class Layout(object):
def __init__(self):
self.solver = Solver()
def update(self, element):
self.solver.autosolve = False
for constraint in element.constraints[1]:
self.solver.remove_constraint(constraint)
for constraint in element.constraints[0]:
self.solver.add_constraint(constraint)
element.constraints = [], element.constraints[0]
self.solver.autosolve = True
class Layout(object):
def __init__(self):
self.solver = Solver()
self.introns = WeakValueDictionary()
def update(self, element):
self.solver.autosolve = False
for constraint in element.constraints[1]:
self.solver.remove_constraint(constraint)
for constraint in element.constraints[0]:
self.solver.add_constraint(constraint)
element.constraints = [], element.constraints[0]
self.solver.autosolve = True
def add_intron(self, source, intron):
self.introns[source] = intron
def pop_intron(self, source):
return self.introns.pop(source)
def get_intron(self, source, otherwise=None):
return self.introns.get(source, otherwise)
def initialize(self, constraints):
""" Initializes the solver by creating all of the necessary
constraints for this components layout children and adding them
to the solver.
Parameters
----------
constraints : Iterable
An iterable that yields the constraints to add to the
solvers.
"""
self._initialized = False
self._solver = solver = Solver(autosolve=False)
for cn in constraints:
solver.add_constraint(cn)
solver.autosolve = True
self._initialized = True
class engine(object):
def __init__(self):
self.solver = Solver()
self.solver.autosolve = True
def require(self, rule):
self.solver.add_constraint(rule)
def prefer(self, rule):
rule.strength = strong
self.solver.add_constraint(rule)
def guide(self, rule):
rule.strength = weak
self.solver.add_constraint(rule)
def __init__(self):
self.solver = Solver()
self.introns = WeakValueDictionary()
class LayoutManager(object):
""" A class which uses a casuarius solver to manage a system
of constraints.
"""
def __init__(self):
self._solver = Solver(autosolve=False)
self._initialized = False
self._running = False
def initialize(self, constraints):
""" Initialize the solver with the given constraints.
Parameters
----------
constraints : Iterable
An iterable that yields the constraints to add to the
solvers.
"""
if self._initialized:
raise RuntimeError('Solver already initialized')
solver = self._solver
solver.autosolve = False
for cn in constraints:
solver.add_constraint(cn)
solver.autosolve = True
self._initialized = True
def replace_constraints(self, old_cns, new_cns):
""" Replace constraints in the solver.
Parameters
----------
old_cns : list
The list of casuarius constraints to remove from the
solver.
new_cns : list
The list of casuarius constraints to add to the solver.
"""
if not self._initialized:
raise RuntimeError('Solver not yet initialized')
solver = self._solver
solver.autosolve = False
for cn in old_cns:
solver.remove_constraint(cn)
for cn in new_cns:
solver.add_constraint(cn)
solver.autosolve = True
def layout(self, cb, width, height, size, strength=medium, weight=1.0):
""" Perform an iteration of the solver for the new width and
height constraint variables.
Parameters
----------
cb : callable
A callback which will be called when new values from the
solver are available. This will be called from within a
solver context while the solved values are valid. Thus
the new values should be consumed before the callback
returns.
width : Constraint Variable
The constraint variable representing the width of the
main layout container.
height : Constraint Variable
The constraint variable representing the height of the
main layout container.
size : (int, int)
The (width, height) size tuple which is the current size
of the main layout container.
strength : casuarius strength, optional
The strength with which to perform the layout using the
current size of the container. i.e. the strength of the
resize. The default is casuarius.medium.
weight : float, optional
The weight to apply to the strength. The default is 1.0
"""
if not self._initialized:
raise RuntimeError('Layout with uninitialized solver')
if self._running:
return
try:
self._running = True
w, h = size
values = [(width, w), (height, h)]
with self._solver.suggest_values(values, strength, weight):
cb()
finally:
self._running = False
def get_min_size(self, width, height, strength=medium, weight=0.1):
""" Run an iteration of the solver with the suggested size of the
component set to (0, 0). This will cause the solver to effectively
compute the minimum size that the window can be to solve the
system.
Parameters
----------
width : Constraint Variable
The constraint variable representing the width of the
main layout container.
height : Constraint Variable
The constraint variable representing the height of the
main layout container.
strength : casuarius strength, optional
The strength with which to perform the layout using the
current size of the container. i.e. the strength of the
resize. The default is casuarius.medium.
weight : float, optional
The weight to apply to the strength. The default is 0.1
so that constraints of medium strength but default weight
have a higher precedence than the minimum size.
Returns
-------
result : (float, float)
The floating point (min_width, min_height) size of the
container which would best satisfy the set of constraints.
"""
if not self._initialized:
raise RuntimeError('Get min size on uninitialized solver')
values = [(width, 0.0), (height, 0.0)]
with self._solver.suggest_values(values, strength, weight):
min_width = width.value
min_height = height.value
return (min_width, min_height)
def get_max_size(self, width, height, strength=medium, weight=0.1):
""" Run an iteration of the solver with the suggested size of
the component set to a very large value. This will cause the
solver to effectively compute the maximum size that the window
can be to solve the system. The return value is a tuple numbers.
If one of the numbers is -1, it indicates there is no maximum in
that direction.
Parameters
----------
width : Constraint Variable
The constraint variable representing the width of the
main layout container.
height : Constraint Variable
The constraint variable representing the height of the
main layout container.
strength : casuarius strength, optional
The strength with which to perform the layout using the
current size of the container. i.e. the strength of the
resize. The default is casuarius.medium.
weight : float, optional
The weight to apply to the strength. The default is 0.1
so that constraints of medium strength but default weight
have a higher precedence than the minimum size.
Returns
-------
result : (float or -1, float or -1)
The floating point (max_width, max_height) size of the
container which would best satisfy the set of constraints.
"""
if not self._initialized:
raise RuntimeError('Get max size on uninitialized solver')
max_val = 2**24 - 1 # Arbitrary, but the max allowed by Qt.
values = [(width, max_val), (height, max_val)]
with self._solver.suggest_values(values, strength, weight):
max_width = width.value
max_height = height.value
width_diff = abs(max_val - int(round(max_width)))
height_diff = abs(max_val - int(round(max_height)))
if width_diff <= 1:
max_width = -1
if height_diff <= 1:
max_height = -1
return (max_width, max_height)
def __init__(self):
self._solver = Solver(autosolve=False)
self._initialized = False
self._running = False
def __init__(self):
self.solver = Solver()
self.solver.autosolve = True
def __init__(self):
self.solver = Solver()
