10}".format(
'_'.join([
filenames[i].split('/')[-1].split('_')[0],
filenames[i].split('/')[-1].split('_')[3]
]), redshifts[i], bestTypes[i][0], bestTypes[i][1],
bestTypes[i][2], matchesFlag[i].replace(' matches', ''),
wikiClassifications[i]))
# Plot one of the matches
classification.plot_with_gui(indexToPlot=0)
# for path, subdirs, files in os.walk(directory):
# for name in files:
# if name.lower().endswith(extensions):
# filenames.append(os.path.join(path, name).replace(directory, ''))
# filenames.sort()
# for f in filenames:
# print("('%s', 0)," % f)
#Convert files to fits
#Get redshifts from MARZ
filenames = [os.path.join(directory, i[0]) for i in atels]
knownRedshifts = [i[1] for i in atels]
classification = astrodash.Classify(filenames, knownRedshifts)
bestFits, bestTypes, rejectionLabels = classification.list_best_matches(n=5)
# SAVE BEST MATCHES
print bestFits
f = open('classification_results.txt', 'w')
for i in range(len(atels)):
f.write("%s %s %s %s\n %s\n\n" %
(atels[i][0], atels[i][1], bestTypes[i], rejectionLabels[i],
bestFits[i]))
f.close()
print("Finished classifying %d spectra!" % len(atels))
# PLOT SPECTRUM ON GUI
# classification.plot_with_gui(indexToPlot=0)
('ATEL_9961_Run28/DES16C3elb_C3_combined_161225_v10_b00.dat', 0.429),
('ATEL_9961_Run28/DES16X3dvb_X3_combined_161225_v10_b00.dat', 0.329),
('ATEL_9961_Run28/DES16C2ege_C2_combined_161225_v10_b00.dat', 0.348),
('ATEL_9961_Run28/DES16X3eww_X3_combined_161225_v10_b00.dat', 0.445),
('ATEL_9961_Run28/DES16X3enk_X3_combined_161225_v10_b00.dat', 0.331),
('ATEL_9961_Run28/DES16S1ffb_S1_combined_161226_v10_b00.dat', 0.164),
('ATEL_9961_Run28/DES16C1fgm_C1_combined_161226_v10_b00.dat', 0.361),
('ATEL_9961_Run28/DES16X2dzz_X2_combined_161226_v10_b00.dat', 0.325),
('ATEL_9961_Run28/DES16X1few_X1_combined_161227_v10_b00.dat', 0.311),
('ATEL_9961_Run28/DES16X1chc_X1_combined_161227_v10_b00.dat', 0.043),
('ATEL_9961_Run28/DES16S2ffk_S2_combined_161227_v10_b00.dat', 0.373)]
filenames = [os.path.join(directoryPath, i[0]) for i in atels]
knownRedshifts = [i[1] for i in atels]
classification = astrodash.Classify(filenames, knownRedshifts, classifyHost=False, smooth=5, knownZ=True)
bestFits, redshifts, bestTypes, rejectionLabels, reliableFlags = classification.list_best_matches(n=5)
# SAVE BEST MATCHES
print(bestFits)
f = open('classification_results.txt', 'w')
for i in range(len(filenames)):
f.write("%s z=%s %s %s %s\n %s\n\n" % (filenames[i].strip(directoryPath), redshifts[i], bestTypes[i], reliableFlags[i], rejectionLabels[i], bestFits[i]))
f.close()
print("Finished classifying %d spectra!" % len(filenames))
# PLOT SPECTRUM ON GUI
classification.plot_with_gui(indexToPlot=18)
import astrodash
import numpy as np
wave = np.arange(2500, 10000, 2)
flux = np.ones(len(wave))
filename = np.array([wave, flux])
# filename = open('/Users/danmuth/PycharmProjects/DASH/templates/superfit_templates/sne/Ib/sn1983N.p12.dat', 'r')
# filename = 'osc-SN2002er-10'
redshift = 0
classification = astrodash.Classify([filename], [redshift],
classifyHost=False,
knownZ=True,
smooth=6,
rlapScores=True)
bestFits, redshifts, bestTypes, rlapFlag, matchesFlag = classification.list_best_matches(
n=5)
print(bestFits)
classification.plot_with_gui(indexToPlot=0)
('VLT/DES16S2dgb_all.txt', 0.57),
('VLT/DES16S2ejs_all.txt', 0.38),
# ('VLT/DES16X1ept_all.txt', 0.),
('VLT/DES16X2aey_all.txt', 0.210),
# ('VLT/DES16X2afc_all.txt', 0.),
('VLT/DES16X3bdj_all.txt', 0.200),
('VLT/DES16X3bfv_all.txt', 0.60),
('VLT/DES16X3cdv_all.txt', 0.824),
('VLT/DES16X3cer_all.txt', 1.16),
('VLT/DES16X3cxy_all.txt', 0.688), #0.50
# ('VLT/DES16X3eyo_all.txt', 0.)
]
filenames = [os.path.join(directory, i[0]) for i in atels]
knownRedshifts = [i[1] for i in atels]
classification = astrodash.Classify(filenames,
knownRedshifts,
classifyHost=True,
rlapScores=True,
knownZ=True)
bestFits, redshifts, bestTypes, rejectionLabels, reliableFlags, redshiftErrs = classification.list_best_matches(
n=5)
# SAVE BEST MATCHES
print(bestFits)
print("Finished classifying %d spectra!" % len(atels))
# PLOT SPECTRUM ON GUI
classification.plot_with_gui(indexToPlot=1)
#ZTF18aajnqmp
# In[19]:
output_list = []
counter = 0
for i in np.unique(SEDM_ML_sample["ZTF_Name"]):
try:
filenames = []
for j in (SEDM_ML_sample[np.where(
i == SEDM_ML_sample["ZTF_Name"])]["Version"]):
filenames.append(sample_location + str(j))
classification = astrodash.Classify(filenames,
classifyHost=False,
knownZ=False,
smooth=6,
rlapScores=True)
output = classification.list_best_matches(n=15,
saveFilename=dash + i +
'.txt')
output_name = np.append(np.asarray(output), i)
output_list.append(output_name)
if (counter % 100 == 0):
print(counter)
counter += 1
except:
print(i)
output_list = np.asarray(output_list)
# In[40]:
