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fermiPy/fermipy | fermipy/utils.py | project | def project(lon0, lat0, lon1, lat1):
"""This function performs a stereographic projection on the unit
vector (lon1,lat1) with the pole defined at the reference unit
vector (lon0,lat0)."""
costh = np.cos(np.pi / 2. - lat0)
cosphi = np.cos(lon0)
sinth = np.sin(np.pi / 2. - lat0)
sinphi = np.... | python | def project(lon0, lat0, lon1, lat1):
"""This function performs a stereographic projection on the unit
vector (lon1,lat1) with the pole defined at the reference unit
vector (lon0,lat0)."""
costh = np.cos(np.pi / 2. - lat0)
cosphi = np.cos(lon0)
sinth = np.sin(np.pi / 2. - lat0)
sinphi = np.... | [
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fermiPy/fermipy | fermipy/utils.py | separation_cos_angle | def separation_cos_angle(lon0, lat0, lon1, lat1):
"""Evaluate the cosine of the angular separation between two
direction vectors."""
return (np.sin(lat1) * np.sin(lat0) + np.cos(lat1) * np.cos(lat0) *
np.cos(lon1 - lon0)) | python | def separation_cos_angle(lon0, lat0, lon1, lat1):
"""Evaluate the cosine of the angular separation between two
direction vectors."""
return (np.sin(lat1) * np.sin(lat0) + np.cos(lat1) * np.cos(lat0) *
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fermiPy/fermipy | fermipy/utils.py | angle_to_cartesian | def angle_to_cartesian(lon, lat):
"""Convert spherical coordinates to cartesian unit vectors."""
theta = np.array(np.pi / 2. - lat)
return np.vstack((np.sin(theta) * np.cos(lon),
np.sin(theta) * np.sin(lon),
np.cos(theta))).T | python | def angle_to_cartesian(lon, lat):
"""Convert spherical coordinates to cartesian unit vectors."""
theta = np.array(np.pi / 2. - lat)
return np.vstack((np.sin(theta) * np.cos(lon),
np.sin(theta) * np.sin(lon),
np.cos(theta))).T | [
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fermiPy/fermipy | fermipy/utils.py | create_model_name | def create_model_name(src):
"""Generate a name for a source object given its spatial/spectral
properties.
Parameters
----------
src : `~fermipy.roi_model.Source`
A source object.
Returns
-------
name : str
A source name.
"""
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spatial_type = src['S... | python | def create_model_name(src):
"""Generate a name for a source object given its spatial/spectral
properties.
Parameters
----------
src : `~fermipy.roi_model.Source`
A source object.
Returns
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name : str
A source name.
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fermiPy/fermipy | fermipy/utils.py | cov_to_correlation | def cov_to_correlation(cov):
"""Compute the correlation matrix given the covariance matrix.
Parameters
----------
cov : `~numpy.ndarray`
N x N matrix of covariances among N parameters.
Returns
-------
corr : `~numpy.ndarray`
N x N matrix of correlations among N parameters.
... | python | def cov_to_correlation(cov):
"""Compute the correlation matrix given the covariance matrix.
Parameters
----------
cov : `~numpy.ndarray`
N x N matrix of covariances among N parameters.
Returns
-------
corr : `~numpy.ndarray`
N x N matrix of correlations among N parameters.
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fermiPy/fermipy | fermipy/utils.py | ellipse_to_cov | def ellipse_to_cov(sigma_maj, sigma_min, theta):
"""Compute the covariance matrix in two variables x and y given
the std. deviation along the semi-major and semi-minor axes and
the rotation angle of the error ellipse.
Parameters
----------
sigma_maj : float
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"""Compute the covariance matrix in two variables x and y given
the std. deviation along the semi-major and semi-minor axes and
the rotation angle of the error ellipse.
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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
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"""Compute the delta-loglikehood values that corresponds to an
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cl : float
Confidence level.
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fermiPy/fermipy | fermipy/utils.py | find_function_root | def find_function_root(fn, x0, xb, delta=0.0, bounds=None):
"""Find the root of a function: f(x)+delta in the interval encompassed
by x0 and xb.
Parameters
----------
fn : function
Python function.
x0 : float
Fixed bound for the root search. This will either be used as
t... | python | def find_function_root(fn, x0, xb, delta=0.0, bounds=None):
"""Find the root of a function: f(x)+delta in the interval encompassed
by x0 and xb.
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----------
fn : function
Python function.
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Fixed bound for the root search. This will either be used as
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fermiPy/fermipy | fermipy/utils.py | get_parameter_limits | def get_parameter_limits(xval, loglike, cl_limit=0.95, cl_err=0.68269, tol=1E-2,
bounds=None):
"""Compute upper/lower limits, peak position, and 1-sigma errors
from a 1-D likelihood function. This function uses the
delta-loglikelihood method to evaluate parameter limits by
sear... | python | def get_parameter_limits(xval, loglike, cl_limit=0.95, cl_err=0.68269, tol=1E-2,
bounds=None):
"""Compute upper/lower limits, peak position, and 1-sigma errors
from a 1-D likelihood function. This function uses the
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fermiPy/fermipy | fermipy/utils.py | parabola | def parabola(xy, amplitude, x0, y0, sx, sy, theta):
"""Evaluate a 2D parabola given by:
f(x,y) = f_0 - (1/2) * \delta^T * R * \Sigma * R^T * \delta
where
\delta = [(x - x_0), (y - y_0)]
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"""Evaluate a 2D parabola given by:
f(x,y) = f_0 - (1/2) * \delta^T * R * \Sigma * R^T * \delta
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fermiPy/fermipy | fermipy/utils.py | get_region_mask | def get_region_mask(z, delta, xy=None):
"""Get mask of connected region within delta of max(z)."""
if xy is None:
ix, iy = np.unravel_index(np.argmax(z), z.shape)
else:
ix, iy = xy
mz = (z > z[ix, iy] - delta)
labels = label(mz)[0]
mz &= labels == labels[ix, iy]
return mz | python | def get_region_mask(z, delta, xy=None):
"""Get mask of connected region within delta of max(z)."""
if xy is None:
ix, iy = np.unravel_index(np.argmax(z), z.shape)
else:
ix, iy = xy
mz = (z > z[ix, iy] - delta)
labels = label(mz)[0]
mz &= labels == labels[ix, iy]
return mz | [
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fermiPy/fermipy | fermipy/utils.py | fit_parabola | def fit_parabola(z, ix, iy, dpix=3, zmin=None):
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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
-------
... | 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
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fermiPy/fermipy | fermipy/utils.py | val_to_bin | def val_to_bin(edges, x):
"""Convert axis coordinate to bin index."""
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"""Convert axis coordinate to bin index."""
ibin = np.digitize(np.array(x, ndmin=1), edges) - 1
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fermiPy/fermipy | fermipy/utils.py | val_to_edge | def val_to_edge(edges, x):
"""Convert axis coordinate to bin index."""
edges = np.array(edges)
w = edges[1:] - edges[:-1]
w = np.insert(w, 0, w[0])
ibin = np.digitize(np.array(x, ndmin=1), edges - 0.5 * w) - 1
ibin[ibin < 0] = 0
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"""Convert axis coordinate to bin index."""
edges = np.array(edges)
w = edges[1:] - edges[:-1]
w = np.insert(w, 0, w[0])
ibin = np.digitize(np.array(x, ndmin=1), edges - 0.5 * w) - 1
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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)
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edges_lo = np.linspace(edges[0] - numlo * binsz, edges[0], nu... | 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))
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"""Convert a FITS recarray to a python dictionary."""
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cols[col] = np.array(col_data, dtype=float)
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"""Convert a FITS recarray to a python dictionary."""
cols = {}
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fermiPy/fermipy | fermipy/utils.py | prettify_xml | def prettify_xml(elem):
"""Return a pretty-printed XML string for the Element.
"""
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import xml.etree.cElementTree as et
rough_string = et.tostring(elem, 'utf-8')
reparsed = minidom.parseString(rough_string)
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"""
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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
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d0 : dict
The input dictionary.
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fermiPy/fermipy | fermipy/utils.py | convolve2d_disk | def convolve2d_disk(fn, r, sig, nstep=200):
"""Evaluate the convolution f'(r) = f(r) * g(r) where f(r) is
azimuthally symmetric function in two dimensions and g is a
step function given by:
g(r) = H(1-r/s)
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----------
fn : function
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fermiPy/fermipy | fermipy/utils.py | convolve2d_gauss | def convolve2d_gauss(fn, r, sig, nstep=200):
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fermiPy/fermipy | fermipy/utils.py | make_pixel_distance | def make_pixel_distance(shape, xpix=None, ypix=None):
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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):
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Standard deviation in degrees.
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----------
sigma : float
Standard deviation in degrees.
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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):
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----------
radius : float
Disk radius in deg.
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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
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"""
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"""Make a kernel for a PSF-convolved 2D disk.
Parameters
----------
psf : `~fermipy.irfs.PSFModel`
sigma : float
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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,
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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):
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fermiPy/fermipy | fermipy/utils.py | overlap_slices | def overlap_slices(large_array_shape, small_array_shape, position):
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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('.'))
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"""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')
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""" Return the list of source for which energy dispersion should be turned off """
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l += [model_comp.info.source_name]
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l = []
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelInfo.make_srcmap_manifest | def make_srcmap_manifest(self, components, name_factory):
""" Build a yaml file that specfies how to make the srcmap files for a particular model
Parameters
----------
components : list
The binning components used in this analysis
name_factory : `NameFactory`
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""" Build a yaml file that specfies how to make the srcmap files for a particular model
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components : list
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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... | 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
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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)
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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
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----------
diffuse_yaml : str
Name of the yaml file with the library of diffuse component definitions
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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)
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""" 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)
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelManager.make_srcmap_manifest | def make_srcmap_manifest(self, modelkey, components, data):
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----------
modelkey : str
Key used to identify this particular model
components : list
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fermiPy/fermipy | fermipy/diffuse/model_manager.py | ModelManager.make_fermipy_config_yaml | def make_fermipy_config_yaml(self, modelkey, components, data, **kwargs):
"""Build a fermipy top-level yaml configuration file
Parameters
----------
modelkey : str
Key used to identify this particular model
components : list
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modelkey : str
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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)
... | 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()
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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):
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fermiPy/fermipy | fermipy/validate/utils.py | get_files | def get_files(files, extnames=['.root']):
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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 ... | 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 = {}
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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.
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----------
tree : `ROOT.TTree`
Input tree/chain.
selection : str
Cut string defining the event list.
fraction : float
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"""
Set the event list for a tree or chain.
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tree : `ROOT.TTree`
Input tree/chain.
selection : str
Cut string defining the event list.
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"""An iterative source-finding algorithm that uses likelihood
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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
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... | python | def localize(self, name, **kwargs):
"""Find the best-fit position of a source. Localization is
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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': kwarg... | 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)
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | make_nfs_path | def make_nfs_path(path):
"""Make a nfs version of a file path.
This just puts /nfs at the beginning instead of /gpfs"""
if os.path.isabs(path):
fullpath = path
else:
fullpath = os.path.abspath(path)
if len(fullpath) < 6:
return fullpath
if fullpath[0:6] == '/gpfs/':
... | python | def make_nfs_path(path):
"""Make a nfs version of a file path.
This just puts /nfs at the beginning instead of /gpfs"""
if os.path.isabs(path):
fullpath = path
else:
fullpath = os.path.abspath(path)
if len(fullpath) < 6:
return fullpath
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | make_gpfs_path | def make_gpfs_path(path):
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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,
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subproc = subprocess... | python | def get_lsf_status():
"""Count and print the number of jobs in various LSF states
"""
status_count = {'RUN': 0,
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'SUSP': 0,
'USUSP': 0,
'NJOB': 0,
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | build_bsub_command | def build_bsub_command(command_template, lsf_args):
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fermiPy/fermipy | fermipy/jobs/slac_impl.py | get_slac_default_args | def get_slac_default_args(job_time=1500):
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Parameters
----------
job_time : int
Expected max length of the job, in seconds.
This is used to select the batch queue and set the
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job_time : int
Expected max length of the job, in seconds.
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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):
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Parameters
----------
link : `fermipy.jobs.chain.Link`
The link used to invoke the command we are running
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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):
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if link is None:
return JobStatus.no_job
if job_dict is None:
job_keys = link.jobs.keys()
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job_keys = sorted(job_dict.keys())
... | 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
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job_keys = link.jobs.keys()
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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')
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files = [scfile]
else:
files = [line.strip() for ... | 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')
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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 = []
with fits.open(fitsfile, memmap=True) as h:
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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))
... | 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))
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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)
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np.random.seed(seed) | python | def set_random_seed(self, 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._... | python | def reload_source(self, name, init_source=True):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_source_morphology | def set_source_morphology(self, name, **kwargs):
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Parameters
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name : str
Source name.
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Spatial model name (PointSource, RadialGaussian, etc.).
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_source_spectrum | def set_source_spectrum(self, name, spectrum_type='PowerLaw',
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_source_dnde | def set_source_dnde(self, name, dnde, update_source=True):
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name : str
Source name.
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loge : `~numpy.ndarray`
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._create_filefunction | def _create_filefunction(self, name, spectrum_pars):
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spectrum_pars = {} if spectrum_pars is None else spectrum_pars
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loge = spectrum_pars.get('loge')
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e... | python | def _create_filefunction(self, name, spectrum_pars):
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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:
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self.logger.error('Working directory does not exist.')
return
regex = self.config['fileio']['outd... | python | def stage_output(self):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.stage_input | def stage_input(self):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.setup | def setup(self, init_sources=True, overwrite=False, **kwargs):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._create_likelihood | def _create_likelihood(self, srcmdl=None):
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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
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then the model maps will be generated using the current
parameters of the ROI."""
for i, c in enumerate(self._com... | python | def generate_model(self, model_name=None):
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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):
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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):
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A ... | python | def get_sources(self, cuts=None, distance=None, skydir=None,
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use_single_psf=False, **kwargs):
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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
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names : list
List of str source names to add.
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List of str source names to add.
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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
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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):
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Parameters
----------
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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,
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Parameters
----------
names : list
List of source names.
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List of source names.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.free_sources | def free_sources(self, free=True, pars=None, cuts=None,
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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):
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Update the value of a parameter. Parameter bounds will
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter_scale | def set_parameter_scale(self, name, par, scale):
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name = self.roi.get_source_by_name(name).name
idx = self.like.par_index(name, par)
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current_scale = self... | python | def set_parameter_scale(self, name, par, scale):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter_bounds | def set_parameter_bounds(self, name, par, bounds):
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name : str
Source name.
par : str
Parameter name.
bounds : list
Upper and lower bound.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.set_parameter_error | def set_parameter_error(self, name, par, error):
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name : str
Source name.
par : str
Parameter name.
error : float
The value for the parameter error
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.lock_parameter | def lock_parameter(self, name, par, lock=True):
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parameters.
Parameters
----------
name : str
Source name.
par : str
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.free_parameter | def free_parameter(self, name, par, free=True):
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Parameters
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Source name.
par : str
Parameter name.
"""
name = self.get_source_name(name)
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Source name.
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Parameter name.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.lock_source | def lock_source(self, name, lock=True):
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Source name.
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.free_source | def free_source(self, name, free=True, pars=None, **kwargs):
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Source name.
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Choose whether to free (free=True) or fix (free=False)
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name : str
Source name.
free : bool
Choose whether to free (free=True) or fix (free=False).
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.free_index | def free_index(self, name, free=True, **kwargs):
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Parameters
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name : str
Source name.
free : bool
Choose whether to free (free=True) or fix (free=False).
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src = self.roi.get_source_by_name(name... | python | def free_index(self, name, free=True, **kwargs):
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Choose whether to free (free=True) or fix (free=False).
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.free_shape | def free_shape(self, name, free=True, **kwargs):
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Parameters
----------
name : str
Source name.
free : bool
Choose whether to free (free=True) or fix (free=False).
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Choose whether to free (free=True) or fix (free=False).
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.get_source_name | def get_source_name(self, name):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.profile_norm | def profile_norm(self, name, logemin=None, logemax=None, reoptimize=False,
xvals=None, npts=None, fix_shape=True, savestate=True,
**kwargs):
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Parameters
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name : str
Source name.
... | python | def profile_norm(self, name, logemin=None, logemax=None, reoptimize=False,
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis.remove_priors | def remove_priors(self):
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par.removePrior() | python | def remove_priors(self):
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fermiPy/fermipy | fermipy/gtanalysis.py | GTAnalysis._create_optObject | def _create_optObject(self, **kwargs):
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"\"Start... | Run the likelihood optimization. This will execute a fit of all
parameters that are currently free in the model and update the
charateristics of the corresponding model components (TS,
npred, etc.). The fit will be repeated N times (set with the
`retries` parameter) until a fit quality... | [
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"compo... | train | https://github.com/fermiPy/fermipy/blob/9df5e7e3728307fd58c5bba36fd86783c39fbad4/fermipy/gtanalysis.py#L2903-L3020 |
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