class MultiSampleSequencer:
def __init__(self, settings: dict):
self.transport = Sequencer(state=settings)
self.interface = ConsoleInterface(self.transport)
def run(self) -> None:
self.transport.run()
def test_sequencer_compare():
"""
Test an old output to ensure we don't regress
"""
input_dir = "data/sumaila/input"
csv_file = os.path.join(input_dir, "metrics-local.csv")
shp_file = os.path.join(input_dir, "networks-proposed.shp")
nwp = NetworkPlan.from_files(shp_file, csv_file, prioritize='Population')
model = Sequencer(nwp, 'Demand...Projected.nodal.demand.per.year')
model.sequence()
expected_dir = "data/sumaila/expected_output"
exp_csv_file = os.path.join(expected_dir, "sequenced-results.csv")
exp_df = pd.read_csv(exp_csv_file)
# exp_shp_file = os.path.join(expected_dir, "edges.shp")
# expected_nwp = NetworkPlan(shp_file, exp_csv_file, prioritize='Population')
# now compare results to expected
#expected_net = expected_nwp.network
compare_fields = ['Sequence..Vertex.id', 'Sequence..Far.sighted.sequence']
# exp_node_dict = expected_net.nodes(data=True)
# exp_node_tups = [tuple(map(d.get, compare_fields)) for d in exp_node_dict]
exp_node_tups = map(tuple, exp_df[compare_fields].values)
seq_node_tups = map(tuple, model.output_frame[compare_fields].values)
exp_node_tups = filter(lambda tup: tup[0] > 0, exp_node_tups)
seq_node_tups = filter(lambda tup: tup[0] > 0, seq_node_tups)
seq_node_tups = map(lambda tup: tuple(map(int, tup)), seq_node_tups)
# seq_node_tups = [tuple(map(seq_node_dict[d].get, compare_fields)) for d in seq_node_dict]
assert sorted(exp_node_tups, key=lambda tup: tup[0]) == \
sorted(seq_node_tups, key=lambda tup: tup[0]),\
"expected nodes do not match sequenced"
def sequence_total_grid_mv_line_network(target_folder, infrastructure_graph):
drafts_folder = make_folder(join(target_folder, 'drafts'))
graph = infrastructure_graph
if not graph.edges():
return {} # The network is empty and there is nothing to sequence
node_table = get_table_from_graph(
graph, ['longitude', 'latitude', 'population', 'peak_demand_in_kw'])
node_table = node_table.rename(columns={'longitude': 'X', 'latitude': 'Y'})
node_table_path = join(drafts_folder, 'nodes-sequencer.csv')
node_table.to_csv(node_table_path)
edge_shapefile_path = join(drafts_folder, 'edges.shp')
nwp = NetworkPlan.from_files(edge_shapefile_path,
node_table_path,
prioritize='population',
proj='+proj=longlat +datum=WGS84 +no_defs')
model = Sequencer(nwp, 'peak.demand.in.kw')
model.sequence()
order_series = model.output_frame['Sequence..Far.sighted.sequence']
for index, order in order_series.iteritems():
node_id = model.output_frame['Unnamed..0'][index]
graph.node[node_id]['grid_mv_network_connection_order'] = order
for node1_id, node2_id, edge_d in graph.cycle_edges():
node1_d = infrastructure_graph.node[node1_id]
node2_d = infrastructure_graph.node[node2_id]
edge_d['grid_mv_network_connection_order'] = min(
node1_d.get('grid_mv_network_connection_order', float('inf')),
node2_d.get('grid_mv_network_connection_order', float('inf')))
return {'infrastructure_graph': graph}
def test_sequencer_compare():
"""
Test an old output to ensure we don't regress
"""
input_dir = "data/sumaila/input"
csv_file = os.path.join(input_dir, "metrics-local.csv")
shp_file = os.path.join(input_dir, "networks-proposed.shp")
nwp = NetworkPlan.from_files(shp_file, csv_file, prioritize='Population')
model = Sequencer(nwp, 'Demand...Projected.nodal.demand.per.year')
model.sequence()
expected_dir = "data/sumaila/expected_output"
exp_csv_file = os.path.join(expected_dir, "sequenced-results.csv")
exp_df = pd.read_csv(exp_csv_file)
# exp_shp_file = os.path.join(expected_dir, "edges.shp")
# expected_nwp = NetworkPlan(shp_file, exp_csv_file, prioritize='Population')
# now compare results to expected
#expected_net = expected_nwp.network
compare_fields = ['Sequence..Vertex.id', 'Sequence..Far.sighted.sequence']
# exp_node_dict = expected_net.nodes(data=True)
# exp_node_tups = [tuple(map(d.get, compare_fields)) for d in exp_node_dict]
exp_node_tups = map(tuple, exp_df[compare_fields].values)
seq_node_tups = map(tuple, model.output_frame[compare_fields].values)
exp_node_tups = filter(lambda tup: tup[0] > 0, exp_node_tups)
seq_node_tups = filter(lambda tup: tup[0] > 0, seq_node_tups)
seq_node_tups = map(lambda tup: tuple(map(int, tup)), seq_node_tups)
# seq_node_tups = [tuple(map(seq_node_dict[d].get, compare_fields)) for d in seq_node_dict]
assert sorted(exp_node_tups, key=lambda tup: tup[0]) == \
sorted(seq_node_tups, key=lambda tup: tup[0]),\
"expected nodes do not match sequenced"
def main():
sequencer = Sequencer()
sequencer.add_steps(KICK, [0, 3, 7])
sequencer.add_steps(SNARE, [2, 6])
sequencer.add_steps(HAT, [0, 1, 2, 3, 4, 5, 6, 7])
while True:
tempo = sequencer.get_tempo()
step = sequencer.get_step()
if len(step) == 0:
LED_Action((25, 25, 25))
elif len(step) == 1:
play_sample(step[0][0])
LED_Action(step[0][1])
else:
color = (0, 0, 0)
i = 0
for sample in step:
if i == 0:
audio = sample[0]
else:
audio = audio.overlay(sample[0])
color = tuple(map(operator.add, color, sample[1]))
i += 1
play_sample(audio)
LED_Action(color)
time.sleep(tempo)
def sequence_total_grid_mv_line_network(target_folder, infrastructure_graph):
drafts_folder = make_folder(join(target_folder, 'drafts'))
graph = infrastructure_graph
if not graph.edges():
return {} # The network is empty and there is nothing to sequence
node_table = get_table_from_graph(graph, [
'longitude', 'latitude', 'population', 'peak_demand_in_kw'])
node_table = node_table.rename(columns={'longitude': 'X', 'latitude': 'Y'})
node_table_path = join(drafts_folder, 'nodes-sequencer.csv')
node_table.to_csv(node_table_path)
edge_shapefile_path = join(drafts_folder, 'edges.shp')
nwp = NetworkPlan.from_files(
edge_shapefile_path, node_table_path, prioritize='population',
proj='+proj=longlat +datum=WGS84 +no_defs')
model = Sequencer(nwp, 'peak.demand.in.kw')
model.sequence()
order_series = model.output_frame['Sequence..Far.sighted.sequence']
for index, order in order_series.iteritems():
node_id = model.output_frame['Unnamed..0'][index]
graph.node[node_id]['grid_mv_network_connection_order'] = order
for node1_id, node2_id, edge_d in graph.cycle_edges():
node1_d = infrastructure_graph.node[node1_id]
node2_d = infrastructure_graph.node[node2_id]
edge_d['grid_mv_network_connection_order'] = min(
node1_d.get('grid_mv_network_connection_order', float('inf')),
node2_d.get('grid_mv_network_connection_order', float('inf')))
return {'infrastructure_graph': graph}
def test_set_note_for_step_range_sets_value_and_hold_state_correctly_on_all_steps(
self):
sequencer = Sequencer(0, 1, 4, 4)
sequencer.set_note_for_step_range(0, 3, 12)
self.assertEquals(sequencer.steps[0].value, 12)
self.assertFalse(sequencer.steps[0].is_hold)
self.assertEquals(sequencer.steps[1].value, 12)
self.assertTrue(sequencer.steps[1].is_hold)
self.assertEquals(sequencer.steps[2].value, 12)
self.assertTrue(sequencer.steps[2].is_hold)
self.assertEquals(sequencer.steps[3].value, 12)
self.assertTrue(sequencer.steps[3].is_hold)
def test_sequencer_follows_topology():
"""Tests that the sequencer doesn't skip nodes in the network"""
nwp = get_network_plan()
model = Sequencer(nwp, 'Demand')
results = model.sequence()
fnodes = results['Sequence..Upstream.id']
node_seq_num = {node: seq_num for seq_num, node in
results['Sequence..Vertex.id'].iteritems()}
#For each from_node, assert that the sequencer has already pointed to it or its a root
eq_(np.all([fnode in nwp.roots or node_seq_num[fnode] < seq_num
for seq_num, fnode in fnodes.iteritems()]), True)
def __init__(self, midiController, midiInput, midiOutput debug):
serial = spi(port=0, device=0, gpio=noop())
self.device = max7219(serial,
cascaded=2,
rotate=1)
print("Created device")
self.printMsg("ESID", font=TINY_FONT)
self.launchpad['in'] = mido.open_input(midiController)
self.launchpad['out'] = mido.open_output(midiController)
if debug:
self.interface['in'] = mido.open_input()
self.interface['out'] = mido.open_output()
else:
self.interface['in'] = mido.open_input(midiInput)
self.interface['out'] = mido.open_output(midiOutput)
self.sequences = []
new = False
if new:
self.sequences.append(Sequencer(36,
"KK",
self.launchpad,
self.interface,
outgoingCC=[10,12],
silent=False,
new=True))
self.sequences.append(Sequencer(38,
"SD",
self.launchpad,
self.interface,
outgoingCC=[18,19],
silent=True,
new=True))
self.sequences.append(Sequencer(39,
"OH",
self.launchpad,
self.interface,
outgoingCC=[58,59],
silent=True,
new=True))
self.sequences.append(Sequencer(46,
"CY",
self.launchpad,
self.interface,
outgoingCC=[50,51],
silent=True,
new=True))
for seq in self.sequences:
self.saveSequence(seq)
else:
self.loadSequences()
def test_init_calculates_step_count_correctly(self):
sequencer = Sequencer(0, 1, 4, 4)
self.assertEquals(sequencer.bars, 1)
self.assertEquals(sequencer.beats_per_bar, 4)
self.assertEquals(sequencer.step_subdivision, 4)
self.assertEquals(sequencer.step_count, 16)
self.assertEquals(len(sequencer.steps.keys()), 16)
sequencer = Sequencer(0, 4, 4, 4)
self.assertEquals(sequencer.bars, 4)
self.assertEquals(sequencer.beats_per_bar, 4)
self.assertEquals(sequencer.step_subdivision, 4)
self.assertEquals(sequencer.step_count, 64)
self.assertEquals(len(sequencer.steps.keys()), 64)
def generate_beat():
# Print a newline
print("")
# Print the header
print(colorama.Fore.BLACK + colorama.Back.GREEN + "Enter the settings for the new beat (press ENTER for the default value)")
# Ask for the time signature to use
while True:
timeSignatureInput = input(" Enter the time signature (%s): " % defaults["timeSignature"])
timeSignature = time_signature(timeSignatureInput or defaults["timeSignature"])
if timeSignature:
break
else:
print(colorama.Fore.RED + " Invalid time signature, make sure you use the format '7/8'. Please try again.")
# Ask for the bpm to use
while True:
try:
bpmInput = input(" Enter the desired BPM, between 30 and 200 (%d): " % defaults["bpm"])
bpm = int(bpmInput or defaults["bpm"])
if bpm < 30 or bpm > 200:
raise ValueError
break
except ValueError:
print(colorama.Fore.RED + " Invalid number, please try again.")
# Create a new sequencer and return it
return Sequencer.generate_irregular_beat(bpm,timeSignature[0],timeSignature[1]);
def opt_fun(params_values):
sequencer = Sequencer(channels, channels_awg, awg_info, channels_delay)
params = {}
list_index_acc = 0
for ii, params_key in enumerate(params_init.keys()):
index = list_index_acc + ii
if 'list' in params_key:
params[params_key] = params_values[index:index +
len(params_init[params_key]
)]
list_index_acc += len(params_init[params_key]) - 1
else:
params[params_key] = params_values[index]
print("params: %s" % params)
multiple_sequences, readout_time_list = arb_optimization_neldermead(
sequencer, params, plot=True)
awg_readout_time_list = get_awg_readout_time(readout_time_list)
data, measured_data, dt = run_qutip_experiment(
multiple_sequences, awg_readout_time_list['m8195a'], plot=True)
Pe_list = measured_data[:, 1]
print("Current value: %s" % Pe_list[0])
return (1 - Pe_list[0])
def __init__(self, midi_in, midi_out):
super(Sequency, self).__init__()
self._midi_out = midi_out
self._midi_in = midi_in
# Clock messages are ignored by default
midi_in.ignore_types(timing=False)
midi_in.set_callback(self._handle_input)
self._lp = LaunchpadMini(midi_in, midi_out)
self._controller = MidiToControl()
self._boot_combo = []
self._reboot_set = False
self._control_mode = CONTROL_MODE.DEFAULT
sequence_states = self._load_from_state()
self._clock = Clock()
self._sequences = []
self._active_sequence = 0
for i in range(0, 8):
self._sequences.append(
Sequencer(i, self._on_trigger, sequence_states[i]))
self.start()
def loadSequences(self):
fileCount = 0
for seqfile in sorted(os.listdir(self.sequenceDir)):
print(seqfile)
if fileCount == 4:
return
fileCount += 1
with open(os.path.join(self.sequenceDir, seqfile), 'r', encoding='utf-8') as fp:
data = json.load(fp)
sequence = []
for step in data['sequence']:
sequence.append(Step(step['note'],
step['led'],
step['incommingCC'],
self.launchpad['out'],
step['active']))
for cc in step['cc']:
sequence[-1].addCc(cc["cc"], cc["value"])
temp = {}
self.sequences.append(Sequencer(data['note'],
data['name'],
self.launchpad,
self.interface,
outgoingCC=data['outgoingCC'],
silent=data['silent']))
self.sequences[-1].sequence = sequence
def test_accumulate_demand():
"""Tests that the accumulated demand is correct"""
nwp = get_network_plan()
# Build dictionary of accumulated values for each node
acc_dicts = {
node: Sequencer(nwp, 'Demand').accumulate(node)
for node in nwp.network.node.keys()
}
# Dictionary of known accumulated demand computed manually
demands = {
0: (100 + 50 + 25 + 12 + 6 + 3),
1: (100 + 25 + 12),
2: (50 + 6 + 3),
3: 25,
4: 12,
5: 6,
6: 3
}
# Assert that accumulate method and manual computation are equal
eq_(
np.all([
acc_dicts[node]['demand'] == demands[node]
for node in nwp.network.node.keys()
]), True)
def test_sequencer_follows_topology():
"""Tests that the sequencer doesn't skip nodes in the network"""
nwp = get_network_plan()
model = Sequencer(nwp, 'Demand')
results = model.sequence()
fnodes = results['Sequence..Upstream.id']
node_seq_num = {
node: seq_num
for seq_num, node in results['Sequence..Vertex.id'].iteritems()
}
#For each from_node, assert that the sequencer has already pointed to it or its a root
eq_(
np.all([
fnode in nwp.roots or node_seq_num[fnode] < seq_num
for seq_num, fnode in fnodes.iteritems()
]), True)
def test_sequencer_with_fakes():
"""
Make sure we work with fake nodes
"""
# for now, just make sure it runs without exceptions
metrics, network, node_rank, edge_rank = gen_data_with_fakes()
nwp = NetworkPlan(network, metrics, prioritize='Population', proj='wgs4')
model = Sequencer(nwp, 'Demand...Projected.nodal.demand.per.year')
results = model.sequence()
node_ids = results['Sequence..Vertex.id']
sequence_ids = results['Sequence..Far.sighted.sequence']
actual_node_rank = dict(zip(node_ids, sequence_ids))
actual_edge_rank = {k: v['rank'] for k, v in
model.networkplan.network.edge.iteritems()}
assert node_rank == actual_node_rank,\
"Node sequencing is not what was expected"
assert edge_rank == actual_edge_rank,\
"Edge sequencing is not what was expected"
def __init__(self, platform):
platform.add_extension(ttl_extension)
sys_clock_pads = platform.request("clk156")
self.clock_domains.cd_sys = ClockDomain(reset_less=True)
self.specials += Instance("IBUFGDS",
i_I=sys_clock_pads.p, i_IB=sys_clock_pads.n,
o_O=self.cd_sys.clk)
sys_clk_freq = 156000000
self.comb += platform.request("sfp_tx_disable_n").eq(1)
gtx = GTXReceiver(
clock_pads=platform.request("sgmii_clock"),
rx_pads=platform.request("sfp_rx"),
sys_clk_freq=sys_clk_freq)
self.submodules += gtx
# clean up GTX clock using Si5324
i2c_master = I2CMaster(platform.request("i2c"))
sequencer = Sequencer(get_i2c_program(sys_clk_freq))
si5324_clock_router = Si5324ClockRouter(platform, sys_clk_freq)
self.submodules += i2c_master, sequencer, si5324_clock_router
self.comb += sequencer.bus.connect(i2c_master.bus)
# decode frames
back_buffer = Signal(32)
front_buffer = Signal(32)
frame_hi = Signal()
self.sync.rx_clean += [
If(gtx.decoders[0].k,
front_buffer.eq(back_buffer),
frame_hi.eq(0)
).Else(
If(frame_hi,
back_buffer[16:].eq(
Cat(gtx.decoders[0].d, gtx.decoders[1].d))
).Else(
back_buffer[:16].eq(
Cat(gtx.decoders[0].d, gtx.decoders[1].d))
),
frame_hi.eq(1)
)
]
# drive TTLs
self.comb += [
platform.request("user_sma_gpio_p").eq(front_buffer[0]),
platform.request("user_sma_gpio_n").eq(front_buffer[1])
]
for i in range(8):
self.comb += platform.request("user_led").eq(front_buffer[2+i])
for i in range(22):
self.comb += platform.request("ttl").eq(front_buffer[10+i])
def test_sequencer_with_fakes():
"""
Make sure we work with fake nodes
"""
# for now, just make sure it runs without exceptions
metrics, network, node_rank, edge_rank = gen_data_with_fakes()
nwp = NetworkPlan(network, metrics, prioritize='Population', proj='wgs4')
model = Sequencer(nwp, 'Demand...Projected.nodal.demand.per.year')
results = model.sequence()
node_ids = results['Sequence..Vertex.id']
sequence_ids = results['Sequence..Far.sighted.sequence']
actual_node_rank = dict(zip(node_ids, sequence_ids))
actual_edge_rank = {
k: v['rank']
for k, v in model.networkplan.network.edge.iteritems()
}
assert node_rank == actual_node_rank,\
"Node sequencing is not what was expected"
assert edge_rank == actual_edge_rank,\
"Edge sequencing is not what was expected"
def test_process_step_only_starts_current_note_when_previous_note_is_empty(
self, mock_send_message, mock_get_previous_step):
mock_get_previous_step.return_value = Note(None, is_hold=False)
sequencer = Sequencer(0, 1, 4, 4)
sequencer.set_midi_channel(0)
sequencer.process_step(Note(1, is_hold=False))
self.assertEquals(mock_send_message.call_count, 1)
self.assertEquals(mock_send_message.call_args[0], ('NoteOn', 0, 1))
def fit(fold_nr, train_set, test_set, img_rows=96, img_cols=96, n_imgs=10 ** 4, batch_size=32, workers=1):
X_train, y_train, X_test, y_test = extract_and_normalize_data(train_set, test_set)
# Done With Preprocessing! :)
x, y = np.meshgrid(np.arange(img_rows), np.arange(img_cols), indexing='ij')
elastic = partial(elastic_transform, x=x, y=y, alpha=img_rows * 1.5, sigma=img_rows * 0.07)
# we create two instances with the same arguments
data_gen_args = dict(
featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=10.,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=True,
zoom_range=[1, 1.2],
fill_mode='constant',
preprocessing_function=elastic)
training_sequence = Sequencer(X_train, y_train, sequence_size=n_imgs, batch_size=batch_size,
data_gen_args=data_gen_args)
raw_model = UNet((img_rows, img_cols, 1), start_ch=8, depth=7, batchnorm=True, dropout=0.5, maxpool=True,
residual=True)
model = ModelMGPU(raw_model, 2)
model.summary(print_fn=logging.info)
model_checkpoint = ModelCheckpoint(
'../data/weights-' + str(fold_nr) + '.h5', monitor='val_loss', save_best_only=True)
metrics_callback = MetricsCallback(X_train, y_train, X_test, y_test, test_set)
c_backs = [model_checkpoint, LoggingWriter(), metrics_callback]
model.compile(optimizer=Adam(lr=0.001), loss=binary_crossentropy, metrics=[dice_coef])
history = model.fit_generator(
training_sequence,
epochs=50,
verbose=1,
shuffle=True,
validation_data=(X_test, y_test),
callbacks=c_backs,
workers=workers,
use_multiprocessing=True)
logging.info(history.history)
plot_learning_performance(history, 'loss-' + str(fold_nr) + '.png')
metrics_callback.save('metrics-' + str(fold_nr) + '.png')
def resultsA(self):
records = self.getRecords(self.basefile)
if len(records) > 1:
raise ImportError('FORMATTING ERROR in base text file');
sequencer = Sequencer()
results = []
for record in records:
record['sequence'] = record['sequence'].replace("\n", "")
frames = list()
for i in range (0, 3):
sequence = sequencer.getExtrons(record,i)
frames.append(sequence)
result = {
'name' : record['name'],
'extrons' : frames.copy()
}
results.append(result)
frames.clear()
file = open("woosh_output_a.txt", 'w')
print('=============================== R E S U L T S ===============================')
file.write('=============================== R E S U L T S ===============================\n')
for result in results:
print(result['name'])
file.write(result['name'])
i = 1
for frame in result['extrons']:
print('\n ------------ Frame ', i, ' ------------')
file.write('\n ------------ Frame ' + str(i) + ' ------------')
for sequence in frame:
print(sequence)
file.write(('\n' + sequence))
i+=1
print('**********************************************')
file.write('\n**********************************************\n\n')
file.close()
def test01_instant(self):
"""test01_instant()
"""
# sequ: Sequencer = Sequencer(0.1) # sequencer with an interval of .1 sec
# sequencer with an interval of 15.0 sec
sequ: Sequencer = Sequencer(5.0) # init for 5 sec delay
self.assertAlmostEqual(5.0, sequ.delayseconds)
self.assertEqual(20, len(sequ._to_do_sched))
self.assertFalse(sequ.do_it_now())
self.assertEqual(0, sequ.skipped)
initiallastsched = sequ._to_do_sched[-1]
cnt: int = 0
starttime = monotonic()
while not sequ.do_it_now():
Sleep(0.25)
self.assertEqual(0, sequ.skipped)
cnt += 1
endtime = monotonic()
toloop = endtime - starttime
self.assertAlmostEqual(10.0, round(toloop, 0))
currentlastsched = sequ._to_do_sched[-1]
delay1 = currentlastsched - initiallastsched
self.assertAlmostEqual(5.0, delay1)
self.assertTrue(38 < cnt < 41)
sched: List[float] = list(sequ._to_do_sched)
ll: List[float] = []
for i in range(1, len(sched)):
ll.append(sched[i] - sched[i - 1])
self.assertEqual(5.0, mean(ll))
sequ = Sequencer(1) # init for 1 sec delay
Sleep(20.5)
self.assertEqual(20, len(sequ._to_do_sched))
self.assertTrue(sequ.do_it_now())
self.assertEqual(20, len(sequ._to_do_sched))
self.assertEqual(11, sequ.skipped)
sequ = Sequencer(5.0) # init for 5 sec delay
waittime: float = sequ.get_nxt_wait()
Sleep(waittime - 0.5)
waittime = sequ.get_nxt_wait()
self.assertAlmostEqual(0.5, waittime, places=1)
a = 0
def test_process_steps_stops_previous_note_when_previous_note_is_hold_and_current_note_is_empty(
self, mock_send_message, mock_get_previous_step):
mock_get_previous_step.return_value = Note(99, is_hold=True)
sequencer = Sequencer(0, 1, 4, 4)
sequencer.set_midi_channel(0)
sequencer.process_step(Note(None, is_hold=False))
self.assertEquals(mock_send_message.call_count, 1)
self.assertEquals(mock_send_message.call_args_list[0][0],
('NoteOff', 0, 99))
def test(): seq = Sequencer() bass = Drum(BassDrum()) snare = Drum(SnareDrum()) bass.get_beat(0).set_enabled(True) bass.get_beat(8).set_enabled(True) snare.get_beat(4).set_enabled(True) snare.get_beat(12).set_enabled(True) seq.add_drum(bass) seq.add_drum(snare) seq.start() sleep(3)
def test_accumulate_cost():
"""Tests that the accumulates costs are correct"""
nwp = get_network_plan()
# Build dictionary of accumulated values for each node
acc_dicts = {
node: Sequencer(nwp, 'Demand').accumulate(node)
for node in nwp.network.node.keys()
}
def get_distance(f, t):
return nwp._distance(f, t)
# Manually compute downstream distances
costs = {
0:
sum([
get_distance(0, 1),
get_distance(0, 2),
get_distance(1, 3),
get_distance(1, 4),
get_distance(2, 5),
get_distance(2, 6)
]),
1:
sum([get_distance(0, 1),
get_distance(1, 3),
get_distance(1, 4)]),
2:
sum([get_distance(0, 2),
get_distance(2, 5),
get_distance(2, 6)]),
3:
get_distance(1, 3),
4:
get_distance(1, 4),
5:
get_distance(2, 5),
6:
get_distance(2, 6)
}
costs = {
node: (acc_dicts[node]['cost'], costs[node])
for node in nwp.network.node.keys()
}
eq_(np.all(map(lambda tup: np.allclose(*tup), costs.values())), True)
def main():
d = {}
for word in open(utils.dict_file):
d[word.strip()] = 0
for word, ac in Sequencer(utils.train_file).Generator():
if ac:
d[word] += 1
sd = set()
for word in open('simple1000.txt'):
sd.add(word.strip())
arr = sorted(d.values())
n = sum(arr)
print 'n =', n
chisq, p = stats.chisquare(map(lambda x: float(x) / n, arr))
print chisq, p
print 'avg =', float(n) / len(d)
print 'savg = ', float(sum(d[x] for x in d.keys() if x in sd)) / 1000
def build(self):
self.initialize_app_state()
self.sequencers = [
Sequencer(id=_id,
bars=1,
beats_per_bar=4,
steps_per_beat=4,
midi_channel=0) for _id in range(8)
]
self.active_sequencer = self.sequencers[0]
self.sequencer_view = SequencerView()
self.sequencer_view.menu.sequencer_spinner.values = [
'Sequencer #{}'.format(sequencer_id)
for sequencer_id in range(len(self.sequencers))
]
self.sequencer_view.update_ui(None)
return self.sequencer_view
class Test_getReadingFrame(unittest.TestCase):
seq = Sequencer()
def testNotFound(self):
start = self.seq.getReadingFrame("AGGATACACACA")
self.assertEqual(start, -1)
def testFirstCodonStart(self):
start = self.seq.getReadingFrame("ATGATACACACA")
self.assertEqual(start, 0)
def testLastCodonStart(self):
start = self.seq.getReadingFrame("AGGATACACATG")
self.assertEqual(start, 9)
def testMiddleCodonStart(self):
start = self.seq.getReadingFrame("AGATGCACACA")
self.assertEqual(start, 2)
def test_set_note_for_step_range_wont_override_a_step_that_is_already_set(
self):
sequencer = Sequencer(0, 1, 4, 4)
sequencer.set_note_for_step(2, 12)
sequencer.set_note_for_step_range(0, 3, 99)
self.assertEquals(sequencer.steps[0].value, 99)
self.assertFalse(sequencer.steps[0].is_hold)
self.assertEquals(sequencer.steps[1].value, 99)
self.assertTrue(sequencer.steps[1].is_hold)
# Test previous step hasn't been overridden
self.assertEquals(sequencer.steps[2].value, 12)
self.assertFalse(sequencer.steps[2].is_hold)
# Test next step after non-hold step is blank
self.assertIsNone(sequencer.steps[3].value)
self.assertFalse(sequencer.steps[3].is_hold)
def opt_fun(params_values):
sequencer = Sequencer(channels, channels_awg, awg_info, channels_delay)
params = {}
for ii, params_key in enumerate(params_init.keys()):
params[params_key] = params_values[ii]
print("params: %s" % params)
multiple_sequences, readout_time_list = sideband_optimization_neldermead(
sequencer, params, plot=True)
awg_readout_time_list = get_awg_readout_time(readout_time_list)
data, measured_data, dt = run_qutip_experiment(
multiple_sequences, awg_readout_time_list['m8195a'], plot=True)
Pe_list = measured_data[:, 1]
print("Current value: %s" % Pe_list[0])
return (Pe_list[0])
def main():
# Test sur l'utilisation du programme
if len(sys.argv) != 2:
print("Utilisation : python3 wirefish.py <nom du fichier>")
exit(1)
# Récuperer les fichiers
dirname = os.path.dirname(__file__)
if not os.path.exists(os.path.join(dirname, '../inputs/')):
os.makedirs(os.path.join(dirname, '../inputs/'))
if not os.path.exists(os.path.join(dirname, '../outputs/')):
os.makedirs(os.path.join(dirname, '../outputs/'))
inputname = sys.argv[1]
inputpath = os.path.join(dirname, '../inputs/' + inputname)
outputname = sys.argv[1].replace('/', '_') + '.txt'
outputpath = os.path.join(dirname, '../outputs/analyse_' + outputname)
if not os.path.isfile(inputpath):
print("{} n'existe pas ".format(inputpath))
exit(1)
# Construction du dictionnaire de la trace.
trace_dict = Sequencer(get_trames_list(inputpath)).sequence()
# A partir de trace_dict, générer le fichier texte résumant l'analyse du fichier.
with open(outputpath, 'w') as f:
f.write('Analyse du fichier : ' + sys.argv[1] + '\n\n' +
get_text_output(trace_dict))
# Construction de l'arbe de la trace à partir de trace_dict
retval = {"name": "Fichier analysé : " + inputname, "children": []}
tree = get_trace_tree(trace_dict, retval)
# Afficher l'interface
Interface(tree).main()
class Test_translateDNA(unittest.TestCase):
seq = Sequencer()
dnaSeq = {'name': 'Test Sequence', 'sequence': ""}
def test1_basic(self):
self.dnaSeq['sequence'] = "ATGATAATCTTTGTTGTGTAA"
start = self.seq.getReadingFrame(self.dnaSeq['sequence'])
protein = self.seq.translate_dna(self.dnaSeq)
self.assertEqual(protein, "MIIFVV")
def test2_middleStart(self):
self.dnaSeq['sequence'] = "ATAATCTTTATGGTTGTGTAG"
protein = self.seq.translate_dna(self.dnaSeq)
self.assertEqual(protein, "MVV")
def test3_earlyStop(self):
self.dnaSeq['sequence'] = "ATGATAATCTAATTGTTGTGTAA"
protein = self.seq.translate_dna(self.dnaSeq)
self.assertEqual(protein, "MII")
def test3_midStartEarlyStop(self):
self.dnaSeq['sequence'] = "ATAATCTTTATGGTTTGAGTGTAG"
protein = self.seq.translate_dna(self.dnaSeq)
self.assertEqual(protein, "MV")
from nmigen import *
from nmigen.asserts import *
from nmigen.hdl.ast import *
from nmigen.back import pysim
from sequencer import Sequencer
from arch import Registers
from incdec import IncDec
from mcycler import MCycler
from muxing import *
if __name__ == "__main__":
m = Module()
m.submodules.sequencer = sequencer = Sequencer()
m.submodules.registers = registers = Registers()
m.submodules.mcycler = mcycler = MCycler()
m.submodules.incdec = incdec = IncDec(16)
addrBus = Signal(16)
dataBus = Signal(8)
m.d.comb += [
mcycler.addr.eq(addrBus),
mcycler.refresh_addr.eq(addrBus),
mcycler.cycle.eq(sequencer.cycle),
mcycler.extend.eq(sequencer.extend),
mcycler.busreq.eq(0),
]
m.d.comb += [
incdec.input.eq(addrBus),
MAG_PAN = -1 * STEREO_WIDTH
HANNABIELL_PAN = 1 * STEREO_WIDTH
MAG_AGE_TYPE = "decay"
HANNABIELL_AGE_TYPE = "rejuvinate"
from sequencer import Sequencer
import logging
logging.basicConfig(filename="play.log",
level=logging.DEBUG,
filemode="w",
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
sequencer = Sequencer()
sequencer.load_sounds("sound/*/*.wav")
# Buses
sequencer.add_bus("bubbles")
sequencer.set_bus_params("bubbles", {
"reverb_room": 0.5,
"reverb_mix": 0.6
})
sequencer.add_bus("long_reverb")
sequencer.set_bus_params("long_reverb", {
"reverb_room": 0.85,
"reverb_mix": 0.75
n = len(word)
for i in xrange(27):
if appear[i]:
score *= self.appear_prob[n][i]
else:
score *= 1.0 - self.appear_prob[n][i]
return score
def Predict(self, word):
if not self.predict_ready:
raise Exception('prediction is not ready')
if len(word) not in utils.train_range:
return False
return self.Score(word) > self.threshold[len(word)]
if __name__ == '__main__':
from sequencer import Sequencer
maid = AppearSep()
maid.Train(Sequencer(utils.train_file).Generator())
ac = [0] * 40
cnt = [0] * 40
for w, tar in Sequencer(utils.valid_file).Generator():
if len(w) in utils.train_range:
cnt[len(w)] += 1
if maid.Predict(w) == tar:
ac[len(w)] += 1
for i in utils.train_range:
print 'Validation', i, float(ac[i] * 100) / cnt[i]
print 'Validation', float(sum(ac) * 100) / sum(cnt)