def rect_init(self):
try:
a = float(self.rect_left.text())
b = float(self.rect_right.text())
acc = float(self.rect_accuracy.text())
min_intervals = float(self.rect_min.text())
max_intervals = float(self.rect_max.text())
if not validate_params(a, b, min_intervals, max_intervals):
raise ValueError
self.error.setText('')
delta = abs(b - a)
self.update_graph(a - delta * 0.1, b + delta * 0.1)
if self.rect_type.currentIndex() == 0:
data = imath.rectangles_left(self.function, a, b, acc,
min_intervals, max_intervals)
elif self.rect_type.currentIndex() == 1:
data = imath.rectangles_right(self.function, a, b, acc,
min_intervals, max_intervals)
elif self.rect_type.currentIndex() == 2:
data = imath.rectangles_middle(self.function, a, b, acc,
min_intervals, max_intervals)
self.data = data
model = tablemodel.TableModel(data, ["Sum", "N"])
self.table.setModel(model)
self.table.update()
except ValueError:
self.error.setText('Invalid params')
except imath.RequirementException:
self.error.setText("Convergence requirements faild")
except imath.BadParamsExceptions:
self.error.setText('Invalid params')
def HordesInit(self):
try:
a = float(self.left.text())
b = float(self.right.text())
acc = float(self.accuracyhords.text())
if not self.validateParams(a, b, a):
raise ValueError
self.error.setText('')
delta = abs( b - a )
self.updateGraph( a - delta * 0.1, b + delta * 0.1 )
data = numath.Hordes(self.function, a, b, acc)
self.data = data
model = tablemodel.TableModel(data, ["a", "b", "x", "F(a)", "F(b)", "F(x)", "|x_(i+1) - x_i"])
self.table.setModel(model)
self.table.update()
except ValueError:
self.error.setText('Invalid params')
except numath.RequirementException:
self.error.setText("Convergence requirements faild")
def NewtonInit(self):
try:
a = float(self.leftNewtone.text())
b = float(self.rightNewtone.text())
approx = float(self.approxNewton.text())
acc = float(self.accuracyNewton.text())
if not self.validateParams(a, b, approx):
raise ValueError
self.error.setText('')
delta = abs( b - a )
self.updateGraph( a - delta * 0.1, b + delta * 0.1 )
data = numath.Newton(self.function, a, b, approx, acc)
self.data = data
model = tablemodel.TableModel(data, ["x", "f(x)", "f'(x)", "x_(n+1)", "|x_(n+1) - x_n|"])
self.table.setModel(model)
self.table.update()
except ValueError:
self.error.setText('Invalid params')
except numath.RequirementException:
self.error.setText("Convergence requirements faild")
def IterationInit(self):
try:
a = float(self.leftIter.text())
b = float(self.rightIter.text())
approx = float(self.approx_Iter.text())
acc = float(self.approx_Iter.text())
if not self.validateParams(a, b, approx):
raise ValueError
self.error.setText('')
delta = abs( b - a )
self.updateGraph( a - delta * 0.1, b + delta * 0.1 )
data = numath.Iteration(self.function, a, b, approx, acc)
self.data = data
model = tablemodel.TableModel(data, ["x_i", "x_(i+1)", "phi(x)", "f(x)", "|x_(i+1) - x_i|"])
self.table.setModel(model)
self.table.update()
except ValueError:
self.error.setText("Invalid Params")
except numath.RequirementException:
self.error.setText("Convergence requirements faild")
except numath.RootOutOfRange:
self.error.setText("Root out of range")
def trapeze_init(self):
try:
a = float(self.trapeze_left.text())
b = float(self.trapeze_right.text())
acc = float(self.trapeze_accuracy.text())
min_intervals = float(self.trapeze_min.text())
max_intervals = float(self.trapeze_max.text())
if not validate_params(a, b, min_intervals, max_intervals):
raise ValueError
self.error.setText('')
delta = abs(b - a)
self.update_graph(a - delta * 0.1, b + delta * 0.1)
data = imath.trapeze(self.function, a, b, acc, min_intervals,
max_intervals)
self.data = data
model = tablemodel.TableModel(data, ["Sum", "N"])
self.table.setModel(model)
self.table.update()
except ValueError:
self.error.setText("Invalid Params")
except imath.RequirementException:
self.error.setText("Convergence requirements faild")
except imath.BadParamsExceptions:
self.error.setText('Invalid params')