# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from astropy.table import Table, Column, vstack
from astropy.io import ascii
from astropy.units import Quantity
from ..extern.pathlib import Path
__all__ = [
'ObservationGroupAxis',
'ObservationGroups',
]
[docs]class ObservationGroups(object):
"""Observation groups.
Class to define observation groups useful for organizing observation
lists into groups of observations with similar properties.
The properties and their binning are specified via
`~gammapy.data.ObservationGroupAxis` objects.
The class takes as input a list of `~gammapy.data.ObservationGroupAxis`
objects and defines one group for each possible combination of the
bins defined in all axes (cartesian product).
The groups are identified by a unique ``GROUP_ID`` int value.
The definitions of the groups are internally stored as a
`~astropy.table.Table` object, the ``obs_groups_table`` member.
The axis parameters should be either dimensionless or castable
into `~astropy.units.Quantity` objects.
For details on the grouping of observations in a list, please
refer to the `~gammapy.data.ObservationGroups.apply` method.
See also :ref:`obs_observation_grouping`.
Parameters
----------
axes : list of `~gammapy.data.ObservationGroupAxis`
List of observation group axes.
Examples
--------
Define an observation grouping:
.. code:: python
alt = Angle([0, 30, 60, 90], 'deg')
az = Angle([-90, 90, 270], 'deg')
ntels = np.array([3, 4])
obs_groups = ObservationGroups([
ObservationGroupAxis('ALT', alt, fmt='edges'),
ObservationGroupAxis('AZ', az, fmt='edges'),
ObservationGroupAxis('N_TELS', ntels, fmt='values'),
])
Print the observation group table (group definitions):
>>> print(obs_groups.obs_groups_table)
Print the observation group axes:
>>> print(obs_groups.info)
Group the observations of an observation list and print them:
>>> obs_table_grouped = obs_groups.apply(obs_table)
>>> print(obs_table_grouped)
Get the observations of a particular group and print them:
>>> obs_table_group8 = obs_groups.get_group_of_observations(obs_table_grouped, 8)
>>> print(obs_table_group8)
"""
def __init__(self, axes):
self.axes = axes
self.obs_groups_table = ObservationGroups.axes_to_table(axes)
@property
def n_groups(self):
"""Number of groups (int)."""
return len(self.obs_groups_table)
@property
def list_of_groups(self):
"""List of groups (`~numpy.ndarray`)."""
return self.obs_groups_table['GROUP_ID'].data
[docs] @staticmethod
def axes_to_table(axes):
"""Fill the observation group axes into a table.
Define one row for each possible combination of the
observation group axis bins. Each row will represent
an observation group.
Parameters
----------
axes : `~gammapy.data.ObservationGroupAxis`
List of observation group axes.
Returns
-------
table : `~astropy.table.Table`
Table containing the observation group definitions.
"""
# define table column data
column_data_min = []
column_data_max = []
# loop over observation axes
for i_axis in range(len(axes)):
if axes[i_axis].fmt == 'values':
column_data_min.append(axes[i_axis].bins)
column_data_max.append(axes[i_axis].bins)
elif axes[i_axis].fmt == 'edges':
column_data_min.append(axes[i_axis].bins[:-1])
column_data_max.append(axes[i_axis].bins[1:])
# define grids of column data
ndim = len(axes)
s0 = (1,) * ndim
expanding_arrays = [x.reshape(s0[:i] + (-1,) + s0[i + 1::])
for i, x in enumerate(column_data_min)]
column_data_expanded_min = np.broadcast_arrays(*expanding_arrays)
expanding_arrays = [x.reshape(s0[:i] + (-1,) + s0[i + 1::])
for i, x in enumerate(column_data_max)]
column_data_expanded_max = np.broadcast_arrays(*expanding_arrays)
# recover units
for i_dim in range(ndim):
column_data_expanded_min[i_dim] = _recover_units(column_data_expanded_min[i_dim],
column_data_min[i_dim])
column_data_expanded_max[i_dim] = _recover_units(column_data_expanded_max[i_dim],
column_data_max[i_dim])
# Make the table
table = Table()
for i_axis in range(len(axes)):
if axes[i_axis].fmt == 'values':
table[axes[i_axis].name] = column_data_expanded_min[i_axis].flatten()
elif axes[i_axis].fmt == 'edges':
table[axes[i_axis].name + "_MIN"] = column_data_expanded_min[i_axis].flatten()
table[axes[i_axis].name + "_MAX"] = column_data_expanded_max[i_axis].flatten()
ObservationGroups._add_group_id(table, axes)
return table
@staticmethod
def _add_group_id(table, axes):
# Compute number of groups
n_groups = 1
for i_axis in range(len(axes)):
n_groups *= axes[i_axis].n_bins
# fill table, with first the obs group IDs, then the axis columns
group_id = Column(name='GROUP_ID', data=np.arange(n_groups))
table.add_column(group_id, index=0)
return table
[docs] @staticmethod
def table_to_axes(table):
"""Define observation group axis list from a table.
Interpret the combinations of bins from a table of groups
in order to define the corresponding observation group axes.
Parameters
----------
table : `~astropy.table.Table`
Table containing the observation group definitions.
Returns
-------
axes : `~gammapy.data.ObservationGroupAxis`
List of observation group axes.
"""
# subset table: remove obs groups column
if table.colnames[0] == 'GROUP_ID':
table = table[table.colnames[1:]]
axes = []
for i_col, col_name in enumerate(table.columns):
data = np.unique(table[col_name].data)
# recover units
data = _recover_units(data, table[col_name])
axes.append(ObservationGroupAxis(col_name, data, fmt='values'))
# format will be reviewed in a further step
# detect range variables and eventually merge columns
for i_col in range(len(axes)):
try:
split_name_min = axes[i_col].name.rsplit("_", 1)
split_name_max = axes[i_col + 1].name.rsplit("_", 1)
do_split = (
split_name_min[-1] == 'MIN' and
split_name_max[-1] == 'MAX' and
split_name_min[0] == split_name_max[0]
)
if do_split:
min_values = axes[i_col].bins
max_values = axes[i_col + 1].bins
edges = np.unique(np.append(min_values, max_values))
# recover units
edges = _recover_units(edges, min_values)
axes[i_col] = ObservationGroupAxis(split_name_min[0], edges, fmt='edges')
axes.pop(i_col + 1) # remove next entry on the list
except:
pass
return axes
[docs] @classmethod
def read(cls, filename):
"""Read observation group definitions from ECSV file.
Using `~astropy.table.Table` and `~astropy.io.ascii`.
Parameters
----------
filename : str
Name of the file.
Returns
-------
obs_groups : `~gammapy.data.ObservationGroups`
Observation groups object.
"""
table = ascii.read(filename)
axes = ObservationGroups.table_to_axes(table)
return cls(axes=axes)
[docs] def write(self, outfile, overwrite=False):
"""Write observation group definitions to ECSV file.
Using `~astropy.table.Table` and `~astropy.io.ascii`.
Parameters
----------
outfile : str
Name of the file.
overwrite : bool, optional
Flag to control file overwriting.
"""
# there is no overwrite option in `~astropy.io.ascii`
if not Path(outfile).is_file() or overwrite:
ascii.write(self.obs_groups_table, outfile,
format='ecsv', fast_writer=False)
@property
def info(self):
"""Info string (str)."""
s = ''
# loop over observation axes
for i_axis in range(len(self.axes)):
s += self.axes[i_axis].info
if i_axis < len(self.axes) - 1:
s += '\n'
return s
[docs] def info_group(self, group_id):
"""Group info string.
Parameters
----------
group_id : int
ID of the group to gather info on.
Returns
-------
s : str
Group info string.
"""
s = 'group {}:'.format(group_id)
# find group row in obs groups table
group_ids = self.obs_groups_table['GROUP_ID'].data
group_index = np.where(group_ids == group_id)
row = group_index[0][0]
# loop over observation axes
for i_axis in range(len(self.axes)):
if i_axis != 0:
s += ','
s += ' ' + self.axes[i_axis].name + ' = '
if self.axes[i_axis].fmt == 'edges':
s += '['
s += str(self.obs_groups_table[self.axes[i_axis].name + '_MIN'][row])
s += ', '
s += str(self.obs_groups_table[self.axes[i_axis].name + '_MAX'][row])
s += ')'
elif self.axes[i_axis].fmt == 'values':
s += str(self.obs_groups_table[self.axes[i_axis].name][row])
s += ' ' + str(self.axes[i_axis].bins.unit)
return s
[docs] def apply(self, obs_table):
"""Group observations in a list according to the defined groups.
The method returns the same observation table with an extra
column in the 1st position indicating the group ID of each
observation.
The algorithm expects the same format (naming and variable
definition range) for both the grouping axis definition and
the corresponding variable in the table. For instance, if the
azimuth axis binning is defined as ``AZ`` with bin edges
``[-90, 90, 270]`` (North and South bins), the input obs table
should have an azimuth column defined as ``AZ`` and wrapped
at ``270 deg``. This can easily be done by calling:
>>> obs_table['AZ'] = Angle(obs_table['AZ']).wrap_at(Angle(270., 'deg'))
Parameters
----------
obs_table : `~gammapy.data.ObservationTable`
Observation list to group.
Returns
-------
obs_table_grouped : `~gammapy.data.ObservationTable`
Grouped observation list.
"""
# read the obs groups table row by row (i.e. 1 group at
# a time) and lookup the range/value for each parameter
n_axes = len(self.axes)
list_obs_table_grouped = []
for i_row in range(self.n_groups):
i_group = self.obs_groups_table['GROUP_ID'][i_row]
# loop over obs group axes to find out the names and formats
# of the parameters to define the selection criteria
obs_table_selected = obs_table
for i_axis in range(n_axes):
name = self.axes[i_axis].name
fmt = self.axes[i_axis].fmt
if fmt == 'edges':
min_value = _recover_units(self.obs_groups_table[name + '_MIN'][i_row],
self.obs_groups_table[name + '_MIN'])
max_value = _recover_units(self.obs_groups_table[name + '_MAX'][i_row],
self.obs_groups_table[name + '_MAX'])
elif fmt == 'values':
min_value = _recover_units(self.obs_groups_table[name][i_row],
self.obs_groups_table[name])
max_value = min_value
# apply selection to the table
selection = dict(type='par_box', variable=name,
value_range=(min_value, max_value))
obs_table_selected = obs_table_selected.select_observations(selection)
# define group and fill in list of grouped observation tables
group_id_data = i_group * np.ones(len(obs_table_selected), dtype=np.int)
obs_table_selected.add_column(Column(name='GROUP_ID', data=group_id_data),
index=0)
list_obs_table_grouped.append(obs_table_selected)
# stack all groups
obs_table_grouped = vstack(list_obs_table_grouped)
return obs_table_grouped
[docs] def get_group_of_observations(self, obs_table, group,
inverted=False, apply_grouping=False):
"""Select the runs corresponding to a particular group.
If the inverted flag is activated, the selection is applied to
exclude the indicated group and keep all others.
Parameters
----------
obs_table : `~gammapy.data.ObservationTable`
Observation list to select from.
group : int
Group ID to select.
inverted : bool, optional
Invert selection: exclude the indicated group and keep the rest.
apply_grouping : bool, optional
Flag to indicate if the observation grouping should take place.
Returns
-------
obs_table_group : `~gammapy.data.ObservationTable`
Observation list of a specific group.
"""
if apply_grouping:
obs_table = self.apply(obs_table)
selection = dict(type='par_box', variable='GROUP_ID',
value_range=(group, group), inverted=inverted)
return obs_table.select_observations(selection)
[docs]class ObservationGroupAxis(object):
"""Observation group axis.
Class to define an axis along which to define bins for creating observation groups.
Two kinds of axis are supported, depending on the value of the ``fmt`` parameter:
- ``fmt='edges'`` for continuous axes (e.g. altitude angle)
- ``fmt='values'`` for discrete axes (e.g. number of telescopes)
In both cases, both, dimensionless and
`~astropy.units.Quantity`-like parameter axes are supported.
See also :ref:`obs_observation_grouping`.
Parameters
----------
name : str
Name of the parameter to bin.
bins : int, float or `~astropy.units.Quantity`-like
Array of values or bin edges, depending on the ``fmt`` parameter.
fmt : {'edges', 'values'}
Format of binning
Examples
--------
Examples how to create ObservationGroupAxis objects:
.. code-block:: python
zenith = Angle([0, 30, 40, 50], 'deg')
zenith_axis = ObservationGroupAxis('ALT', alt, fmt='edges')
ntels = [3, 4]
ntels_axis = ObservationGroupAxis('N_TELS', ntels, fmt='values')
"""
def __init__(self, name, bins, fmt):
if fmt not in ['edges', 'values']:
raise ValueError("Invalid fmt: {}.".format(fmt))
self.name = name
self.bins = np.asanyarray(bins)
self.fmt = fmt
@property
def n_bins(self):
"""Number of bins (int)."""
if self.fmt == 'edges':
return len(self.bins) - 1
elif self.fmt == 'values':
return len(self.bins)
[docs] def get_bin(self, bin_id):
"""Get bin (int, float or `~astropy.units.Quantity`-like).
Value or tuple of bin edges (depending on the ``fmt`` parameter)
for the specified bin.
Parameters
----------
bin_id : int
ID of the bin to retrieve.
Returns
-------
bin : int, float or `~astropy.units.Quantity`-like
Value or tuple of bin edges, depending on the ``fmt`` parameter.
"""
if self.fmt == 'edges':
return self.bins[bin_id], self.bins[bin_id + 1]
elif self.fmt == 'values':
return self.bins[bin_id]
@property
def get_bins(self):
"""List of bins (int, float or `~astropy.units.Quantity`-like).
List of bin edges or values (depending on the ``fmt`` parameter)
for all bins.
"""
bins = []
for i_bin in np.arange(self.n_bins):
bins.append(self.get_bin(i_bin))
return bins
[docs] @classmethod
def from_column(cls, col):
"""Import from astropy column.
Parameters
----------
col : `~astropy.table.Column`
Column with the axis info.
"""
return cls(
name=col.name,
bins=col,
format=col.meta['axis_format']
)
[docs] def to_column(self):
"""Convert to astropy column.
Returns
-------
col : `~astropy.table.Column`
Column with the axis info.
"""
col = Column(data=self.bins, name=self.name)
col.meta['axis_format'] = self.fmt
return col
@property
def info(self):
"""Info string (str)."""
s = "{} {} {}".format(self.name, self.fmt, self.bins)
return s
def _recover_units(array, as_units):
"""Utility function to recover units.
After some numpy operations, `~astropy.units.Quantity`-like objects
loose their units. This function shoul recover them.
Parameters
----------
array : `~numpy.ndarray` or `~astropy.units.Quantity`-like
Array without units.
as_units : int, float or `~astropy.units.Quantity`-like
Structure to imitate the units.
Returns
-------
array : int, float or `~astropy.units.Quantity`-like
Array with units.
"""
# TODO: this seems wrong to do stuff like this. Review and maybe remove?
try:
return Quantity(np.array(array), as_units.unit)
except:
# return unmodified
return array