def main():
parser = argparse.ArgumentParser()
parser.add_argument('img1', help=('Image1 - use magnitude'))
parser.add_argument('img2', help=('Image2 - use phase'))
args = parser.parse_args()
images = (args.img1, args.img2)
for img in images:
try:
with open(img, 'rb'):
pass
except IOError:
print >> sys.stderr, "%s could not be opened" % img
return 1
img1, img2 = image2array(images[0]), image2array(images[1])
if img1.shape != img2.shape:
print >> sys.stderr, "Images should have the same dimensions"
return 2
G, H = fft(img1), fft(img2)
magG = get_magnitude(G)
phaseH = get_phase(H)
K = image_from_mag_phase(magG, phaseH)
img3 = fft(K, True)
plot([img1, img2, prepare_show(img3, False)])
return 0
def main(args):
"""
Plot job execution times.
"""
with open(args.fconfig, 'rb') as fp:
args_fconfig = utils.dict_to_class(json.load(fp))
utils.plot(args_fconfig)
return None
def plot_results(self):
"""
Plots acquisition results in a new window
"""
from utils import plot
rr_file = "{}.rr.txt".format(self.acquisition_path.encode("utf-8"))
tag_file = "{}.tag.txt".format(self.acquisition_path.encode("utf-8"))
plot(rr_file, tag_file)
def runLR(x, truth, show=False, X=None, truthX=None):
w = train(x,truth)
#print "w = " + str(w)
prediction = np.sign(np.dot(x,w))
green = x[(prediction == 1), 1:]
red = x[(prediction < 1), 1:]
plot(green, red, w, show=show, axis=312 if X == None else 323)
right = x[(prediction == truth),1:]
wrong = x[(prediction != truth),1:]
plot(right, wrong, w, show=show, axis=313 if X == None else 325)
wrongIn = wrong[:,0].size
if X != None:
predictionOut = np.sign(np.dot(X,w))
green = X[(predictionOut == 1), 1:]
red = X[(predictionOut < 1), 1:]
plot(green, red, w, show=show, axis=324)
right = X[(predictionOut == truthX),1:]
wrong = X[(predictionOut != truthX),1:]
plot(right, wrong, w, show=show, axis=326)
return wrongIn, wrong[:,0].size, w
def main():
parser = argparse.ArgumentParser()
parser.add_argument('img', help=('Path of the image to perform the'
' transform'))
parser.add_argument('--rmin', type=int, help='rmin parameter', default=23)
parser.add_argument('--rmax', type=int, help='rmax parameter', default=48)
args = parser.parse_args()
try:
with open(args.img, 'rb'):
pass
except IOError:
print >> sys.stderr, "%s could not be opened" % args.img
return 1
rmin_range = (0, 100)
if not (rmin_range[0] < args.rmin < rmin_range[1]):
print >> sys.stderr, "rmin should be between %d and %d" % rmin_range
return 2
if not (rmin_range[0] < args.rmax < rmin_range[1]):
print >> sys.stderr, "rmax should be between %d and %d" % rmin_range
return 2
if args.rmin >= args.rmax:
print >> sys.stderr, 'rmin should be less than rmax'
return 3
aimg = image2array(args.img)
freq_ = fft(aimg)
shift_freq = fftshift(freq_)
center = (shift_freq.shape[0] / 2, shift_freq.shape[1] / 2)
distance_array = euclidean_distance(shift_freq, center)
mask = (distance_array < args.rmin) != (distance_array > args.rmax)
shift_freq_cpy = shift_freq.copy()
shift_freq_cpy[mask] = 0
output = fft(fftshift(shift_freq_cpy, True), True)
plot([aimg, prepare_show(shift_freq), prepare_show(shift_freq_cpy),
prepare_show(output, False)])
return 0
def main():
p = argparse.ArgumentParser()
p.add_argument("-i", "--interactive", action="store_true")
kwargs = vars(p.parse_args())
plt.rcParams["interactive"] = kwargs["interactive"]
with utils.plot(__file__):
plot(**kwargs)
def gradient_step(i, state, mod):
params = get_params(state)
mod.prior.hyp = params[0]
mod.likelihood.hyp = params[1]
# grad(Filter) + Smoother:
neg_log_marg_lik, gradients = mod.run()
# neg_log_marg_lik, gradients = mod.run_two_stage() # <-- less elegant but reduces compile time
prior_params = softplus_list(params[0])
print('iter %2d: var_f=%1.2f len_f=%1.2f, nlml=%2.2f' %
(i, prior_params[0], prior_params[1], neg_log_marg_lik))
if plot_intermediate:
plot(mod, i)
return opt_update(i, gradients, state)
def train(self):
rewardperep = []
for i in range(self.episode):
state = self.state_to_index(self.env.reset())
totalreward = 0
for j in range(self.horizon):
action = self.act(state, deterministic=False)
next_state, reward, done, info = self.env.step(action)
next_state = self.state_to_index(next_state)
self.update(state, action, reward, next_state)
totalreward += reward
state = next_state
if done == True:
break
rewardperep.append(totalreward)
plot(rewardperep, 'results/qlambdalearning_traffic_returns.png')
def train(gan, datasets):
real_example = gan.input()
latent = gan.sample()
fake_example = gan.generator(latent)
real_logits = gan.discriminator(real_example)
fake_logits = gan.discriminator(fake_example)
predict = tf.less_equal((fake_logits), 0.5)
# p_label = tf.argmax(false_labels,1,output_type=tf.int32)
acc = tf.reduce_mean(tf.cast(predict, tf.float32))
tf.summary.scalar('acc', acc)
g_loss = gan.compute_G_loss(fake_logits,
tf.ones(shape=[gan.batch_size, 1]))
# d_loss = gan.compute_D_loss(real_logits,tf.ones(shape=[gan.batch_size,1]))+\
# gan.compute_D_loss(fake_logits,tf.zeros(shape=[gan.batch_size,1]))
fake_d_loss = gan.compute_fake_D_loss(fake_logits,
tf.zeros_like(fake_logits))
real_d_loss = gan.compute_real_D_loss(real_logits,
tf.ones_like(real_logits))
d_loss = fake_d_loss + real_d_loss
d_op, g_op = gan.train_op(g_loss=g_loss, d_loss=d_loss)
summary_op = tf.summary.merge_all()
writer = tf.summary.FileWriter(FLAGS.ckpt, graph=tf.get_default_graph())
saver = tf.train.Saver()
global_step = tf.train.get_or_create_global_step()
global_step = tf.assign_add(global_step, 1)
init_op = tf.global_variables_initializer()
train_op = [d_op, g_op]
with tf.Session() as sess:
for step in range(FLAGS.max_steps):
utils.load_or_initial_model(sess, FLAGS.ckpt, saver, init_op)
a = 0
G_step = 0
D_step = 0
i = 0
if not os.path.exists('out/'):
os.makedirs('out/')
for step in range(FLAGS.max_steps):
data = datasets.next_batch(gan.batch_size)
feed_dict = {real_example: data[0]}
a, loss_d, loss_g, _, g_step, summary_str = \
sess.run([acc, d_loss, g_loss, train_op, global_step, summary_op], feed_dict=feed_dict)
D_step += 1
G_step += 1
if g_step % 1000 == 0:
print(g_step, loss_d, loss_g, a, D_step, G_step)
samples = sess.run(fake_example)
samples = samples[:16]
writer.add_summary(summary_str, global_step=g_step)
# saver.save(sess, FLAGS.ckpt, global_step=g_step)
fig = utils.plot(samples)
plt.savefig('out/{}.png'.format(str(i).zfill(3)),
bbox_inches='tight')
i += 1
plt.close(fig)
def pso_svm(data):
# 初始化参数
particle_position_vector = np.array([
np.array([random.random() * 10,
random.random() * 10]) for _ in range(args.n_particles)
])
pbest_position = particle_position_vector
pbest_fitness_value = np.array(
[float('inf') for _ in range(args.n_particles)])
gbest_fitness_value = np.array([float('inf'), float('inf')])
gbest_position = np.array([float('inf'), float('inf')])
velocity_vector = ([np.array([0, 0]) for _ in range(args.n_particles)])
iteration = 0
while iteration < args.n_iterations:
plot(particle_position_vector)
for i in range(args.n_particles):
fitness_cadidate = fitness_function(particle_position_vector[i],
data)
print("error of particle-", i, "is (training, test)",
fitness_cadidate, " At (gamma, c): ",
particle_position_vector[i])
if (pbest_fitness_value[i] > fitness_cadidate[1]):
pbest_fitness_value[i] = fitness_cadidate[1]
pbest_position[i] = particle_position_vector[i]
if (gbest_fitness_value[1] > fitness_cadidate[1]):
gbest_fitness_value = fitness_cadidate
gbest_position = particle_position_vector[i]
elif (gbest_fitness_value[1] == fitness_cadidate[1]
and gbest_fitness_value[0] > fitness_cadidate[0]):
gbest_fitness_value = fitness_cadidate
gbest_position = particle_position_vector[i]
for i in range(args.n_particles):
new_velocity = (
args.W * velocity_vector[i]) + (args.c1 * random.random()) * (
pbest_position[i] - particle_position_vector[i]
) + (args.c2 * random.random()) * (gbest_position -
particle_position_vector[i])
new_position = new_velocity + particle_position_vector[i]
particle_position_vector[i] = new_position
iteration = iteration + 1
def train(model, x_train_data, y_train_data, x_test_data, y_test_data):
verbose = 0
if DEBUG >= 2:
model.summary()
verbose = 1
history = model.fit(x_train_data,
y_train_data,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
validation_split=0.05,
verbose=verbose,
shuffle=True)
import utils
if DEBUG >= 3:
if args['save_config']:
utils.plot(history.history, 'train.png')
else:
utils.plot(history.history)
model.reset_states()
prediction = model.predict(x_test_data)
log = False
if DEBUG >= 1:
log = True
under, over, avg_err = utils.predict_result(prediction, y_test_data, log)
# print('{0}>{1}, {2}<{3}, {4:.3f}<0.02'.format(under, len(prediction)/2, over, len(prediction)/5, abs(avg_err)))
err = float(np.max(y_test_data)) * 0.006
total = len(y_test_data)
result = {
'under': under,
'under_r': under * 100 / total,
'over': over,
'over_r': over * 100 / total,
'avg_err': avg_err
}
if result['under_r'] > 60 and result['over_r'] < 20 and abs(avg_err) < err:
model.save('{0}_lstm_b{1}p{2}_{3:.1f}_{4:.1f}_{5:.3f}.h5'.format(
name, BLOCK_SIZE, PREDICT_PERIOD, result['under_r'],
result['over_r'], avg_err))
if DEBUG >= 3:
if (args['save_config']):
utils.plot_predict(prediction, y_test_data, 'predict.png')
else:
utils.plot_predict(prediction, y_test_data)
return result
def __call__(self,
modelname,
split_rate=.9,
seq_length=30,
batch_size=8,
num_layers=2):
train_size = int(self.prices.train_size * split_rate)
X = self.prices.X[train_size:train_size + 300, :]
X = torch.unsqueeze(torch.from_numpy(X).float(), 1)
X_test, Y_test = utils.data_process(X, X.shape[0], seq_length)
model = torch.load(modelname + '.model')
model.eval()
loss_fn = nn.MSELoss()
with torch.no_grad():
loss_sum = 0
Y_pred = model(X_test[:, :batch_size, :])
Y_pred = torch.squeeze(Y_pred[num_layers - 1, :, :])
for i in range(batch_size, X_test.shape[1], batch_size):
y = model(X_test[:, i:i + batch_size, :])
y = torch.squeeze(y[num_layers - 1, :, :])
Y_pred = torch.cat((Y_pred, y))
loss = loss_fn(Y_test[i:i + batch_size, :], y)
loss_sum += loss.item()
print(loss_sum)
Y_pred.resize_(Y_pred.shape[0] * Y_pred.shape[1])
Y_test.resize_(Y_test.shape[0] * Y_test.shape[1])
my_pred = pd.DataFrame(columns=['pred', 'actual'])
my_pred.head()
my_pred['pred'] = Y_pred.numpy()
my_pred['actual'] = Y_test.numpy()
#/content/drive/My Drive/abc/
my_pred.to_csv('Stock_rnn.csv', sep=',', encoding='utf-8')
utils.plot([Y_pred.shape[0], Y_test.shape[0]],
[Y_pred.numpy(), Y_test.numpy()], ['blue', 'red'],
'Time (Days)', 'Price',
'Sample ' + modelname + ' Price Result',
['Prediction', 'Ground Truth'])
def __call__(self,
model_name,
hidden_size=128,
seq_length=30,
split_rate=.9,
batch_size=8,
num_epochs=500,
num_layers=2):
train_size = int(self.prices.train_size * split_rate)
X = torch.unsqueeze(
torch.from_numpy(self.prices.X[:train_size, :]).float(), 1)
X_train, Y_train = utils.data_process(X, train_size, seq_length)
if model_name == 'LSTM':
model = SimpleLSTM(self.window_size,
hidden_size,
num_layers=num_layers)
else:
model = SimpleGRU(self.window_size,
hidden_size,
num_layers=num_layers)
loss_fn = nn.MSELoss()
optimizer = optim.Adam(model.parameters())
loss_plt = []
for epoch in range(num_epochs):
loss_sum = 0
for i in range(0, X_train.shape[1] - batch_size, batch_size):
Y_pred = model(X_train[:, i:i + batch_size, :])
Y_pred = torch.squeeze(Y_pred[num_layers - 1, :, :])
loss = loss_fn(Y_train[i:i + batch_size, :], Y_pred)
loss_sum += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch [%d] finished, Loss Sum: %f' % (epoch, loss_sum))
loss_plt.append(loss_sum)
torch.save(model, model_name + '.model')
utils.plot([len(loss_plt)], [np.array(loss_plt)], 'black', 'Epoch',
'Loss Sum', 'MSE Loss Function')
def main():
EPOCH = 10
MIN_FREQ = 5
SHUFFLE = True
trn_texts = open("trn.data").read().strip().split("\n")
trn_labels = open("trn.label").read().strip().split("\n")
dev_texts = open("dev.data").read().strip().split("\n")
dev_labels = open("dev.label").read().strip().split("\n")
print('perceptron')
print('-' * 40)
print('trn data size:', len(trn_texts))
print('dev data size:', len(dev_texts))
bag_of_words = BagOfWords(True, True, MIN_FREQ)
trn_data = bag_of_words.fit_transform(trn_texts, trn_labels)
dev_data = bag_of_words.transform(dev_texts)
print('min vocabulary freq:', MIN_FREQ)
print('vocabulary size:', len(trn_data[0]))
print('shuffle after epoch:', SHUFFLE)
perceptron = Perceptron(bag_of_words)
print('training start\n')
start = time()
print('data accurary')
print_cells(['epoch', 'trn', 'dev'], 9)
print('-' * 30)
trn_acc = []
dev_acc = []
def epoch_callback(i):
trn_acc.append(perceptron.accuracy(trn_data, trn_labels))
dev_acc.append(perceptron.accuracy(dev_data, dev_labels))
print_cells([i, trn_acc[i], dev_acc[i]], 9)
perceptron.fit(trn_data, trn_labels, EPOCH, SHUFFLE, epoch_callback)
print('\ntraining end')
print('duration:', round(time() - start))
plot(list(range(0, EPOCH)), trn_acc, dev_acc, 'Perceptron')
def main():
# For custom use, rewrite this section with your own data
###################################################################
taskname = "SUMMARIZATION"
HJ, model_probs, labels, length_list = get_data()
###################################################################
HUSE, HUSEQ, HUSED = calculate_HUSE(HJ, model_probs, labels, length_list)
# OUTPUT
print("For the task of {}".format(taskname))
print("Overall HUSE score is: {}".format(HUSE))
print("HUSE-Q (just human) score is: {}".format(HUSEQ))
print("HUSE-D score is: {}".format(HUSED))
# Plot saved to {taskname}.pdf
plot(taskname, HJ, model_probs, labels, length_list)
def p01e(train_path, eval_path, pred_path):
"""Gaussian discriminant analysis
Args:
train_path: path to csv file for training data
eval_path: path to csv file for validation data
pred_path: path to save predictions
"""
x_train, y_train = utils.load_dataset(train_path, add_intercept=False)
model = GaussianDiscriminantAnalysis()
model.fit(x_train, y_train)
x_val, y_val = utils.load_dataset(eval_path, add_intercept=False)
y_pred = model.predict(x_val)
utils.plot(x_val, y_val, model.theta, "{}.png".format(pred_path))
# Use np.savetxt to save outputs from validation set to pred_path
np.savetxt(pred_path, y_pred)
def p01b(train_path, eval_path, pred_path):
"""Logistic regression with Newton's Method
Args:
train_path: Path to CSV file containing dataset for training.
eval_path: Path to CSV file containing dataset for evaluation.
pred_path: Path to save predictions.
"""
# Train classifier
x_train, y_train = utils.load_dataset(train_path, add_intercept=True)
model = LogisticRegression(eps=1e-5)
model.fit(x_train, y_train)
# Validate classifier
x_val, y_val = utils.load_dataset(eval_path, add_intercept=True)
y_pred = model.predict(x_val)
utils.plot(x_val, y_val, model.theta, "{}.png".format(pred_path))
np.savetxt(pred_path, y_pred)
def draw_mobility(countries):
FEATURES_VALUES = utils.features_values()
value_vars = utils.MOBILITY
data = FEATURES_VALUES.melt(id_vars=['Date', 'CountryName'],
value_vars=value_vars,
var_name='Measures',
value_name="Value")
data['Measures'] = data.apply(pretty_name, axis=1)
for country in countries:
utils.plot('line',
data.loc[data['CountryName'] == country],
'data_visualization_mobility_' + country,
'Date',
'Value',
'Variation of activity [%]',
hue='Measures',
legend_pos=None)
def plot_motion_z_position(pool_physics, request):
show_plots, save_plots = request.config.getoption(
'--show-plots'), request.config.getoption('--save-plots')
if not (show_plots or save_plots):
yield
return
from utils import plot_ball_motion as plot
yield
test_name = '_'.join(
[request.function.__name__, pool_physics.ball_collision_model])
plot(0,
pool_physics,
title=test_name + " ($z$ position)",
coords=(2, ),
collision_depth=1,
filename=os.path.join(PLOTS_DIR, test_name +
'-z.png') if save_plots else None,
show=show_plots)
def generate(self, sess, feed_dict, index):
samples = sess.run(self.gen_input, feed_dict=feed_dict)
fig = utils.plot(samples[0:16, :])
path = os.path.join(FLAGS.train_dir, 'out/')
if not os.path.exists(path):
os.makedirs(path)
plt.savefig(os.path.join(path + '{}.png'.format(str(index).zfill(3))), bbox_inches='tight')
plt.close(fig)
def plot_predictions(self, X, filepath=None, title='Predictions'):
if filepath is None:
filepath = self.results_dir + '/predictions.png'
pred = self.predict(X)
images = np.concatenate([
np.transpose(X, (1, 0, 2, 3, 4)),
np.transpose(pred, (1, 0, 2, 3, 4))
],
axis=0)
images = np.reshape(
images,
(2 * X.shape[0] * X.shape[1], X.shape[2], X.shape[3], X.shape[4]))
plot(
filepath, title,
make_mosaic(images,
nrows=X.shape[0],
ncols=int(2 * X.shape[1]),
clip=True))
def plot_initial_positions(pool_physics, pool_table, request):
show_plots, save_plots = request.config.getoption(
'--show-plots'), request.config.getoption('--save-plots')
if not (show_plots or save_plots):
yield
return
from utils import plot_motion_timelapse as plot
yield
test_name = '_'.join(
[request.function.__name__, pool_physics.ball_collision_model])
plot(pool_physics,
table=pool_table,
nt=0,
t_0=0.0,
t_1=0.0,
title=test_name + ' (initial positions)',
filename=os.path.join(PLOTS_DIR, test_name + '-initial-positions.png')
if save_plots else None,
show=show_plots)
def main():
for episode in range(NUM_OF_EPISODES):
game = env.Easy21()
state = game.get_state()
action = epsilon_greedy_action(state)
is_terminal = False
while not is_terminal:
new_state, reward, is_terminal = game.step(action)
new_action = epsilon_greedy_action(new_state)
# Update the visit count.
N[state[0], state[1], action] += 1
# Update the Q value.
# How much we expect the our current Q-value to be wrong. We estimate the true Q
# value by looking at the obtained reward and the next Q value we would pick.
error = (reward + get_Q(new_state, new_action)) - get_Q(
state, action)
# Step-size for the update. Decreases over time, when we visit the state more and
# more we are more confident of our value, so we update it less. The fact that alpha
# will eventually go to zero is essential for convergence of the algorithm toward
# the optimal policy.
alpha = 1 / N[state[0], state[1], action]
# Update the Q values using Sarsa algorithm.
# Q(S, A) <-- Q(S, A) + alpha*( reward + discount*Q(S', A') - Q(S, A) )
# Note that discount is 1 in this case.
Q[state[0], state[1],
action] = get_Q(state, action) + alpha * error
# Move to the new state-action pair.
state = new_state
action = new_action
if episode % PRINT_EVERY == 0:
print(episode)
#print(Q)
#utils.plot(Q)
utils.plot(Q)
def gradient_step(i, state, mod):
params = get_params(state)
mod.prior.hyp = params[0]
mod.likelihood.hyp = params[1]
# grad(Filter) + Smoother:
# neg_log_marg_lik, gradients = mod.run()
neg_log_marg_lik, gradients = mod.run_two_stage()
prior_params = softplus_list(params[0])
# print('iter %2d: var1=%1.2f len1=%1.2f om1=%1.2f var2=%1.2f len2=%1.2f om2=%1.2f var3=%1.2f len3=%1.2f om3=%1.2f '
# 'var4=%1.2f len4=%1.2f var5=%1.2f len5=%1.2f var6=%1.2f len6=%1.2f '
# 'vary=%1.2f, nlml=%2.2f' %
# (i, prior_params[0][0], prior_params[0][1], prior_params[0][2],
# prior_params[1][0], prior_params[1][1], prior_params[1][2],
# prior_params[2][0], prior_params[2][1], prior_params[2][2],
# prior_params[3][0], prior_params[3][1],
# prior_params[4][0], prior_params[4][1],
# prior_params[5][0], prior_params[5][1],
# softplus(params[1]), neg_log_marg_lik))
# print('iter %2d: len1=%1.2f om1=%1.2f len2=%1.2f om2=%1.2f len3=%1.2f om3=%1.2f '
# 'var4=%1.2f len4=%1.2f var5=%1.2f len5=%1.2f var6=%1.2f len6=%1.2f '
# 'vary=%1.2f, nlml=%2.2f' %
# (i, prior_params[0][0], prior_params[0][1],
# prior_params[1][0], prior_params[1][1],
# prior_params[2][0], prior_params[2][1],
# prior_params[3][0], prior_params[3][1],
# prior_params[4][0], prior_params[4][1],
# prior_params[5][0], prior_params[5][1],
# softplus(params[1]), neg_log_marg_lik))
print(
'iter %2d: len1=%1.2f om1=%1.2f len2=%1.2f om2=%1.2f len3=%1.2f om3=%1.2f '
'len4=%1.2f len5=%1.2f len6=%1.2f '
'vary=%1.2f, nlml=%2.2f' %
(i, prior_params[0][0], prior_params[0][1], prior_params[1][0],
prior_params[1][1], prior_params[2][0], prior_params[2][1],
prior_params[3], prior_params[4], prior_params[5], softplus(
params[1]), neg_log_marg_lik))
if plot_intermediate:
plot(mod, i)
return opt_update(i, gradients, state)
def gradient_step(i, state, mod):
params = get_params(state)
mod.prior.hyp = params[0]
mod.likelihood.hyp = params[1]
# grad(Filter) + Smoother:
batch_ind = np.random.permutation(N)[:N_batch]
# grad(Filter) + Smoother:
neg_log_marg_lik, gradients = mod.run(batch_ind=batch_ind)
nlml = neg_log_marg_lik * N / N_batch
print('iter %2d: nlml=%2.2f' %
(i, nlml))
if plot_intermediate:
plot(mod, i)
return opt_update(i, gradients, state)
def main():
"""
Main function to drive the simulator. The expected usage is:
./sim.py -c <path to circuit netlist>
"""
# set up argument parser
parser = ArgumentParser()
parser.add_argument('-c', help='Circuit netlist file', required=True)
parser.add_argument('-o', help='Output .wav file', default=None)
try:
# extract command line arguments
args = parser.parse_args()
netlist = Netlist(args.c)
circuit = netlist.as_circuit()
# solve the circuit at every timestamp for the input signal
timescale, input_signal, vout = circuit.transient()
vout = np.array(vout)
t_start, t_end = circuit.timescale()
# write data to output wavfile
outfile = args.o
if outfile is not None:
rate = timescale[1] - timescale[0]
fs = int(1.0 / rate)
max_amp = np.max(np.abs(vout))
sigf32 = (vout / max_amp).astype(np.float32)
sd.play(sigf32, fs * 100)
# wavfile.write(outfile, fs, sigf32)
# plot the results
plt.subplot(1, 2, 1)
plot(t_start, t_end, input_signal, title="Input Signal")
plt.subplot(1, 2, 2)
plot(t_start, t_end, vout, title="Output Signal")
plt.show()
except IOError as e:
parser.print_help()
print("\nIOError {}".format(e))
sys.exit(-1)
def _plot_and_write(plot_dict,
loc,
x_label="",
y_label="",
title="",
kind='line',
legend=True,
moving_average=False):
for key in plot_dict:
plot(data={key: plot_dict[key]},
loc=loc + str(key) + ".pdf",
x_label=x_label,
y_label=y_label,
title=title,
kind=kind,
legend=legend,
index_col=None,
moving_average=moving_average)
write_to_csv(data={key: plot_dict[key]}, loc=loc + ".csv")
def silence(self):
threshold = [-1*int(10*i) for i in range(1, 11)][::-1]
precisionArr = []
recallArr = []
FscoreArr = []
for value in threshold:
print("threshold: {} being evaluated".format(value))
valueResults = []
for i, filename in enumerate(self.files):
print("Executing file {} number {}/{}".format(filename, i+1, len(self.files)), end='\r')
audio, sr, channels, _, _, _ = std.AudioLoader(filename=filename)()
audio = np.sum(audio, axis=1)/channels
_, ret = essStartstopDetector(audio, threshold=value)
valueResults.append((filename.replace(self.wavDatasetPath, ""), ret))
print('')
valueResults = sorted(valueResults, key=lambda x: x[0])
_, precision, recall = self.evaluateValue(valueResults, "Clicks")
precisionArr.append(precision)
recallArr.append(recall)
FscoreArr.append((1 + Fbeta**2) * precision * recall / (Fbeta**2 * precision + recall))
u.plot("./results/silencethreshold.png", precision=precisionArr,recall=recallArr, Fscore=FscoreArr, x_values=threshold)
frameSize = [int(2**i) for i in range(5,10)]
precisionArr = []
recallArr = []
FscoreArr = []
for value in frameSize:
print("frameSize: {} being evaluated".format(value))
valueResults = []
for i, filename in enumerate(self.files):
print("Executing file {} number {}/{}".format(filename, i+1, len(self.files)), end='\r')
audio, sr, channels, _, _, _ = std.AudioLoader(filename=filename)()
audio = np.sum(audio, axis=1)/channels
_, ret = essStartstopDetector(audio, frameSize=value, hopSize=value)
valueResults.append((filename.replace(self.wavDatasetPath, ""), ret))
print('')
valueResults = sorted(valueResults, key=lambda x: x[0])
_, precision, recall = self.evaluateValue(valueResults, "Clicks")
precisionArr.append(precision)
recallArr.append(recall)
FscoreArr.append((1 + Fbeta**2) * precision * recall / (Fbeta**2 * precision + recall))
u.plot("./results/silenceframeSize.png", precision=precisionArr, recall=recallArr, Fscore=FscoreArr, x_values=frameSize)
def summary():
message = '数据统计'
vars = dict(request.vars)
labels = []
sizes = []
try:
if 'submit' in vars:
table_name = vars['table_name']
data_type = vars['data_type']
start_date = vars['start']
end_date = vars['end']
sql = f'select branch_name, {data_type} from {table_name} where open_date between "{start_date}" and "{end_date}" group by branch_name;'
table_values = bankdb.executesql(sql)
labels = [row[0] for row in table_values]
sizes = [float(row[1]) for row in table_values]
utils.plot(labels, sizes)
except Exception as e:
response.flash = sql + str(e)
massage = sql
return dict(locals())
def draw_death_over_mobility(countries):
FEATURES_VALUES = utils.features_values()
value_vars = ['{}_15days'.format(mobility) for mobility in utils.MOBILITY]
data = FEATURES_VALUES.melt(
id_vars=['CountryName', 'ConfirmedDeaths', 'R'],
value_vars=value_vars,
var_name='Measures',
value_name="Value")
data['Measures'] = data.apply(pretty_name, axis=1)
for country in countries:
utils.plot('scatter',
data.loc[data['CountryName'] == country],
'data_visualization_death_to_measure_' + country,
'Value',
'R',
'Rt',
x_label='mobility',
hue='Measures',
legend_pos=None)
def plot_history(self, sid: int = None, show_trades: bool = False):
"""provides a plot of the batl/batb over time"""
if sid is None:
sid = max(self.batb_history,
key=lambda k: len(self.batb_history[k]))
fig = plot(sid, self.batb_history[sid], self.batl_history[sid])
if show_trades:
fig = plot_trades(fig, self.trade_times[sid],
self.trade_price[sid], self.trade_direction[sid])
return fig
def plot_final_positions(pool_physics, pool_table, request):
show_plots, save_plots = request.config.getoption('--show-plots'), request.config.getoption('--save-plots')
if not (show_plots or save_plots):
yield
return
from utils import plot_motion_timelapse as plot
yield
test_name = '_'.join([request.function.__name__, pool_physics.ball_collision_model])
events = pool_physics.events
if events:
t1 = events[-1].t
if events[-1].T < float('inf'):
t1 += events[-1].T
else:
t1 = 0.0
plot(pool_physics, table=pool_table,
nt=0,
t_0=t1, t_1=t1,
title=test_name + ' (final positions)',
filename=os.path.join(PLOTS_DIR, test_name + '-final-positions.png') if save_plots else None,
show=show_plots)
def runPla(x, f, w=np.zeros((3)), show=False, X=None):
truth = np.sign(np.dot(x,f))
green = x[(truth == 1), 1:]
red = x[(truth < 1), 1:]
plot(green, red, f, show=show, axis=311)
right = np.empty([0,2])
wrong = np.empty([0,2])
count = 0
while right[:].size < x[:].size:
prediction = np.sign(np.dot(x,w))
#print "prediction = " + str(prediction)
green = x[(prediction == 1), 1:]
red = x[(prediction < 1), 1:]
plot(green, red, w, show=show, axis=312)
right = x[(prediction == truth),1:]
wrong = x[(prediction != truth),1:]
plot(right, wrong, w, show=show, axis=313)
if wrong.size == 0:
break
w = train(w, x, prediction, truth)
count += 1
if show:
pause(.1)
return count, w
def main(bee_count, move_count, cities_count):
all_cities = utils.loadCities(cities_count)
super_extra_ultra_special_intergalactic_bee = Bee()
bees = []
for i in range(bee_count):
bees.append(Bee())
while super_extra_ultra_special_intergalactic_bee.is_not_complete(all_cities):
for bee in bees:
bee.move(move_count, all_cities)
bees = sorted(bees, key=lambda be: be.distance, reverse=False)
super_extra_ultra_special_intergalactic_bee = bees[0]
max_distance = max(bees, key=lambda b: b.distance).distance
min_distance = min(bees, key=lambda b: b.distance).distance
middle_distance_difference = (max_distance + min_distance) / 2
recruiters = []
for bee in bees:
if bee.distance > middle_distance_difference:
bee.change_role(True)
recruiters.append(bee)
else:
bee.change_role(False)
for bee in bees:
if not bee.recruiter:
rndm = rand.uniform(0, 1)
selected_bee = Bee()
if rndm < 0.5:
selected_bee = recruiters[rand.randrange(0, len(recruiters) - 1)]
else:
selected_bee = super_extra_ultra_special_intergalactic_bee
bee.replace_cities(selected_bee.visited_cities[:])
utils.plot(super_extra_ultra_special_intergalactic_bee.visited_cities)
def runPla(x, f, w=np.zeros((3)), show=False, X=None):
truth = np.sign(np.dot(x, f))
green = x[(truth == 1), 1:]
red = x[(truth < 1), 1:]
plot(green, red, f, show=show, axis=311)
right = np.empty([0, 2])
wrong = np.empty([0, 2])
count = 0
while right[:].size < x[:].size:
prediction = np.sign(np.dot(x, w))
#print "prediction = " + str(prediction)
green = x[(prediction == 1), 1:]
red = x[(prediction < 1), 1:]
plot(green, red, w, show=show, axis=312)
right = x[(prediction == truth), 1:]
wrong = x[(prediction != truth), 1:]
plot(right, wrong, w, show=show, axis=313)
if wrong.size == 0:
break
w = train(w, x, prediction, truth)
count += 1
if show:
pause(.1)
return count, w
def main():
warnings.filterwarnings('ignore')
seed = 1234
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
parser = arg_parser()
args = parser.parse_args()
if osp.exists('../trained_models') is False:
os.makedirs('../trained_models')
if osp.exists('../predictions') is False:
os.makedirs('../predictions')
if args.model == 'unet':
model = UNet(3, 4)
elif args.model == 'resunet_a':
model = ResUNet_a(3, 4)
else:
model = smp.Unet(args.model, encoder_weights='imagenet',
classes=4, activation='sigmoid')
model_trainer = Trainer(model, loss=args.loss)
model_trainer.start()
losses = model_trainer.losses
dice_scores = model_trainer.dice_scores
iou_scores = model_trainer.iou_scores
if osp.exists('../results') is False:
os.makedirs('../results')
plot(losses, name='{}_Loss'.format(args.loss))
plot(dice_scores, name='Dice_score')
plot(iou_scores, name='IoU_score')
def cond_samples(f, replay_buffer, args, fresh=False):
if fresh:
replay_buffer = uncond_samples(f, args, save=False)
n_it = replay_buffer.size(0) // 100
all_y = []
for i in range(n_it):
x = replay_buffer[i * 100:(i + 1) * 100].to(device)
y = f.classify(x).max(1)[1]
all_y.append(y)
all_y = torch.cat(all_y, 0)
each_class = [replay_buffer[all_y == l] for l in range(10)]
print([len(c) for c in each_class])
for i in range(100):
this_im = []
for l in range(10):
this_l = each_class[l][i * 10:(i + 1) * 10]
this_im.append(this_l)
this_im = torch.cat(this_im, 0)
if this_im.size(0) > 0:
plot("./save/cond_samples/samples_{}.png".format(i), this_im)
print(i)
def shouldExplore(self):
return random.random() <= self.epsilon
if __name__ == "__main__":
actualActionValues = utils.generateActionValues(10);
bandit = MultiArmedBandit(10, actualActionValues);
agent = AgentEpsilonGreedy(bandit, 10, 0.1);
mse = []
for _ in range(0, 10000):
agent.learn()
mse += [np.mean(np.square(actualActionValues - agent.actionValueEstimates))];
print actualActionValues;
print agent.actionValueEstimates;
utils.plot(mse);
class AgentSoftmax(Agent):
def __init__(self, bandit, numberOfArms, epsilon, temperature):
Agent.__init__(self, bandit, numberOfArms);
self.epsilon = epsilon;
self.temperature = temperature;
self.armSelectionCount = np.zeros(numberOfArms);
def getAlpha(self, arm):
return 1.0 / self.armSelectionCount[arm - 1];
def chooseArm(self):
choices = [math.exp(q/self.temperature) for q in self.actionValueEstimates ]
totalSum = sum(choices)
(d["num_filled"] == num_filled) &
(d["num_shells"] >= num_shells_range[0]) &
(d["num_shells"] <= num_shells_range[1]) &
(d["freq"] == 1.0)]
num_particles = num_filled * (num_filled + 1)
energy_type = {"ground": "ground state",
"add": "addition",
"rm": "removal"}[label]
fig, ax = plt.subplots()
fig.set_size_inches((4, 3))
for method, case in d.groupby("method"):
case = case.sort_values("num_shells")
xs = case["num_shells"].astype(int)
ys = case["energy"]
ax.plot(xs, ys, "-x", label=utils.METHOD_LABEL[method],
color=utils.METHOD_COLOR[method])
ax.get_xaxis().set_major_locator(
matplotlib.ticker.MaxNLocator(integer=True))
ax.set_xlabel("K (number of shells)")
ax.set_ylabel("E (energy)")
ax.legend()
fig.tight_layout()
utils.savefig(fig,
"by-num-shells-{num_particles}-{num_filled}-"
"{label}-{ml}-{interaction}"
.format(**locals()))
with utils.plot(__file__, call=plot) as interactive:
if not interactive:
plot("ground", freq=1.0, num_filled=2, num_shells_range=[4, 15])
def get_reliability(self):
# finding sources
self.source_finder(image=self.imagename, lsmname=self.poslsm,
thresh=self.pos_smooth, **self.opts_pos)
self.source_finder(image=self.negativeimage, lsmname=self.neglsm,
thresh=self.neg_smooth, **self.opts_neg)
# removing sources within a specified radius
self.remove_sources_within(catalog=self.poslsm, rel_excl_src=
self.rel_excl_src)
self.remove_sources_within(catalog=self.neglsm, rel_excl_src=
self.rel_excl_src)
# add local variance as a parameter
if self.do_local_var:
utils.local_variance(self.imagedata, self.header,
catalog=self.poslsm, wcs=self.wcs,
pixelsize=self.pixelsize, local_region=
self.local_var_region, savefig=False,
highvariance_factor=None, prefix=self.prefix,
neg_side=True)
utils.local_variance(self.imagedata, self.header,
catalog=self.neglsm, wcs=self.wcs,
pixelsize=self.pixelsize, local_region=
self.local_var_region, savefig=False,
highvariance_factor=None, prefix=self.prefix, neg_side=True)
# compute correlation if only do_psf_corr = True
#and the psf is provided
if self.do_psf_corr and self.psfname:
utils.psf_image_correlation(
catalog=self.poslsm, psfimage=self.psfname,
imagedata=self.imagedata, header=self.header,
wcs=self.wcs, pixelsize=self.pixelsize,
corr_region=self.psf_corr_region, prefix= self.prefix)
utils.psf_image_correlation(
catalog=self.neglsm, psfimage=self.psfname,
imagedata=self.imagedata, header=self.header,
wcs=self.wcs, pixelsize=self.pixelsize,
corr_region=self.psf_corr_region, prefix=self.prefix)
##TODO verbose vs. logging
pmodel = Tigger.load(self.poslsm, verbose=self.loglevel)
nmodel = Tigger.load(self.neglsm, verbose=self.loglevel)
posSources = pmodel.sources
negSources = nmodel.sources
npsrc = len(posSources)
nnsrc = len(negSources)
positive, labels = self.params(posSources, pmodel)
negative, labels = self.params(negSources, nmodel)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i]*((4.0/((nplanes+2)*
npsrc))**(1.0/(nplanes+4.0)))
pcov = utils.gaussian_kde_set_covariance(positive.T, cov)
ncov = utils.gaussian_kde_set_covariance(negative.T, cov)
# get number densities
nps = pcov(positive.T) * npsrc
nns = ncov(positive.T) * nnsrc
# define reliability of positive catalog
rel = (nps-nns)/nps
for src, rf in zip(posSources, rel):
src.setAttribute("rel", rf)
out_lsm = self.poslsm
pmodel.save(out_lsm)
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive, negative, rel=rel, labels=labels,
savefig=savefig, prefix=self.prefix)
return self.poslsm, self.neglsm
sum = 0.0
run = 0.0
while run < runs:
f = points2weights(np.random.random((2,2)) * 2 - 1)
x = np.insert(np.random.random((N,2)) * 2 - 1,0,np.ones((1,N)), axis=1)
testSet = np.insert(np.random.random((N*10,2)) * 2 - 1,0,np.ones((1,N*10)), axis=1)
y = np.sign(np.dot(x,f))
testTruth = np.sign(np.dot(testSet,f))
w = np.zeros(3)
green = x[(y == 1), 1:]
red = x[(y != 1), 1:]
plot(green, red, f, axis=311, show=True)
count = 0
while count < 1000:
oldW = np.copy(w)
element = 0
while element < N:
w += lr * gradient(x[element],y[element],w)
element += 1
prediction = np.sign(np.dot(x,np.transpose(w)))
right = x[(prediction == y),1:]
wrong = x[(prediction != y),1:]
plot(right, wrong, w, show=False, axis=313)#, xlim=[-5,5], ylim=[-5,5])
#pause(.05)
if mag(oldW, w) < 0.01:
break
#!/usr/bin/env python3
# Generates ../FigureFiles/fig-by-freq-*.svg from the data files.
import argparse, itertools, os, sys
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import utils
p = argparse.ArgumentParser()
p.add_argument("-i", "--interactive", action="store_true")
p.add_argument("-t", "--title", action="store_true")
p.add_argument("-int", "--interaction", default="normal")
p.add_argument("label", metavar="type", help="ground, add, or rm")
kwargs = vars(p.parse_args())
plt.rcParams["interactive"] = kwargs["interactive"]
with utils.plot(__file__):
d = utils.load_all()
num_shells = 10
num_filled = 2
interaction = kwargs["interaction"]
label = kwargs["label"]
ml = utils.label_num_filled_to_ml(label, num_filled)
d = d[(d["method"] != "imsrg[f]+eom[n]") &
(d["method"] != "magnus_quads+eom") &
(d["interaction"] == interaction) &
(d["label"] == label) &
(d["num_shells"] == num_shells) &
(d["num_filled"] == num_filled) &
# filter out higher frequencies because they stretch the plot too much
(d["freq"] <= 1.0) &
(d["ml"] == ml)]
s2, r, done, info = env.step(a)
r = -1 * (0.5 - s2[0])
f = features.get_features(s2)
#s = s2
reward += r
if done:
break
total_reward.append(reward)
return sum(total_reward)/len(total_reward)
env = gym.make('MountainCar-v0')
num_action = env.action_space.n
num_base_func = 12
nb = num_base_func*num_action+1
sigma = 0.5 #2.5
num_iterations = 10
gamma = 0.999
ep = 0.2 #0.2
num_sampling = 100
features = RBF(env,sigma,num_base_func)
if __name__ == '__main__':
w,error_list,reward_list = LSPI(plot=True)
try:
test(w,render=True)
finally:
print w
utils.plot(error_list,reward_list)
aveW = np.zeros((1,6))
show = False
for i in range(0,runs):
print "Running test # " + str(i)
x = np.insert(np.random.random((d,2)) * 2 - 1,0,np.ones((1,d)), axis=1)
x = np.append(x, x[:,1:2] * x[:,2:3], axis=1)
x = np.append(x, np.square(x[:,1:3]), axis=1)
truth = np.sign(np.square(x[:,1]) + np.square(x[:,2]) - .6)
noise = np.append(np.ones((d*.9)), np.ones((d - d*.9)) * -1, axis=0)
np.random.shuffle(noise)
truth *= noise
i,o,w = lr.runLR(x, truth, show=show)
plotX = np.linspace(-1,1,1000)
plotY = np.sqrt(np.square(plotX) * -1 + .6)
plotX = np.append(plotX, plotX, axis=1)
plotY = np.append(plotY, -plotY, axis=1)
green = x[(truth == 1), 1:]
red = x[(truth < 1), 1:]
plot(green, red, [7,1,7], axis=311, show=show, other=[plotX, plotY,'b-'])
pause(.1)
wrongIn += i
aveW = aveW + w
print "Average of " + str(wrongIn/runs) + " wrong in sample per run"
fractionWrong = (wrongIn/runs)/d
print "%f incorrect on average in sample"%(fractionWrong)
print "ave w is " + str(aveW/runs)
def get_reliability(self):
# finding sources
self.log.info(" Extracting the sources on both sides ")
pfile = self.prefix + self.catalogue_format + ".fits"
nfile = self.prefix + "_negative" + self.catalogue_format + ".fits"
# i need to catch mmap.mmap error here
# running a source finder
self.source_finder(image=self.negimage,
output=nfile, thresh=self.neg_smooth,
savemask=self.savemaskneg,
prefix=self.prefix, **self.opts_neg)
self.source_finder(image=self.imagename,
output=pfile, thresh=self.pos_smooth,
savemask=self.savemaskpos,
prefix=self.prefix, **self.opts_pos)
self.log.info(" Source Finder completed successfully ")
pmodel, positive, labels = self.params(pfile)
nmodel, negative, labels = self.params(nfile)
# setting up a kernel, Gaussian kernel
bandwidth = []
for plane in negative.T:
bandwidth.append(plane.std())
nplanes = len(labels)
cov = numpy.zeros([nplanes, nplanes])
nnsrc = len(negative)
npsrc = len(positive)
self.log.info(" There are %d positive and %d negtive detections "%(npsrc, nnsrc))
if nnsrc == 0 or npsrc ==0:
self.log.error("The resulting array has length of 0 thus cannot compute"
" the reliability. Aborting.")
self.log.info(" Computing the reliabilities ")
for i in range(nplanes):
for j in range(nplanes):
if i == j:
cov[i, j] = bandwidth[i]*((4.0/((nplanes+2)*
nnsrc))**(1.0/(nplanes+4.0)))
self.log.info("The resulting covariance matrix is %r"%cov)
pcov = utils.gaussian_kde_set_covariance(positive.T, cov)
ncov = utils.gaussian_kde_set_covariance(negative.T, cov)
# get number densities
nps = pcov(positive.T) * npsrc
nns = ncov(positive.T) * nnsrc
# define reliability of positive catalog
rel = (nps-nns)/nps
for src, rf in zip(pmodel.sources, rel):
src.setAttribute("rel", rf)
self.log.info(" Saved the reliabilities values.")
# remove sources with poor correlation and high reliability,
# the values are currently arbitrary
if self.do_psf_corr and self.derel:
for s in pmodel.sources:
cf, r = s.correlation_factor, s.rel
if cf < 0.006 and r > 0.60:
s.rel = 0.0
if self.makeplots:
savefig = self.prefix + "_planes.png"
utils.plot(positive, negative, rel=rel, labels=labels,
savefig=savefig, prefix=self.prefix)
# removes sources in a given radius from the phase center
if self.radiusrm:
self.log.info(" Remove sources ra, dec, radius of %r"
" from the phase center" %self.radiusrm)
pmodel = self.remove_sources_within(pmodel)
if not self.savefits:
self.log.info(" Deleting the negative image.")
os.system("rm -r %s"%self.negimage)
# Set field Center
pmodel.ra0, pmodel.dec0 = map(numpy.deg2rad, self.wcs.getCentreWCSCoords())
return pmodel, nmodel, self.locstep
# Copyright (c) 2015 Jaakko Luttinen
from utils import plot
import matplotlib.pyplot as plt
if __name__ == "__main__":
plt.figure()
plot("pca", 1, maxiter=200)
plt.xlim(0, 6)
plt.ylim(-7500, -4500)
plt.savefig("fig_pca_01.pdf", frameon=False)
plt.figure()
plot("pca", 2, maxiter=200)
plt.xlim(0, 70)
plt.ylim(-120000, -70000)
plt.savefig("fig_pca_02.pdf", frameon=False)
plt.figure()
plot("mog", 1, maxiter=200)
plt.xlim(0, 3)
plt.ylim(-1000, -750)
plt.savefig("fig_mog_01.pdf", frameon=False)
plt.figure()
plot("mog", 2, maxiter=200)
plt.xlim(0, 150)
plt.ylim(-40000, -32000)
plt.savefig("fig_mog_02.pdf", frameon=False)
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 09 00:57:33 2013
@author: Tejay Cardon
visualize pla
"""
from utils import points2weights, plot
from LinearRegression import runLR
import numpy as np
from matplotlib.pyplot import pause
runs = 10
d = 1000
for i in range(0,runs):
print "Running test # " + str(i)
f = points2weights(np.random.random((2,2)) * 2 - 1)
x = np.insert(np.random.random((d,2)) * 2 - 1,0,np.ones((1,d)), axis=1)
truth = np.sign(np.dot(x,f))
i,o,w = runLR(x, truth, show=True)
green = x[(truth == 1), 1:]
red = x[(truth != 1), 1:]
plot(green, red, f, axis=311, show=True)
pause(.5)
loads_d = []
for i in loads:
loads_d.append(get_num_dict(i))
chord_loads.append(get_50_percent(loads_d[0]))
vserver_loads.append(get_50_percent(loads_d[1]))
x_values.append(next(get_numbers(f)))
plt.figure().set_size_inches(6.5,5)
plt.xlabel("#Nodes")
plt.ylabel("% of nodes storing 50% of data")
from matplotlib.ticker import EngFormatter
formatter = EngFormatter(places=0)
plt.gca().xaxis.set_major_formatter(formatter)
plt.ylim(0,0.5)
plt.xlim(0,1000000)
out_file = "intro_lb_chord.pdf"
d1 = prepare(x_values,chord_loads)
d2 = prepare(x_values,vserver_loads)
d1['label'] = 'Neighbor Replication'
d1['linestyle'] = 'dashed'
d2['label'] = "Virtual Servers"
plot(out_file,d1,d2)
