def from_config(cls, cp, section, variable_args): """Returns a distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. By default, only the name of the distribution (`uniform_angle`) needs to be specified. This will results in a uniform prior on `[0, 2pi)`. To make the domain cyclic, add `cyclic_domain =`. To specify boundaries that are not `[0, 2pi)`, add `(min|max)-var` arguments, where `var` is the name of the variable. For example, this will initialize a variable called `theta` with a uniform distribution on `[0, 2pi)` without cyclic boundaries: .. code-block:: ini [{section}-theta] name = uniform_angle This will make the domain cyclic on `[0, 2pi)`: .. code-block:: ini [{section}-theta] name = uniform_angle cyclic_domain = Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by ``VARARGS_DELIM``. These must appear in the "tag" part of the section header. Returns ------- UniformAngle A distribution instance from the pycbc.inference.prior module. """ # we'll retrieve the setting for cyclic_domain directly additional_opts = { 'cyclic_domain': cp.has_option_tag(section, 'cyclic_domain', variable_args) } return bounded.bounded_from_config(cls, cp, section, variable_args, bounds_required=False, additional_opts=additional_opts)
def from_config(cls, cp, section, variable_args): """Returns a distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. By default, only the name of the distribution (`uniform_angle`) needs to be specified. This will results in a uniform prior on `[0, 2pi)`. To make the domain cyclic, add `cyclic_domain =`. To specify boundaries that are not `[0, 2pi)`, add `(min|max)-var` arguments, where `var` is the name of the variable. For example, this will initialize a variable called `theta` with a uniform distribution on `[0, 2pi)` without cyclic boundaries: .. code-block:: ini [{section}-theta] name = uniform_angle This will make the domain cyclic on `[0, 2pi)`: .. code-block:: ini [{section}-theta] name = uniform_angle cyclic_domain = Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by ``VARARGS_DELIM``. These must appear in the "tag" part of the section header. Returns ------- UniformAngle A distribution instance from the pycbc.inference.prior module. """ # we'll retrieve the setting for cyclic_domain directly additional_opts = {'cyclic_domain': cp.has_option_tag(section, 'cyclic_domain', variable_args)} return bounded.bounded_from_config(cls, cp, section, variable_args, bounds_required=False, additional_opts=additional_opts)
def from_config(cls, cp, section, variable_args): """Returns a Gaussian distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. Boundary arguments should be provided in the same way as described in `get_param_bounds_from_config`. In addition, the mean and variance of each parameter can be specified by setting `{param}_mean` and `{param}_var`, respectively. For example, the following would create a truncated Gaussian distribution between 0 and 6.28 for a parameter called `phi` with mean 3.14 and variance 0.5 that is cyclic: .. code-block:: ini [{section}-{tag}] min-phi = 0 max-phi = 6.28 phi_mean = 3.14 phi_var = 0.5 cyclic = Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by `prior.VARARGS_DELIM`. These must appear in the "tag" part of the section header. Returns ------- Gaussain A distribution instance from the pycbc.inference.prior module. """ return bounded.bounded_from_config(cls, cp, section, variable_args, bounds_required=False)
def from_config(cls, cp, section, variable_args): """Returns a distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. The file to construct the distribution from must be provided by setting `filename`. Boundary arguments can be provided in the same way as described in `get_param_bounds_from_config`. .. code-block:: ini [{section}-{tag}] name = fromfile filename = ra_prior.hdf min-ra = 0 max-ra = 6.28 Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by `prior.VARARGS_DELIM`. These must appear in the "tag" part of the section header. Returns ------- BoundedDist A distribution instance from the pycbc.inference.prior module. """ return bounded.bounded_from_config(cls, cp, section, variable_args, bounds_required=False)
def from_config(cls, cp, section, variable_args): """Returns a distribution based on a configuration file. The parameters for the distribution are retrieved from the section titled "[`section`-`variable_args`]" in the config file. Parameters ---------- cp : pycbc.workflow.WorkflowConfigParser A parsed configuration file that contains the distribution options. section : str Name of the section in the configuration file. variable_args : str The names of the parameters for this distribution, separated by `prior.VARARGS_DELIM`. These must appear in the "tag" part of the section header. Returns ------- UniformAngle A distribution instance from the pycbc.inference.prior module. """ return bounded.bounded_from_config(cls, cp, section, variable_args, bounds_required=False)