def assemble_scaling_matrix(scaling=True):
"""Create and return the per-model scaling matrices.
@keyword scaling: If True, diagonal scaling is enabled during optimisation to allow the problem to be better conditioned.
@type scaling: bool
@return: The list of diagonal and square scaling matrices.
@rtype: list of numpy rank-2, float64 array or list of None
"""
# The specific analysis API object and parameter object.
api = return_api()
param_object = return_parameter_object()
# Initialise.
scaling_matrix = []
# Loop over the models.
for model_info in api.model_loop():
# No diagonal scaling.
if not scaling:
scaling_matrix.append(None)
continue
# Get the parameter names.
names = api.get_param_names(model_info)
# No parameters for this model.
if names == None or len(names) == 0:
scaling_matrix.append(None)
continue
# The parameter number.
n = len(names)
# Initialise.
scaling_matrix.append(identity(n, float64))
i = 0
# Update the diagonal with the parameter specific scaling factor.
for i in range(n):
scaling_matrix[-1][i,
i] = param_object.scaling(names[i],
model_info=model_info)
# Return the matrix.
return scaling_matrix
def assemble_scaling_matrix(scaling=True):
"""Create and return the per-model scaling matrices.
@keyword scaling: If True, diagonal scaling is enabled during optimisation to allow the problem to be better conditioned.
@type scaling: bool
@return: The list of diagonal and square scaling matrices.
@rtype: list of numpy rank-2, float64 array or list of None
"""
# The specific analysis API object and parameter object.
api = return_api()
param_object = return_parameter_object()
# Initialise.
scaling_matrix = []
# Loop over the models.
for model_info in api.model_loop():
# No diagonal scaling.
if not scaling:
scaling_matrix.append(None)
continue
# Get the parameter names.
names = api.get_param_names(model_info)
# No parameters for this model.
if names == None or len(names) == 0:
scaling_matrix.append(None)
continue
# The parameter number.
n = len(names)
# Initialise.
scaling_matrix.append(identity(n, float64))
i = 0
# Update the diagonal with the parameter specific scaling factor.
for i in range(n):
scaling_matrix[-1][i, i] = param_object.scaling(names[i], model_info=model_info)
# Return the matrix.
return scaling_matrix
def grid_setup(lower=None,
upper=None,
inc=None,
verbosity=1,
skip_preset=True):
"""Determine the per-model grid bounds, allowing for the zooming grid search.
@keyword lower: The user supplied lower bounds of the grid search which must be equal to the number of parameters in the model.
@type lower: list of numbers
@keyword upper: The user supplied upper bounds of the grid search which must be equal to the number of parameters in the model.
@type upper: list of numbers
@keyword inc: The user supplied grid search increments.
@type inc: int or list of int
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword skip_preset: This argument, when True, allows any parameter which already has a value set to be skipped in the grid search.
@type skip_preset: bool
@return: The per-model grid upper and lower bounds. The first dimension of each structure corresponds to the model, the second the model parameters.
@rtype: tuple of lists of lists of float, lists of lists of float, list of lists of int
"""
# The specific analysis API object and parameter object.
api = return_api()
param_object = return_parameter_object()
# Initialise.
model_lower = []
model_upper = []
model_inc = []
# Loop over the models.
for model_info in api.model_loop():
# Get the parameter names and current values.
names = api.get_param_names(model_info)
values = api.get_param_values(model_info)
# No parameters for this model.
if names == None or len(names) == 0:
model_lower.append([])
model_upper.append([])
model_inc.append([])
continue
# The parameter number.
n = len(names)
# Make sure that the length of the parameter array is > 0.
if n == 0:
raise RelaxError(
"Cannot run a grid search on a model with zero parameters.")
# Check that the user supplied bound lengths are ok.
if lower != None and len(lower) != n:
raise RelaxLenError('lower bounds', n)
if upper != None and len(upper) != n:
raise RelaxLenError('upper bounds', n)
# Check the user supplied increments.
if isinstance(inc, list) and len(inc) != n:
raise RelaxLenError('increment', n)
if isinstance(inc, list):
for i in range(n):
if not (isinstance(inc[i], int) or inc[i] == None):
raise RelaxIntListIntError('increment', inc)
elif not isinstance(inc, int):
raise RelaxIntListIntError('increment', inc)
# Convert to the model increment list.
if isinstance(inc, int):
model_inc.append([inc] * n)
else:
model_inc.append(inc)
# Print out the model title.
api.print_model_title(prefix="Grid search setup: ",
model_info=model_info)
# The grid zoom level.
zoom = 0
if hasattr(cdp, 'grid_zoom_level'):
zoom = cdp.grid_zoom_level
zoom_factor = 1.0 / 2.0**zoom
if zoom > 0:
print(
"Zooming grid level of %s, scaling the grid size by a factor of %s.\n"
% (zoom, zoom_factor))
# Append empty lists for the bounds to be built up.
model_lower.append([])
model_upper.append([])
# Loop over the parameters.
data = []
for i in range(n):
# A comment for user feedback.
comment = 'Default bounds'
if lower != None and upper != None:
comment = 'User supplied lower and upper bound'
elif lower != None:
comment = 'User supplied lower bound'
elif upper != None:
comment = 'User supplied upper bound'
# Alias the number of increments for this parameter.
incs = model_inc[-1][i]
# Error checking for increment values of None.
if incs == None and values[i] in [None, {}, []]:
raise RelaxError(
"The parameter '%s' has no preset value, therefore a grid increment of None is not valid."
% names[i])
# The lower bound for this parameter.
if lower != None:
lower_i = lower[i]
else:
lower_i = param_object.grid_lower(names[i],
incs=incs,
model_info=model_info)
# The upper bound for this parameter.
if upper != None:
upper_i = upper[i]
else:
upper_i = param_object.grid_upper(names[i],
incs=incs,
model_info=model_info)
# The skipping logic.
skip = False
if skip_preset:
# Override the flag if the zoom is on.
if zoom:
skip = False
# No preset value.
elif values[i] in [None, {}, []]:
skip = False
# The preset value is a NaN value due to numpy conversions of None.
elif isNaN(values[i]):
skip = False
# Ok, now the parameter can be skipped.
else:
skip = True
# Override the skip flag if the incs value is None.
if incs == None:
skip = True
# Skip preset values.
if skip:
lower_i = values[i]
upper_i = values[i]
model_inc[-1][i] = incs = 1
comment = 'Preset value'
# Zooming grid.
elif zoom:
# The full size and scaled size.
size = upper_i - lower_i
zoom_size = size * zoom_factor
half_size = zoom_size / 2.0
comment = 'Zoom grid width of %s %s' % (
zoom_size, param_object.units(names[i]))
# The new size around the current value.
lower_zoom = values[i] - half_size
upper_zoom = values[i] + half_size
# Outside of the original lower bound, so shift the grid to fit.
if zoom > 0 and lower_zoom < lower_i:
# The amount to shift by.
shift = lower_i - lower_zoom
# Set the new bounds.
upper_i = upper_zoom + shift
# Outside of the original upper bound, so shift the grid to fit.
elif zoom > 0 and upper_zoom > upper_i:
# The amount to shift by.
shift = upper_i - upper_zoom
# Set the new bounds.
lower_i = lower_zoom + shift
# Inside the original bounds.
else:
lower_i = lower_zoom
upper_i = upper_zoom
# Add to the data list for printing out.
data.append([
names[i],
"%15s" % lower_i,
"%15s" % upper_i,
"%15s" % incs, comment
])
# Scale the bounds.
scaling = param_object.scaling(names[i], model_info=model_info)
lower_i /= scaling
upper_i /= scaling
# Append.
model_lower[-1].append(lower_i)
model_upper[-1].append(upper_i)
# Printout.
if verbosity:
write_data(out=sys.stdout,
headings=[
"Parameter", "Lower bound", "Upper bound",
"Increments", "Comment"
],
data=data)
sys.stdout.write('\n')
# Return the bounds.
return model_lower, model_upper, model_inc
def grid_setup(lower=None, upper=None, inc=None, verbosity=1, skip_preset=True):
"""Determine the per-model grid bounds, allowing for the zooming grid search.
@keyword lower: The user supplied lower bounds of the grid search which must be equal to the number of parameters in the model.
@type lower: list of numbers
@keyword upper: The user supplied upper bounds of the grid search which must be equal to the number of parameters in the model.
@type upper: list of numbers
@keyword inc: The user supplied grid search increments.
@type inc: int or list of int
@keyword verbosity: The amount of information to print. The higher the value, the greater the verbosity.
@type verbosity: int
@keyword skip_preset: This argument, when True, allows any parameter which already has a value set to be skipped in the grid search.
@type skip_preset: bool
@return: The per-model grid upper and lower bounds. The first dimension of each structure corresponds to the model, the second the model parameters.
@rtype: tuple of lists of lists of float, lists of lists of float, list of lists of int
"""
# The specific analysis API object and parameter object.
api = return_api()
param_object = return_parameter_object()
# Initialise.
model_lower = []
model_upper = []
model_inc = []
# Loop over the models.
for model_info in api.model_loop():
# Get the parameter names and current values.
names = api.get_param_names(model_info)
values = api.get_param_values(model_info)
# No parameters for this model.
if names == None or len(names) == 0:
model_lower.append([])
model_upper.append([])
model_inc.append([])
continue
# The parameter number.
n = len(names)
# Make sure that the length of the parameter array is > 0.
if n == 0:
raise RelaxError("Cannot run a grid search on a model with zero parameters.")
# Check that the user supplied bound lengths are ok.
if lower != None and len(lower) != n:
raise RelaxLenError('lower bounds', n)
if upper != None and len(upper) != n:
raise RelaxLenError('upper bounds', n)
# Check the user supplied increments.
if isinstance(inc, list) and len(inc) != n:
raise RelaxLenError('increment', n)
if isinstance(inc, list):
for i in range(n):
if not (isinstance(inc[i], int) or inc[i] == None):
raise RelaxIntListIntError('increment', inc)
elif not isinstance(inc, int):
raise RelaxIntListIntError('increment', inc)
# Convert to the model increment list.
if isinstance(inc, int):
model_inc.append([inc]*n)
else:
model_inc.append(inc)
# Print out the model title.
api.print_model_title(prefix="Grid search setup: ", model_info=model_info)
# The grid zoom level.
zoom = 0
if hasattr(cdp, 'grid_zoom_level'):
zoom = cdp.grid_zoom_level
zoom_factor = 1.0 / 2.0**zoom
if zoom > 0:
print("Zooming grid level of %s, scaling the grid size by a factor of %s.\n" % (zoom, zoom_factor))
# Append empty lists for the bounds to be built up.
model_lower.append([])
model_upper.append([])
# Loop over the parameters.
data = []
for i in range(n):
# A comment for user feedback.
comment = 'Default bounds'
if lower != None and upper != None:
comment = 'User supplied lower and upper bound'
elif lower != None:
comment = 'User supplied lower bound'
elif upper != None:
comment = 'User supplied upper bound'
# Alias the number of increments for this parameter.
incs = model_inc[-1][i]
# Error checking for increment values of None.
if incs == None and values[i] in [None, {}, []]:
raise RelaxError("The parameter '%s' has no preset value, therefore a grid increment of None is not valid." % names[i])
# The lower bound for this parameter.
if lower != None:
lower_i = lower[i]
else:
lower_i = param_object.grid_lower(names[i], incs=incs, model_info=model_info)
# The upper bound for this parameter.
if upper != None:
upper_i = upper[i]
else:
upper_i = param_object.grid_upper(names[i], incs=incs, model_info=model_info)
# The skipping logic.
skip = False
if skip_preset:
# Override the flag if the zoom is on.
if zoom:
skip = False
# No preset value.
elif values[i] in [None, {}, []]:
skip = False
# The preset value is a NaN value due to numpy conversions of None.
elif isNaN(values[i]):
skip = False
# Ok, now the parameter can be skipped.
else:
skip = True
# Override the skip flag if the incs value is None.
if incs == None:
skip = True
# Skip preset values.
if skip:
lower_i = values[i]
upper_i = values[i]
model_inc[-1][i] = incs = 1
comment = 'Preset value'
# Zooming grid.
elif zoom:
# The full size and scaled size.
size = upper_i - lower_i
zoom_size = size * zoom_factor
half_size = zoom_size / 2.0
comment = 'Zoom grid width of %s %s' % (zoom_size, param_object.units(names[i]))
# The new size around the current value.
lower_zoom = values[i] - half_size
upper_zoom = values[i] + half_size
# Outside of the original lower bound, so shift the grid to fit.
if zoom > 0 and lower_zoom < lower_i:
# The amount to shift by.
shift = lower_i - lower_zoom
# Set the new bounds.
upper_i = upper_zoom + shift
# Outside of the original upper bound, so shift the grid to fit.
elif zoom > 0 and upper_zoom > upper_i:
# The amount to shift by.
shift = upper_i - upper_zoom
# Set the new bounds.
lower_i = lower_zoom + shift
# Inside the original bounds.
else:
lower_i = lower_zoom
upper_i = upper_zoom
# Add to the data list for printing out.
data.append([names[i], "%15s" % lower_i, "%15s" % upper_i, "%15s" % incs, comment])
# Scale the bounds.
scaling = param_object.scaling(names[i], model_info=model_info)
lower_i /= scaling
upper_i /= scaling
# Append.
model_lower[-1].append(lower_i)
model_upper[-1].append(upper_i)
# Printout.
if verbosity:
write_data(out=sys.stdout, headings=["Parameter", "Lower bound", "Upper bound", "Increments", "Comment"], data=data)
sys.stdout.write('\n')
# Return the bounds.
return model_lower, model_upper, model_inc
