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fermiPy/fermipy | fermipy/utils.py | onesided_cl_to_dlnl | def onesided_cl_to_dlnl(cl):
"""Compute the delta-loglikehood values that corresponds to an
upper limit of the given confidence level.
Parameters
----------
cl : float
Confidence level.
Returns
-------
dlnl : float
Delta-loglikelihood value with respect to the maximum of the
likelihood function.
"""
alpha = 1.0 - cl
return 0.5 * np.power(np.sqrt(2.) * special.erfinv(1 - 2 * alpha), 2.) | python | def onesided_cl_to_dlnl(cl):
"""Compute the delta-loglikehood values that corresponds to an
upper limit of the given confidence level.
Parameters
----------
cl : float
Confidence level.
Returns
-------
dlnl : float
Delta-loglikelihood value with respect to the maximum of the
likelihood function.
"""
alpha = 1.0 - cl
return 0.5 * np.power(np.sqrt(2.) * special.erfinv(1 - 2 * alpha), 2.) | [
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Confidence level.
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fermiPy/fermipy | fermipy/utils.py | split_bin_edges | def split_bin_edges(edges, npts=2):
"""Subdivide an array of bins by splitting each bin into ``npts``
subintervals.
Parameters
----------
edges : `~numpy.ndarray`
Bin edge array.
npts : int
Number of intervals into which each bin will be subdivided.
Returns
-------
edges : `~numpy.ndarray`
Subdivided bin edge array.
"""
if npts < 2:
return edges
x = (edges[:-1, None] +
(edges[1:, None] - edges[:-1, None]) *
np.linspace(0.0, 1.0, npts + 1)[None, :])
return np.unique(np.ravel(x)) | python | def split_bin_edges(edges, npts=2):
"""Subdivide an array of bins by splitting each bin into ``npts``
subintervals.
Parameters
----------
edges : `~numpy.ndarray`
Bin edge array.
npts : int
Number of intervals into which each bin will be subdivided.
Returns
-------
edges : `~numpy.ndarray`
Subdivided bin edge array.
"""
if npts < 2:
return edges
x = (edges[:-1, None] +
(edges[1:, None] - edges[:-1, None]) *
np.linspace(0.0, 1.0, npts + 1)[None, :])
return np.unique(np.ravel(x)) | [
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fermiPy/fermipy | fermipy/utils.py | extend_array | def extend_array(edges, binsz, lo, hi):
"""Extend an array to encompass lo and hi values."""
numlo = int(np.ceil((edges[0] - lo) / binsz))
numhi = int(np.ceil((hi - edges[-1]) / binsz))
edges = copy.deepcopy(edges)
if numlo > 0:
edges_lo = np.linspace(edges[0] - numlo * binsz, edges[0], numlo + 1)
edges = np.concatenate((edges_lo[:-1], edges))
if numhi > 0:
edges_hi = np.linspace(edges[-1], edges[-1] + numhi * binsz, numhi + 1)
edges = np.concatenate((edges, edges_hi[1:]))
return edges | python | def extend_array(edges, binsz, lo, hi):
"""Extend an array to encompass lo and hi values."""
numlo = int(np.ceil((edges[0] - lo) / binsz))
numhi = int(np.ceil((hi - edges[-1]) / binsz))
edges = copy.deepcopy(edges)
if numlo > 0:
edges_lo = np.linspace(edges[0] - numlo * binsz, edges[0], numlo + 1)
edges = np.concatenate((edges_lo[:-1], edges))
if numhi > 0:
edges_hi = np.linspace(edges[-1], edges[-1] + numhi * binsz, numhi + 1)
edges = np.concatenate((edges, edges_hi[1:]))
return edges | [
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fermiPy/fermipy | fermipy/utils.py | fits_recarray_to_dict | def fits_recarray_to_dict(table):
"""Convert a FITS recarray to a python dictionary."""
cols = {}
for icol, col in enumerate(table.columns.names):
col_data = table.data[col]
if type(col_data[0]) == np.float32:
cols[col] = np.array(col_data, dtype=float)
elif type(col_data[0]) == np.float64:
cols[col] = np.array(col_data, dtype=float)
elif type(col_data[0]) == str:
cols[col] = np.array(col_data, dtype=str)
elif type(col_data[0]) == np.string_:
cols[col] = np.array(col_data, dtype=str)
elif type(col_data[0]) == np.int16:
cols[col] = np.array(col_data, dtype=int)
elif type(col_data[0]) == np.ndarray:
cols[col] = np.array(col_data)
else:
raise Exception(
'Unrecognized column type: %s %s' % (col, str(type(col_data))))
return cols | python | def fits_recarray_to_dict(table):
"""Convert a FITS recarray to a python dictionary."""
cols = {}
for icol, col in enumerate(table.columns.names):
col_data = table.data[col]
if type(col_data[0]) == np.float32:
cols[col] = np.array(col_data, dtype=float)
elif type(col_data[0]) == np.float64:
cols[col] = np.array(col_data, dtype=float)
elif type(col_data[0]) == str:
cols[col] = np.array(col_data, dtype=str)
elif type(col_data[0]) == np.string_:
cols[col] = np.array(col_data, dtype=str)
elif type(col_data[0]) == np.int16:
cols[col] = np.array(col_data, dtype=int)
elif type(col_data[0]) == np.ndarray:
cols[col] = np.array(col_data)
else:
raise Exception(
'Unrecognized column type: %s %s' % (col, str(type(col_data))))
return cols | [
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fermiPy/fermipy | fermipy/utils.py | merge_dict | def merge_dict(d0, d1, add_new_keys=False, append_arrays=False):
"""Recursively merge the contents of python dictionary d0 with
the contents of another python dictionary, d1.
Parameters
----------
d0 : dict
The input dictionary.
d1 : dict
Dictionary to be merged with the input dictionary.
add_new_keys : str
Do not skip keys that only exist in d1.
append_arrays : bool
If an element is a numpy array set the value of that element by
concatenating the two arrays.
"""
if d1 is None:
return d0
elif d0 is None:
return d1
elif d0 is None and d1 is None:
return {}
od = {}
for k, v in d0.items():
t0 = None
t1 = None
if k in d0:
t0 = type(d0[k])
if k in d1:
t1 = type(d1[k])
if k not in d1:
od[k] = copy.deepcopy(d0[k])
elif isinstance(v, dict) and isinstance(d1[k], dict):
od[k] = merge_dict(d0[k], d1[k], add_new_keys, append_arrays)
elif isinstance(v, list) and isstr(d1[k]):
od[k] = d1[k].split(',')
elif isinstance(v, dict) and d1[k] is None:
od[k] = copy.deepcopy(d0[k])
elif isinstance(v, np.ndarray) and append_arrays:
od[k] = np.concatenate((v, d1[k]))
elif (d0[k] is not None and d1[k] is not None) and t0 != t1:
if t0 == dict or t0 == list:
raise Exception('Conflicting types in dictionary merge for '
'key %s %s %s' % (k, t0, t1))
od[k] = t0(d1[k])
else:
od[k] = copy.copy(d1[k])
if add_new_keys:
for k, v in d1.items():
if k not in d0:
od[k] = copy.deepcopy(d1[k])
return od | python | def merge_dict(d0, d1, add_new_keys=False, append_arrays=False):
"""Recursively merge the contents of python dictionary d0 with
the contents of another python dictionary, d1.
Parameters
----------
d0 : dict
The input dictionary.
d1 : dict
Dictionary to be merged with the input dictionary.
add_new_keys : str
Do not skip keys that only exist in d1.
append_arrays : bool
If an element is a numpy array set the value of that element by
concatenating the two arrays.
"""
if d1 is None:
return d0
elif d0 is None:
return d1
elif d0 is None and d1 is None:
return {}
od = {}
for k, v in d0.items():
t0 = None
t1 = None
if k in d0:
t0 = type(d0[k])
if k in d1:
t1 = type(d1[k])
if k not in d1:
od[k] = copy.deepcopy(d0[k])
elif isinstance(v, dict) and isinstance(d1[k], dict):
od[k] = merge_dict(d0[k], d1[k], add_new_keys, append_arrays)
elif isinstance(v, list) and isstr(d1[k]):
od[k] = d1[k].split(',')
elif isinstance(v, dict) and d1[k] is None:
od[k] = copy.deepcopy(d0[k])
elif isinstance(v, np.ndarray) and append_arrays:
od[k] = np.concatenate((v, d1[k]))
elif (d0[k] is not None and d1[k] is not None) and t0 != t1:
if t0 == dict or t0 == list:
raise Exception('Conflicting types in dictionary merge for '
'key %s %s %s' % (k, t0, t1))
od[k] = t0(d1[k])
else:
od[k] = copy.copy(d1[k])
if add_new_keys:
for k, v in d1.items():
if k not in d0:
od[k] = copy.deepcopy(d1[k])
return od | [
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fermiPy/fermipy | fermipy/utils.py | tolist | def tolist(x):
""" convenience function that takes in a
nested structure of lists and dictionaries
and converts everything to its base objects.
This is useful for dupming a file to yaml.
(a) numpy arrays into python lists
>>> type(tolist(np.asarray(123))) == int
True
>>> tolist(np.asarray([1,2,3])) == [1,2,3]
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(b) numpy strings into python strings.
>>> tolist([np.asarray('cat')])==['cat']
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>>> ordered=OrderedDict(a=1, b=2)
>>> type(tolist(ordered)) == dict
True
(d) converts unicode to regular strings
>>> type(u'a') == str
False
>>> type(tolist(u'a')) == str
True
(e) converts numbers & bools in strings to real represntation,
(i.e. '123' -> 123)
>>> type(tolist(np.asarray('123'))) == int
True
>>> type(tolist('123')) == int
True
>>> tolist('False') == False
True
"""
if isinstance(x, list):
return map(tolist, x)
elif isinstance(x, dict):
return dict((tolist(k), tolist(v)) for k, v in x.items())
elif isinstance(x, np.ndarray) or isinstance(x, np.number):
# note, call tolist again to convert strings of numbers to numbers
return tolist(x.tolist())
elif isinstance(x, OrderedDict):
return dict(x)
elif isinstance(x, np.bool_):
return bool(x)
elif isstr(x) or isinstance(x, np.str):
x = str(x) # convert unicode & numpy strings
try:
return int(x)
except:
try:
return float(x)
except:
if x == 'True':
return True
elif x == 'False':
return False
else:
return x
else:
return x | python | def tolist(x):
""" convenience function that takes in a
nested structure of lists and dictionaries
and converts everything to its base objects.
This is useful for dupming a file to yaml.
(a) numpy arrays into python lists
>>> type(tolist(np.asarray(123))) == int
True
>>> tolist(np.asarray([1,2,3])) == [1,2,3]
True
(b) numpy strings into python strings.
>>> tolist([np.asarray('cat')])==['cat']
True
(c) an ordered dict to a dict
>>> ordered=OrderedDict(a=1, b=2)
>>> type(tolist(ordered)) == dict
True
(d) converts unicode to regular strings
>>> type(u'a') == str
False
>>> type(tolist(u'a')) == str
True
(e) converts numbers & bools in strings to real represntation,
(i.e. '123' -> 123)
>>> type(tolist(np.asarray('123'))) == int
True
>>> type(tolist('123')) == int
True
>>> tolist('False') == False
True
"""
if isinstance(x, list):
return map(tolist, x)
elif isinstance(x, dict):
return dict((tolist(k), tolist(v)) for k, v in x.items())
elif isinstance(x, np.ndarray) or isinstance(x, np.number):
# note, call tolist again to convert strings of numbers to numbers
return tolist(x.tolist())
elif isinstance(x, OrderedDict):
return dict(x)
elif isinstance(x, np.bool_):
return bool(x)
elif isstr(x) or isinstance(x, np.str):
x = str(x) # convert unicode & numpy strings
try:
return int(x)
except:
try:
return float(x)
except:
if x == 'True':
return True
elif x == 'False':
return False
else:
return x
else:
return x | [
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fermiPy/fermipy | fermipy/utils.py | make_gaussian_kernel | def make_gaussian_kernel(sigma, npix=501, cdelt=0.01, xpix=None, ypix=None):
"""Make kernel for a 2D gaussian.
Parameters
----------
sigma : float
Standard deviation in degrees.
"""
sigma /= cdelt
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dxy = make_pixel_distance(npix, xpix, ypix)
k = fn(dxy, sigma)
k /= (np.sum(k) * np.radians(cdelt) ** 2)
return k | python | def make_gaussian_kernel(sigma, npix=501, cdelt=0.01, xpix=None, ypix=None):
"""Make kernel for a 2D gaussian.
Parameters
----------
sigma : float
Standard deviation in degrees.
"""
sigma /= cdelt
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fermiPy/fermipy | fermipy/utils.py | make_disk_kernel | def make_disk_kernel(radius, npix=501, cdelt=0.01, xpix=None, ypix=None):
"""Make kernel for a 2D disk.
Parameters
----------
radius : float
Disk radius in deg.
"""
radius /= cdelt
def fn(t, s): return 0.5 * (np.sign(s - t) + 1.0)
dxy = make_pixel_distance(npix, xpix, ypix)
k = fn(dxy, radius)
k /= (np.sum(k) * np.radians(cdelt) ** 2)
return k | python | def make_disk_kernel(radius, npix=501, cdelt=0.01, xpix=None, ypix=None):
"""Make kernel for a 2D disk.
Parameters
----------
radius : float
Disk radius in deg.
"""
radius /= cdelt
def fn(t, s): return 0.5 * (np.sign(s - t) + 1.0)
dxy = make_pixel_distance(npix, xpix, ypix)
k = fn(dxy, radius)
k /= (np.sum(k) * np.radians(cdelt) ** 2)
return k | [
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radius : float
Disk radius in deg. | [
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fermiPy/fermipy | fermipy/utils.py | make_cdisk_kernel | def make_cdisk_kernel(psf, sigma, npix, cdelt, xpix, ypix, psf_scale_fn=None,
normalize=False):
"""Make a kernel for a PSF-convolved 2D disk.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
sigma : float
68% containment radius in degrees.
"""
sigma /= 0.8246211251235321
dtheta = psf.dtheta
egy = psf.energies
x = make_pixel_distance(npix, xpix, ypix)
x *= cdelt
k = np.zeros((len(egy), npix, npix))
for i in range(len(egy)):
def fn(t): return psf.eval(i, t, scale_fn=psf_scale_fn)
psfc = convolve2d_disk(fn, dtheta, sigma)
k[i] = np.interp(np.ravel(x), dtheta, psfc).reshape(x.shape)
if normalize:
k /= (np.sum(k, axis=0)[np.newaxis, ...] * np.radians(cdelt) ** 2)
return k | python | def make_cdisk_kernel(psf, sigma, npix, cdelt, xpix, ypix, psf_scale_fn=None,
normalize=False):
"""Make a kernel for a PSF-convolved 2D disk.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
sigma : float
68% containment radius in degrees.
"""
sigma /= 0.8246211251235321
dtheta = psf.dtheta
egy = psf.energies
x = make_pixel_distance(npix, xpix, ypix)
x *= cdelt
k = np.zeros((len(egy), npix, npix))
for i in range(len(egy)):
def fn(t): return psf.eval(i, t, scale_fn=psf_scale_fn)
psfc = convolve2d_disk(fn, dtheta, sigma)
k[i] = np.interp(np.ravel(x), dtheta, psfc).reshape(x.shape)
if normalize:
k /= (np.sum(k, axis=0)[np.newaxis, ...] * np.radians(cdelt) ** 2)
return k | [
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fermiPy/fermipy | fermipy/utils.py | make_radial_kernel | def make_radial_kernel(psf, fn, sigma, npix, cdelt, xpix, ypix, psf_scale_fn=None,
normalize=False, klims=None, sparse=False):
"""Make a kernel for a general radially symmetric 2D function.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
fn : callable
Function that evaluates the kernel at a radial coordinate r.
sigma : float
68% containment radius in degrees.
"""
if klims is None:
egy = psf.energies
else:
egy = psf.energies[klims[0]:klims[1] + 1]
ang_dist = make_pixel_distance(npix, xpix, ypix) * cdelt
max_ang_dist = np.max(ang_dist) + cdelt
#dtheta = np.linspace(0.0, (np.max(ang_dist) * 1.05)**0.5, 200)**2.0
# z = create_kernel_function_lookup(psf, fn, sigma, egy,
# dtheta, psf_scale_fn)
shape = (len(egy), npix, npix)
k = np.zeros(shape)
r99 = psf.containment_angle(energies=egy, fraction=0.997)
r34 = psf.containment_angle(energies=egy, fraction=0.34)
rmin = np.maximum(r34 / 4., 0.01)
rmax = np.maximum(r99, 0.1)
if sigma is not None:
rmin = np.maximum(rmin, 0.5 * sigma)
rmax = np.maximum(rmax, 2.0 * r34 + 3.0 * sigma)
rmax = np.minimum(rmax, max_ang_dist)
for i in range(len(egy)):
rebin = min(int(np.ceil(cdelt / rmin[i])), 8)
if sparse:
dtheta = np.linspace(0.0, rmax[i]**0.5, 100)**2.0
else:
dtheta = np.linspace(0.0, max_ang_dist**0.5, 200)**2.0
z = eval_radial_kernel(psf, fn, sigma, i, dtheta, psf_scale_fn)
xdist = make_pixel_distance(npix * rebin,
xpix * rebin + (rebin - 1.0) / 2.,
ypix * rebin + (rebin - 1.0) / 2.)
xdist *= cdelt / float(rebin)
#x = val_to_pix(dtheta, np.ravel(xdist))
if sparse:
m = np.ravel(xdist) < rmax[i]
kk = np.zeros(xdist.size)
#kk[m] = map_coordinates(z, [x[m]], order=2, prefilter=False)
kk[m] = np.interp(np.ravel(xdist)[m], dtheta, z)
kk = kk.reshape(xdist.shape)
else:
kk = np.interp(np.ravel(xdist), dtheta, z).reshape(xdist.shape)
# kk = map_coordinates(z, [x], order=2,
# prefilter=False).reshape(xdist.shape)
if rebin > 1:
kk = sum_bins(kk, 0, rebin)
kk = sum_bins(kk, 1, rebin)
k[i] = kk / float(rebin)**2
k = k.reshape((len(egy),) + ang_dist.shape)
if normalize:
k /= (np.sum(k, axis=0)[np.newaxis, ...] * np.radians(cdelt) ** 2)
return k | python | def make_radial_kernel(psf, fn, sigma, npix, cdelt, xpix, ypix, psf_scale_fn=None,
normalize=False, klims=None, sparse=False):
"""Make a kernel for a general radially symmetric 2D function.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
fn : callable
Function that evaluates the kernel at a radial coordinate r.
sigma : float
68% containment radius in degrees.
"""
if klims is None:
egy = psf.energies
else:
egy = psf.energies[klims[0]:klims[1] + 1]
ang_dist = make_pixel_distance(npix, xpix, ypix) * cdelt
max_ang_dist = np.max(ang_dist) + cdelt
#dtheta = np.linspace(0.0, (np.max(ang_dist) * 1.05)**0.5, 200)**2.0
# z = create_kernel_function_lookup(psf, fn, sigma, egy,
# dtheta, psf_scale_fn)
shape = (len(egy), npix, npix)
k = np.zeros(shape)
r99 = psf.containment_angle(energies=egy, fraction=0.997)
r34 = psf.containment_angle(energies=egy, fraction=0.34)
rmin = np.maximum(r34 / 4., 0.01)
rmax = np.maximum(r99, 0.1)
if sigma is not None:
rmin = np.maximum(rmin, 0.5 * sigma)
rmax = np.maximum(rmax, 2.0 * r34 + 3.0 * sigma)
rmax = np.minimum(rmax, max_ang_dist)
for i in range(len(egy)):
rebin = min(int(np.ceil(cdelt / rmin[i])), 8)
if sparse:
dtheta = np.linspace(0.0, rmax[i]**0.5, 100)**2.0
else:
dtheta = np.linspace(0.0, max_ang_dist**0.5, 200)**2.0
z = eval_radial_kernel(psf, fn, sigma, i, dtheta, psf_scale_fn)
xdist = make_pixel_distance(npix * rebin,
xpix * rebin + (rebin - 1.0) / 2.,
ypix * rebin + (rebin - 1.0) / 2.)
xdist *= cdelt / float(rebin)
#x = val_to_pix(dtheta, np.ravel(xdist))
if sparse:
m = np.ravel(xdist) < rmax[i]
kk = np.zeros(xdist.size)
#kk[m] = map_coordinates(z, [x[m]], order=2, prefilter=False)
kk[m] = np.interp(np.ravel(xdist)[m], dtheta, z)
kk = kk.reshape(xdist.shape)
else:
kk = np.interp(np.ravel(xdist), dtheta, z).reshape(xdist.shape)
# kk = map_coordinates(z, [x], order=2,
# prefilter=False).reshape(xdist.shape)
if rebin > 1:
kk = sum_bins(kk, 0, rebin)
kk = sum_bins(kk, 1, rebin)
k[i] = kk / float(rebin)**2
k = k.reshape((len(egy),) + ang_dist.shape)
if normalize:
k /= (np.sum(k, axis=0)[np.newaxis, ...] * np.radians(cdelt) ** 2)
return k | [
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fermiPy/fermipy | fermipy/utils.py | make_psf_kernel | def make_psf_kernel(psf, npix, cdelt, xpix, ypix, psf_scale_fn=None, normalize=False):
"""
Generate a kernel for a point-source.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
npix : int
Number of pixels in X and Y dimensions.
cdelt : float
Pixel size in degrees.
"""
egy = psf.energies
x = make_pixel_distance(npix, xpix, ypix)
x *= cdelt
k = np.zeros((len(egy), npix, npix))
for i in range(len(egy)):
k[i] = psf.eval(i, x, scale_fn=psf_scale_fn)
if normalize:
k /= (np.sum(k, axis=0)[np.newaxis, ...] * np.radians(cdelt) ** 2)
return k | python | def make_psf_kernel(psf, npix, cdelt, xpix, ypix, psf_scale_fn=None, normalize=False):
"""
Generate a kernel for a point-source.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
npix : int
Number of pixels in X and Y dimensions.
cdelt : float
Pixel size in degrees.
"""
egy = psf.energies
x = make_pixel_distance(npix, xpix, ypix)
x *= cdelt
k = np.zeros((len(egy), npix, npix))
for i in range(len(egy)):
k[i] = psf.eval(i, x, scale_fn=psf_scale_fn)
if normalize:
k /= (np.sum(k, axis=0)[np.newaxis, ...] * np.radians(cdelt) ** 2)
return k | [
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fermiPy/fermipy | fermipy/utils.py | overlap_slices | def overlap_slices(large_array_shape, small_array_shape, position):
"""
Modified version of `~astropy.nddata.utils.overlap_slices`.
Get slices for the overlapping part of a small and a large array.
Given a certain position of the center of the small array, with
respect to the large array, tuples of slices are returned which can be
used to extract, add or subtract the small array at the given
position. This function takes care of the correct behavior at the
boundaries, where the small array is cut of appropriately.
Parameters
----------
large_array_shape : tuple
Shape of the large array.
small_array_shape : tuple
Shape of the small array.
position : tuple
Position of the small array's center, with respect to the large array.
Coordinates should be in the same order as the array shape.
Returns
-------
slices_large : tuple of slices
Slices in all directions for the large array, such that
``large_array[slices_large]`` extracts the region of the large array
that overlaps with the small array.
slices_small : slice
Slices in all directions for the small array, such that
``small_array[slices_small]`` extracts the region that is inside the
large array.
"""
# Get edge coordinates
edges_min = [int(pos - small_shape // 2) for (pos, small_shape) in
zip(position, small_array_shape)]
edges_max = [int(pos + (small_shape - small_shape // 2)) for
(pos, small_shape) in
zip(position, small_array_shape)]
# Set up slices
slices_large = tuple(slice(max(0, edge_min), min(large_shape, edge_max))
for (edge_min, edge_max, large_shape) in
zip(edges_min, edges_max, large_array_shape))
slices_small = tuple(slice(max(0, -edge_min),
min(large_shape - edge_min,
edge_max - edge_min))
for (edge_min, edge_max, large_shape) in
zip(edges_min, edges_max, large_array_shape))
return slices_large, slices_small | python | def overlap_slices(large_array_shape, small_array_shape, position):
"""
Modified version of `~astropy.nddata.utils.overlap_slices`.
Get slices for the overlapping part of a small and a large array.
Given a certain position of the center of the small array, with
respect to the large array, tuples of slices are returned which can be
used to extract, add or subtract the small array at the given
position. This function takes care of the correct behavior at the
boundaries, where the small array is cut of appropriately.
Parameters
----------
large_array_shape : tuple
Shape of the large array.
small_array_shape : tuple
Shape of the small array.
position : tuple
Position of the small array's center, with respect to the large array.
Coordinates should be in the same order as the array shape.
Returns
-------
slices_large : tuple of slices
Slices in all directions for the large array, such that
``large_array[slices_large]`` extracts the region of the large array
that overlaps with the small array.
slices_small : slice
Slices in all directions for the small array, such that
``small_array[slices_small]`` extracts the region that is inside the
large array.
"""
# Get edge coordinates
edges_min = [int(pos - small_shape // 2) for (pos, small_shape) in
zip(position, small_array_shape)]
edges_max = [int(pos + (small_shape - small_shape // 2)) for
(pos, small_shape) in
zip(position, small_array_shape)]
# Set up slices
slices_large = tuple(slice(max(0, edge_min), min(large_shape, edge_max))
for (edge_min, edge_max, large_shape) in
zip(edges_min, edges_max, large_array_shape))
slices_small = tuple(slice(max(0, -edge_min),
min(large_shape - edge_min,
edge_max - edge_min))
for (edge_min, edge_max, large_shape) in
zip(edges_min, edges_max, large_array_shape))
return slices_large, slices_small | [
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Position of the small array's center, with respect to the large array.
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Slices in all directions for the large array, such that
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | make_library | def make_library(**kwargs):
"""Build and return a ModelManager object and fill the associated model library
"""
library_yaml = kwargs.pop('library', 'models/library.yaml')
comp_yaml = kwargs.pop('comp', 'config/binning.yaml')
basedir = kwargs.pop('basedir', os.path.abspath('.'))
model_man = kwargs.get('ModelManager', ModelManager(basedir=basedir))
model_comp_dict = model_man.make_library(library_yaml, library_yaml, comp_yaml)
return dict(model_comp_dict=model_comp_dict,
ModelManager=model_man) | python | def make_library(**kwargs):
"""Build and return a ModelManager object and fill the associated model library
"""
library_yaml = kwargs.pop('library', 'models/library.yaml')
comp_yaml = kwargs.pop('comp', 'config/binning.yaml')
basedir = kwargs.pop('basedir', os.path.abspath('.'))
model_man = kwargs.get('ModelManager', ModelManager(basedir=basedir))
model_comp_dict = model_man.make_library(library_yaml, library_yaml, comp_yaml)
return dict(model_comp_dict=model_comp_dict,
ModelManager=model_man) | [
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelInfo.edisp_disable_list | def edisp_disable_list(self):
""" Return the list of source for which energy dispersion should be turned off """
l = []
for model_comp in self.model_components.values():
if model_comp.edisp_disable:
l += [model_comp.info.source_name]
return l | python | def edisp_disable_list(self):
""" Return the list of source for which energy dispersion should be turned off """
l = []
for model_comp in self.model_components.values():
if model_comp.edisp_disable:
l += [model_comp.info.source_name]
return l | [
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelInfo.make_model_rois | def make_model_rois(self, components, name_factory):
""" Make the fermipy roi_model objects for each of a set of binning components """
ret_dict = {}
# Figure out which sources need to be split by components
master_roi_source_info = {}
sub_comp_sources = {}
for comp_name, model_comp in self.model_components.items():
comp_info = model_comp.info
if comp_info.components is None:
master_roi_source_info[comp_name] = model_comp
else:
sub_comp_sources[comp_name] = model_comp
# Build the xml for the master
master_roi = SourceFactory.make_roi(master_roi_source_info)
master_xml_mdl = name_factory.master_srcmdl_xml(
modelkey=self.model_name)
print("Writing master ROI model to %s" % master_xml_mdl)
master_roi.write_xml(master_xml_mdl)
ret_dict['master'] = master_roi
# Now deal with the components
for comp in components:
zcut = "zmax%i" % comp.zmax
compkey = "%s_%s" % (zcut, comp.make_key(
'{ebin_name}_{evtype_name}'))
# name_keys = dict(zcut=zcut,
# modelkey=self.model_name,
# component=compkey)
comp_roi_source_info = {}
for comp_name, model_comp in sub_comp_sources.items():
comp_info = model_comp.info
if comp_info.selection_dependent:
key = comp.make_key('{ebin_name}_{evtype_name}')
elif comp_info.moving:
key = zcut
info_clone = comp_info.components[key].clone_and_merge_sub(key)
comp_roi_source_info[comp_name] =\
ModelComponent(info=info_clone,
spectrum=model_comp.spectrum)
# Build the xml for the component
comp_roi = SourceFactory.make_roi(comp_roi_source_info)
comp_xml_mdl = name_factory.comp_srcmdl_xml(modelkey=self.model_name,
component=compkey)
print("Writing component ROI model to %s" % comp_xml_mdl)
comp_roi.write_xml(comp_xml_mdl)
ret_dict[compkey] = comp_roi
return ret_dict | python | def make_model_rois(self, components, name_factory):
""" Make the fermipy roi_model objects for each of a set of binning components """
ret_dict = {}
# Figure out which sources need to be split by components
master_roi_source_info = {}
sub_comp_sources = {}
for comp_name, model_comp in self.model_components.items():
comp_info = model_comp.info
if comp_info.components is None:
master_roi_source_info[comp_name] = model_comp
else:
sub_comp_sources[comp_name] = model_comp
# Build the xml for the master
master_roi = SourceFactory.make_roi(master_roi_source_info)
master_xml_mdl = name_factory.master_srcmdl_xml(
modelkey=self.model_name)
print("Writing master ROI model to %s" % master_xml_mdl)
master_roi.write_xml(master_xml_mdl)
ret_dict['master'] = master_roi
# Now deal with the components
for comp in components:
zcut = "zmax%i" % comp.zmax
compkey = "%s_%s" % (zcut, comp.make_key(
'{ebin_name}_{evtype_name}'))
# name_keys = dict(zcut=zcut,
# modelkey=self.model_name,
# component=compkey)
comp_roi_source_info = {}
for comp_name, model_comp in sub_comp_sources.items():
comp_info = model_comp.info
if comp_info.selection_dependent:
key = comp.make_key('{ebin_name}_{evtype_name}')
elif comp_info.moving:
key = zcut
info_clone = comp_info.components[key].clone_and_merge_sub(key)
comp_roi_source_info[comp_name] =\
ModelComponent(info=info_clone,
spectrum=model_comp.spectrum)
# Build the xml for the component
comp_roi = SourceFactory.make_roi(comp_roi_source_info)
comp_xml_mdl = name_factory.comp_srcmdl_xml(modelkey=self.model_name,
component=compkey)
print("Writing component ROI model to %s" % comp_xml_mdl)
comp_roi.write_xml(comp_xml_mdl)
ret_dict[compkey] = comp_roi
return ret_dict | [
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelManager.read_model_yaml | def read_model_yaml(self, modelkey):
""" Read the yaml file for the diffuse components
"""
model_yaml = self._name_factory.model_yaml(modelkey=modelkey,
fullpath=True)
model = yaml.safe_load(open(model_yaml))
return model | python | def read_model_yaml(self, modelkey):
""" Read the yaml file for the diffuse components
"""
model_yaml = self._name_factory.model_yaml(modelkey=modelkey,
fullpath=True)
model = yaml.safe_load(open(model_yaml))
return model | [
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelManager.make_library | def make_library(self, diffuse_yaml, catalog_yaml, binning_yaml):
""" Build up the library of all the components
Parameters
----------
diffuse_yaml : str
Name of the yaml file with the library of diffuse component definitions
catalog_yaml : str
Name of the yaml file width the library of catalog split definitions
binning_yaml : str
Name of the yaml file with the binning definitions
"""
ret_dict = {}
#catalog_dict = yaml.safe_load(open(catalog_yaml))
components_dict = Component.build_from_yamlfile(binning_yaml)
diffuse_ret_dict = make_diffuse_comp_info_dict(GalpropMapManager=self._gmm,
DiffuseModelManager=self._dmm,
library=diffuse_yaml,
components=components_dict)
catalog_ret_dict = make_catalog_comp_dict(library=catalog_yaml,
CatalogSourceManager=self._csm)
ret_dict.update(diffuse_ret_dict['comp_info_dict'])
ret_dict.update(catalog_ret_dict['comp_info_dict'])
self._library.update(ret_dict)
return ret_dict | python | def make_library(self, diffuse_yaml, catalog_yaml, binning_yaml):
""" Build up the library of all the components
Parameters
----------
diffuse_yaml : str
Name of the yaml file with the library of diffuse component definitions
catalog_yaml : str
Name of the yaml file width the library of catalog split definitions
binning_yaml : str
Name of the yaml file with the binning definitions
"""
ret_dict = {}
#catalog_dict = yaml.safe_load(open(catalog_yaml))
components_dict = Component.build_from_yamlfile(binning_yaml)
diffuse_ret_dict = make_diffuse_comp_info_dict(GalpropMapManager=self._gmm,
DiffuseModelManager=self._dmm,
library=diffuse_yaml,
components=components_dict)
catalog_ret_dict = make_catalog_comp_dict(library=catalog_yaml,
CatalogSourceManager=self._csm)
ret_dict.update(diffuse_ret_dict['comp_info_dict'])
ret_dict.update(catalog_ret_dict['comp_info_dict'])
self._library.update(ret_dict)
return ret_dict | [
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelManager.make_model_info | def make_model_info(self, modelkey):
""" Build a dictionary with the information for a particular model.
Parameters
----------
modelkey : str
Key used to identify this particular model
Return `ModelInfo`
"""
model = self.read_model_yaml(modelkey)
sources = model['sources']
components = OrderedDict()
spec_model_yaml = self._name_factory.fullpath(localpath=model['spectral_models'])
self._spec_lib.update(yaml.safe_load(open(spec_model_yaml)))
for source, source_info in sources.items():
model_type = source_info.get('model_type', None)
par_overrides = source_info.get('par_overides', None)
version = source_info['version']
spec_type = source_info['SpectrumType']
edisp_disable = source_info.get('edisp_disable', False)
sourcekey = "%s_%s" % (source, version)
if model_type == 'galprop_rings':
comp_info_dict = self.gmm.diffuse_comp_info_dicts(version)
def_spec_type = spec_type['default']
for comp_key, comp_info in comp_info_dict.items():
model_comp = ModelComponent(info=comp_info,
spectrum=\
self._spec_lib[spec_type.get(comp_key,
def_spec_type)],
par_overrides=par_overrides,
edisp_disable=edisp_disable)
components[comp_key] = model_comp
elif model_type == 'Catalog':
comp_info_dict = self.csm.split_comp_info_dict(source, version)
def_spec_type = spec_type['default']
for comp_key, comp_info in comp_info_dict.items():
model_comp = ModelComponent(info=comp_info,
spectrum=\
self._spec_lib[spec_type.get(comp_key,
def_spec_type)],
par_overrides=par_overrides,
edisp_disable=edisp_disable)
components[comp_key] = model_comp
else:
comp_info = self.dmm.diffuse_comp_info(sourcekey)
model_comp = ModelComponent(info=comp_info,
spectrum=self._spec_lib[spec_type],
par_overrides=par_overrides,
edisp_disable=edisp_disable)
components[sourcekey] = model_comp
ret_val = ModelInfo(model_name=modelkey,
model_components=components)
self._models[modelkey] = ret_val
return ret_val | python | def make_model_info(self, modelkey):
""" Build a dictionary with the information for a particular model.
Parameters
----------
modelkey : str
Key used to identify this particular model
Return `ModelInfo`
"""
model = self.read_model_yaml(modelkey)
sources = model['sources']
components = OrderedDict()
spec_model_yaml = self._name_factory.fullpath(localpath=model['spectral_models'])
self._spec_lib.update(yaml.safe_load(open(spec_model_yaml)))
for source, source_info in sources.items():
model_type = source_info.get('model_type', None)
par_overrides = source_info.get('par_overides', None)
version = source_info['version']
spec_type = source_info['SpectrumType']
edisp_disable = source_info.get('edisp_disable', False)
sourcekey = "%s_%s" % (source, version)
if model_type == 'galprop_rings':
comp_info_dict = self.gmm.diffuse_comp_info_dicts(version)
def_spec_type = spec_type['default']
for comp_key, comp_info in comp_info_dict.items():
model_comp = ModelComponent(info=comp_info,
spectrum=\
self._spec_lib[spec_type.get(comp_key,
def_spec_type)],
par_overrides=par_overrides,
edisp_disable=edisp_disable)
components[comp_key] = model_comp
elif model_type == 'Catalog':
comp_info_dict = self.csm.split_comp_info_dict(source, version)
def_spec_type = spec_type['default']
for comp_key, comp_info in comp_info_dict.items():
model_comp = ModelComponent(info=comp_info,
spectrum=\
self._spec_lib[spec_type.get(comp_key,
def_spec_type)],
par_overrides=par_overrides,
edisp_disable=edisp_disable)
components[comp_key] = model_comp
else:
comp_info = self.dmm.diffuse_comp_info(sourcekey)
model_comp = ModelComponent(info=comp_info,
spectrum=self._spec_lib[spec_type],
par_overrides=par_overrides,
edisp_disable=edisp_disable)
components[sourcekey] = model_comp
ret_val = ModelInfo(model_name=modelkey,
model_components=components)
self._models[modelkey] = ret_val
return ret_val | [
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelManager.get_sub_comp_info | def get_sub_comp_info(source_info, comp):
"""Build and return information about a sub-component for a particular selection
"""
sub_comps = source_info.get('components', None)
if sub_comps is None:
return source_info.copy()
moving = source_info.get('moving', False)
selection_dependent = source_info.get('selection_dependent', False)
if selection_dependent:
key = comp.make_key('{ebin_name}_{evtype_name}')
elif moving:
key = "zmax%i" % comp.zmax
ret_dict = source_info.copy()
ret_dict.update(sub_comps[key])
return ret_dict | python | def get_sub_comp_info(source_info, comp):
"""Build and return information about a sub-component for a particular selection
"""
sub_comps = source_info.get('components', None)
if sub_comps is None:
return source_info.copy()
moving = source_info.get('moving', False)
selection_dependent = source_info.get('selection_dependent', False)
if selection_dependent:
key = comp.make_key('{ebin_name}_{evtype_name}')
elif moving:
key = "zmax%i" % comp.zmax
ret_dict = source_info.copy()
ret_dict.update(sub_comps[key])
return ret_dict | [
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fermiPy/fermipy | fermipy/validate/utils.py | replace_aliases | def replace_aliases(cut_dict, aliases):
"""Substitute aliases in a cut dictionary."""
for k, v in cut_dict.items():
for k0, v0 in aliases.items():
cut_dict[k] = cut_dict[k].replace(k0, '(%s)' % v0) | python | def replace_aliases(cut_dict, aliases):
"""Substitute aliases in a cut dictionary."""
for k, v in cut_dict.items():
for k0, v0 in aliases.items():
cut_dict[k] = cut_dict[k].replace(k0, '(%s)' % v0) | [
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fermiPy/fermipy | fermipy/validate/utils.py | get_files | def get_files(files, extnames=['.root']):
"""Extract a list of file paths from a list containing both paths
and file lists with one path per line."""
files_out = []
for f in files:
mime = mimetypes.guess_type(f)
if os.path.splitext(f)[1] in extnames:
files_out += [f]
elif mime[0] == 'text/plain':
files_out += list(np.loadtxt(f, unpack=True, dtype='str'))
else:
raise Exception('Unrecognized input type.')
return files_out | python | def get_files(files, extnames=['.root']):
"""Extract a list of file paths from a list containing both paths
and file lists with one path per line."""
files_out = []
for f in files:
mime = mimetypes.guess_type(f)
if os.path.splitext(f)[1] in extnames:
files_out += [f]
elif mime[0] == 'text/plain':
files_out += list(np.loadtxt(f, unpack=True, dtype='str'))
else:
raise Exception('Unrecognized input type.')
return files_out | [
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fermiPy/fermipy | fermipy/validate/utils.py | get_cuts_from_xml | def get_cuts_from_xml(xmlfile):
"""Extract event selection strings from the XML file."""
root = ElementTree.ElementTree(file=xmlfile).getroot()
event_maps = root.findall('EventMap')
alias_maps = root.findall('AliasDict')[0]
event_classes = {}
event_types = {}
event_aliases = {}
for m in event_maps:
if m.attrib['altName'] == 'EVENT_CLASS':
for c in m.findall('EventCategory'):
event_classes[c.attrib['name']] = strip(
c.find('ShortCut').text)
elif m.attrib['altName'] == 'EVENT_TYPE':
for c in m.findall('EventCategory'):
event_types[c.attrib['name']] = strip(c.find('ShortCut').text)
for m in alias_maps.findall('Alias'):
event_aliases[m.attrib['name']] = strip(m.text)
replace_aliases(event_aliases, event_aliases.copy())
replace_aliases(event_aliases, event_aliases.copy())
replace_aliases(event_classes, event_aliases)
replace_aliases(event_types, event_aliases)
event_selections = {}
event_selections.update(event_classes)
event_selections.update(event_types)
event_selections.update(event_aliases)
return event_selections | python | def get_cuts_from_xml(xmlfile):
"""Extract event selection strings from the XML file."""
root = ElementTree.ElementTree(file=xmlfile).getroot()
event_maps = root.findall('EventMap')
alias_maps = root.findall('AliasDict')[0]
event_classes = {}
event_types = {}
event_aliases = {}
for m in event_maps:
if m.attrib['altName'] == 'EVENT_CLASS':
for c in m.findall('EventCategory'):
event_classes[c.attrib['name']] = strip(
c.find('ShortCut').text)
elif m.attrib['altName'] == 'EVENT_TYPE':
for c in m.findall('EventCategory'):
event_types[c.attrib['name']] = strip(c.find('ShortCut').text)
for m in alias_maps.findall('Alias'):
event_aliases[m.attrib['name']] = strip(m.text)
replace_aliases(event_aliases, event_aliases.copy())
replace_aliases(event_aliases, event_aliases.copy())
replace_aliases(event_classes, event_aliases)
replace_aliases(event_types, event_aliases)
event_selections = {}
event_selections.update(event_classes)
event_selections.update(event_types)
event_selections.update(event_aliases)
return event_selections | [
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fermiPy/fermipy | fermipy/validate/utils.py | set_event_list | def set_event_list(tree, selection=None, fraction=None, start_fraction=None):
"""
Set the event list for a tree or chain.
Parameters
----------
tree : `ROOT.TTree`
Input tree/chain.
selection : str
Cut string defining the event list.
fraction : float
Fraction of the total file to include in the event list
starting from the *end* of the file.
"""
import ROOT
elist = rand_str()
if selection is None:
cuts = ''
else:
cuts = selection
if fraction is None or fraction >= 1.0:
n = tree.Draw(">>%s" % elist, cuts, "goff")
tree.SetEventList(ROOT.gDirectory.Get(elist))
elif start_fraction is None:
nentries = int(tree.GetEntries())
first_entry = min(int((1.0 - fraction) * nentries), nentries)
n = tree.Draw(">>%s" % elist, cuts, "goff", nentries, first_entry)
tree.SetEventList(ROOT.gDirectory.Get(elist))
else:
nentries = int(tree.GetEntries())
first_entry = min(int(start_fraction * nentries), nentries)
n = first_entry + int(nentries * fraction)
n = tree.Draw(">>%s" % elist, cuts, "goff",
n - first_entry, first_entry)
tree.SetEventList(ROOT.gDirectory.Get(elist))
return n | python | def set_event_list(tree, selection=None, fraction=None, start_fraction=None):
"""
Set the event list for a tree or chain.
Parameters
----------
tree : `ROOT.TTree`
Input tree/chain.
selection : str
Cut string defining the event list.
fraction : float
Fraction of the total file to include in the event list
starting from the *end* of the file.
"""
import ROOT
elist = rand_str()
if selection is None:
cuts = ''
else:
cuts = selection
if fraction is None or fraction >= 1.0:
n = tree.Draw(">>%s" % elist, cuts, "goff")
tree.SetEventList(ROOT.gDirectory.Get(elist))
elif start_fraction is None:
nentries = int(tree.GetEntries())
first_entry = min(int((1.0 - fraction) * nentries), nentries)
n = tree.Draw(">>%s" % elist, cuts, "goff", nentries, first_entry)
tree.SetEventList(ROOT.gDirectory.Get(elist))
else:
nentries = int(tree.GetEntries())
first_entry = min(int(start_fraction * nentries), nentries)
n = first_entry + int(nentries * fraction)
n = tree.Draw(">>%s" % elist, cuts, "goff",
n - first_entry, first_entry)
tree.SetEventList(ROOT.gDirectory.Get(elist))
return n | [
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fermiPy/fermipy | fermipy/sourcefind.py | SourceFind.localize | def localize(self, name, **kwargs):
"""Find the best-fit position of a source. Localization is
performed in two steps. First a TS map is computed centered
on the source with half-width set by ``dtheta_max``. A fit is
then performed to the maximum TS peak in this map. The source
position is then further refined by scanning the likelihood in
the vicinity of the peak found in the first step. The size of
the scan region is set to encompass the 99% positional
uncertainty contour as determined from the peak fit.
Parameters
----------
name : str
Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
analysis.
"""
timer = Timer.create(start=True)
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['localize'],
optimizer=self.defaults['optimizer'])
schema.add_option('use_cache', True)
schema.add_option('prefix', '')
config = utils.create_dict(self.config['localize'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Running localization for %s' % name)
free_state = FreeParameterState(self)
loc = self._localize(name, **config)
free_state.restore()
self.logger.info('Finished localization.')
if config['make_plots']:
self._plotter.make_localization_plots(loc, self.roi,
prefix=config['prefix'])
outfile = \
utils.format_filename(self.workdir, 'loc',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
if config['write_fits']:
loc['file'] = os.path.basename(outfile) + '.fits'
self._make_localize_fits(loc, outfile + '.fits',
**config)
if config['write_npy']:
np.save(outfile + '.npy', dict(loc))
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return loc | python | def localize(self, name, **kwargs):
"""Find the best-fit position of a source. Localization is
performed in two steps. First a TS map is computed centered
on the source with half-width set by ``dtheta_max``. A fit is
then performed to the maximum TS peak in this map. The source
position is then further refined by scanning the likelihood in
the vicinity of the peak found in the first step. The size of
the scan region is set to encompass the 99% positional
uncertainty contour as determined from the peak fit.
Parameters
----------
name : str
Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
analysis.
"""
timer = Timer.create(start=True)
name = self.roi.get_source_by_name(name).name
schema = ConfigSchema(self.defaults['localize'],
optimizer=self.defaults['optimizer'])
schema.add_option('use_cache', True)
schema.add_option('prefix', '')
config = utils.create_dict(self.config['localize'],
optimizer=self.config['optimizer'])
config = schema.create_config(config, **kwargs)
self.logger.info('Running localization for %s' % name)
free_state = FreeParameterState(self)
loc = self._localize(name, **config)
free_state.restore()
self.logger.info('Finished localization.')
if config['make_plots']:
self._plotter.make_localization_plots(loc, self.roi,
prefix=config['prefix'])
outfile = \
utils.format_filename(self.workdir, 'loc',
prefix=[config['prefix'],
name.lower().replace(' ', '_')])
if config['write_fits']:
loc['file'] = os.path.basename(outfile) + '.fits'
self._make_localize_fits(loc, outfile + '.fits',
**config)
if config['write_npy']:
np.save(outfile + '.npy', dict(loc))
self.logger.info('Execution time: %.2f s', timer.elapsed_time)
return loc | [
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then performed to the maximum TS peak in this map. The source
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Parameters
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Source name.
{options}
optimizer : dict
Dictionary that overrides the default optimizer settings.
Returns
-------
localize : dict
Dictionary containing results of the localization
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fermiPy/fermipy | fermipy/sourcefind.py | SourceFind._fit_position_tsmap | def _fit_position_tsmap(self, name, **kwargs):
"""Localize a source from its TS map."""
prefix = kwargs.get('prefix', '')
dtheta_max = kwargs.get('dtheta_max', 0.5)
zmin = kwargs.get('zmin', -3.0)
kw = {
'map_size': 2.0 * dtheta_max,
'write_fits': kwargs.get('write_fits', False),
'write_npy': kwargs.get('write_npy', False),
'use_pylike': kwargs.get('use_pylike', True),
'max_kernel_radius': self.config['tsmap']['max_kernel_radius'],
'loglevel': logging.DEBUG
}
src = self.roi.copy_source(name)
if src['SpatialModel'] in ['RadialDisk', 'RadialGaussian']:
kw['max_kernel_radius'] = max(kw['max_kernel_radius'],
2.0 * src['SpatialWidth'])
skydir = kwargs.get('skydir', src.skydir)
tsmap = self.tsmap(utils.join_strings([prefix, name.lower().
replace(' ', '_')]),
model=src.data,
map_skydir=skydir,
exclude=[name],
make_plots=False, **kw)
# Find peaks with TS > 4
peaks = find_peaks(tsmap['ts'], 4.0, 0.2)
peak_best = None
o = {}
for p in sorted(peaks, key=lambda t: t['amp'], reverse=True):
xy = p['ix'], p['iy']
ts_value = tsmap['ts'].data[xy[1], xy[0]]
posfit = fit_error_ellipse(tsmap['ts'], xy=xy, dpix=2,
zmin=max(zmin, -ts_value * 0.5))
offset = posfit['skydir'].separation(self.roi[name].skydir).deg
if posfit['fit_success'] and posfit['fit_inbounds']:
peak_best = p
break
if peak_best is None:
ts_value = np.max(tsmap['ts'].data)
posfit = fit_error_ellipse(tsmap['ts'], dpix=2,
zmin=max(zmin, -ts_value * 0.5))
o.update(posfit)
pix = posfit['skydir'].to_pixel(self.geom.wcs)
o['xpix'] = float(pix[0])
o['ypix'] = float(pix[1])
o['skydir'] = posfit['skydir'].transform_to('icrs')
o['pos_offset'] = posfit['skydir'].separation(
self.roi[name].skydir).deg
o['loglike'] = 0.5 * posfit['zoffset']
o['tsmap'] = tsmap['ts']
return o | python | def _fit_position_tsmap(self, name, **kwargs):
"""Localize a source from its TS map."""
prefix = kwargs.get('prefix', '')
dtheta_max = kwargs.get('dtheta_max', 0.5)
zmin = kwargs.get('zmin', -3.0)
kw = {
'map_size': 2.0 * dtheta_max,
'write_fits': kwargs.get('write_fits', False),
'write_npy': kwargs.get('write_npy', False),
'use_pylike': kwargs.get('use_pylike', True),
'max_kernel_radius': self.config['tsmap']['max_kernel_radius'],
'loglevel': logging.DEBUG
}
src = self.roi.copy_source(name)
if src['SpatialModel'] in ['RadialDisk', 'RadialGaussian']:
kw['max_kernel_radius'] = max(kw['max_kernel_radius'],
2.0 * src['SpatialWidth'])
skydir = kwargs.get('skydir', src.skydir)
tsmap = self.tsmap(utils.join_strings([prefix, name.lower().
replace(' ', '_')]),
model=src.data,
map_skydir=skydir,
exclude=[name],
make_plots=False, **kw)
# Find peaks with TS > 4
peaks = find_peaks(tsmap['ts'], 4.0, 0.2)
peak_best = None
o = {}
for p in sorted(peaks, key=lambda t: t['amp'], reverse=True):
xy = p['ix'], p['iy']
ts_value = tsmap['ts'].data[xy[1], xy[0]]
posfit = fit_error_ellipse(tsmap['ts'], xy=xy, dpix=2,
zmin=max(zmin, -ts_value * 0.5))
offset = posfit['skydir'].separation(self.roi[name].skydir).deg
if posfit['fit_success'] and posfit['fit_inbounds']:
peak_best = p
break
if peak_best is None:
ts_value = np.max(tsmap['ts'].data)
posfit = fit_error_ellipse(tsmap['ts'], dpix=2,
zmin=max(zmin, -ts_value * 0.5))
o.update(posfit)
pix = posfit['skydir'].to_pixel(self.geom.wcs)
o['xpix'] = float(pix[0])
o['ypix'] = float(pix[1])
o['skydir'] = posfit['skydir'].transform_to('icrs')
o['pos_offset'] = posfit['skydir'].separation(
self.roi[name].skydir).deg
o['loglike'] = 0.5 * posfit['zoffset']
o['tsmap'] = tsmap['ts']
return o | [
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | get_lsf_status | def get_lsf_status():
"""Count and print the number of jobs in various LSF states
"""
status_count = {'RUN': 0,
'PEND': 0,
'SUSP': 0,
'USUSP': 0,
'NJOB': 0,
'UNKNWN': 0}
try:
subproc = subprocess.Popen(['bjobs'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
subproc.stderr.close()
output = subproc.stdout.readlines()
except OSError:
return status_count
for line in output[1:]:
line = line.strip().split()
# Protect against format of multiproc jobs
if len(line) < 5:
continue
status_count['NJOB'] += 1
for k in status_count:
if line[2] == k:
status_count[k] += 1
return status_count | python | def get_lsf_status():
"""Count and print the number of jobs in various LSF states
"""
status_count = {'RUN': 0,
'PEND': 0,
'SUSP': 0,
'USUSP': 0,
'NJOB': 0,
'UNKNWN': 0}
try:
subproc = subprocess.Popen(['bjobs'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
subproc.stderr.close()
output = subproc.stdout.readlines()
except OSError:
return status_count
for line in output[1:]:
line = line.strip().split()
# Protect against format of multiproc jobs
if len(line) < 5:
continue
status_count['NJOB'] += 1
for k in status_count:
if line[2] == k:
status_count[k] += 1
return status_count | [
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | build_bsub_command | def build_bsub_command(command_template, lsf_args):
"""Build and return a lsf batch command template
The structure will be 'bsub -s <key> <value> <command_template>'
where <key> and <value> refer to items in lsf_args
"""
if command_template is None:
return ""
full_command = 'bsub -o {logfile}'
for key, value in lsf_args.items():
full_command += ' -%s' % key
if value is not None:
full_command += ' %s' % value
full_command += ' %s' % command_template
return full_command | python | def build_bsub_command(command_template, lsf_args):
"""Build and return a lsf batch command template
The structure will be 'bsub -s <key> <value> <command_template>'
where <key> and <value> refer to items in lsf_args
"""
if command_template is None:
return ""
full_command = 'bsub -o {logfile}'
for key, value in lsf_args.items():
full_command += ' -%s' % key
if value is not None:
full_command += ' %s' % value
full_command += ' %s' % command_template
return full_command | [
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | SlacInterface.dispatch_job_hook | def dispatch_job_hook(self, link, key, job_config, logfile, stream=sys.stdout):
"""Send a single job to the LSF batch
Parameters
----------
link : `fermipy.jobs.chain.Link`
The link used to invoke the command we are running
key : str
A string that identifies this particular instance of the job
job_config : dict
A dictionrary with the arguments for the job. Used with
the self._command_template job template
logfile : str
The logfile for this job, may be used to check for success/ failure
"""
full_sub_dict = job_config.copy()
if self._no_batch:
full_command = "%s >& %s" % (
link.command_template().format(**full_sub_dict), logfile)
else:
full_sub_dict['logfile'] = logfile
full_command_template = build_bsub_command(
link.command_template(), self._lsf_args)
full_command = full_command_template.format(**full_sub_dict)
logdir = os.path.dirname(logfile)
print_bsub = True
if self._dry_run:
if print_bsub:
stream.write("%s\n" % full_command)
return 0
try:
os.makedirs(logdir)
except OSError:
pass
proc = subprocess.Popen(full_command.split(),
stderr=stream,
stdout=stream)
proc.communicate()
return proc.returncode | python | def dispatch_job_hook(self, link, key, job_config, logfile, stream=sys.stdout):
"""Send a single job to the LSF batch
Parameters
----------
link : `fermipy.jobs.chain.Link`
The link used to invoke the command we are running
key : str
A string that identifies this particular instance of the job
job_config : dict
A dictionrary with the arguments for the job. Used with
the self._command_template job template
logfile : str
The logfile for this job, may be used to check for success/ failure
"""
full_sub_dict = job_config.copy()
if self._no_batch:
full_command = "%s >& %s" % (
link.command_template().format(**full_sub_dict), logfile)
else:
full_sub_dict['logfile'] = logfile
full_command_template = build_bsub_command(
link.command_template(), self._lsf_args)
full_command = full_command_template.format(**full_sub_dict)
logdir = os.path.dirname(logfile)
print_bsub = True
if self._dry_run:
if print_bsub:
stream.write("%s\n" % full_command)
return 0
try:
os.makedirs(logdir)
except OSError:
pass
proc = subprocess.Popen(full_command.split(),
stderr=stream,
stdout=stream)
proc.communicate()
return proc.returncode | [
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | SlacInterface.submit_jobs | def submit_jobs(self, link, job_dict=None, job_archive=None, stream=sys.stdout):
"""Submit all the jobs in job_dict """
if link is None:
return JobStatus.no_job
if job_dict is None:
job_keys = link.jobs.keys()
else:
job_keys = sorted(job_dict.keys())
# copy & reverse the keys b/c we will be popping item off the back of
# the list
unsubmitted_jobs = job_keys
unsubmitted_jobs.reverse()
failed = False
if unsubmitted_jobs:
if stream != sys.stdout:
sys.stdout.write('Submitting jobs (%i): ' %
len(unsubmitted_jobs))
sys.stdout.flush()
while unsubmitted_jobs:
status = get_lsf_status()
njob_to_submit = min(self._max_jobs - status['NJOB'],
self._jobs_per_cycle,
len(unsubmitted_jobs))
if self._dry_run:
njob_to_submit = len(unsubmitted_jobs)
for i in range(njob_to_submit):
job_key = unsubmitted_jobs.pop()
# job_details = job_dict[job_key]
job_details = link.jobs[job_key]
job_config = job_details.job_config
if job_details.status == JobStatus.failed:
clean_job(job_details.logfile, {}, self._dry_run)
# clean_job(job_details.logfile,
# job_details.outfiles, self.args['dry_run'])
job_config['logfile'] = job_details.logfile
new_job_details = self.dispatch_job(
link, job_key, job_archive, stream)
if new_job_details.status == JobStatus.failed:
failed = True
clean_job(new_job_details.logfile,
new_job_details.outfiles, self._dry_run)
link.jobs[job_key] = new_job_details
if unsubmitted_jobs:
if stream != sys.stdout:
sys.stdout.write('.')
sys.stdout.flush()
stream.write('Sleeping %.0f seconds between submission cycles\n' %
self._time_per_cycle)
time.sleep(self._time_per_cycle)
if failed:
return JobStatus.failed
if stream != sys.stdout:
sys.stdout.write('!\n')
return JobStatus.done | python | def submit_jobs(self, link, job_dict=None, job_archive=None, stream=sys.stdout):
"""Submit all the jobs in job_dict """
if link is None:
return JobStatus.no_job
if job_dict is None:
job_keys = link.jobs.keys()
else:
job_keys = sorted(job_dict.keys())
# copy & reverse the keys b/c we will be popping item off the back of
# the list
unsubmitted_jobs = job_keys
unsubmitted_jobs.reverse()
failed = False
if unsubmitted_jobs:
if stream != sys.stdout:
sys.stdout.write('Submitting jobs (%i): ' %
len(unsubmitted_jobs))
sys.stdout.flush()
while unsubmitted_jobs:
status = get_lsf_status()
njob_to_submit = min(self._max_jobs - status['NJOB'],
self._jobs_per_cycle,
len(unsubmitted_jobs))
if self._dry_run:
njob_to_submit = len(unsubmitted_jobs)
for i in range(njob_to_submit):
job_key = unsubmitted_jobs.pop()
# job_details = job_dict[job_key]
job_details = link.jobs[job_key]
job_config = job_details.job_config
if job_details.status == JobStatus.failed:
clean_job(job_details.logfile, {}, self._dry_run)
# clean_job(job_details.logfile,
# job_details.outfiles, self.args['dry_run'])
job_config['logfile'] = job_details.logfile
new_job_details = self.dispatch_job(
link, job_key, job_archive, stream)
if new_job_details.status == JobStatus.failed:
failed = True
clean_job(new_job_details.logfile,
new_job_details.outfiles, self._dry_run)
link.jobs[job_key] = new_job_details
if unsubmitted_jobs:
if stream != sys.stdout:
sys.stdout.write('.')
sys.stdout.flush()
stream.write('Sleeping %.0f seconds between submission cycles\n' %
self._time_per_cycle)
time.sleep(self._time_per_cycle)
if failed:
return JobStatus.failed
if stream != sys.stdout:
sys.stdout.write('!\n')
return JobStatus.done | [
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fermiPy/fermipy | fermipy/gtanalysis.py | create_sc_table | def create_sc_table(scfile, colnames=None):
"""Load an FT2 file from a file or list of files."""
if utils.is_fits_file(scfile) and colnames is None:
return create_table_from_fits(scfile, 'SC_DATA')
if utils.is_fits_file(scfile):
files = [scfile]
else:
files = [line.strip() for line in open(scfile, 'r')]
tables = [create_table_from_fits(f, 'SC_DATA', colnames)
for f in files]
return vstack(tables) | python | def create_sc_table(scfile, colnames=None):
"""Load an FT2 file from a file or list of files."""
if utils.is_fits_file(scfile) and colnames is None:
return create_table_from_fits(scfile, 'SC_DATA')
if utils.is_fits_file(scfile):
files = [scfile]
else:
files = [line.strip() for line in open(scfile, 'r')]
tables = [create_table_from_fits(f, 'SC_DATA', colnames)
for f in files]
return vstack(tables) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | create_table_from_fits | def create_table_from_fits(fitsfile, hduname, colnames=None):
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file."""
if colnames is None:
return Table.read(fitsfile, hduname)
cols = []
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data = h[hduname].data.field(k)
cols += [Column(name=k, data=data)]
return Table(cols) | python | def create_table_from_fits(fitsfile, hduname, colnames=None):
"""Memory efficient function for loading a table from a FITS
file."""
if colnames is None:
return Table.read(fitsfile, hduname)
cols = []
with fits.open(fitsfile, memmap=True) as h:
for k in colnames:
data = h[hduname].data.field(k)
cols += [Column(name=k, data=data)]
return Table(cols) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | get_spectral_index | def get_spectral_index(src, egy):
"""Compute the local spectral index of a source."""
delta = 1E-5
f0 = src.spectrum()(pyLike.dArg(egy * (1 - delta)))
f1 = src.spectrum()(pyLike.dArg(egy * (1 + delta)))
if f0 > 0 and f1 > 0:
gamma = np.log10(f0 / f1) / np.log10((1 - delta) / (1 + delta))
else:
gamma = np.nan
return gamma | python | def get_spectral_index(src, egy):
"""Compute the local spectral index of a source."""
delta = 1E-5
f0 = src.spectrum()(pyLike.dArg(egy * (1 - delta)))
f1 = src.spectrum()(pyLike.dArg(egy * (1 + delta)))
if f0 > 0 and f1 > 0:
gamma = np.log10(f0 / f1) / np.log10((1 - delta) / (1 + delta))
else:
gamma = np.nan
return gamma | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.create | def create(cls, infile, config=None, params=None, mask=None):
"""Create a new instance of GTAnalysis from an analysis output file
generated with `~fermipy.GTAnalysis.write_roi`. By default
the new instance will inherit the configuration of the saved
analysis instance. The configuration may be overriden by
passing a configuration file path with the ``config``
argument.
Parameters
----------
infile : str
Path to the ROI results file.
config : str
Path to a configuration file. This will override the
configuration in the ROI results file.
params : str
Path to a yaml file with updated parameter values
mask : str
Path to a fits file with an updated mask
"""
infile = os.path.abspath(infile)
roi_file, roi_data = utils.load_data(infile)
if config is None:
config = roi_data['config']
validate = False
else:
validate = True
gta = cls(config, validate=validate)
gta.setup(init_sources=False)
gta.load_roi(infile, params=params, mask=mask)
return gta | python | def create(cls, infile, config=None, params=None, mask=None):
"""Create a new instance of GTAnalysis from an analysis output file
generated with `~fermipy.GTAnalysis.write_roi`. By default
the new instance will inherit the configuration of the saved
analysis instance. The configuration may be overriden by
passing a configuration file path with the ``config``
argument.
Parameters
----------
infile : str
Path to the ROI results file.
config : str
Path to a configuration file. This will override the
configuration in the ROI results file.
params : str
Path to a yaml file with updated parameter values
mask : str
Path to a fits file with an updated mask
"""
infile = os.path.abspath(infile)
roi_file, roi_data = utils.load_data(infile)
if config is None:
config = roi_data['config']
validate = False
else:
validate = True
gta = cls(config, validate=validate)
gta.setup(init_sources=False)
gta.load_roi(infile, params=params, mask=mask)
return gta | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.clone | def clone(self, config, **kwargs):
"""Make a clone of this analysis instance."""
gta = GTAnalysis(config, **kwargs)
gta._roi = copy.deepcopy(self.roi)
return gta | python | def clone(self, config, **kwargs):
"""Make a clone of this analysis instance."""
gta = GTAnalysis(config, **kwargs)
gta._roi = copy.deepcopy(self.roi)
return gta | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_random_seed | def set_random_seed(self, seed):
"""Set the seed for the random number generator"""
self.config['mc']['seed'] = seed
np.random.seed(seed) | python | def set_random_seed(self, seed):
"""Set the seed for the random number generator"""
self.config['mc']['seed'] = seed
np.random.seed(seed) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.reload_source | def reload_source(self, name, init_source=True):
"""Delete and reload a source in the model. This will update
the spatial model of this source to the one defined in the XML
model."""
for c in self.components:
c.reload_source(name)
if init_source:
self._init_source(name)
self.like.model = self.like.components[0].model | python | def reload_source(self, name, init_source=True):
"""Delete and reload a source in the model. This will update
the spatial model of this source to the one defined in the XML
model."""
for c in self.components:
c.reload_source(name)
if init_source:
self._init_source(name)
self.like.model = self.like.components[0].model | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_source_morphology | def set_source_morphology(self, name, **kwargs):
"""Set the spatial model of a source.
Parameters
----------
name : str
Source name.
spatial_model : str
Spatial model name (PointSource, RadialGaussian, etc.).
spatial_pars : dict
Dictionary of spatial parameters (optional).
use_cache : bool
Generate the spatial model by interpolating the cached source
map.
use_pylike : bool
"""
name = self.roi.get_source_by_name(name).name
src = self.roi[name]
spatial_model = kwargs.get('spatial_model', src['SpatialModel'])
spatial_pars = kwargs.get('spatial_pars', {})
use_pylike = kwargs.get('use_pylike', True)
psf_scale_fn = kwargs.get('psf_scale_fn', None)
update_source = kwargs.get('update_source', False)
if hasattr(pyLike.BinnedLikelihood, 'setSourceMapImage') and not use_pylike:
src.set_spatial_model(spatial_model, spatial_pars)
self._update_srcmap(src.name, src, psf_scale_fn=psf_scale_fn)
else:
src = self.delete_source(name, loglevel=logging.DEBUG,
save_template=False)
src.set_spatial_model(spatial_model, spatial_pars)
self.add_source(src.name, src, init_source=False,
use_pylike=use_pylike, loglevel=logging.DEBUG)
if update_source:
self.update_source(name) | python | def set_source_morphology(self, name, **kwargs):
"""Set the spatial model of a source.
Parameters
----------
name : str
Source name.
spatial_model : str
Spatial model name (PointSource, RadialGaussian, etc.).
spatial_pars : dict
Dictionary of spatial parameters (optional).
use_cache : bool
Generate the spatial model by interpolating the cached source
map.
use_pylike : bool
"""
name = self.roi.get_source_by_name(name).name
src = self.roi[name]
spatial_model = kwargs.get('spatial_model', src['SpatialModel'])
spatial_pars = kwargs.get('spatial_pars', {})
use_pylike = kwargs.get('use_pylike', True)
psf_scale_fn = kwargs.get('psf_scale_fn', None)
update_source = kwargs.get('update_source', False)
if hasattr(pyLike.BinnedLikelihood, 'setSourceMapImage') and not use_pylike:
src.set_spatial_model(spatial_model, spatial_pars)
self._update_srcmap(src.name, src, psf_scale_fn=psf_scale_fn)
else:
src = self.delete_source(name, loglevel=logging.DEBUG,
save_template=False)
src.set_spatial_model(spatial_model, spatial_pars)
self.add_source(src.name, src, init_source=False,
use_pylike=use_pylike, loglevel=logging.DEBUG)
if update_source:
self.update_source(name) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_source_spectrum | def set_source_spectrum(self, name, spectrum_type='PowerLaw',
spectrum_pars=None, update_source=True):
"""Set the spectral model of a source. This function can be
used to change the spectral type of a source or modify its
spectral parameters. If called with
spectrum_type='FileFunction' and spectrum_pars=None, the
source spectrum will be replaced with a FileFunction with the
same differential flux distribution as the original spectrum.
Parameters
----------
name : str
Source name.
spectrum_type : str
Spectrum type (PowerLaw, etc.).
spectrum_pars : dict
Dictionary of spectral parameters (optional).
update_source : bool
Recompute all source characteristics (flux, TS, NPred)
using the new spectral model of the source.
"""
name = self.roi.get_source_by_name(name).name
src = self.roi[name]
spectrum_pars = {} if spectrum_pars is None else spectrum_pars
if (self.roi[name]['SpectrumType'] == 'PowerLaw' and
spectrum_type == 'LogParabola'):
spectrum_pars.setdefault('beta', {'value': 0.0, 'scale': 1.0,
'min': 0.0, 'max': 1.0})
spectrum_pars.setdefault('Eb', src.spectral_pars['Scale'])
spectrum_pars.setdefault('norm', src.spectral_pars['Prefactor'])
if 'alpha' not in spectrum_pars:
spectrum_pars['alpha'] = src.spectral_pars['Index']
spectrum_pars['alpha']['value'] *= -1.0
if spectrum_pars['alpha']['scale'] == -1.0:
spectrum_pars['alpha']['value'] *= -1.0
spectrum_pars['alpha']['scale'] *= -1.0
if spectrum_type == 'FileFunction':
self._create_filefunction(name, spectrum_pars)
else:
fn = gtutils.create_spectrum_from_dict(spectrum_type,
spectrum_pars)
self.like.setSpectrum(str(name), fn)
# Get parameters
src = self.components[0].like.logLike.getSource(str(name))
pars_dict = gtutils.get_function_pars_dict(src.spectrum())
self.roi[name]['SpectrumType'] = spectrum_type
self.roi[name].set_spectral_pars(pars_dict)
for c in self.components:
c.roi[name]['SpectrumType'] = spectrum_type
c.roi[name].set_spectral_pars(pars_dict)
if update_source:
self.update_source(name) | python | def set_source_spectrum(self, name, spectrum_type='PowerLaw',
spectrum_pars=None, update_source=True):
"""Set the spectral model of a source. This function can be
used to change the spectral type of a source or modify its
spectral parameters. If called with
spectrum_type='FileFunction' and spectrum_pars=None, the
source spectrum will be replaced with a FileFunction with the
same differential flux distribution as the original spectrum.
Parameters
----------
name : str
Source name.
spectrum_type : str
Spectrum type (PowerLaw, etc.).
spectrum_pars : dict
Dictionary of spectral parameters (optional).
update_source : bool
Recompute all source characteristics (flux, TS, NPred)
using the new spectral model of the source.
"""
name = self.roi.get_source_by_name(name).name
src = self.roi[name]
spectrum_pars = {} if spectrum_pars is None else spectrum_pars
if (self.roi[name]['SpectrumType'] == 'PowerLaw' and
spectrum_type == 'LogParabola'):
spectrum_pars.setdefault('beta', {'value': 0.0, 'scale': 1.0,
'min': 0.0, 'max': 1.0})
spectrum_pars.setdefault('Eb', src.spectral_pars['Scale'])
spectrum_pars.setdefault('norm', src.spectral_pars['Prefactor'])
if 'alpha' not in spectrum_pars:
spectrum_pars['alpha'] = src.spectral_pars['Index']
spectrum_pars['alpha']['value'] *= -1.0
if spectrum_pars['alpha']['scale'] == -1.0:
spectrum_pars['alpha']['value'] *= -1.0
spectrum_pars['alpha']['scale'] *= -1.0
if spectrum_type == 'FileFunction':
self._create_filefunction(name, spectrum_pars)
else:
fn = gtutils.create_spectrum_from_dict(spectrum_type,
spectrum_pars)
self.like.setSpectrum(str(name), fn)
# Get parameters
src = self.components[0].like.logLike.getSource(str(name))
pars_dict = gtutils.get_function_pars_dict(src.spectrum())
self.roi[name]['SpectrumType'] = spectrum_type
self.roi[name].set_spectral_pars(pars_dict)
for c in self.components:
c.roi[name]['SpectrumType'] = spectrum_type
c.roi[name].set_spectral_pars(pars_dict)
if update_source:
self.update_source(name) | [
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spectrum_type : str
Spectrum type (PowerLaw, etc.).
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_source_dnde | def set_source_dnde(self, name, dnde, update_source=True):
"""Set the differential flux distribution of a source with the
FileFunction spectral type.
Parameters
----------
name : str
Source name.
dnde : `~numpy.ndarray`
Array of differential flux values (cm^{-2} s^{-1} MeV^{-1}).
"""
name = self.roi.get_source_by_name(name).name
if self.roi[name]['SpectrumType'] != 'FileFunction':
msg = 'Wrong spectral type: %s' % self.roi[name]['SpectrumType']
self.logger.error(msg)
raise Exception(msg)
xy = self.get_source_dnde(name)
if len(dnde) != len(xy[0]):
msg = 'Wrong length for dnde array: %i' % len(dnde)
self.logger.error(msg)
raise Exception(msg)
for c in self.components:
src = c.like.logLike.getSource(str(name))
spectrum = src.spectrum()
file_function = pyLike.FileFunction_cast(spectrum)
file_function.setSpectrum(10**xy[0], dnde)
if update_source:
self.update_source(name) | python | def set_source_dnde(self, name, dnde, update_source=True):
"""Set the differential flux distribution of a source with the
FileFunction spectral type.
Parameters
----------
name : str
Source name.
dnde : `~numpy.ndarray`
Array of differential flux values (cm^{-2} s^{-1} MeV^{-1}).
"""
name = self.roi.get_source_by_name(name).name
if self.roi[name]['SpectrumType'] != 'FileFunction':
msg = 'Wrong spectral type: %s' % self.roi[name]['SpectrumType']
self.logger.error(msg)
raise Exception(msg)
xy = self.get_source_dnde(name)
if len(dnde) != len(xy[0]):
msg = 'Wrong length for dnde array: %i' % len(dnde)
self.logger.error(msg)
raise Exception(msg)
for c in self.components:
src = c.like.logLike.getSource(str(name))
spectrum = src.spectrum()
file_function = pyLike.FileFunction_cast(spectrum)
file_function.setSpectrum(10**xy[0], dnde)
if update_source:
self.update_source(name) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.get_source_dnde | def get_source_dnde(self, name):
"""Return differential flux distribution of a source. For
sources with FileFunction spectral type this returns the
internal differential flux array.
Returns
-------
loge : `~numpy.ndarray`
Array of energies at which the differential flux is
evaluated (log10(E/MeV)).
dnde : `~numpy.ndarray`
Array of differential flux values (cm^{-2} s^{-1} MeV^{-1})
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"""
name = self.roi.get_source_by_name(name).name
if self.roi[name]['SpectrumType'] != 'FileFunction':
src = self.components[0].like.logLike.getSource(str(name))
spectrum = src.spectrum()
file_function = pyLike.FileFunction_cast(spectrum)
loge = file_function.log_energy()
logdnde = file_function.log_dnde()
loge = np.log10(np.exp(loge))
dnde = np.exp(logdnde)
return loge, dnde
else:
ebinsz = (self.log_energies[-1] -
self.log_energies[0]) / self.enumbins
loge = utils.extend_array(self.log_energies, ebinsz, 0.5, 6.5)
dnde = np.array([self.like[name].spectrum()(pyLike.dArg(10 ** egy))
for egy in loge])
return loge, dnde | python | def get_source_dnde(self, name):
"""Return differential flux distribution of a source. For
sources with FileFunction spectral type this returns the
internal differential flux array.
Returns
-------
loge : `~numpy.ndarray`
Array of energies at which the differential flux is
evaluated (log10(E/MeV)).
dnde : `~numpy.ndarray`
Array of differential flux values (cm^{-2} s^{-1} MeV^{-1})
evaluated at energies in ``loge``.
"""
name = self.roi.get_source_by_name(name).name
if self.roi[name]['SpectrumType'] != 'FileFunction':
src = self.components[0].like.logLike.getSource(str(name))
spectrum = src.spectrum()
file_function = pyLike.FileFunction_cast(spectrum)
loge = file_function.log_energy()
logdnde = file_function.log_dnde()
loge = np.log10(np.exp(loge))
dnde = np.exp(logdnde)
return loge, dnde
else:
ebinsz = (self.log_energies[-1] -
self.log_energies[0]) / self.enumbins
loge = utils.extend_array(self.log_energies, ebinsz, 0.5, 6.5)
dnde = np.array([self.like[name].spectrum()(pyLike.dArg(10 ** egy))
for egy in loge])
return loge, dnde | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._create_filefunction | def _create_filefunction(self, name, spectrum_pars):
"""Replace the spectrum of an existing source with a
FileFunction."""
spectrum_pars = {} if spectrum_pars is None else spectrum_pars
if 'loge' in spectrum_pars:
loge = spectrum_pars.get('loge')
else:
ebinsz = (self.log_energies[-1] -
self.log_energies[0]) / self.enumbins
loge = utils.extend_array(self.log_energies, ebinsz, 0.5, 6.5)
# Get the values
dnde = np.zeros(len(loge))
if 'dnde' in spectrum_pars:
dnde = spectrum_pars.get('dnde')
else:
dnde = np.array([self.like[name].spectrum()(pyLike.dArg(10 ** egy))
for egy in loge])
filename = \
os.path.join(self.workdir,
'%s_filespectrum.txt' % (name.lower().replace(' ', '_')))
# Create file spectrum txt file
np.savetxt(filename, np.vstack((10**loge, dnde)).T)
self.like.setSpectrum(name, str('FileFunction'))
self.roi[name]['Spectrum_Filename'] = filename
# Update
for c in self.components:
src = c.like.logLike.getSource(str(name))
spectrum = src.spectrum()
spectrum.getParam(str('Normalization')).setBounds(1E-3, 1E3)
file_function = pyLike.FileFunction_cast(spectrum)
file_function.readFunction(str(filename))
c.roi[name]['Spectrum_Filename'] = filename | python | def _create_filefunction(self, name, spectrum_pars):
"""Replace the spectrum of an existing source with a
FileFunction."""
spectrum_pars = {} if spectrum_pars is None else spectrum_pars
if 'loge' in spectrum_pars:
loge = spectrum_pars.get('loge')
else:
ebinsz = (self.log_energies[-1] -
self.log_energies[0]) / self.enumbins
loge = utils.extend_array(self.log_energies, ebinsz, 0.5, 6.5)
# Get the values
dnde = np.zeros(len(loge))
if 'dnde' in spectrum_pars:
dnde = spectrum_pars.get('dnde')
else:
dnde = np.array([self.like[name].spectrum()(pyLike.dArg(10 ** egy))
for egy in loge])
filename = \
os.path.join(self.workdir,
'%s_filespectrum.txt' % (name.lower().replace(' ', '_')))
# Create file spectrum txt file
np.savetxt(filename, np.vstack((10**loge, dnde)).T)
self.like.setSpectrum(name, str('FileFunction'))
self.roi[name]['Spectrum_Filename'] = filename
# Update
for c in self.components:
src = c.like.logLike.getSource(str(name))
spectrum = src.spectrum()
spectrum.getParam(str('Normalization')).setBounds(1E-3, 1E3)
file_function = pyLike.FileFunction_cast(spectrum)
file_function.readFunction(str(filename))
c.roi[name]['Spectrum_Filename'] = filename | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.stage_output | def stage_output(self):
"""Copy data products to final output directory."""
if self.workdir == self.outdir:
return
elif not os.path.isdir(self.workdir):
self.logger.error('Working directory does not exist.')
return
regex = self.config['fileio']['outdir_regex']
savefits = self.config['fileio']['savefits']
files = os.listdir(self.workdir)
self.logger.info('Staging files to %s', self.outdir)
fitsfiles = []
for c in self.components:
for f in c.files.values():
if f is None:
continue
fitsfiles += [os.path.basename(f)]
for f in files:
wpath = os.path.join(self.workdir, f)
opath = os.path.join(self.outdir, f)
if not utils.match_regex_list(regex, os.path.basename(f)):
continue
if os.path.isfile(opath) and filecmp.cmp(wpath, opath, False):
continue
if not savefits and f in fitsfiles:
continue
self.logger.debug('Copying ' + f)
self.logger.info('Copying ' + f)
shutil.copy(wpath, self.outdir)
self.logger.info('Finished.') | python | def stage_output(self):
"""Copy data products to final output directory."""
if self.workdir == self.outdir:
return
elif not os.path.isdir(self.workdir):
self.logger.error('Working directory does not exist.')
return
regex = self.config['fileio']['outdir_regex']
savefits = self.config['fileio']['savefits']
files = os.listdir(self.workdir)
self.logger.info('Staging files to %s', self.outdir)
fitsfiles = []
for c in self.components:
for f in c.files.values():
if f is None:
continue
fitsfiles += [os.path.basename(f)]
for f in files:
wpath = os.path.join(self.workdir, f)
opath = os.path.join(self.outdir, f)
if not utils.match_regex_list(regex, os.path.basename(f)):
continue
if os.path.isfile(opath) and filecmp.cmp(wpath, opath, False):
continue
if not savefits and f in fitsfiles:
continue
self.logger.debug('Copying ' + f)
self.logger.info('Copying ' + f)
shutil.copy(wpath, self.outdir)
self.logger.info('Finished.') | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.stage_input | def stage_input(self):
"""Copy input files to working directory."""
if self.workdir == self.outdir:
return
elif not os.path.isdir(self.workdir):
self.logger.error('Working directory does not exist.')
return
self.logger.info('Staging files to %s', self.workdir)
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for f in os.listdir(self.outdir)]
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for c in self.components:
for f in c.files.values():
if f is None:
continue
wpath = os.path.join(self.workdir, os.path.basename(f))
opath = os.path.join(self.outdir, os.path.basename(f))
if os.path.isfile(wpath):
continue
elif os.path.isfile(opath):
self.logger.debug('Copying ' + os.path.basename(f))
shutil.copy(opath, self.workdir)
self.logger.info('Finished.') | python | def stage_input(self):
"""Copy input files to working directory."""
if self.workdir == self.outdir:
return
elif not os.path.isdir(self.workdir):
self.logger.error('Working directory does not exist.')
return
self.logger.info('Staging files to %s', self.workdir)
files = [os.path.join(self.outdir, f)
for f in os.listdir(self.outdir)]
regex = copy.deepcopy(self.config['fileio']['workdir_regex'])
for f in files:
if not os.path.isfile(f):
continue
if not utils.match_regex_list(regex, os.path.basename(f)):
continue
self.logger.debug('Copying ' + os.path.basename(f))
shutil.copy(f, self.workdir)
for c in self.components:
for f in c.files.values():
if f is None:
continue
wpath = os.path.join(self.workdir, os.path.basename(f))
opath = os.path.join(self.outdir, os.path.basename(f))
if os.path.isfile(wpath):
continue
elif os.path.isfile(opath):
self.logger.debug('Copying ' + os.path.basename(f))
shutil.copy(opath, self.workdir)
self.logger.info('Finished.') | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._create_likelihood | def _create_likelihood(self, srcmdl=None):
"""Instantiate the likelihood object for each component and
create a SummedLikelihood."""
self._like = SummedLikelihood()
for c in self.components:
c._create_binned_analysis(srcmdl)
self._like.addComponent(c.like)
self.like.model = self.like.components[0].model
self._fitcache = None
self._init_roi_model() | python | def _create_likelihood(self, srcmdl=None):
"""Instantiate the likelihood object for each component and
create a SummedLikelihood."""
self._like = SummedLikelihood()
for c in self.components:
c._create_binned_analysis(srcmdl)
self._like.addComponent(c.like)
self.like.model = self.like.components[0].model
self._fitcache = None
self._init_roi_model() | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.generate_model | def generate_model(self, model_name=None):
"""Generate model maps for all components. model_name should
be a unique identifier for the model. If model_name is None
then the model maps will be generated using the current
parameters of the ROI."""
for i, c in enumerate(self._components):
c.generate_model(model_name=model_name) | python | def generate_model(self, model_name=None):
"""Generate model maps for all components. model_name should
be a unique identifier for the model. If model_name is None
then the model maps will be generated using the current
parameters of the ROI."""
for i, c in enumerate(self._components):
c.generate_model(model_name=model_name) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_energy_range | def set_energy_range(self, logemin, logemax):
"""Set the energy bounds of the analysis. This restricts the
evaluation of the likelihood to the data that falls in this
range. Input values will be rounded to the closest bin edge
value. If either argument is None then the lower or upper
bound of the analysis instance will be used.
Parameters
----------
logemin : float
Lower energy bound in log10(E/MeV).
logemax : float
Upper energy bound in log10(E/MeV).
Returns
-------
eminmax : array
Minimum and maximum energy in log10(E/MeV).
"""
if logemin is None:
logemin = self.log_energies[0]
else:
imin = int(utils.val_to_edge(self.log_energies, logemin)[0])
logemin = self.log_energies[imin]
if logemax is None:
logemax = self.log_energies[-1]
else:
imax = int(utils.val_to_edge(self.log_energies, logemax)[0])
logemax = self.log_energies[imax]
self._loge_bounds = np.array([logemin, logemax])
self._roi_data['loge_bounds'] = np.copy(self.loge_bounds)
for c in self.components:
c.set_energy_range(logemin, logemax)
return self._loge_bounds | python | def set_energy_range(self, logemin, logemax):
"""Set the energy bounds of the analysis. This restricts the
evaluation of the likelihood to the data that falls in this
range. Input values will be rounded to the closest bin edge
value. If either argument is None then the lower or upper
bound of the analysis instance will be used.
Parameters
----------
logemin : float
Lower energy bound in log10(E/MeV).
logemax : float
Upper energy bound in log10(E/MeV).
Returns
-------
eminmax : array
Minimum and maximum energy in log10(E/MeV).
"""
if logemin is None:
logemin = self.log_energies[0]
else:
imin = int(utils.val_to_edge(self.log_energies, logemin)[0])
logemin = self.log_energies[imin]
if logemax is None:
logemax = self.log_energies[-1]
else:
imax = int(utils.val_to_edge(self.log_energies, logemax)[0])
logemax = self.log_energies[imax]
self._loge_bounds = np.array([logemin, logemax])
self._roi_data['loge_bounds'] = np.copy(self.loge_bounds)
for c in self.components:
c.set_energy_range(logemin, logemax)
return self._loge_bounds | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.model_counts_map | def model_counts_map(self, name=None, exclude=None, use_mask=False):
"""Return the model counts map for a single source, a list of
sources, or for the sum of all sources in the ROI. The
exclude parameter can be used to exclude one or more
components when generating the model map.
Parameters
----------
name : str or list of str
Parameter controlling the set of sources for which the
model counts map will be calculated. If name=None the
model map will be generated for all sources in the ROI.
exclude : str or list of str
List of sources that will be excluded when calculating the
model map.
use_mask : bool
Parameter that specifies in the model counts map should include
mask pixels (i.e., ones whose weights are <= 0)
Returns
-------
map : `~gammapy.maps.Map`
"""
maps = [c.model_counts_map(name, exclude, use_mask=use_mask)
for c in self.components]
return skymap.coadd_maps(self.geom, maps) | python | def model_counts_map(self, name=None, exclude=None, use_mask=False):
"""Return the model counts map for a single source, a list of
sources, or for the sum of all sources in the ROI. The
exclude parameter can be used to exclude one or more
components when generating the model map.
Parameters
----------
name : str or list of str
Parameter controlling the set of sources for which the
model counts map will be calculated. If name=None the
model map will be generated for all sources in the ROI.
exclude : str or list of str
List of sources that will be excluded when calculating the
model map.
use_mask : bool
Parameter that specifies in the model counts map should include
mask pixels (i.e., ones whose weights are <= 0)
Returns
-------
map : `~gammapy.maps.Map`
"""
maps = [c.model_counts_map(name, exclude, use_mask=use_mask)
for c in self.components]
return skymap.coadd_maps(self.geom, maps) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.model_counts_spectrum | def model_counts_spectrum(self, name, logemin=None, logemax=None,
summed=False, weighted=False):
"""Return the predicted number of model counts versus energy
for a given source and energy range. If summed=True return
the counts spectrum summed over all components otherwise
return a list of model spectra. If weighted=True return
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"""
if logemin is None:
logemin = self.log_energies[0]
if logemax is None:
logemax = self.log_energies[-1]
if summed:
cs = np.zeros(self.enumbins)
imin = utils.val_to_bin_bounded(self.log_energies,
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imax = utils.val_to_bin_bounded(self.log_energies,
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for c in self.components:
ecenter = 0.5 * (c.log_energies[:-1] + c.log_energies[1:])
counts = c.model_counts_spectrum(name, self.log_energies[0],
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cs += np.histogram(ecenter,
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return cs[imin:imax]
else:
cs = []
for c in self.components:
cs += [c.model_counts_spectrum(name, logemin,
logemax, weighted=weighted)]
return cs | python | def model_counts_spectrum(self, name, logemin=None, logemax=None,
summed=False, weighted=False):
"""Return the predicted number of model counts versus energy
for a given source and energy range. If summed=True return
the counts spectrum summed over all components otherwise
return a list of model spectra. If weighted=True return
the weighted version of the counts spectrum
"""
if logemin is None:
logemin = self.log_energies[0]
if logemax is None:
logemax = self.log_energies[-1]
if summed:
cs = np.zeros(self.enumbins)
imin = utils.val_to_bin_bounded(self.log_energies,
logemin + 1E-7)[0]
imax = utils.val_to_bin_bounded(self.log_energies,
logemax - 1E-7)[0] + 1
for c in self.components:
ecenter = 0.5 * (c.log_energies[:-1] + c.log_energies[1:])
counts = c.model_counts_spectrum(name, self.log_energies[0],
self.log_energies[-1], weighted)
cs += np.histogram(ecenter,
weights=counts,
bins=self.log_energies)[0]
return cs[imin:imax]
else:
cs = []
for c in self.components:
cs += [c.model_counts_spectrum(name, logemin,
logemax, weighted=weighted)]
return cs | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.get_sources | def get_sources(self, cuts=None, distance=None, skydir=None,
minmax_ts=None, minmax_npred=None, exclude=None,
square=False):
"""Retrieve list of sources in the ROI satisfying the given
selections.
Returns
-------
srcs : list
A list of `~fermipy.roi_model.Model` objects.
"""
coordsys = self.config['binning']['coordsys']
return self.roi.get_sources(skydir, distance, cuts,
minmax_ts, minmax_npred,
exclude, square,
coordsys=coordsys) | python | def get_sources(self, cuts=None, distance=None, skydir=None,
minmax_ts=None, minmax_npred=None, exclude=None,
square=False):
"""Retrieve list of sources in the ROI satisfying the given
selections.
Returns
-------
srcs : list
A list of `~fermipy.roi_model.Model` objects.
"""
coordsys = self.config['binning']['coordsys']
return self.roi.get_sources(skydir, distance, cuts,
minmax_ts, minmax_npred,
exclude, square,
coordsys=coordsys) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.add_source | def add_source(self, name, src_dict, free=None, init_source=True,
save_source_maps=True, use_pylike=True,
use_single_psf=False, **kwargs):
"""Add a source to the ROI model. This function may be called
either before or after `~fermipy.gtanalysis.GTAnalysis.setup`.
Parameters
----------
name : str
Source name.
src_dict : dict or `~fermipy.roi_model.Source` object
Dictionary or source object defining the source properties
(coordinates, spectral parameters, etc.).
free : bool
Initialize the source with a free normalization parameter.
use_pylike : bool
Create source maps with pyLikelihood.
use_single_psf : bool
Use the PSF model calculated for the ROI center. If false
then a new model will be generated using the position of
the source.
"""
if self.roi.has_source(name):
msg = 'Source %s already exists.' % name
self.logger.error(msg)
raise Exception(msg)
loglevel = kwargs.pop('loglevel', self.loglevel)
self.logger.log(loglevel, 'Adding source ' + name)
src = self.roi.create_source(name, src_dict, rescale=True)
self.make_template(src)
for c in self.components:
c.add_source(name, src_dict, free=free,
save_source_maps=save_source_maps,
use_pylike=use_pylike,
use_single_psf=use_single_psf)
if self._like is None:
return
if self.config['gtlike']['edisp'] and src.name not in \
self.config['gtlike']['edisp_disable']:
self.set_edisp_flag(src.name, True)
self.like.syncSrcParams(str(name))
self.like.model = self.like.components[0].model
# if free is not None:
# self.free_norm(name, free, loglevel=logging.DEBUG)
if init_source:
self._init_source(name)
self._update_roi()
if self._fitcache is not None:
self._fitcache.update_source(name) | python | def add_source(self, name, src_dict, free=None, init_source=True,
save_source_maps=True, use_pylike=True,
use_single_psf=False, **kwargs):
"""Add a source to the ROI model. This function may be called
either before or after `~fermipy.gtanalysis.GTAnalysis.setup`.
Parameters
----------
name : str
Source name.
src_dict : dict or `~fermipy.roi_model.Source` object
Dictionary or source object defining the source properties
(coordinates, spectral parameters, etc.).
free : bool
Initialize the source with a free normalization parameter.
use_pylike : bool
Create source maps with pyLikelihood.
use_single_psf : bool
Use the PSF model calculated for the ROI center. If false
then a new model will be generated using the position of
the source.
"""
if self.roi.has_source(name):
msg = 'Source %s already exists.' % name
self.logger.error(msg)
raise Exception(msg)
loglevel = kwargs.pop('loglevel', self.loglevel)
self.logger.log(loglevel, 'Adding source ' + name)
src = self.roi.create_source(name, src_dict, rescale=True)
self.make_template(src)
for c in self.components:
c.add_source(name, src_dict, free=free,
save_source_maps=save_source_maps,
use_pylike=use_pylike,
use_single_psf=use_single_psf)
if self._like is None:
return
if self.config['gtlike']['edisp'] and src.name not in \
self.config['gtlike']['edisp_disable']:
self.set_edisp_flag(src.name, True)
self.like.syncSrcParams(str(name))
self.like.model = self.like.components[0].model
# if free is not None:
# self.free_norm(name, free, loglevel=logging.DEBUG)
if init_source:
self._init_source(name)
self._update_roi()
if self._fitcache is not None:
self._fitcache.update_source(name) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.add_sources_from_roi | def add_sources_from_roi(self, names, roi, free=False, **kwargs):
"""Add multiple sources to the current ROI model copied from another ROI model.
Parameters
----------
names : list
List of str source names to add.
roi : `~fermipy.roi_model.ROIModel` object
The roi model from which to add sources.
free : bool
Initialize the source with a free normalization paramter.
"""
for name in names:
self.add_source(name, roi[name].data, free=free, **kwargs) | python | def add_sources_from_roi(self, names, roi, free=False, **kwargs):
"""Add multiple sources to the current ROI model copied from another ROI model.
Parameters
----------
names : list
List of str source names to add.
roi : `~fermipy.roi_model.ROIModel` object
The roi model from which to add sources.
free : bool
Initialize the source with a free normalization paramter.
"""
for name in names:
self.add_source(name, roi[name].data, free=free, **kwargs) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.delete_source | def delete_source(self, name, save_template=True, delete_source_map=False,
build_fixed_wts=True, **kwargs):
"""Delete a source from the ROI model.
Parameters
----------
name : str
Source name.
save_template : bool
Keep the SpatialMap FITS template associated with this
source.
delete_source_map : bool
Delete the source map associated with this source from the
source maps file.
Returns
-------
src : `~fermipy.roi_model.Model`
The deleted source object.
"""
if not self.roi.has_source(name):
self.logger.error('No source with name: %s', name)
return
loglevel = kwargs.pop('loglevel', self.loglevel)
self.logger.log(loglevel, 'Deleting source %s', name)
# STs require a source to be freed before deletion
if self.like is not None:
self.free_norm(name, loglevel=logging.DEBUG)
for c in self.components:
c.delete_source(name, save_template=save_template,
delete_source_map=delete_source_map,
build_fixed_wts=build_fixed_wts)
src = self.roi.get_source_by_name(name)
self.roi.delete_sources([src])
if self.like is not None:
self.like.model = self.like.components[0].model
self._update_roi()
return src | python | def delete_source(self, name, save_template=True, delete_source_map=False,
build_fixed_wts=True, **kwargs):
"""Delete a source from the ROI model.
Parameters
----------
name : str
Source name.
save_template : bool
Keep the SpatialMap FITS template associated with this
source.
delete_source_map : bool
Delete the source map associated with this source from the
source maps file.
Returns
-------
src : `~fermipy.roi_model.Model`
The deleted source object.
"""
if not self.roi.has_source(name):
self.logger.error('No source with name: %s', name)
return
loglevel = kwargs.pop('loglevel', self.loglevel)
self.logger.log(loglevel, 'Deleting source %s', name)
# STs require a source to be freed before deletion
if self.like is not None:
self.free_norm(name, loglevel=logging.DEBUG)
for c in self.components:
c.delete_source(name, save_template=save_template,
delete_source_map=delete_source_map,
build_fixed_wts=build_fixed_wts)
src = self.roi.get_source_by_name(name)
self.roi.delete_sources([src])
if self.like is not None:
self.like.model = self.like.components[0].model
self._update_roi()
return src | [
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Delete the source map associated with this source from the
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.delete_sources | def delete_sources(self, cuts=None, distance=None,
skydir=None, minmax_ts=None, minmax_npred=None,
exclude=None, square=False, names=None):
"""Delete sources in the ROI model satisfying the given
selection criteria.
Parameters
----------
cuts : dict
Dictionary of [min,max] selections on source properties.
distance : float
Cut on angular distance from ``skydir``. If None then no
selection will be applied.
skydir : `~astropy.coordinates.SkyCoord`
Reference sky coordinate for ``distance`` selection. If
None then the distance selection will be applied with
respect to the ROI center.
minmax_ts : list
Select sources that have TS in the range [min,max]. If
either min or max are None then only a lower (upper) bound
will be applied. If this parameter is none no selection
will be applied.
minmax_npred : list
Select sources that have npred in the range [min,max]. If
either min or max are None then only a lower (upper) bound
will be applied. If this parameter is none no selection
will be applied.
square : bool
Switch between applying a circular or square (ROI-like)
selection on the maximum projected distance from the ROI
center.
names : list
Select sources matching a name in this list.
Returns
-------
srcs : list
A list of `~fermipy.roi_model.Model` objects.
"""
srcs = self.roi.get_sources(skydir=skydir, distance=distance, cuts=cuts,
minmax_ts=minmax_ts, minmax_npred=minmax_npred,
exclude=exclude, square=square,
coordsys=self.config[
'binning']['coordsys'],
names=names)
for s in srcs:
self.delete_source(s.name, build_fixed_wts=False)
if self.like is not None:
# Build fixed model weights in one pass
for c in self.components:
c.like.logLike.buildFixedModelWts()
self._update_roi()
return srcs | python | def delete_sources(self, cuts=None, distance=None,
skydir=None, minmax_ts=None, minmax_npred=None,
exclude=None, square=False, names=None):
"""Delete sources in the ROI model satisfying the given
selection criteria.
Parameters
----------
cuts : dict
Dictionary of [min,max] selections on source properties.
distance : float
Cut on angular distance from ``skydir``. If None then no
selection will be applied.
skydir : `~astropy.coordinates.SkyCoord`
Reference sky coordinate for ``distance`` selection. If
None then the distance selection will be applied with
respect to the ROI center.
minmax_ts : list
Select sources that have TS in the range [min,max]. If
either min or max are None then only a lower (upper) bound
will be applied. If this parameter is none no selection
will be applied.
minmax_npred : list
Select sources that have npred in the range [min,max]. If
either min or max are None then only a lower (upper) bound
will be applied. If this parameter is none no selection
will be applied.
square : bool
Switch between applying a circular or square (ROI-like)
selection on the maximum projected distance from the ROI
center.
names : list
Select sources matching a name in this list.
Returns
-------
srcs : list
A list of `~fermipy.roi_model.Model` objects.
"""
srcs = self.roi.get_sources(skydir=skydir, distance=distance, cuts=cuts,
minmax_ts=minmax_ts, minmax_npred=minmax_npred,
exclude=exclude, square=square,
coordsys=self.config[
'binning']['coordsys'],
names=names)
for s in srcs:
self.delete_source(s.name, build_fixed_wts=False)
if self.like is not None:
# Build fixed model weights in one pass
for c in self.components:
c.like.logLike.buildFixedModelWts()
self._update_roi()
return srcs | [
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Cut on angular distance from ``skydir``. If None then no
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Reference sky coordinate for ``distance`` selection. If
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Switch between applying a circular or square (ROI-like)
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Select sources matching a name in this list.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.free_sources_by_name | def free_sources_by_name(self, names, free=True, pars=None,
**kwargs):
"""Free all sources with names matching ``names``.
Parameters
----------
names : list
List of source names.
free : bool
Choose whether to free (free=True) or fix (free=False)
source parameters.
pars : list
Set a list of parameters to be freed/fixed for each
source. If none then all source parameters will be
freed/fixed. If pars='norm' then only normalization
parameters will be freed.
Returns
-------
srcs : list
A list of `~fermipy.roi_model.Model` objects.
"""
if names is None:
return
names = [names] if not isinstance(names, list) else names
names = [self.roi.get_source_by_name(t).name for t in names]
srcs = [s for s in self.roi.sources if s.name in names]
for s in srcs:
self.free_source(s.name, free=free, pars=pars, **kwargs)
return srcs | python | def free_sources_by_name(self, names, free=True, pars=None,
**kwargs):
"""Free all sources with names matching ``names``.
Parameters
----------
names : list
List of source names.
free : bool
Choose whether to free (free=True) or fix (free=False)
source parameters.
pars : list
Set a list of parameters to be freed/fixed for each
source. If none then all source parameters will be
freed/fixed. If pars='norm' then only normalization
parameters will be freed.
Returns
-------
srcs : list
A list of `~fermipy.roi_model.Model` objects.
"""
if names is None:
return
names = [names] if not isinstance(names, list) else names
names = [self.roi.get_source_by_name(t).name for t in names]
srcs = [s for s in self.roi.sources if s.name in names]
for s in srcs:
self.free_source(s.name, free=free, pars=pars, **kwargs)
return srcs | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter | def set_parameter(self, name, par, value, true_value=True, scale=None,
bounds=None, error=None, update_source=True):
"""
Update the value of a parameter. Parameter bounds will
automatically be adjusted to encompass the new parameter
value.
Parameters
----------
name : str
Source name.
par : str
Parameter name.
value : float
Parameter value. By default this argument should be the
unscaled (True) parameter value.
scale : float
Parameter scale (optional). Value argument is interpreted
with respect to the scale parameter if it is provided.
error : float
Parameter error (optional). By default this argument should be the
unscaled (True) parameter value.
update_source : bool
Update the source dictionary for the object.
"""
name = self.roi.get_source_by_name(name).name
idx = self.like.par_index(name, par)
current_bounds = list(self.like.model[idx].getBounds())
if scale is not None:
self.like[idx].setScale(scale)
else:
scale = self.like.model[idx].getScale()
if true_value:
current_bounds[0] = min(current_bounds[0], value / scale)
current_bounds[1] = max(current_bounds[1], value / scale)
if error is not None:
error = error / scale
else:
current_bounds[0] = min(current_bounds[0], value)
current_bounds[1] = max(current_bounds[1], value)
# update current bounds to encompass new value
self.like[idx].setBounds(*current_bounds)
if true_value:
for p in self.like[idx].pars:
p.setTrueValue(value)
else:
self.like[idx].setValue(value)
if bounds is not None:
if true_value:
bounds[0] = min(bounds[0], value / scale)
bounds[1] = max(bounds[1], value / scale)
else:
bounds[0] = min(bounds[0], value)
bounds[1] = max(bounds[1], value)
# For some reason the numerical accuracy is causing this to throw exceptions.
try:
if bounds is not None:
self.like[idx].setBounds(*bounds)
except RuntimeError:
self.logger.warning(
"Caught failure on setBounds for %s::%s." % (name, par))
pass
if error is not None:
self.like[idx].setError(error)
self._sync_params(name)
if update_source:
self.update_source(name) | python | def set_parameter(self, name, par, value, true_value=True, scale=None,
bounds=None, error=None, update_source=True):
"""
Update the value of a parameter. Parameter bounds will
automatically be adjusted to encompass the new parameter
value.
Parameters
----------
name : str
Source name.
par : str
Parameter name.
value : float
Parameter value. By default this argument should be the
unscaled (True) parameter value.
scale : float
Parameter scale (optional). Value argument is interpreted
with respect to the scale parameter if it is provided.
error : float
Parameter error (optional). By default this argument should be the
unscaled (True) parameter value.
update_source : bool
Update the source dictionary for the object.
"""
name = self.roi.get_source_by_name(name).name
idx = self.like.par_index(name, par)
current_bounds = list(self.like.model[idx].getBounds())
if scale is not None:
self.like[idx].setScale(scale)
else:
scale = self.like.model[idx].getScale()
if true_value:
current_bounds[0] = min(current_bounds[0], value / scale)
current_bounds[1] = max(current_bounds[1], value / scale)
if error is not None:
error = error / scale
else:
current_bounds[0] = min(current_bounds[0], value)
current_bounds[1] = max(current_bounds[1], value)
# update current bounds to encompass new value
self.like[idx].setBounds(*current_bounds)
if true_value:
for p in self.like[idx].pars:
p.setTrueValue(value)
else:
self.like[idx].setValue(value)
if bounds is not None:
if true_value:
bounds[0] = min(bounds[0], value / scale)
bounds[1] = max(bounds[1], value / scale)
else:
bounds[0] = min(bounds[0], value)
bounds[1] = max(bounds[1], value)
# For some reason the numerical accuracy is causing this to throw exceptions.
try:
if bounds is not None:
self.like[idx].setBounds(*bounds)
except RuntimeError:
self.logger.warning(
"Caught failure on setBounds for %s::%s." % (name, par))
pass
if error is not None:
self.like[idx].setError(error)
self._sync_params(name)
if update_source:
self.update_source(name) | [
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Parameter scale (optional). Value argument is interpreted
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Parameter error (optional). By default this argument should be the
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter_scale | def set_parameter_scale(self, name, par, scale):
"""Update the scale of a parameter while keeping its value constant."""
name = self.roi.get_source_by_name(name).name
idx = self.like.par_index(name, par)
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self.like[idx].setScale(scale)
self.like[idx].setValue(current_value * current_scale / scale)
self.like[idx].setBounds(current_bounds[0] * current_scale / scale,
current_bounds[1] * current_scale / scale)
self._sync_params(name) | python | def set_parameter_scale(self, name, par, scale):
"""Update the scale of a parameter while keeping its value constant."""
name = self.roi.get_source_by_name(name).name
idx = self.like.par_index(name, par)
current_bounds = list(self.like.model[idx].getBounds())
current_scale = self.like.model[idx].getScale()
current_value = self.like[idx].getValue()
self.like[idx].setScale(scale)
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter_bounds | def set_parameter_bounds(self, name, par, bounds):
"""Set the bounds on the scaled value of a parameter.
Parameters
----------
name : str
Source name.
par : str
Parameter name.
bounds : list
Upper and lower bound.
"""
idx = self.like.par_index(name, par)
self.like[idx].setBounds(*bounds)
self._sync_params(name) | python | def set_parameter_bounds(self, name, par, bounds):
"""Set the bounds on the scaled value of a parameter.
Parameters
----------
name : str
Source name.
par : str
Parameter name.
bounds : list
Upper and lower bound.
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idx = self.like.par_index(name, par)
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter_error | def set_parameter_error(self, name, par, error):
"""Set the error on the value of a parameter.
Parameters
----------
name : str
Source name.
par : str
Parameter name.
error : float
The value for the parameter error
"""
idx = self.like.par_index(name, par)
self.like[idx].setError(error)
self._sync_params(name) | python | def set_parameter_error(self, name, par, error):
"""Set the error on the value of a parameter.
Parameters
----------
name : str
Source name.
par : str
Parameter name.
error : float
The value for the parameter error
"""
idx = self.like.par_index(name, par)
self.like[idx].setError(error)
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.get_source_name | def get_source_name(self, name):
"""Return the name of a source as it is defined in the
pyLikelihood model object."""
if name not in self.like.sourceNames():
name = self.roi.get_source_by_name(name).name
return name | python | def get_source_name(self, name):
"""Return the name of a source as it is defined in the
pyLikelihood model object."""
if name not in self.like.sourceNames():
name = self.roi.get_source_by_name(name).name
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.constrain_norms | def constrain_norms(self, srcNames, cov_scale=1.0):
"""Constrain the normalizations of one or more sources by
adding gaussian priors with sigma equal to the parameter
error times a scaling factor."""
# Get the covariance matrix
for name in srcNames:
par = self.like.normPar(name)
err = par.error()
val = par.getValue()
if par.error() == 0.0 or not par.isFree():
continue
self.add_gauss_prior(name, par.getName(),
val, err * cov_scale) | python | def constrain_norms(self, srcNames, cov_scale=1.0):
"""Constrain the normalizations of one or more sources by
adding gaussian priors with sigma equal to the parameter
error times a scaling factor."""
# Get the covariance matrix
for name in srcNames:
par = self.like.normPar(name)
err = par.error()
val = par.getValue()
if par.error() == 0.0 or not par.isFree():
continue
self.add_gauss_prior(name, par.getName(),
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.remove_priors | def remove_priors(self):
"""Clear all priors."""
for src in self.roi.sources:
for par in self.like[src.name].funcs["Spectrum"].params.values():
par.removePrior() | python | def remove_priors(self):
"""Clear all priors."""
for src in self.roi.sources:
for par in self.like[src.name].funcs["Spectrum"].params.values():
par.removePrior() | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._create_optObject | def _create_optObject(self, **kwargs):
""" Make MINUIT or NewMinuit type optimizer object """
optimizer = kwargs.get('optimizer',
self.config['optimizer']['optimizer'])
if optimizer.upper() == 'MINUIT':
optObject = pyLike.Minuit(self.like.logLike)
elif optimizer.upper() == 'NEWMINUIT':
optObject = pyLike.NewMinuit(self.like.logLike)
else:
optFactory = pyLike.OptimizerFactory_instance()
optObject = optFactory.create(str(optimizer), self.like.logLike)
return optObject | python | def _create_optObject(self, **kwargs):
""" Make MINUIT or NewMinuit type optimizer object """
optimizer = kwargs.get('optimizer',
self.config['optimizer']['optimizer'])
if optimizer.upper() == 'MINUIT':
optObject = pyLike.Minuit(self.like.logLike)
elif optimizer.upper() == 'NEWMINUIT':
optObject = pyLike.NewMinuit(self.like.logLike)
else:
optFactory = pyLike.OptimizerFactory_instance()
optObject = optFactory.create(str(optimizer), self.like.logLike)
return optObject | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.load_xml | def load_xml(self, xmlfile):
"""Load model definition from XML.
Parameters
----------
xmlfile : str
Name of the input XML file.
"""
self.logger.info('Loading XML')
for c in self.components:
c.load_xml(xmlfile)
for name in self.like.sourceNames():
self.update_source(name)
self._fitcache = None
self.logger.info('Finished Loading XML') | python | def load_xml(self, xmlfile):
"""Load model definition from XML.
Parameters
----------
xmlfile : str
Name of the input XML file.
"""
self.logger.info('Loading XML')
for c in self.components:
c.load_xml(xmlfile)
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self._fitcache = None
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.load_parameters_from_yaml | def load_parameters_from_yaml(self, yamlfile, update_sources=False):
"""Load model parameters from yaml
Parameters
----------
yamlfile : str
Name of the input yaml file.
"""
d = utils.load_yaml(yamlfile)
for src, src_pars in d.items():
for par_name, par_dict in src_pars.items():
if par_name in ['SpectrumType']:
continue
par_value = par_dict.get('value', None)
par_error = par_dict.get('error', None)
par_scale = par_dict.get('scale', None)
par_min = par_dict.get('min', None)
par_max = par_dict.get('max', None)
par_free = par_dict.get('free', None)
if par_min is not None and par_max is not None:
par_bounds = [par_min, par_max]
else:
par_bounds = None
try:
self.set_parameter(src, par_name, par_value, true_value=False,
scale=par_scale, bounds=par_bounds, error=par_error,
update_source=update_sources)
except RuntimeError as msg:
self.logger.warn(msg)
self.logger.warn("Did not set parameter %s:%s"%(src,par_name))
continue
except Exception as msg:
self.logger.warn(msg)
continue
if par_free is not None:
self.free_parameter(src, par_name, par_free)
self._sync_params_state() | python | def load_parameters_from_yaml(self, yamlfile, update_sources=False):
"""Load model parameters from yaml
Parameters
----------
yamlfile : str
Name of the input yaml file.
"""
d = utils.load_yaml(yamlfile)
for src, src_pars in d.items():
for par_name, par_dict in src_pars.items():
if par_name in ['SpectrumType']:
continue
par_value = par_dict.get('value', None)
par_error = par_dict.get('error', None)
par_scale = par_dict.get('scale', None)
par_min = par_dict.get('min', None)
par_max = par_dict.get('max', None)
par_free = par_dict.get('free', None)
if par_min is not None and par_max is not None:
par_bounds = [par_min, par_max]
else:
par_bounds = None
try:
self.set_parameter(src, par_name, par_value, true_value=False,
scale=par_scale, bounds=par_bounds, error=par_error,
update_source=update_sources)
except RuntimeError as msg:
self.logger.warn(msg)
self.logger.warn("Did not set parameter %s:%s"%(src,par_name))
continue
except Exception as msg:
self.logger.warn(msg)
continue
if par_free is not None:
self.free_parameter(src, par_name, par_free)
self._sync_params_state() | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._restore_counts_maps | def _restore_counts_maps(self):
"""
Revert counts maps to their state prior to injecting any simulated
components.
"""
for c in self.components:
c.restore_counts_maps()
if hasattr(self.like.components[0].logLike, 'setCountsMap'):
self._init_roi_model()
else:
self.write_xml('tmp')
self._like = SummedLikelihood()
for i, c in enumerate(self._components):
c._create_binned_analysis()
self._like.addComponent(c.like)
self._init_roi_model()
self.load_xml('tmp') | python | def _restore_counts_maps(self):
"""
Revert counts maps to their state prior to injecting any simulated
components.
"""
for c in self.components:
c.restore_counts_maps()
if hasattr(self.like.components[0].logLike, 'setCountsMap'):
self._init_roi_model()
else:
self.write_xml('tmp')
self._like = SummedLikelihood()
for i, c in enumerate(self._components):
c._create_binned_analysis()
self._like.addComponent(c.like)
self._init_roi_model()
self.load_xml('tmp') | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.simulate_source | def simulate_source(self, src_dict=None):
"""
Inject simulated source counts into the data.
Parameters
----------
src_dict : dict
Dictionary defining the spatial and spectral properties of
the source that will be injected.
"""
self._fitcache = None
if src_dict is None:
src_dict = {}
else:
src_dict = copy.deepcopy(src_dict)
skydir = wcs_utils.get_target_skydir(src_dict, self.roi.skydir)
src_dict.setdefault('ra', skydir.ra.deg)
src_dict.setdefault('dec', skydir.dec.deg)
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
src_dict.setdefault('Prefactor', 1E-13)
self.add_source('mcsource', src_dict, free=True,
init_source=False)
for c in self.components:
c.simulate_roi('mcsource', clear=False)
self.delete_source('mcsource')
if hasattr(self.like.components[0].logLike, 'setCountsMap'):
self._init_roi_model()
else:
self.write_xml('tmp')
self._like = SummedLikelihood()
for i, c in enumerate(self._components):
c._create_binned_analysis('tmp.xml')
self._like.addComponent(c.like)
self._init_roi_model()
self.load_xml('tmp') | python | def simulate_source(self, src_dict=None):
"""
Inject simulated source counts into the data.
Parameters
----------
src_dict : dict
Dictionary defining the spatial and spectral properties of
the source that will be injected.
"""
self._fitcache = None
if src_dict is None:
src_dict = {}
else:
src_dict = copy.deepcopy(src_dict)
skydir = wcs_utils.get_target_skydir(src_dict, self.roi.skydir)
src_dict.setdefault('ra', skydir.ra.deg)
src_dict.setdefault('dec', skydir.dec.deg)
src_dict.setdefault('SpatialModel', 'PointSource')
src_dict.setdefault('SpatialWidth', 0.3)
src_dict.setdefault('Index', 2.0)
src_dict.setdefault('Prefactor', 1E-13)
self.add_source('mcsource', src_dict, free=True,
init_source=False)
for c in self.components:
c.simulate_roi('mcsource', clear=False)
self.delete_source('mcsource')
if hasattr(self.like.components[0].logLike, 'setCountsMap'):
self._init_roi_model()
else:
self.write_xml('tmp')
self._like = SummedLikelihood()
for i, c in enumerate(self._components):
c._create_binned_analysis('tmp.xml')
self._like.addComponent(c.like)
self._init_roi_model()
self.load_xml('tmp') | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.simulate_roi | def simulate_roi(self, name=None, randomize=True, restore=False):
"""Generate a simulation of the ROI using the current best-fit model
and replace the data counts cube with this simulation. The
simulation is created by generating an array of Poisson random
numbers with expectation values drawn from the model cube of
the binned analysis instance. This function will update the
counts cube both in memory and in the source map file. The
counts cube can be restored to its original state by calling
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Parameters
----------
name : str
Name of the model component to be simulated. If None then
the whole ROI will be simulated.
restore : bool
Restore the data counts cube to its original state.
"""
self.logger.info('Simulating ROI')
self._fitcache = None
if restore:
self.logger.info('Restoring')
self._restore_counts_maps()
self.logger.info('Finished')
return
for c in self.components:
c.simulate_roi(name=name, clear=True, randomize=randomize)
if hasattr(self.like.components[0].logLike, 'setCountsMap'):
self._init_roi_model()
else:
self.write_xml('tmp')
self._like = SummedLikelihood()
for i, c in enumerate(self._components):
c._create_binned_analysis('tmp.xml')
self._like.addComponent(c.like)
self._init_roi_model()
self.load_xml('tmp')
self.logger.info('Finished') | python | def simulate_roi(self, name=None, randomize=True, restore=False):
"""Generate a simulation of the ROI using the current best-fit model
and replace the data counts cube with this simulation. The
simulation is created by generating an array of Poisson random
numbers with expectation values drawn from the model cube of
the binned analysis instance. This function will update the
counts cube both in memory and in the source map file. The
counts cube can be restored to its original state by calling
this method with ``restore`` = True.
Parameters
----------
name : str
Name of the model component to be simulated. If None then
the whole ROI will be simulated.
restore : bool
Restore the data counts cube to its original state.
"""
self.logger.info('Simulating ROI')
self._fitcache = None
if restore:
self.logger.info('Restoring')
self._restore_counts_maps()
self.logger.info('Finished')
return
for c in self.components:
c.simulate_roi(name=name, clear=True, randomize=randomize)
if hasattr(self.like.components[0].logLike, 'setCountsMap'):
self._init_roi_model()
else:
self.write_xml('tmp')
self._like = SummedLikelihood()
for i, c in enumerate(self._components):
c._create_binned_analysis('tmp.xml')
self._like.addComponent(c.like)
self._init_roi_model()
self.load_xml('tmp')
self.logger.info('Finished') | [
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Restore the data counts cube to its original state. | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.load_roi | def load_roi(self, infile, reload_sources=False, params=None, mask=None):
"""This function reloads the analysis state from a previously
saved instance generated with
`~fermipy.gtanalysis.GTAnalysis.write_roi`.
Parameters
----------
infile : str
reload_sources : bool
Regenerate source maps for non-diffuse sources.
params : str
Path to a yaml file with updated parameter values
mask : str
Path to a fits file with an updated mask
"""
infile = utils.resolve_path(infile, workdir=self.workdir)
roi_file, roi_data = utils.load_data(infile, workdir=self.workdir)
self.logger.info('Loading ROI file: %s', roi_file)
key_map = {'dfde': 'dnde',
'dfde100': 'dnde100',
'dfde1000': 'dnde1000',
'dfde10000': 'dnde10000',
'dfde_index': 'dnde_index',
'dfde100_index': 'dnde100_index',
'dfde1000_index': 'dnde1000_index',
'dfde10000_index': 'dnde10000_index',
'e2dfde': 'e2dnde',
'e2dfde100': 'e2dnde100',
'e2dfde1000': 'e2dnde1000',
'e2dfde10000': 'e2dnde10000',
'Npred': 'npred',
'Npred_wt': 'npred_wt',
'logLike': 'loglike',
'dlogLike': 'dloglike',
'emin': 'e_min',
'ectr': 'e_ctr',
'emax': 'e_max',
'logemin': 'loge_min',
'logectr': 'loge_ctr',
'logemax': 'loge_max',
'ref_dfde': 'ref_dnde',
'ref_e2dfde': 'ref_e2dnde',
'ref_dfde_emin': 'ref_dnde_e_min',
'ref_dfde_emax': 'ref_dnde_e_max',
}
self._roi_data = utils.update_keys(roi_data['roi'], key_map)
if 'erange' in self._roi_data:
self._roi_data['loge_bounds'] = self._roi_data.pop('erange')
self._loge_bounds = self._roi_data.setdefault('loge_bounds',
self.loge_bounds)
sources = roi_data.pop('sources')
sources = utils.update_keys(sources, key_map)
for k0, v0 in sources.items():
for k, v in defaults.source_flux_output.items():
if k not in v0:
continue
if v[2] == float and isinstance(v0[k], np.ndarray):
sources[k0][k], sources[k0][k + '_err'] \
= v0[k][0], v0[k][1]
self.roi.load_sources(sources.values())
for i, c in enumerate(self.components):
if 'src_expscale' in self._roi_data['components'][i]:
c._src_expscale = copy.deepcopy(self._roi_data['components']
[i]['src_expscale'])
self._create_likelihood(infile)
self.set_energy_range(self.loge_bounds[0], self.loge_bounds[1])
if params is not None:
self.load_parameters_from_yaml(params)
if mask is not None:
self.set_weights_map(mask, update_roi=False)
if reload_sources:
names = [s.name for s in self.roi.sources if not s.diffuse]
self.reload_sources(names, False)
self.logger.info('Finished Loading ROI') | python | def load_roi(self, infile, reload_sources=False, params=None, mask=None):
"""This function reloads the analysis state from a previously
saved instance generated with
`~fermipy.gtanalysis.GTAnalysis.write_roi`.
Parameters
----------
infile : str
reload_sources : bool
Regenerate source maps for non-diffuse sources.
params : str
Path to a yaml file with updated parameter values
mask : str
Path to a fits file with an updated mask
"""
infile = utils.resolve_path(infile, workdir=self.workdir)
roi_file, roi_data = utils.load_data(infile, workdir=self.workdir)
self.logger.info('Loading ROI file: %s', roi_file)
key_map = {'dfde': 'dnde',
'dfde100': 'dnde100',
'dfde1000': 'dnde1000',
'dfde10000': 'dnde10000',
'dfde_index': 'dnde_index',
'dfde100_index': 'dnde100_index',
'dfde1000_index': 'dnde1000_index',
'dfde10000_index': 'dnde10000_index',
'e2dfde': 'e2dnde',
'e2dfde100': 'e2dnde100',
'e2dfde1000': 'e2dnde1000',
'e2dfde10000': 'e2dnde10000',
'Npred': 'npred',
'Npred_wt': 'npred_wt',
'logLike': 'loglike',
'dlogLike': 'dloglike',
'emin': 'e_min',
'ectr': 'e_ctr',
'emax': 'e_max',
'logemin': 'loge_min',
'logectr': 'loge_ctr',
'logemax': 'loge_max',
'ref_dfde': 'ref_dnde',
'ref_e2dfde': 'ref_e2dnde',
'ref_dfde_emin': 'ref_dnde_e_min',
'ref_dfde_emax': 'ref_dnde_e_max',
}
self._roi_data = utils.update_keys(roi_data['roi'], key_map)
if 'erange' in self._roi_data:
self._roi_data['loge_bounds'] = self._roi_data.pop('erange')
self._loge_bounds = self._roi_data.setdefault('loge_bounds',
self.loge_bounds)
sources = roi_data.pop('sources')
sources = utils.update_keys(sources, key_map)
for k0, v0 in sources.items():
for k, v in defaults.source_flux_output.items():
if k not in v0:
continue
if v[2] == float and isinstance(v0[k], np.ndarray):
sources[k0][k], sources[k0][k + '_err'] \
= v0[k][0], v0[k][1]
self.roi.load_sources(sources.values())
for i, c in enumerate(self.components):
if 'src_expscale' in self._roi_data['components'][i]:
c._src_expscale = copy.deepcopy(self._roi_data['components']
[i]['src_expscale'])
self._create_likelihood(infile)
self.set_energy_range(self.loge_bounds[0], self.loge_bounds[1])
if params is not None:
self.load_parameters_from_yaml(params)
if mask is not None:
self.set_weights_map(mask, update_roi=False)
if reload_sources:
names = [s.name for s in self.roi.sources if not s.diffuse]
self.reload_sources(names, False)
self.logger.info('Finished Loading ROI') | [
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Parameters
----------
infile : str
reload_sources : bool
Regenerate source maps for non-diffuse sources.
params : str
Path to a yaml file with updated parameter values
mask : str
Path to a fits file with an updated mask | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.write_roi | def write_roi(self, outfile=None,
save_model_map=False, **kwargs):
"""Write current state of the analysis to a file. This method
writes an XML model definition, a ROI dictionary, and a FITS
source catalog file. A previously saved analysis state can be
reloaded from the ROI dictionary file with the
`~fermipy.gtanalysis.GTAnalysis.load_roi` method.
Parameters
----------
outfile : str
String prefix of the output files. The extension of this
string will be stripped when generating the XML, YAML and
npy filenames.
make_plots : bool
Generate diagnostic plots.
save_model_map : bool
Save the current counts model to a FITS file.
"""
# extract the results in a convenient format
make_plots = kwargs.get('make_plots', False)
save_weight_map = kwargs.get('save_weight_map', False)
if outfile is None:
pathprefix = os.path.join(self.config['fileio']['workdir'],
'results')
elif not os.path.isabs(outfile):
pathprefix = os.path.join(self.config['fileio']['workdir'],
outfile)
else:
pathprefix = outfile
pathprefix = utils.strip_suffix(pathprefix,
['fits', 'yaml', 'npy'])
# pathprefix, ext = os.path.splitext(pathprefix)
prefix = os.path.basename(pathprefix)
xmlfile = pathprefix + '.xml'
fitsfile = pathprefix + '.fits'
npyfile = pathprefix + '.npy'
self.write_xml(xmlfile)
self.write_fits(fitsfile)
if not self.config['gtlike']['use_external_srcmap']:
for c in self.components:
c.like.logLike.saveSourceMaps(str(c.files['srcmap']))
if save_model_map:
self.write_model_map(prefix)
if save_weight_map:
self.write_weight_map(prefix)
o = {}
o['roi'] = copy.deepcopy(self._roi_data)
o['config'] = copy.deepcopy(self.config)
o['version'] = fermipy.__version__
o['stversion'] = fermipy.get_st_version()
o['sources'] = {}
for s in self.roi.sources:
o['sources'][s.name] = copy.deepcopy(s.data)
for i, c in enumerate(self.components):
o['roi']['components'][i][
'src_expscale'] = copy.deepcopy(c.src_expscale)
self.logger.info('Writing %s...', npyfile)
np.save(npyfile, o)
if make_plots:
self.make_plots(prefix, None,
**kwargs.get('plotting', {})) | python | def write_roi(self, outfile=None,
save_model_map=False, **kwargs):
"""Write current state of the analysis to a file. This method
writes an XML model definition, a ROI dictionary, and a FITS
source catalog file. A previously saved analysis state can be
reloaded from the ROI dictionary file with the
`~fermipy.gtanalysis.GTAnalysis.load_roi` method.
Parameters
----------
outfile : str
String prefix of the output files. The extension of this
string will be stripped when generating the XML, YAML and
npy filenames.
make_plots : bool
Generate diagnostic plots.
save_model_map : bool
Save the current counts model to a FITS file.
"""
# extract the results in a convenient format
make_plots = kwargs.get('make_plots', False)
save_weight_map = kwargs.get('save_weight_map', False)
if outfile is None:
pathprefix = os.path.join(self.config['fileio']['workdir'],
'results')
elif not os.path.isabs(outfile):
pathprefix = os.path.join(self.config['fileio']['workdir'],
outfile)
else:
pathprefix = outfile
pathprefix = utils.strip_suffix(pathprefix,
['fits', 'yaml', 'npy'])
# pathprefix, ext = os.path.splitext(pathprefix)
prefix = os.path.basename(pathprefix)
xmlfile = pathprefix + '.xml'
fitsfile = pathprefix + '.fits'
npyfile = pathprefix + '.npy'
self.write_xml(xmlfile)
self.write_fits(fitsfile)
if not self.config['gtlike']['use_external_srcmap']:
for c in self.components:
c.like.logLike.saveSourceMaps(str(c.files['srcmap']))
if save_model_map:
self.write_model_map(prefix)
if save_weight_map:
self.write_weight_map(prefix)
o = {}
o['roi'] = copy.deepcopy(self._roi_data)
o['config'] = copy.deepcopy(self.config)
o['version'] = fermipy.__version__
o['stversion'] = fermipy.get_st_version()
o['sources'] = {}
for s in self.roi.sources:
o['sources'][s.name] = copy.deepcopy(s.data)
for i, c in enumerate(self.components):
o['roi']['components'][i][
'src_expscale'] = copy.deepcopy(c.src_expscale)
self.logger.info('Writing %s...', npyfile)
np.save(npyfile, o)
if make_plots:
self.make_plots(prefix, None,
**kwargs.get('plotting', {})) | [
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outfile : str
String prefix of the output files. The extension of this
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make_plots : bool
Generate diagnostic plots.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.make_plots | def make_plots(self, prefix, mcube_map=None, **kwargs):
"""Make diagnostic plots using the current ROI model."""
#mcube_maps = kwargs.pop('mcube_maps', None)
if mcube_map is None:
mcube_map = self.model_counts_map()
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.run(self, mcube_map, prefix=prefix, **kwargs) | python | def make_plots(self, prefix, mcube_map=None, **kwargs):
"""Make diagnostic plots using the current ROI model."""
#mcube_maps = kwargs.pop('mcube_maps', None)
if mcube_map is None:
mcube_map = self.model_counts_map()
plotter = plotting.AnalysisPlotter(self.config['plotting'],
fileio=self.config['fileio'],
logging=self.config['logging'])
plotter.run(self, mcube_map, prefix=prefix, **kwargs) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.update_source | def update_source(self, name, paramsonly=False, reoptimize=False, **kwargs):
"""Update the dictionary for this source.
Parameters
----------
name : str
paramsonly : bool
reoptimize : bool
Re-fit background parameters in likelihood scan.
"""
npts = self.config['gtlike']['llscan_npts']
optimizer = kwargs.get('optimizer', self.config['optimizer'])
sd = self.get_src_model(name, paramsonly, reoptimize, npts,
optimizer=optimizer)
src = self.roi.get_source_by_name(name)
src.update_data(sd) | python | def update_source(self, name, paramsonly=False, reoptimize=False, **kwargs):
"""Update the dictionary for this source.
Parameters
----------
name : str
paramsonly : bool
reoptimize : bool
Re-fit background parameters in likelihood scan.
"""
npts = self.config['gtlike']['llscan_npts']
optimizer = kwargs.get('optimizer', self.config['optimizer'])
sd = self.get_src_model(name, paramsonly, reoptimize, npts,
optimizer=optimizer)
src = self.roi.get_source_by_name(name)
src.update_data(sd) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.compute_srcprob | def compute_srcprob(self,xmlfile=None, overwrite=False):
"""Run the gtsrcprob app with the current model or a user provided xmlfile"""
for i,c in enumerate(self.components):
# compute diffuse response, necessary for srcprob
c._diffrsp_app(xmlfile=xmlfile)
# compute srcprob
c._srcprob_app(xmlfile = xmlfile, overwrite = overwrite) | python | def compute_srcprob(self,xmlfile=None, overwrite=False):
"""Run the gtsrcprob app with the current model or a user provided xmlfile"""
for i,c in enumerate(self.components):
# compute diffuse response, necessary for srcprob
c._diffrsp_app(xmlfile=xmlfile)
# compute srcprob
c._srcprob_app(xmlfile = xmlfile, overwrite = overwrite) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.reload_source | def reload_source(self, name):
"""Recompute the source map for a single source in the model.
"""
src = self.roi.get_source_by_name(name)
if hasattr(self.like.logLike, 'loadSourceMap'):
self.like.logLike.loadSourceMap(str(name), True, False)
srcmap_utils.delete_source_map(self.files['srcmap'], name)
self.like.logLike.saveSourceMaps(str(self.files['srcmap']))
self._scale_srcmap(self._src_expscale, check_header=False,
names=[name])
self.like.logLike.buildFixedModelWts()
else:
self.write_xml('tmp')
src = self.delete_source(name)
self.add_source(name, src, free=True)
self.load_xml('tmp') | python | def reload_source(self, name):
"""Recompute the source map for a single source in the model.
"""
src = self.roi.get_source_by_name(name)
if hasattr(self.like.logLike, 'loadSourceMap'):
self.like.logLike.loadSourceMap(str(name), True, False)
srcmap_utils.delete_source_map(self.files['srcmap'], name)
self.like.logLike.saveSourceMaps(str(self.files['srcmap']))
self._scale_srcmap(self._src_expscale, check_header=False,
names=[name])
self.like.logLike.buildFixedModelWts()
else:
self.write_xml('tmp')
src = self.delete_source(name)
self.add_source(name, src, free=True)
self.load_xml('tmp') | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.reload_sources | def reload_sources(self, names):
"""Recompute the source map for a list of sources in the model.
"""
try:
self.like.logLike.loadSourceMaps(names, True, True)
# loadSourceMaps doesn't overwrite the header so we need
# to ignore EXPSCALE by setting check_header=False
self._scale_srcmap(self._src_expscale, check_header=False,
names=names)
except:
for name in names:
self.reload_source(name) | python | def reload_sources(self, names):
"""Recompute the source map for a list of sources in the model.
"""
try:
self.like.logLike.loadSourceMaps(names, True, True)
# loadSourceMaps doesn't overwrite the header so we need
# to ignore EXPSCALE by setting check_header=False
self._scale_srcmap(self._src_expscale, check_header=False,
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except:
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._create_source | def _create_source(self, src):
"""Create a pyLikelihood Source object from a
`~fermipy.roi_model.Model` object."""
if src['SpatialType'] == 'SkyDirFunction':
pylike_src = pyLike.PointSource(self.like.logLike.observation())
pylike_src.setDir(src.skydir.ra.deg, src.skydir.dec.deg, False,
False)
elif src['SpatialType'] == 'SpatialMap':
filepath = str(utils.path_to_xmlpath(src['Spatial_Filename']))
sm = pyLike.SpatialMap(filepath)
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'RadialProfile':
filepath = str(utils.path_to_xmlpath(src['Spatial_Filename']))
sm = pyLike.RadialProfile(filepath)
sm.setCenter(src['ra'], src['dec'])
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'RadialGaussian':
sm = pyLike.RadialGaussian(src.skydir.ra.deg, src.skydir.dec.deg,
src.spatial_pars['Sigma']['value'])
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'RadialDisk':
sm = pyLike.RadialDisk(src.skydir.ra.deg, src.skydir.dec.deg,
src.spatial_pars['Radius']['value'])
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'MapCubeFunction':
filepath = str(utils.path_to_xmlpath(src['Spatial_Filename']))
mcf = pyLike.MapCubeFunction2(filepath)
pylike_src = pyLike.DiffuseSource(mcf,
self.like.logLike.observation(),
False)
else:
raise Exception('Unrecognized spatial type: %s',
src['SpatialType'])
if src['SpectrumType'] == 'FileFunction':
fn = gtutils.create_spectrum_from_dict(src['SpectrumType'],
src.spectral_pars)
file_function = pyLike.FileFunction_cast(fn)
filename = str(os.path.expandvars(src['Spectrum_Filename']))
file_function.readFunction(filename)
elif src['SpectrumType'] == 'DMFitFunction':
fn = pyLike.DMFitFunction()
fn = gtutils.create_spectrum_from_dict(src['SpectrumType'],
src.spectral_pars, fn)
filename = str(os.path.expandvars(src['Spectrum_Filename']))
fn.readFunction(filename)
else:
fn = gtutils.create_spectrum_from_dict(src['SpectrumType'],
src.spectral_pars)
pylike_src.setSpectrum(fn)
pylike_src.setName(str(src.name))
return pylike_src | python | def _create_source(self, src):
"""Create a pyLikelihood Source object from a
`~fermipy.roi_model.Model` object."""
if src['SpatialType'] == 'SkyDirFunction':
pylike_src = pyLike.PointSource(self.like.logLike.observation())
pylike_src.setDir(src.skydir.ra.deg, src.skydir.dec.deg, False,
False)
elif src['SpatialType'] == 'SpatialMap':
filepath = str(utils.path_to_xmlpath(src['Spatial_Filename']))
sm = pyLike.SpatialMap(filepath)
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'RadialProfile':
filepath = str(utils.path_to_xmlpath(src['Spatial_Filename']))
sm = pyLike.RadialProfile(filepath)
sm.setCenter(src['ra'], src['dec'])
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'RadialGaussian':
sm = pyLike.RadialGaussian(src.skydir.ra.deg, src.skydir.dec.deg,
src.spatial_pars['Sigma']['value'])
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'RadialDisk':
sm = pyLike.RadialDisk(src.skydir.ra.deg, src.skydir.dec.deg,
src.spatial_pars['Radius']['value'])
pylike_src = pyLike.DiffuseSource(sm,
self.like.logLike.observation(),
False)
elif src['SpatialType'] == 'MapCubeFunction':
filepath = str(utils.path_to_xmlpath(src['Spatial_Filename']))
mcf = pyLike.MapCubeFunction2(filepath)
pylike_src = pyLike.DiffuseSource(mcf,
self.like.logLike.observation(),
False)
else:
raise Exception('Unrecognized spatial type: %s',
src['SpatialType'])
if src['SpectrumType'] == 'FileFunction':
fn = gtutils.create_spectrum_from_dict(src['SpectrumType'],
src.spectral_pars)
file_function = pyLike.FileFunction_cast(fn)
filename = str(os.path.expandvars(src['Spectrum_Filename']))
file_function.readFunction(filename)
elif src['SpectrumType'] == 'DMFitFunction':
fn = pyLike.DMFitFunction()
fn = gtutils.create_spectrum_from_dict(src['SpectrumType'],
src.spectral_pars, fn)
filename = str(os.path.expandvars(src['Spectrum_Filename']))
fn.readFunction(filename)
else:
fn = gtutils.create_spectrum_from_dict(src['SpectrumType'],
src.spectral_pars)
pylike_src.setSpectrum(fn)
pylike_src.setName(str(src.name))
return pylike_src | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.set_exposure_scale | def set_exposure_scale(self, name, scale=None):
"""Set the exposure correction of a source.
Parameters
----------
name : str
Source name.
scale : factor
Exposure scale factor (1.0 = nominal exposure).
"""
name = self.roi.get_source_by_name(name).name
if scale is None and name not in self._src_expscale:
return
elif scale is None:
scale = self._src_expscale.get(name, 1.0)
else:
self._src_expscale[name] = scale
self._scale_srcmap({name: scale}) | python | def set_exposure_scale(self, name, scale=None):
"""Set the exposure correction of a source.
Parameters
----------
name : str
Source name.
scale : factor
Exposure scale factor (1.0 = nominal exposure).
"""
name = self.roi.get_source_by_name(name).name
if scale is None and name not in self._src_expscale:
return
elif scale is None:
scale = self._src_expscale.get(name, 1.0)
else:
self._src_expscale[name] = scale
self._scale_srcmap({name: scale}) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.set_energy_range | def set_energy_range(self, logemin, logemax):
"""Set the energy range of the analysis.
Parameters
----------
logemin: float
Lower end of energy range in log10(E/MeV).
logemax : float
Upper end of energy range in log10(E/MeV).
"""
if logemin is None:
logemin = self.log_energies[0]
if logemax is None:
logemax = self.log_energies[-1]
imin = int(utils.val_to_edge(self.log_energies, logemin)[0])
imax = int(utils.val_to_edge(self.log_energies, logemax)[0])
if imin - imax == 0:
imin = int(len(self.log_energies) - 1)
imax = int(len(self.log_energies) - 1)
klims = self.like.logLike.klims()
if imin != klims[0] or imax != klims[1]:
self.like.selectEbounds(imin, imax)
return np.array([self.log_energies[imin], self.log_energies[imax]]) | python | def set_energy_range(self, logemin, logemax):
"""Set the energy range of the analysis.
Parameters
----------
logemin: float
Lower end of energy range in log10(E/MeV).
logemax : float
Upper end of energy range in log10(E/MeV).
"""
if logemin is None:
logemin = self.log_energies[0]
if logemax is None:
logemax = self.log_energies[-1]
imin = int(utils.val_to_edge(self.log_energies, logemin)[0])
imax = int(utils.val_to_edge(self.log_energies, logemax)[0])
if imin - imax == 0:
imin = int(len(self.log_energies) - 1)
imax = int(len(self.log_energies) - 1)
klims = self.like.logLike.klims()
if imin != klims[0] or imax != klims[1]:
self.like.selectEbounds(imin, imax)
return np.array([self.log_energies[imin], self.log_energies[imax]]) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.counts_map | def counts_map(self):
"""Return 3-D counts map for this component as a Map object.
Returns
-------
map : `~fermipy.skymap.MapBase`
"""
try:
if isinstance(self.like, gtutils.SummedLikelihood):
cmap = self.like.components[0].logLike.countsMap()
p_method = cmap.projection().method()
else:
cmap = self.like.logLike.countsMap()
p_method = cmap.projection().method()
except Exception:
p_method = 0
if p_method == 0: # WCS
z = cmap.data()
z = np.array(z).reshape(self.enumbins, self.npix, self.npix)
return WcsNDMap(copy.deepcopy(self.geom), z)
elif p_method == 1: # HPX
z = cmap.data()
z = np.array(z).reshape(self.enumbins, np.max(self.geom.npix))
return HpxNDMap(copy.deepcopy(self.geom), z)
else:
self.logger.error('Did not recognize CountsMap type %i' % p_method,
exc_info=True)
return None | python | def counts_map(self):
"""Return 3-D counts map for this component as a Map object.
Returns
-------
map : `~fermipy.skymap.MapBase`
"""
try:
if isinstance(self.like, gtutils.SummedLikelihood):
cmap = self.like.components[0].logLike.countsMap()
p_method = cmap.projection().method()
else:
cmap = self.like.logLike.countsMap()
p_method = cmap.projection().method()
except Exception:
p_method = 0
if p_method == 0: # WCS
z = cmap.data()
z = np.array(z).reshape(self.enumbins, self.npix, self.npix)
return WcsNDMap(copy.deepcopy(self.geom), z)
elif p_method == 1: # HPX
z = cmap.data()
z = np.array(z).reshape(self.enumbins, np.max(self.geom.npix))
return HpxNDMap(copy.deepcopy(self.geom), z)
else:
self.logger.error('Did not recognize CountsMap type %i' % p_method,
exc_info=True)
return None | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.weight_map | def weight_map(self):
"""Return 3-D weights map for this component as a Map object.
Returns
-------
map : `~fermipy.skymap.MapBase`
"""
# EAC we need the try blocks b/c older versions of the ST don't have some of these functions
if isinstance(self.like, gtutils.SummedLikelihood):
cmap = self.like.components[0].logLike.countsMap()
try:
p_method = cmap.projection().method()
except AttributeError:
p_method = 0
try:
if self.like.components[0].logLike.has_weights():
wmap = self.like.components[0].logLike.weightMap()
else:
wmap = None
except Exception:
wmap = None
else:
cmap = self.like.logLike.countsMap()
try:
p_method = cmap.projection().method()
except AttributeError:
p_method = 0
try:
if self.like.logLike.has_weights():
wmap = self.like.logLike.weightMap()
else:
wmap = None
except Exception:
wmap = None
if p_method == 0: # WCS
if wmap is None:
z = np.ones((self.enumbins, self.npix, self.npix))
else:
z = wmap.model()
z = np.array(z).reshape(self.enumbins, self.npix, self.npix)
return WcsNDMap(copy.deepcopy(self._geom), z)
elif p_method == 1: # HPX
nhpix = np.max(self.geom.npix)
if wmap is None:
z = np.ones((self.enumbins, nhpix))
else:
z = wmap.model()
z = np.array(z).reshape(self.enumbins, nhpix)
return HpxNDMap(self.geom, z)
else:
self.logger.error('Did not recognize CountsMap type %i' % p_method,
exc_info=True)
return None | python | def weight_map(self):
"""Return 3-D weights map for this component as a Map object.
Returns
-------
map : `~fermipy.skymap.MapBase`
"""
# EAC we need the try blocks b/c older versions of the ST don't have some of these functions
if isinstance(self.like, gtutils.SummedLikelihood):
cmap = self.like.components[0].logLike.countsMap()
try:
p_method = cmap.projection().method()
except AttributeError:
p_method = 0
try:
if self.like.components[0].logLike.has_weights():
wmap = self.like.components[0].logLike.weightMap()
else:
wmap = None
except Exception:
wmap = None
else:
cmap = self.like.logLike.countsMap()
try:
p_method = cmap.projection().method()
except AttributeError:
p_method = 0
try:
if self.like.logLike.has_weights():
wmap = self.like.logLike.weightMap()
else:
wmap = None
except Exception:
wmap = None
if p_method == 0: # WCS
if wmap is None:
z = np.ones((self.enumbins, self.npix, self.npix))
else:
z = wmap.model()
z = np.array(z).reshape(self.enumbins, self.npix, self.npix)
return WcsNDMap(copy.deepcopy(self._geom), z)
elif p_method == 1: # HPX
nhpix = np.max(self.geom.npix)
if wmap is None:
z = np.ones((self.enumbins, nhpix))
else:
z = wmap.model()
z = np.array(z).reshape(self.enumbins, nhpix)
return HpxNDMap(self.geom, z)
else:
self.logger.error('Did not recognize CountsMap type %i' % p_method,
exc_info=True)
return None | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.model_counts_spectrum | def model_counts_spectrum(self, name, logemin, logemax, weighted=False):
"""Return the model counts spectrum of a source.
Parameters
----------
name : str
Source name.
"""
# EAC, we need this b/c older version of the ST don't have the right signature
try:
cs = np.array(self.like.logLike.modelCountsSpectrum(
str(name), weighted))
except (TypeError, NotImplementedError):
cs = np.array(self.like.logLike.modelCountsSpectrum(str(name)))
imin = utils.val_to_edge(self.log_energies, logemin)[0]
imax = utils.val_to_edge(self.log_energies, logemax)[0]
if imax <= imin:
raise Exception('Invalid energy range.')
return cs[imin:imax] | python | def model_counts_spectrum(self, name, logemin, logemax, weighted=False):
"""Return the model counts spectrum of a source.
Parameters
----------
name : str
Source name.
"""
# EAC, we need this b/c older version of the ST don't have the right signature
try:
cs = np.array(self.like.logLike.modelCountsSpectrum(
str(name), weighted))
except (TypeError, NotImplementedError):
cs = np.array(self.like.logLike.modelCountsSpectrum(str(name)))
imin = utils.val_to_edge(self.log_energies, logemin)[0]
imax = utils.val_to_edge(self.log_energies, logemax)[0]
if imax <= imin:
raise Exception('Invalid energy range.')
return cs[imin:imax] | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.setup | def setup(self, overwrite=False, **kwargs):
"""Run pre-processing step for this component. This will
generate all of the auxiliary files needed to instantiate a
likelihood object. By default this function will skip any
steps for which the output file already exists.
Parameters
----------
overwrite : bool
Run all pre-processing steps even if the output file of
that step is present in the working directory.
"""
loglevel = kwargs.get('loglevel', self.loglevel)
self.logger.log(loglevel, 'Running setup for component %s',
self.name)
use_external_srcmap = self.config['gtlike']['use_external_srcmap']
# Run data selection
if not use_external_srcmap:
self._select_data(overwrite=overwrite, **kwargs)
# Create LT Cube
if self._ext_ltcube is not None:
self.logger.log(loglevel, 'Using external LT cube.')
else:
self._create_ltcube(overwrite=overwrite, **kwargs)
self.logger.debug('Loading LT Cube %s', self.files['ltcube'])
self._ltc = LTCube.create(self.files['ltcube'])
# Extract tmin, tmax from LT cube
self._tmin = self._ltc.tstart
self._tmax = self._ltc.tstop
self.logger.debug('Creating PSF model')
self._psf = irfs.PSFModel.create(self.roi.skydir, self._ltc,
self.config['gtlike']['irfs'],
self.config['selection']['evtype'],
self.energies)
# Bin data and create exposure cube
if not use_external_srcmap:
self._bin_data(overwrite=overwrite, **kwargs)
self._create_expcube(overwrite=overwrite, **kwargs)
# This is needed in case the exposure map is in HEALPix
hpxhduname = "HPXEXPOSURES"
try:
self._bexp = Map.read(self.files['bexpmap'], hdu=hpxhduname)
except KeyError:
self._bexp = Map.read(self.files['bexpmap'])
# Write ROI XML
self.roi.write_xml(self.files['srcmdl'], self.config['model'])
# Create source maps file
if not use_external_srcmap:
self._create_srcmaps(overwrite=overwrite)
if not self.config['data']['cacheft1'] and os.path.isfile(self.files['ft1']):
self.logger.debug('Deleting FT1 file.')
os.remove(self.files['ft1'])
self.logger.log(loglevel, 'Finished setup for component %s',
self.name) | python | def setup(self, overwrite=False, **kwargs):
"""Run pre-processing step for this component. This will
generate all of the auxiliary files needed to instantiate a
likelihood object. By default this function will skip any
steps for which the output file already exists.
Parameters
----------
overwrite : bool
Run all pre-processing steps even if the output file of
that step is present in the working directory.
"""
loglevel = kwargs.get('loglevel', self.loglevel)
self.logger.log(loglevel, 'Running setup for component %s',
self.name)
use_external_srcmap = self.config['gtlike']['use_external_srcmap']
# Run data selection
if not use_external_srcmap:
self._select_data(overwrite=overwrite, **kwargs)
# Create LT Cube
if self._ext_ltcube is not None:
self.logger.log(loglevel, 'Using external LT cube.')
else:
self._create_ltcube(overwrite=overwrite, **kwargs)
self.logger.debug('Loading LT Cube %s', self.files['ltcube'])
self._ltc = LTCube.create(self.files['ltcube'])
# Extract tmin, tmax from LT cube
self._tmin = self._ltc.tstart
self._tmax = self._ltc.tstop
self.logger.debug('Creating PSF model')
self._psf = irfs.PSFModel.create(self.roi.skydir, self._ltc,
self.config['gtlike']['irfs'],
self.config['selection']['evtype'],
self.energies)
# Bin data and create exposure cube
if not use_external_srcmap:
self._bin_data(overwrite=overwrite, **kwargs)
self._create_expcube(overwrite=overwrite, **kwargs)
# This is needed in case the exposure map is in HEALPix
hpxhduname = "HPXEXPOSURES"
try:
self._bexp = Map.read(self.files['bexpmap'], hdu=hpxhduname)
except KeyError:
self._bexp = Map.read(self.files['bexpmap'])
# Write ROI XML
self.roi.write_xml(self.files['srcmdl'], self.config['model'])
# Create source maps file
if not use_external_srcmap:
self._create_srcmaps(overwrite=overwrite)
if not self.config['data']['cacheft1'] and os.path.isfile(self.files['ft1']):
self.logger.debug('Deleting FT1 file.')
os.remove(self.files['ft1'])
self.logger.log(loglevel, 'Finished setup for component %s',
self.name) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._scale_srcmap | def _scale_srcmap(self, scale_map, check_header=True, names=None):
"""Apply exposure corrections to the source map file.
Parameters
----------
scale_map : dict
Dictionary of exposure corrections.
check_header : bool
Check EXPSCALE header keyword to see if an exposure
correction has already been applied to this source.
names : list, optional
Names of sources to which the exposure correction will be
applied. If None then all sources will be corrected.
"""
srcmap = fits.open(self.files['srcmap'])
for hdu in srcmap[1:]:
if hdu.name not in scale_map:
continue
if names is not None and hdu.name not in names:
continue
scale = scale_map[hdu.name]
if scale < 1e-20:
self.logger.warning(
"The expscale parameter was zero, setting it to 1e-8")
scale = 1e-8
if 'EXPSCALE' in hdu.header and check_header:
old_scale = hdu.header['EXPSCALE']
else:
old_scale = 1.0
hdu.data *= scale / old_scale
hdu.header['EXPSCALE'] = (scale,
'Exposure correction applied to this map')
srcmap.writeto(self.files['srcmap'], overwrite=True)
srcmap.close()
# Force reloading the map from disk
for name in scale_map.keys():
self.like.logLike.eraseSourceMap(str(name))
self.like.logLike.buildFixedModelWts() | python | def _scale_srcmap(self, scale_map, check_header=True, names=None):
"""Apply exposure corrections to the source map file.
Parameters
----------
scale_map : dict
Dictionary of exposure corrections.
check_header : bool
Check EXPSCALE header keyword to see if an exposure
correction has already been applied to this source.
names : list, optional
Names of sources to which the exposure correction will be
applied. If None then all sources will be corrected.
"""
srcmap = fits.open(self.files['srcmap'])
for hdu in srcmap[1:]:
if hdu.name not in scale_map:
continue
if names is not None and hdu.name not in names:
continue
scale = scale_map[hdu.name]
if scale < 1e-20:
self.logger.warning(
"The expscale parameter was zero, setting it to 1e-8")
scale = 1e-8
if 'EXPSCALE' in hdu.header and check_header:
old_scale = hdu.header['EXPSCALE']
else:
old_scale = 1.0
hdu.data *= scale / old_scale
hdu.header['EXPSCALE'] = (scale,
'Exposure correction applied to this map')
srcmap.writeto(self.files['srcmap'], overwrite=True)
srcmap.close()
# Force reloading the map from disk
for name in scale_map.keys():
self.like.logLike.eraseSourceMap(str(name))
self.like.logLike.buildFixedModelWts() | [
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Check EXPSCALE header keyword to see if an exposure
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Names of sources to which the exposure correction will be
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._make_scaled_srcmap | def _make_scaled_srcmap(self):
"""Make an exposure cube with the same binning as the counts map."""
self.logger.info('Computing scaled source map.')
bexp0 = fits.open(self.files['bexpmap_roi'])
bexp1 = fits.open(self.config['gtlike']['bexpmap'])
srcmap = fits.open(self.config['gtlike']['srcmap'])
if bexp0[0].data.shape != bexp1[0].data.shape:
raise Exception('Wrong shape for input exposure map file.')
bexp_ratio = bexp0[0].data / bexp1[0].data
self.logger.info(
'Min/Med/Max exposure correction: %f %f %f' % (np.min(bexp_ratio),
np.median(
bexp_ratio),
np.max(bexp_ratio)))
for hdu in srcmap[1:]:
if hdu.name == 'GTI':
continue
if hdu.name == 'EBOUNDS':
continue
hdu.data *= bexp_ratio
srcmap.writeto(self.files['srcmap'], overwrite=True) | python | def _make_scaled_srcmap(self):
"""Make an exposure cube with the same binning as the counts map."""
self.logger.info('Computing scaled source map.')
bexp0 = fits.open(self.files['bexpmap_roi'])
bexp1 = fits.open(self.config['gtlike']['bexpmap'])
srcmap = fits.open(self.config['gtlike']['srcmap'])
if bexp0[0].data.shape != bexp1[0].data.shape:
raise Exception('Wrong shape for input exposure map file.')
bexp_ratio = bexp0[0].data / bexp1[0].data
self.logger.info(
'Min/Med/Max exposure correction: %f %f %f' % (np.min(bexp_ratio),
np.median(
bexp_ratio),
np.max(bexp_ratio)))
for hdu in srcmap[1:]:
if hdu.name == 'GTI':
continue
if hdu.name == 'EBOUNDS':
continue
hdu.data *= bexp_ratio
srcmap.writeto(self.files['srcmap'], overwrite=True) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.simulate_roi | def simulate_roi(self, name=None, clear=True, randomize=True):
"""Simulate the whole ROI or inject a simulation of one or
more model components into the data.
Parameters
----------
name : str
Name of the model component to be simulated. If None then
the whole ROI will be simulated.
clear : bool
Zero the current counts map before injecting the simulation.
randomize : bool
Fill with each pixel with random values drawn from a
poisson distribution. If false then fill each pixel with
the counts expectation value.
"""
cm = self.counts_map()
data = cm.data
m = self.model_counts_map(name)
if clear:
data.fill(0.0)
if randomize:
if m.data.min()<0.:
self.logger.warning('At least on negative value found in model map.'
' Changing it/them to 0')
indexcond = np.where( m.data <0. )
m.data[indexcond]=np.zeros(len(m.data[indexcond]))
data += np.random.poisson(m.data).astype(float)
else:
data += m.data
if hasattr(self.like.logLike, 'setCountsMap'):
self.like.logLike.setCountsMap(np.ravel(data))
srcmap_utils.update_source_maps(self.files['srcmap'],
{'PRIMARY': data},
logger=self.logger)
cm.write(self.files['ccubemc'], overwrite=True, conv='fgst-ccube') | python | def simulate_roi(self, name=None, clear=True, randomize=True):
"""Simulate the whole ROI or inject a simulation of one or
more model components into the data.
Parameters
----------
name : str
Name of the model component to be simulated. If None then
the whole ROI will be simulated.
clear : bool
Zero the current counts map before injecting the simulation.
randomize : bool
Fill with each pixel with random values drawn from a
poisson distribution. If false then fill each pixel with
the counts expectation value.
"""
cm = self.counts_map()
data = cm.data
m = self.model_counts_map(name)
if clear:
data.fill(0.0)
if randomize:
if m.data.min()<0.:
self.logger.warning('At least on negative value found in model map.'
' Changing it/them to 0')
indexcond = np.where( m.data <0. )
m.data[indexcond]=np.zeros(len(m.data[indexcond]))
data += np.random.poisson(m.data).astype(float)
else:
data += m.data
if hasattr(self.like.logLike, 'setCountsMap'):
self.like.logLike.setCountsMap(np.ravel(data))
srcmap_utils.update_source_maps(self.files['srcmap'],
{'PRIMARY': data},
logger=self.logger)
cm.write(self.files['ccubemc'], overwrite=True, conv='fgst-ccube') | [
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Zero the current counts map before injecting the simulation.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._update_srcmap_file | def _update_srcmap_file(self, sources, overwrite=True):
"""Check the contents of the source map file and generate
source maps for any components that are not present."""
if not os.path.isfile(self.files['srcmap']):
return
hdulist = fits.open(self.files['srcmap'])
hdunames = [hdu.name.upper() for hdu in hdulist]
srcmaps = {}
for src in sources:
if src.name.upper() in hdunames and not overwrite:
continue
self.logger.debug('Creating source map for %s', src.name)
srcmaps[src.name] = self._create_srcmap(src.name, src)
if srcmaps:
self.logger.debug(
'Updating source map file for component %s.', self.name)
srcmap_utils.update_source_maps(self.files['srcmap'], srcmaps,
logger=self.logger)
hdulist.close() | python | def _update_srcmap_file(self, sources, overwrite=True):
"""Check the contents of the source map file and generate
source maps for any components that are not present."""
if not os.path.isfile(self.files['srcmap']):
return
hdulist = fits.open(self.files['srcmap'])
hdunames = [hdu.name.upper() for hdu in hdulist]
srcmaps = {}
for src in sources:
if src.name.upper() in hdunames and not overwrite:
continue
self.logger.debug('Creating source map for %s', src.name)
srcmaps[src.name] = self._create_srcmap(src.name, src)
if srcmaps:
self.logger.debug(
'Updating source map file for component %s.', self.name)
srcmap_utils.update_source_maps(self.files['srcmap'], srcmaps,
logger=self.logger)
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._create_srcmap | def _create_srcmap(self, name, src, **kwargs):
"""Generate the source map for a source."""
psf_scale_fn = kwargs.get('psf_scale_fn', None)
skydir = src.skydir
spatial_model = src['SpatialModel']
spatial_width = src['SpatialWidth']
xpix, ypix = self.geom.to_image().coord_to_pix(skydir)
exp = self._bexp.interp_by_coord(
(skydir, self._bexp.geom.axes[0].center))
cache = self._srcmap_cache.get(name, None)
if cache is not None:
k = cache.create_map([ypix, xpix])
else:
k = srcmap_utils.make_srcmap(self._psf, exp, spatial_model,
spatial_width,
npix=self.npix, xpix=xpix, ypix=ypix,
cdelt=self.config['binning']['binsz'],
psf_scale_fn=psf_scale_fn,
sparse=True)
return k | python | def _create_srcmap(self, name, src, **kwargs):
"""Generate the source map for a source."""
psf_scale_fn = kwargs.get('psf_scale_fn', None)
skydir = src.skydir
spatial_model = src['SpatialModel']
spatial_width = src['SpatialWidth']
xpix, ypix = self.geom.to_image().coord_to_pix(skydir)
exp = self._bexp.interp_by_coord(
(skydir, self._bexp.geom.axes[0].center))
cache = self._srcmap_cache.get(name, None)
if cache is not None:
k = cache.create_map([ypix, xpix])
else:
k = srcmap_utils.make_srcmap(self._psf, exp, spatial_model,
spatial_width,
npix=self.npix, xpix=xpix, ypix=ypix,
cdelt=self.config['binning']['binsz'],
psf_scale_fn=psf_scale_fn,
sparse=True)
return k | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._update_srcmap | def _update_srcmap(self, name, src, **kwargs):
"""Update the source map for an existing source in memory."""
k = self._create_srcmap(name, src, **kwargs)
scale = self._src_expscale.get(name, 1.0)
k *= scale
# Force the source map to be cached
# FIXME: No longer necessary to force cacheing in ST after 11-05-02
self.like.logLike.sourceMap(str(name)).model()
self.like.logLike.setSourceMapImage(str(name), np.ravel(k))
self.like.logLike.sourceMap(str(name)).model()
normPar = self.like.normPar(name)
if not normPar.isFree():
self.like.logLike.buildFixedModelWts() | python | def _update_srcmap(self, name, src, **kwargs):
"""Update the source map for an existing source in memory."""
k = self._create_srcmap(name, src, **kwargs)
scale = self._src_expscale.get(name, 1.0)
k *= scale
# Force the source map to be cached
# FIXME: No longer necessary to force cacheing in ST after 11-05-02
self.like.logLike.sourceMap(str(name)).model()
self.like.logLike.setSourceMapImage(str(name), np.ravel(k))
self.like.logLike.sourceMap(str(name)).model()
normPar = self.like.normPar(name)
if not normPar.isFree():
self.like.logLike.buildFixedModelWts() | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.generate_model | def generate_model(self, model_name=None, outfile=None):
"""Generate a counts model map from an XML model file using
gtmodel.
Parameters
----------
model_name : str
Name of the model. If no name is given it will use the
baseline model.
outfile : str
Override the name of the output model file.
"""
if model_name is not None:
model_name = os.path.splitext(model_name)[0]
if model_name is None or model_name == '':
srcmdl = self.files['srcmdl']
else:
srcmdl = self.get_model_path(model_name)
if not os.path.isfile(srcmdl):
raise Exception("Model file does not exist: %s", srcmdl)
if model_name is None:
suffix = self.config['file_suffix']
else:
suffix = '_%s%s' % (model_name, self.config['file_suffix'])
outfile = os.path.join(self.config['fileio']['workdir'],
'mcube%s.fits' % (suffix))
# May consider generating a custom source model file
if not os.path.isfile(outfile):
kw = dict(srcmaps=self.files['srcmap'],
srcmdl=srcmdl,
bexpmap=self.files['bexpmap'],
outfile=outfile,
expcube=self.files['ltcube'],
irfs=self.config['gtlike']['irfs'],
evtype=self.config['selection']['evtype'],
edisp=bool(self.config['gtlike']['edisp']),
outtype='ccube',
chatter=self.config['logging']['chatter'])
run_gtapp('gtmodel', self.logger, kw)
else:
self.logger.info('Skipping gtmodel') | python | def generate_model(self, model_name=None, outfile=None):
"""Generate a counts model map from an XML model file using
gtmodel.
Parameters
----------
model_name : str
Name of the model. If no name is given it will use the
baseline model.
outfile : str
Override the name of the output model file.
"""
if model_name is not None:
model_name = os.path.splitext(model_name)[0]
if model_name is None or model_name == '':
srcmdl = self.files['srcmdl']
else:
srcmdl = self.get_model_path(model_name)
if not os.path.isfile(srcmdl):
raise Exception("Model file does not exist: %s", srcmdl)
if model_name is None:
suffix = self.config['file_suffix']
else:
suffix = '_%s%s' % (model_name, self.config['file_suffix'])
outfile = os.path.join(self.config['fileio']['workdir'],
'mcube%s.fits' % (suffix))
# May consider generating a custom source model file
if not os.path.isfile(outfile):
kw = dict(srcmaps=self.files['srcmap'],
srcmdl=srcmdl,
bexpmap=self.files['bexpmap'],
outfile=outfile,
expcube=self.files['ltcube'],
irfs=self.config['gtlike']['irfs'],
evtype=self.config['selection']['evtype'],
edisp=bool(self.config['gtlike']['edisp']),
outtype='ccube',
chatter=self.config['logging']['chatter'])
run_gtapp('gtmodel', self.logger, kw)
else:
self.logger.info('Skipping gtmodel') | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.write_xml | def write_xml(self, xmlfile):
"""Write the XML model for this analysis component."""
xmlfile = self.get_model_path(xmlfile)
self.logger.info('Writing %s...', xmlfile)
self.like.writeXml(str(xmlfile)) | python | def write_xml(self, xmlfile):
"""Write the XML model for this analysis component."""
xmlfile = self.get_model_path(xmlfile)
self.logger.info('Writing %s...', xmlfile)
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis.get_model_path | def get_model_path(self, name):
"""Infer the path to the XML model name."""
name, ext = os.path.splitext(name)
ext = '.xml'
xmlfile = name + self.config['file_suffix'] + ext
xmlfile = utils.resolve_path(xmlfile,
workdir=self.config['fileio']['workdir'])
return xmlfile | python | def get_model_path(self, name):
"""Infer the path to the XML model name."""
name, ext = os.path.splitext(name)
ext = '.xml'
xmlfile = name + self.config['file_suffix'] + ext
xmlfile = utils.resolve_path(xmlfile,
workdir=self.config['fileio']['workdir'])
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._tscube_app | def _tscube_app(self, xmlfile):
"""Run gttscube as an application."""
xmlfile = self.get_model_path(xmlfile)
outfile = os.path.join(self.config['fileio']['workdir'],
'tscube%s.fits' % (self.config['file_suffix']))
kw = dict(cmap=self.files['ccube'],
expcube=self.files['ltcube'],
bexpmap=self.files['bexpmap'],
irfs=self.config['gtlike']['irfs'],
evtype=self.config['selection']['evtype'],
srcmdl=xmlfile,
nxpix=self.npix, nypix=self.npix,
binsz=self.config['binning']['binsz'],
xref=float(self.roi.skydir.ra.deg),
yref=float(self.roi.skydir.dec.deg),
proj=self.config['binning']['proj'],
stlevel=0,
coordsys=self.config['binning']['coordsys'],
outfile=outfile)
run_gtapp('gttscube', self.logger, kw) | python | def _tscube_app(self, xmlfile):
"""Run gttscube as an application."""
xmlfile = self.get_model_path(xmlfile)
outfile = os.path.join(self.config['fileio']['workdir'],
'tscube%s.fits' % (self.config['file_suffix']))
kw = dict(cmap=self.files['ccube'],
expcube=self.files['ltcube'],
bexpmap=self.files['bexpmap'],
irfs=self.config['gtlike']['irfs'],
evtype=self.config['selection']['evtype'],
srcmdl=xmlfile,
nxpix=self.npix, nypix=self.npix,
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xref=float(self.roi.skydir.ra.deg),
yref=float(self.roi.skydir.dec.deg),
proj=self.config['binning']['proj'],
stlevel=0,
coordsys=self.config['binning']['coordsys'],
outfile=outfile)
run_gtapp('gttscube', self.logger, kw) | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._diffrsp_app | def _diffrsp_app(self,xmlfile=None, **kwargs):
"""
Compute the diffuse response
"""
loglevel = kwargs.get('loglevel', self.loglevel)
self.logger.log(loglevel, 'Computing diffuse repsonce for component %s.',
self.name)
# set the srcmdl
srcmdl_file = self.files['srcmdl']
if xmlfile is not None:
srcmdl_file = self.get_model_path(xmlfile)
kw = dict(evfile=self.files['ft1'],
scfile=self.data_files['scfile'],
irfs = self.config['gtlike']['irfs'],
evtype = self.config['selection']['evtype'],
srcmdl = srcmdl_file)
run_gtapp('gtdiffrsp', self.logger, kw, loglevel=loglevel)
return | python | def _diffrsp_app(self,xmlfile=None, **kwargs):
"""
Compute the diffuse response
"""
loglevel = kwargs.get('loglevel', self.loglevel)
self.logger.log(loglevel, 'Computing diffuse repsonce for component %s.',
self.name)
# set the srcmdl
srcmdl_file = self.files['srcmdl']
if xmlfile is not None:
srcmdl_file = self.get_model_path(xmlfile)
kw = dict(evfile=self.files['ft1'],
scfile=self.data_files['scfile'],
irfs = self.config['gtlike']['irfs'],
evtype = self.config['selection']['evtype'],
srcmdl = srcmdl_file)
run_gtapp('gtdiffrsp', self.logger, kw, loglevel=loglevel)
return | [
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fermiPy/fermipy | fermipy/gtanalysis.py | GTBinnedAnalysis._srcprob_app | def _srcprob_app(self,xmlfile=None, overwrite=False, **kwargs):
"""
Run srcprob for an analysis component as an application
"""
loglevel = kwargs.get('loglevel', self.loglevel)
self.logger.log(loglevel, 'Computing src probability for component %s.',
self.name)
# set the srcmdl
srcmdl_file = self.files['srcmdl']
if xmlfile is not None:
srcmdl_file = self.get_model_path(xmlfile)
# set the outfile
# it's defined here and not in self.files dict
# so that it is copied with the stage_output module
# even if savefits is False
outfile = os.path.join(self.workdir,
'ft1_srcprob{0[file_suffix]:s}.fits'.format(self.config))
kw = dict(evfile=self.files['ft1'],
scfile=self.data_files['scfile'],
outfile= outfile,
irfs = self.config['gtlike']['irfs'],
srcmdl = srcmdl_file)
self.logger.debug(kw)
# run gtapp for the srcprob
if os.path.isfile(outfile) and not overwrite:
self.logger.info('Skipping gtsrcprob')
else:
run_gtapp('gtsrcprob', self.logger, kw, loglevel=loglevel) | python | def _srcprob_app(self,xmlfile=None, overwrite=False, **kwargs):
"""
Run srcprob for an analysis component as an application
"""
loglevel = kwargs.get('loglevel', self.loglevel)
self.logger.log(loglevel, 'Computing src probability for component %s.',
self.name)
# set the srcmdl
srcmdl_file = self.files['srcmdl']
if xmlfile is not None:
srcmdl_file = self.get_model_path(xmlfile)
# set the outfile
# it's defined here and not in self.files dict
# so that it is copied with the stage_output module
# even if savefits is False
outfile = os.path.join(self.workdir,
'ft1_srcprob{0[file_suffix]:s}.fits'.format(self.config))
kw = dict(evfile=self.files['ft1'],
scfile=self.data_files['scfile'],
outfile= outfile,
irfs = self.config['gtlike']['irfs'],
srcmdl = srcmdl_file)
self.logger.debug(kw)
# run gtapp for the srcprob
if os.path.isfile(outfile) and not overwrite:
self.logger.info('Skipping gtsrcprob')
else:
run_gtapp('gtsrcprob', self.logger, kw, loglevel=loglevel) | [
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fermiPy/fermipy | fermipy/jobs/chain.py | purge_dict | def purge_dict(idict):
"""Remove null items from a dictionary """
odict = {}
for key, val in idict.items():
if is_null(val):
continue
odict[key] = val
return odict | python | def purge_dict(idict):
"""Remove null items from a dictionary """
odict = {}
for key, val in idict.items():
if is_null(val):
continue
odict[key] = val
return odict | [
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fermiPy/fermipy | fermipy/jobs/chain.py | Chain.main | def main(cls):
"""Hook to run this `Chain` from the command line """
chain = cls.create()
args = chain._run_argparser(sys.argv[1:])
chain._run_chain(sys.stdout, args.dry_run)
chain._finalize(args.dry_run) | python | def main(cls):
"""Hook to run this `Chain` from the command line """
chain = cls.create()
args = chain._run_argparser(sys.argv[1:])
chain._run_chain(sys.stdout, args.dry_run)
chain._finalize(args.dry_run) | [
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fermiPy/fermipy | fermipy/jobs/chain.py | Chain._set_link | def _set_link(self, linkname, cls, **kwargs):
"""Transfer options kwargs to a `Link` object,
optionally building the `Link if needed.
Parameters
----------
linkname : str
Unique name of this particular link
cls : type
Type of `Link` being created or managed
"""
val_copy = purge_dict(kwargs.copy())
sub_link_prefix = val_copy.pop('link_prefix', '')
link_prefix = self.link_prefix + sub_link_prefix
create_args = dict(linkname=linkname,
link_prefix=link_prefix,
job_archive=val_copy.pop('job_archive', None),
file_stage=val_copy.pop('file_stage', None))
job_args = val_copy
if linkname in self._links:
link = self._links[linkname]
link.update_args(job_args)
else:
link = cls.create(**create_args)
self._links[link.linkname] = link
logfile_default = os.path.join('logs', '%s.log' % link.full_linkname)
logfile = kwargs.setdefault('logfile', logfile_default)
link._register_job(JobDetails.topkey, job_args,
logfile, status=JobStatus.unknown)
return link | python | def _set_link(self, linkname, cls, **kwargs):
"""Transfer options kwargs to a `Link` object,
optionally building the `Link if needed.
Parameters
----------
linkname : str
Unique name of this particular link
cls : type
Type of `Link` being created or managed
"""
val_copy = purge_dict(kwargs.copy())
sub_link_prefix = val_copy.pop('link_prefix', '')
link_prefix = self.link_prefix + sub_link_prefix
create_args = dict(linkname=linkname,
link_prefix=link_prefix,
job_archive=val_copy.pop('job_archive', None),
file_stage=val_copy.pop('file_stage', None))
job_args = val_copy
if linkname in self._links:
link = self._links[linkname]
link.update_args(job_args)
else:
link = cls.create(**create_args)
self._links[link.linkname] = link
logfile_default = os.path.join('logs', '%s.log' % link.full_linkname)
logfile = kwargs.setdefault('logfile', logfile_default)
link._register_job(JobDetails.topkey, job_args,
logfile, status=JobStatus.unknown)
return link | [
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linkname : str
Unique name of this particular link
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Type of `Link` being created or managed | [
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fermiPy/fermipy | fermipy/jobs/chain.py | Chain._set_links_job_archive | def _set_links_job_archive(self):
"""Pass self._job_archive along to links"""
for link in self._links.values():
link._job_archive = self._job_archive | python | def _set_links_job_archive(self):
"""Pass self._job_archive along to links"""
for link in self._links.values():
link._job_archive = self._job_archive | [
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fermiPy/fermipy | fermipy/jobs/chain.py | Chain._run_chain | def _run_chain(self,
stream=sys.stdout,
dry_run=False,
stage_files=True,
force_run=False,
resubmit_failed=False):
"""Run all the links in the chain
Parameters
-----------
stream : `file`
Stream to print to,
Must have 'write' function
dry_run : bool
Print commands but do not run them
stage_files : bool
Stage files to and from the scratch area
force_run : bool
Run jobs, even if they are marked as done
resubmit_failed : bool
Resubmit failed jobs
"""
self._set_links_job_archive()
failed = False
if self._file_stage is not None:
input_file_mapping, output_file_mapping = self._map_scratch_files(
self.sub_files)
if stage_files:
self._file_stage.make_scratch_dirs(input_file_mapping, dry_run)
self._file_stage.make_scratch_dirs(
output_file_mapping, dry_run)
self._stage_input_files(input_file_mapping, dry_run)
for link in self._links.values():
logfile = os.path.join('logs', "%s.log" % link.full_linkname)
link._archive_self(logfile, status=JobStatus.unknown)
key = JobDetails.make_fullkey(link.full_linkname)
if hasattr(link, 'check_status'):
link.check_status(stream, no_wait=True,
check_once=True, do_print=False)
else:
pass
link_status = link.check_job_status(key)
if link_status in [JobStatus.done]:
if not force_run:
print ("Skipping done link", link.full_linkname)
continue
elif link_status in [JobStatus.running]:
if not force_run and not resubmit_failed:
print ("Skipping running link", link.full_linkname)
continue
elif link_status in [JobStatus.failed,
JobStatus.partial_failed]:
if not resubmit_failed:
print ("Skipping failed link", link.full_linkname)
continue
print ("Running link ", link.full_linkname)
link.run_with_log(dry_run=dry_run, stage_files=False,
resubmit_failed=resubmit_failed)
link_status = link.check_jobs_status()
link._set_status_self(status=link_status)
if link_status in [JobStatus.failed, JobStatus.partial_failed]:
print ("Stoping chain execution at failed link %s" %
link.full_linkname)
failed = True
break
# elif link_status in [JobStatus.partial_failed]:
# print ("Resubmitting partially failed link %s" %
# link.full_linkname)
# link.run_with_log(dry_run=dry_run, stage_files=False,
# resubmit_failed=resubmit_failed)
# link_status = link.check_jobs_status()
# link._set_status_self(status=link_status)
# if link_status in [JobStatus.partial_failed]:
# print ("Stoping chain execution: resubmission failed %s" %
# link.full_linkname)
# failed = True
# break
if self._file_stage is not None and stage_files and not failed:
self._stage_output_files(output_file_mapping, dry_run)
chain_status = self.check_links_status()
print ("Chain status: %s" % (JOB_STATUS_STRINGS[chain_status]))
if chain_status == 5:
job_status = 0
else:
job_status = -1
self._write_status_to_log(job_status, stream)
self._set_status_self(status=chain_status)
if self._job_archive:
self._job_archive.file_archive.update_file_status()
self._job_archive.write_table_file() | python | def _run_chain(self,
stream=sys.stdout,
dry_run=False,
stage_files=True,
force_run=False,
resubmit_failed=False):
"""Run all the links in the chain
Parameters
-----------
stream : `file`
Stream to print to,
Must have 'write' function
dry_run : bool
Print commands but do not run them
stage_files : bool
Stage files to and from the scratch area
force_run : bool
Run jobs, even if they are marked as done
resubmit_failed : bool
Resubmit failed jobs
"""
self._set_links_job_archive()
failed = False
if self._file_stage is not None:
input_file_mapping, output_file_mapping = self._map_scratch_files(
self.sub_files)
if stage_files:
self._file_stage.make_scratch_dirs(input_file_mapping, dry_run)
self._file_stage.make_scratch_dirs(
output_file_mapping, dry_run)
self._stage_input_files(input_file_mapping, dry_run)
for link in self._links.values():
logfile = os.path.join('logs', "%s.log" % link.full_linkname)
link._archive_self(logfile, status=JobStatus.unknown)
key = JobDetails.make_fullkey(link.full_linkname)
if hasattr(link, 'check_status'):
link.check_status(stream, no_wait=True,
check_once=True, do_print=False)
else:
pass
link_status = link.check_job_status(key)
if link_status in [JobStatus.done]:
if not force_run:
print ("Skipping done link", link.full_linkname)
continue
elif link_status in [JobStatus.running]:
if not force_run and not resubmit_failed:
print ("Skipping running link", link.full_linkname)
continue
elif link_status in [JobStatus.failed,
JobStatus.partial_failed]:
if not resubmit_failed:
print ("Skipping failed link", link.full_linkname)
continue
print ("Running link ", link.full_linkname)
link.run_with_log(dry_run=dry_run, stage_files=False,
resubmit_failed=resubmit_failed)
link_status = link.check_jobs_status()
link._set_status_self(status=link_status)
if link_status in [JobStatus.failed, JobStatus.partial_failed]:
print ("Stoping chain execution at failed link %s" %
link.full_linkname)
failed = True
break
# elif link_status in [JobStatus.partial_failed]:
# print ("Resubmitting partially failed link %s" %
# link.full_linkname)
# link.run_with_log(dry_run=dry_run, stage_files=False,
# resubmit_failed=resubmit_failed)
# link_status = link.check_jobs_status()
# link._set_status_self(status=link_status)
# if link_status in [JobStatus.partial_failed]:
# print ("Stoping chain execution: resubmission failed %s" %
# link.full_linkname)
# failed = True
# break
if self._file_stage is not None and stage_files and not failed:
self._stage_output_files(output_file_mapping, dry_run)
chain_status = self.check_links_status()
print ("Chain status: %s" % (JOB_STATUS_STRINGS[chain_status]))
if chain_status == 5:
job_status = 0
else:
job_status = -1
self._write_status_to_log(job_status, stream)
self._set_status_self(status=chain_status)
if self._job_archive:
self._job_archive.file_archive.update_file_status()
self._job_archive.write_table_file() | [
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Stream to print to,
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Print commands but do not run them
stage_files : bool
Stage files to and from the scratch area
force_run : bool
Run jobs, even if they are marked as done
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fermiPy/fermipy | fermipy/jobs/chain.py | Chain.clear_jobs | def clear_jobs(self, recursive=True):
"""Clear a dictionary with all the jobs
If recursive is True this will include jobs from all internal `Link`
"""
if recursive:
for link in self._links.values():
link.clear_jobs(recursive)
self.jobs.clear() | python | def clear_jobs(self, recursive=True):
"""Clear a dictionary with all the jobs
If recursive is True this will include jobs from all internal `Link`
"""
if recursive:
for link in self._links.values():
link.clear_jobs(recursive)
self.jobs.clear() | [
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fermiPy/fermipy | fermipy/jobs/chain.py | Chain.check_links_status | def check_links_status(self,
fail_running=False,
fail_pending=False):
""""Check the status of all the jobs run from the
`Link` objects in this `Chain` and return a status
flag that summarizes that.
Parameters
----------
fail_running : `bool`
If True, consider running jobs as failed
fail_pending : `bool`
If True, consider pending jobs as failed
Returns
-------
status : `JobStatus`
Job status flag that summarizes the status of all the jobs,
"""
status_vector = JobStatusVector()
for link in self._links.values():
key = JobDetails.make_fullkey(link.full_linkname)
link_status = link.check_job_status(key,
fail_running=fail_running,
fail_pending=fail_pending)
status_vector[link_status] += 1
return status_vector.get_status() | python | def check_links_status(self,
fail_running=False,
fail_pending=False):
""""Check the status of all the jobs run from the
`Link` objects in this `Chain` and return a status
flag that summarizes that.
Parameters
----------
fail_running : `bool`
If True, consider running jobs as failed
fail_pending : `bool`
If True, consider pending jobs as failed
Returns
-------
status : `JobStatus`
Job status flag that summarizes the status of all the jobs,
"""
status_vector = JobStatusVector()
for link in self._links.values():
key = JobDetails.make_fullkey(link.full_linkname)
link_status = link.check_job_status(key,
fail_running=fail_running,
fail_pending=fail_pending)
status_vector[link_status] += 1
return status_vector.get_status() | [
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... | Check the status of all the jobs run from the
`Link` objects in this `Chain` and return a status
flag that summarizes that.
Parameters
----------
fail_running : `bool`
If True, consider running jobs as failed
fail_pending : `bool`
If True, consider pending jobs as failed
Returns
-------
status : `JobStatus`
Job status flag that summarizes the status of all the jobs, | [
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] | 9df5e7e3728307fd58c5bba36fd86783c39fbad4 | https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/jobs/chain.py#L290-L320 | train | 36,099 |
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