def default(self, obj):
try:
if isinstance(obj, int):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return int(obj)
elif isinstance(obj, float):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return float(obj)
if isinstance(obj, numpy.integer):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return int(obj)
elif isinstance(obj, numpy.floating):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return float(obj)
elif isinstance(obj, complex):
return self.default(numpy.real(obj))
elif isinstance(obj, numpy.ndarray):
return obj.tolist()
else:
return super(NPEncoder, self).default(obj)
except:
logging.error(ExHelper.getException('NPEncoder'))
return None
def saveImage(imageData, ra, dec, scale, fileName):
try:
# Initialize WCS information, http://docs.astropy.org/en/stable/wcs/
wcs = WCS(naxis=2)
# Use the center of the image as projection center
wcs.wcs.crpix = [
imageData.shape[1] / 2. + 0.5, imageData.shape[0] / 2. + 0.5
]
# Set the coordinates of the image center
wcs.wcs.crval = [ra, dec] # Aladin RA goes from 0 to 360 (for J2000)
# Set the pixel scale (in deg/pix)
wcs.wcs.cdelt = [scale, scale]
# Set the coordinate system
wcs.wcs.ctype = ['RA---CAR',
'DEC--CAR'] # ['GLON-CAR', 'GLAT-CAR'] # ra, dec
# And produce a FITS header
header = wcs.to_header()
#Avoid transparent color in Aladin HipsGen
if consts.GEN_ALADIN_READY_FITS:
imageData[imageData < 1] = 1
# We can also just output one of the wavelengths
fits.writeto(fileName, imageData, header=header, clobber=True)
print('Saved: ' + fileName)
except:
print(ExHelper.getException('saveImage'))
def default(self, obj):
try:
if isinstance(obj, int):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return int(obj)
elif isinstance(obj, float):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return float(obj)
if isinstance(obj, numpy.integer):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return int(obj)
elif isinstance(obj, numpy.floating):
if obj > CONFIG.BIG_NUMBER:
return CONFIG.BIG_NUMBER
if obj < -CONFIG.BIG_NUMBER:
return -CONFIG.BIG_NUMBER
return float(obj)
elif isinstance(obj, complex):
return self.default(numpy.real(obj))
elif isinstance(obj, numpy.ndarray):
return obj.tolist()
else:
return super(NPEncoder, self).default(obj)
except:
logging.error(ExHelper.getException('NPEncoder'))
return None
def get(key):
try:
if contains(key):
return cached_datasets[key]
except:
logging.error(ExHelper.getException('dataset_cache.get'))
return None
def get(key):
try:
if contains(key):
return cached_datasets[key]
except:
logging.error(ExHelper.getException('dataset_cache.get'))
return None
def remove(key):
try:
if contains(key):
del cached_datasets[key]
return True
except:
logging.error(ExHelper.getException('dataset_cache.remove'))
return False
def remove(key):
try:
if contains(key):
del cached_datasets[key]
return True
except:
logging.error(ExHelper.getException('dataset_cache.remove'))
return False
def remove_with_prefix(key_prefix):
try:
remove_keys = []
for key in cached_datasets.keys():
if key.startswith(key_prefix):
remove_keys.append(key)
for key in remove_keys:
remove(key)
except:
logging.error(ExHelper.getException('dataset_cache.remove_with_prefix'))
def remove_with_prefix(key_prefix):
try:
remove_keys = []
for key in cached_datasets.keys():
if key.startswith(key_prefix):
remove_keys.append(key)
for key in remove_keys:
remove(key)
except:
logging.error(
ExHelper.getException('dataset_cache.remove_with_prefix'))
def get_intermediate_file(filepath, target):
try:
stingray_object = DaveReader.get_stingray_object(filepath)
if stingray_object:
filename = FileUtils.get_intermediate_filename(
target, filepath, HEN_FILE_EXTENSION)
if DaveReader.save_to_intermediate_file(stingray_object, filename):
return filename
except:
logging.error(ExHelper.getException('get_intermediate_file'))
return None
def get_key(value, strict=False):
try:
m = hashlib.md5()
if strict:
m.update(str(value).encode('utf-8'))
else:
m.update(str(value + str(randint(0,99999))).encode('utf-8'))
ugly_key = str(m.digest())
return "".join(e for e in ugly_key if e.isalnum())
except:
logging.error(ExHelper.getException('dataset_cache.remove_with_prefix'))
return ""
def get_destination(target, filename):
try:
if CONFIG.IS_LOCAL_SERVER:
if filename.startswith('/') and os.path.isfile(filename):
# This is supposed to be an absolute path
return filename
else:
# Relative path
return "/".join([target, filename])
else:
return "/".join([target, secure_filename(filename)])
except:
logging.error(ExHelper.getException('get_destination'))
return ""
def get_key(value, strict=False):
try:
m = hashlib.md5()
if strict:
m.update(str(value).encode('utf-8'))
else:
m.update(str(value + str(randint(0, 99999))).encode('utf-8'))
ugly_key = str(m.digest())
return "".join(e for e in ugly_key if e.isalnum())
except:
logging.error(
ExHelper.getException('dataset_cache.remove_with_prefix'))
return ""
def get_destination(target, filename):
try:
if CONFIG.IS_LOCAL_SERVER:
if filename.startswith('/') and os.path.isfile(filename):
# This is supposed to be an absolute path
return filename
else:
# Relative path
return "/".join([target, filename])
else:
return "/".join([target, secure_filename(filename)])
except:
logging.error(ExHelper.getException('get_destination'))
return ""
def add(key, dataset):
try:
cached_datasets[key] = dataset
except:
logging.error(ExHelper.getException('dataset_cache.add'))
def get_file_dataset(destination, time_offset=0):
dataset = None
cache_key = ""
try:
if destination:
cache_key = get_cache_key_for_destination(destination, time_offset)
if DsCache.contains(cache_key):
logging.debug(
"get_file_dataset: returned cached dataset, cache_key: " +
str(cache_key))
return DsCache.get(cache_key), cache_key
logging.debug("get_file_dataset: reading destination: " +
str(destination))
filename = os.path.splitext(destination)[0]
file_extension_from_file = os.path.splitext(destination)[1]
file_extension = magic.from_file(destination)
logging.debug("File extension: %s" % file_extension)
if file_extension.find("ASCII") == 0:
table_id = "EVENTS"
header_names = [CONFIG.TIME_COLUMN, "PHA", "Color1", "Color2"]
dataset = get_txt_dataset(destination, table_id, header_names)
table = dataset.tables[table_id]
table.add_columns(["AMPLITUDE"])
numValues = len(table.columns[CONFIG.TIME_COLUMN].values)
random_values = np.random.uniform(-1, 1, size=numValues)
table.columns["AMPLITUDE"].values = random_values
elif file_extension.find("FITS") == 0 \
or file_extension.find("gzip") > -1:
# Opening Fits
hdulist = fits.open(destination, memmap=True)
if get_hdu_string_from_hdulist(CONFIG.EVENTS_STRING,
hdulist) != "":
# If EVENTS extension found, consider the Fits as EVENTS Fits
dataset = get_events_fits_dataset_with_stingray(
destination,
hdulist,
dsId='FITS',
hduname=get_hdu_string_from_hdulist(
CONFIG.EVENTS_STRING, hdulist),
column=CONFIG.TIME_COLUMN,
gtistring=CONFIG.GTI_STRING,
extra_colums=['PI', "PHA"],
time_offset=time_offset)
elif 'RATE' in hdulist:
# If RATE extension found, consider the Fits as LIGHTCURVE Fits
dataset = get_lightcurve_fits_dataset_with_stingray(
destination,
hdulist,
hduname='RATE',
column=CONFIG.TIME_COLUMN,
gtistring=CONFIG.GTI_STRING,
time_offset=time_offset)
elif 'EBOUNDS' in hdulist:
# If EBOUNDS extension found, consider the Fits as RMF Fits
dataset = get_fits_dataset(hdulist, "RMF", ["EBOUNDS"])
elif get_hdu_string_from_hdulist(CONFIG.GTI_STRING,
hdulist) != "":
# If not EVENTS or RATE extension found, check if is GTI Fits
dataset = get_gti_fits_dataset_with_stingray(
hdulist,
gtistring=CONFIG.GTI_STRING,
time_offset=time_offset)
else:
logging.warn("Unsupported FITS type! Any table found: " +
CONFIG.EVENTS_STRING + ", RATE, EBOUNDS or " +
CONFIG.GTI_STRING)
elif file_extension == "data" and (file_extension_from_file
in [".p", ".nc"]):
# If file is pickle object, tries to parse it as dataset
dataset = load_dataset_from_intermediate_file(destination)
else:
logging.warn("Unknown file extension: " + str(file_extension) +
" , " + str(file_extension_from_file))
if dataset:
DsCache.add(cache_key, dataset)
logging.debug(
"get_file_dataset, dataset added to cache, cache_key: " +
str(cache_key))
else:
logging.error("get_file_dataset: Destination is empty")
except:
logging.error(ExHelper.getException('get_file_dataset'))
return dataset, cache_key
def contains(key):
try:
return key in cached_datasets
except:
logging.error(ExHelper.getException('dataset_cache.contains'))
return False
def add(key, dataset):
try:
cached_datasets[key] = dataset
except:
logging.error(ExHelper.getException('dataset_cache.add'))
def get_file_dataset(destination, time_offset=0):
dataset = None
cache_key = ""
try:
if destination:
cache_key = get_cache_key_for_destination(destination, time_offset)
if DsCache.contains(cache_key):
logging.debug("get_file_dataset: returned cached dataset, cache_key: " + str(cache_key))
return DsCache.get(cache_key), cache_key
logging.debug("get_file_dataset: reading destination: " + str(destination))
filename = os.path.splitext(destination)[0]
file_extension_from_file = os.path.splitext(destination)[1]
file_extension = magic.from_file(destination)
logging.debug("File extension: %s" % file_extension)
if file_extension.find("ASCII") == 0:
table_id = "EVENTS"
header_names = [CONFIG.TIME_COLUMN, "PHA", "Color1", "Color2"]
dataset = get_txt_dataset(destination, table_id, header_names)
table = dataset.tables[table_id]
table.add_columns(["AMPLITUDE"])
numValues = len(table.columns[CONFIG.TIME_COLUMN].values)
random_values = np.random.uniform(-1, 1, size=numValues)
table.columns["AMPLITUDE"].values = random_values
elif file_extension.find("FITS") == 0 \
or file_extension.find("gzip") > -1:
# Opening Fits
hdulist = fits.open(destination, memmap=True)
if get_hdu_string_from_hdulist(CONFIG.EVENTS_STRING, hdulist) != "":
# If EVENTS extension found, consider the Fits as EVENTS Fits
dataset = get_events_fits_dataset_with_stingray(destination, hdulist, dsId='FITS',
hduname=get_hdu_string_from_hdulist(CONFIG.EVENTS_STRING, hdulist),
column=CONFIG.TIME_COLUMN, gtistring=CONFIG.GTI_STRING,
extra_colums=['PI', "PHA"], time_offset=time_offset)
elif 'RATE' in hdulist:
# If RATE extension found, consider the Fits as LIGHTCURVE Fits
dataset = get_lightcurve_fits_dataset_with_stingray(destination, hdulist, hduname='RATE',
column=CONFIG.TIME_COLUMN, gtistring=CONFIG.GTI_STRING, time_offset=time_offset)
elif 'EBOUNDS' in hdulist:
# If EBOUNDS extension found, consider the Fits as RMF Fits
dataset = get_fits_dataset(hdulist, "RMF", ["EBOUNDS"])
elif get_hdu_string_from_hdulist(CONFIG.GTI_STRING, hdulist) != "":
# If not EVENTS or RATE extension found, check if is GTI Fits
dataset = get_gti_fits_dataset_with_stingray(hdulist,gtistring=CONFIG.GTI_STRING, time_offset=time_offset)
else:
logging.warn("Unsupported FITS type! Any table found: " + CONFIG.EVENTS_STRING + ", RATE, EBOUNDS or " + CONFIG.GTI_STRING)
elif file_extension == "data" and (file_extension_from_file in [".p", ".nc"]):
# If file is pickle object, tries to parse it as dataset
dataset = load_dataset_from_intermediate_file(destination)
else:
logging.warn("Unknown file extension: " + str(file_extension) + " , " + str(file_extension_from_file))
if dataset:
DsCache.add(cache_key, dataset)
logging.debug("get_file_dataset, dataset added to cache, cache_key: " + str(cache_key))
else:
logging.error("get_file_dataset: Destination is empty")
except:
logging.error(ExHelper.getException('get_file_dataset'))
return dataset, cache_key
def bulk_analisys(filenames, plot_configs, outdir):
try:
results = dict()
results["outdir"] = outdir
results["plot_configs"] = []
# For each plot config do a bulk analisys
for plot_config in plot_configs:
plot_config_outdir = "/".join([outdir, plot_config["id"]])
dt = plot_config["dt"]
filters = FltHelper.get_filters_clean_color_filters(
plot_config["filters"])
filters = FltHelper.apply_bin_size_to_filters(filters, dt)
if "class" in plot_config:
if plot_config["class"] == "LcPlot":
args = ['--outdir', plot_config_outdir]
args.extend(['--bintime', str(dt)])
args = add_filter_to_args(args, filters,
CONFIG.TIME_COLUMN,
'--safe-interval')
args = add_filter_to_args(args, filters, "PI",
'--pi-interval')
args = add_filter_to_args(args, filters, "E",
'--e-interval')
#args = add_filter_to_args(args, filters, "PHA", '--pha-interval')
#args = add_filter_to_args(args, filters, "RATE", '--rate-interval')
args.extend(filenames)
logging.debug("Calling MPlcurve, args: " + str(args))
MPlcurve(args)
push_plotconfig_results(results["plot_configs"],
plot_config["id"],
plot_config_outdir)
elif plot_config["class"] == "PDSPlot":
logging.error(
"PDSPlot not supported yet, still work in progress!!")
''' TO MANY QUESTIONS OPENED FOR IMPLEMENT PDS ON BULK ANALYSIS
args = ['--outdir', plot_config_outdir]
args.extend(['--kind', "PDS"])
args.extend(['--bintime', str(dt)])
# Normalization: MaltPyn supports Leahy or rms, now DAVE sends leahy, frac, abs or none
args.extend(['--norm', str(plot_config["norm"])])
# Rebin: is this
args.extend(['--rebin', str(plot_config["norm"])])
args.extend(filenames)
MPfspec(args)
push_plotconfig_results(results["plot_configs"], plot_config["id"], plot_config_outdir)
'''
else:
logging.error("PlotConfig.class not supported!!")
else:
logging.error("PlotConfig has no class key!!")
return results
except:
logging.error(ExHelper.getException('bulk_analisys'))
return None
def contains(key):
try:
return key in cached_datasets
except:
logging.error(ExHelper.getException('dataset_cache.contains'))
return False
