def derive(self, gl=M('Gear (L) Position'), gn=M('Gear (N) Position'), gr=M('Gear (R) Position'), gc=M('Gear (C) Position')): up_state = vstack_params_where_state( (gl, 'Up'), (gn, 'Up'), (gr, 'Up'), (gc, 'Up'), ).all(axis=0) down_state = vstack_params_where_state( (gl, 'Down'), (gn, 'Down'), (gr, 'Down'), (gc, 'Down'), ).all(axis=0) transit_state = vstack_params_where_state( (gl, 'In Transit'), (gn, 'In Transit'), (gr, 'In Transit'), (gc, 'In Transit'), ).any(axis=0) param = first_valid_parameter(gl, gn, gr, gc) self.array = np_ma_masked_zeros_like(param.array) self.array[repair_mask(up_state, repair_duration=None)] = 'Up' self.array[repair_mask(down_state, repair_duration=None)] = 'Down' self.array[repair_mask(transit_state, repair_duration=None)] = 'In Transit' self.array = nearest_neighbour_mask_repair(self.array)
def derive(self, gl=M('Gear (L) On Ground'), gr=M('Gear (R) On Ground'), vert_spd=P('Vertical Speed'), torque=P('Eng (*) Torque Avg'), ac_series=A('Series'), collective=P('Collective')): if gl and gr: delta = abs((gl.offset - gr.offset) * gl.frequency) if 0.75 < delta or delta < 0.25: # If the samples of the left and right gear are close together, # the best representation is to map them onto a single # parameter in which we accept that either wheel on the ground # equates to gear on ground. self.array = np.ma.logical_or(gl.array, gr.array) self.frequency = gl.frequency self.offset = gl.offset return else: # If the paramters are not co-located, then # merge_two_parameters creates the best combination possible. self.array, self.frequency, self.offset = merge_two_parameters(gl, gr) return elif gl or gr: gear = gl or gr self.array = gear.array self.frequency = gear.frequency self.offset = gear.offset elif vert_spd and torque: vert_spd_limit = 100.0 torque_limit = 30.0 if ac_series and ac_series.value == 'Columbia 234': vert_spd_limit = 125.0 torque_limit = 22.0 collective_limit = 15.0 vert_spd_array = align(vert_spd, torque) if vert_spd.hz != torque.hz else vert_spd.array collective_array = align(collective, torque) if collective.hz != torque.hz else collective.array vert_spd_array = moving_average(vert_spd_array) torque_array = moving_average(torque.array) collective_array = moving_average(collective_array) roo_vs_array = runs_of_ones(abs(vert_spd_array) < vert_spd_limit, min_samples=1) roo_torque_array = runs_of_ones(torque_array < torque_limit, min_samples=1) roo_collective_array = runs_of_ones(collective_array < collective_limit, min_samples=1) vs_and_torque = slices_and(roo_vs_array, roo_torque_array) grounded = slices_and(vs_and_torque, roo_collective_array) array = np_ma_zeros_like(vert_spd_array) for _slice in slices_remove_small_slices(grounded, count=2): array[_slice] = 1 array.mask = vert_spd_array.mask | torque_array.mask array.mask = array.mask | collective_array.mask self.array = nearest_neighbour_mask_repair(array) self.frequency = torque.frequency self.offset = torque.offset else: vert_spd_array = align(vert_spd, torque) if vert_spd.hz != torque.hz else vert_spd.array # Introducted for S76 and Bell 212 which do not have Gear On Ground available vert_spd_array = moving_average(vert_spd_array) torque_array = moving_average(torque.array) grounded = slices_and(runs_of_ones(abs(vert_spd_array) < vert_spd_limit, min_samples=1), runs_of_ones(torque_array < torque_limit, min_samples=1)) array = np_ma_zeros_like(vert_spd_array) for _slice in slices_remove_small_slices(grounded, count=2): array[_slice] = 1 array.mask = vert_spd_array.mask | torque_array.mask self.array = nearest_neighbour_mask_repair(array) self.frequency = torque.frequency self.offset = torque.offset else: # should not get here if can_operate is correct raise NotImplementedError()
def derive(self, gl=M('Gear (L) On Ground'), gr=M('Gear (R) On Ground'), vert_spd=P('Vertical Speed'), torque=P('Eng (*) Torque Avg'), ac_series=A('Series'), collective=P('Collective')): if gl and gr: delta = abs((gl.offset - gr.offset) * gl.frequency) if 0.75 < delta or delta < 0.25: # If the samples of the left and right gear are close together, # the best representation is to map them onto a single # parameter in which we accept that either wheel on the ground # equates to gear on ground. self.array = np.ma.logical_or(gl.array, gr.array) self.frequency = gl.frequency self.offset = gl.offset return else: # If the paramters are not co-located, then # merge_two_parameters creates the best combination possible. self.array, self.frequency, self.offset = merge_two_parameters( gl, gr) return elif gl or gr: gear = gl or gr self.array = gear.array self.frequency = gear.frequency self.offset = gear.offset elif vert_spd and torque: vert_spd_limit = 100.0 torque_limit = 30.0 if ac_series and ac_series.value == 'Columbia 234': vert_spd_limit = 125.0 torque_limit = 22.0 collective_limit = 15.0 vert_spd_array = align( vert_spd, torque) if vert_spd.hz != torque.hz else vert_spd.array collective_array = align( collective, torque) if collective.hz != torque.hz else collective.array vert_spd_array = moving_average(vert_spd_array) torque_array = moving_average(torque.array) collective_array = moving_average(collective_array) roo_vs_array = runs_of_ones( abs(vert_spd_array) < vert_spd_limit, min_samples=1) roo_torque_array = runs_of_ones(torque_array < torque_limit, min_samples=1) roo_collective_array = runs_of_ones( collective_array < collective_limit, min_samples=1) vs_and_torque = slices_and(roo_vs_array, roo_torque_array) grounded = slices_and(vs_and_torque, roo_collective_array) array = np_ma_zeros_like(vert_spd_array) for _slice in slices_remove_small_slices(grounded, count=2): array[_slice] = 1 array.mask = vert_spd_array.mask | torque_array.mask array.mask = array.mask | collective_array.mask self.array = nearest_neighbour_mask_repair(array) self.frequency = torque.frequency self.offset = torque.offset else: vert_spd_array = align( vert_spd, torque) if vert_spd.hz != torque.hz else vert_spd.array # Introducted for S76 and Bell 212 which do not have Gear On Ground available vert_spd_array = moving_average(vert_spd_array) torque_array = moving_average(torque.array) grounded = slices_and( runs_of_ones(abs(vert_spd_array) < vert_spd_limit, min_samples=1), runs_of_ones(torque_array < torque_limit, min_samples=1)) array = np_ma_zeros_like(vert_spd_array) for _slice in slices_remove_small_slices(grounded, count=2): array[_slice] = 1 array.mask = vert_spd_array.mask | torque_array.mask self.array = nearest_neighbour_mask_repair(array) self.frequency = torque.frequency self.offset = torque.offset else: # should not get here if can_operate is correct raise NotImplementedError()
def derive(self, vert_spd=P('Vertical Speed'), torque=P('Eng (*) Torque Avg'), ac_series=A('Series'), collective=P('Collective')): vert_spd_limit = 100.0 torque_limit = 30.0 if ac_series and ac_series.value == 'Columbia 234': vert_spd_limit = 125.0 torque_limit = 22.0 collective_limit = 15.0 vert_spd_array = align( vert_spd, torque) if vert_spd.hz != torque.hz else vert_spd.array collective_array = align( collective, torque) if collective.hz != torque.hz else collective.array vert_spd_array = moving_average(vert_spd_array) torque_array = moving_average(torque.array) collective_array = moving_average(collective_array) roo_vs_array = runs_of_ones(abs(vert_spd_array) < vert_spd_limit, min_samples=1) roo_torque_array = runs_of_ones(torque_array < torque_limit, min_samples=1) roo_collective_array = runs_of_ones( collective_array < collective_limit, min_samples=1) vs_and_torque = slices_and(roo_vs_array, roo_torque_array) grounded = slices_and(vs_and_torque, roo_collective_array) array = np_ma_zeros_like(vert_spd_array) for _slice in slices_remove_small_slices(grounded, count=2): array[_slice] = 1 array.mask = vert_spd_array.mask | torque_array.mask array.mask = array.mask | collective_array.mask self.array = nearest_neighbour_mask_repair(array) self.frequency = torque.frequency self.offset = torque.offset else: vert_spd_array = align( vert_spd, torque) if vert_spd.hz != torque.hz else vert_spd.array # Introducted for S76 and Bell 212 which do not have Gear On Ground available vert_spd_array = moving_average(vert_spd_array) torque_array = moving_average(torque.array) grounded = slices_and( runs_of_ones(abs(vert_spd_array) < vert_spd_limit, min_samples=1), runs_of_ones(torque_array < torque_limit, min_samples=1)) array = np_ma_zeros_like(vert_spd_array) for _slice in slices_remove_small_slices(grounded, count=2): array[_slice] = 1 array.mask = vert_spd_array.mask | torque_array.mask self.array = nearest_neighbour_mask_repair(array) self.frequency = torque.frequency self.offset = torque.offset