def run_net_2(training_data,
test_data,
monitor_evaluation_accuracy=True,
monitoring_evaluation_cost=True):
""" Runs net 2 """
n_input, n_hidden, n_out = 784, 100, 10
epochs, mini_batch_size, eta, lmbda = 30, 10, 0.5, 5
net = network_test_2.NetworkTest2([n_input, n_hidden, n_out])
net.stochastic_gradient_descent(
training_data,
epochs,
mini_batch_size,
eta,
lmbda,
evaluation_data=test_data,
monitor_evaluation_accuracy=monitor_evaluation_accuracy,
monitor_evaluation_cost=monitoring_evaluation_cost,
monitor_training_accuracy=True,
monitor_training_cost=True)
if monitor_evaluation_accuracy:
x, y = net.get_accuracy_per_epoch()
plt = plot.Plot(x, 'Epoch', y, 'Accuracy (%)', 'Net 2 Accuracy',
'net_2_accuracy')
plt.plot()
if monitoring_evaluation_cost:
x, y = net.get_cost_per_epoch()
plt = plot.Plot(x, 'Epoch', y, 'Total Cost', 'Net 2 Cost',
'net_2_cost')
plt.plot()
def main_pc():
# this code uses pictures taken manualy
# this is not the correct way to use the code but because of COVID-19, the setup was not obtainable
folder = 'D:\_Udes\S4\Projet\ScanUS\Photos_boite/'
files = os.listdir(folder)
laser_angles = [-5, -3, -2, -1, -0.5, 0, 0.5, 1.5, 2, 3, 4]
my_plot = plot.Plot(name='main plot', range=[0, 10])
position_laser_ref_plaque = Matrix(pos=[259, 512, 150])
angle_laser_cam = 11.1 * 2 * math.pi / 360 + math.atan(259 / 512)
trans_plaque_to_cam_ref = Matrix(angles=[0, 0, angle_laser_cam])
position_laser = mult([trans_plaque_to_cam_ref, position_laser_ref_plaque])
for i in range(len(files)):
filename = folder + files[i]
img = cv2.imread(filename)
x, y, fail = camera.find_red_dot(frame=img)
if not fail:
angle_table = ((i * 11.25) % 360) * 2 * math.pi / 360
angle_laser = laser_angles[i // 32] * 2 * math.pi / 360
p1, v1 = camera.get_red_dot_point_vector_in_world(
angle_table, x, y)
p2, v2 = laser.get_laser_point_vector_in_world(
angle_table=angle_table, angle_wrist=angle_laser)
p, error = intersect(p1, v1, p2, v2)
my_plot.add_point(p + [error])
if i > 11 * 32:
break
input()
my_plot.close()
def test_velocity_dist_default_key():
"""Test velocity distribution."""
tracking = load.Load("tests/tracking.txt")
plotObj = plot.Plot(tracking)
velocityTest = plotObj.velocityDistribution(ids=[0, 1])
refData = tracking.getObjects(0)
a = (np.sqrt(
np.diff(refData.xBody.values)**2 + np.diff(refData.yBody.values)**2)
) / np.diff(refData.imageNumber.values)
refData = tracking.getObjects(1)
b = (np.sqrt(
np.diff(refData.xBody.values)**2 + np.diff(refData.yBody.values)**2)
) / np.diff(refData.imageNumber.values)
pooled = np.concatenate((a, b))
np.testing.assert_array_equal(pooled, velocityTest[1][0])
velocityTest = plotObj.velocityDistribution(ids=[0, 1], pooled=False)
np.testing.assert_array_equal(a, velocityTest[1][0])
np.testing.assert_array_equal(b, velocityTest[1][1])
refData = tracking.getObjectsInFrames(0, indexes=list(range(0, 100)))
a = (np.sqrt(
np.diff(refData.xBody.values)**2 + np.diff(refData.yBody.values)**2)
) / np.diff(refData.imageNumber.values)
velocityTest = plotObj.velocityDistribution(ids=[0],
pooled=True,
indexes=(0, 100))
np.testing.assert_array_equal(a, velocityTest[1][0])
def plot_pdf(self):
"""print self.pdf"""
if self.pdf is None:
print 'PDF not yet computed'
pass
else:
labels = {}
# mind the $ and r's for latex formatted printing
labels['ylabel'] = r'PDF(\langle\bar{' + r'{}'.format(
self.symbol) + r'_{\tau}}\rangle)'
# lacks universality, but based on use is ok
labels['xlabel'] = r'{} / {}'.format(self.symbol, self.unit)
labels['title'] = (r'probability distribution of values of ' +
r'${}$'.format(self.symbol))
labels['opath'] = 'output/'
labels['oname'] = self.name + '_pdf'
kwargs = {
'linestyle': 'none',
'label': '',
'marker': 'o',
'ms': 1.5,
'lw': '1.0',
'color': 'blue'
}
self.pdf_fig = pc.Plot(self.pdf, labels, **kwargs)
self.pdf_fig.save()
self.pdf_fig.cla()
def make_plotlayout(self):
self.plot_layout = QtGui.QGridLayout()
self.plot_layout.setGeometry(QtCore.QRect(200, 200, 200, 200))
#self.plot_layout.
self.plots.append(plot.Plot())
self.root_layout.addLayout(self.plot_layout)
self.plot_layout.addWidget(self.plots[0])
def plotThread(self):
#print("1")
plot1 = plot.Plot("x v y", "x", "y", np.empty(0), np.empty(0))
while(1):
#plot1.addData(time.time(), self.robots[0].getPos()[0])
plot1.addData(self.robots[0].getPos()[0], self.robots[0].getPos()[1])
plot1.plot()
time.sleep(0.01)
def do_GET(s):
"""Respond to a GET request."""
plot.Plot(LOG_FILENAME, PLOT_FILENAME)
s.send_response(200)
s.send_header("Content-type", "image/png")
s.end_headers()
with open(PLOT_FILENAME, 'rb') as f:
s.wfile.write(f.read())
def do_plot_indexes(self, arg):
indexes = arg.split(',', 1)
a_plot = plot.Plot(plot.PlotCellIndex(indexes[0]))
try:
for index in indexes[1:]:
p = plot.PlotCellIndex(index)
a_plot.addSimple(plot.PlotCell((p.data, p.dates)))
finally:
a_plot.plot()
def main():
num = 15
st = stats.Loadstats()
st.loadCpu(True)
st.loadMem(True)
st.loadTemp(True)
time_lbl = []
with open('time.log', 'r') as f:
s = f.readlines()
# print(len(s))
for x in range(len(s) - 1, len(s) - num-1, -1):
time_lbl.append(s[x])
cpu_plt = []
with open('cpu_use.log', 'r') as f:
s = f.readlines()
# print(len(s))
for x in range(len(s) - 1, len(s) - num-1, -1):
cpu_plt.append(s[x].split(',')[:-1])
cpu = plot.Plot()
cpu.plot(cpu_plt, 'm', time_lbl, num, 'cpu_usege.png',
figname = 'CPU utilization', labelname = 'Processor:')
mem_plt = []
with open('mem.log', 'r') as f:
s = f.readlines()
for x in range(len(s) - 1, len(s) - num-1, -1):
mem_plt.append(s[x])
mem = plot.Plot()
mem.plot(mem_plt, 's', time_lbl, num, 'mem_usege.png',
figname = 'MEM utilization', labelname = 'Utilization:')
temp_plt = []
with open('cpu_temp.log', 'r') as f:
s = f.readlines()
for x in range(len(s) - 1, len(s) - num-1, -1):
temp_plt.append(s[x].split(',')[:-1])
temp = plot.Plot()
temp.plot(temp_plt, 'm', time_lbl, num, 'temp.png',
figname = 'CPU Temperature', labelname = 'Core:')
def main():
# ------ CONFIGURE PARAMETERS ------
params = set_params.Params()
# ------ EXECUTE ------
results = adagrad(params)
# ------ PLOT ------
algorithm = "adagrad"
plt = plot.Plot(params)
plt.update_algorithm(algorithm, results, thresholding=True)
plt.plot_all()
def draw_empty(self):
self.canvas.Destroy()
self.toolbar.Destroy()
plt = plot.Plot()
self.canvas = FigureCanvas(self, -1, plt.fig)
self.toolbar = NavigationToolbar(self.canvas)
self.sizer.Add(self.toolbar, 0, wx.EXPAND)
self.sizer.Add(self.canvas, 1, wx.GROW)
self.Layout()
def position_selected(self, index):
item = index.internalPointer()
it = item.data(index.column(), QtCore.Qt.UserRole)
if isinstance(it, position.position):
for i in range(self.plot_layout.count()):
self.plot_layout.takeAt(i)
self.plot_layout.addWidget(plot.Plot(position=it))
elif isinstance(it, position._security):
pass
def run_with_plot(self):
"""Open plot window and run menu in thread"""
thread = MenuThread(self)
self.plot = plot.Plot()
try:
thread.start()
self.plot.run()
finally:
thread.join()
self.plot = None
if thread.exception is not None:
raise thread.exception
def __init__(self, rateTrain=0.0, lr=1e-3, nCell=5, trialID=0):
# path ----
self.modelPath = 'model'
self.figurePath = 'figure'
# ----
# parameter ----
dInput = 6
self.dOutput = 3
self.trialID = trialID
# ----
# func ----
# data
self.myData = data.NankaiData()
# Test data, interval/seq/onehotyear/b
self.xTest, self.seqTest, self.yYearTest, self.yTest = self.myData.TrainTest()
# Eval data nankai rireki
self.xEval, self.seqEval, self.yYearEval, self.yEval = self.myData.Eval()
# plot
self.myPlot = plot.Plot(figurepath=figurePath, trialID=trialID)
# ----
# Placeholder ----
# interval
# pred paramb + vt-1
self.odex = tf.compat.v1.placeholder(tf.float32,shape=[None, None, dInput])
# vt
self.odey = tf.compat.v1.placeholder(tf.float32,shape=[None, self.dOutput])
# ----
# neural network ----
self.Vt = self.odeNN(self.odex)
self.Vt_test = self.odeNN(self.odex, reuse=True)
# ----
# loss ----
self.odeloss = tf.square(self.odey - self.Vt)
self.odeloss_test = tf.square(self.odey - self.Vt_test)
# ----
#pdb.set_trace()
# optimizer ----
odeVars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES,scope='odeNN')
self.optODE = tf.compat.v1.train.AdamOptimizer(lr).minimize(self.odeloss, var_list=odeVars)
# ----
# ----
config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.1,allow_growth=True))
self.sess = tf.compat.v1.Session(config=config)
self.sess.run(tf.compat.v1.global_variables_initializer())
self.saver = tf.compat.v1.train.Saver()
def _build_gui(self):
"""
Builds the interface of this window, the entire tree of widgets.
"""
_vbox = gtk.VBox()
self.add(_vbox)
_toolbar = self._build_toolbar()
_vbox.pack_start(_toolbar, False, False, 5)
self._build_drawing_area(_vbox)
self._plot_window = plot.Plot()
self._plot_window.set_title(TITLE)
self._plot_window.set_icon_from_file(ROBOT_FILE)
def main():
# ------ CONFIGURE PARAMETERS ------
params = set_params.Params()
# ------ EXECUTE ------
results = adam_lb_modified(params)
# ------ PLOT ------
if (params.flipping):
algorithm = "adam-lb-modified-w-flipping"
else:
algorithm = "adam-lb-modified"
plt = plot.Plot(params)
plt.update_algorithm(algorithm, results, thresholding=True)
plt.plot_all()
def plot_data(self):
# mind the $ and r's for latex formatted printing
labels['ylabel'] = r'${} / {}$'.format(symbol, unit)
# lacks universality, but based on use is ok
labels['xlabel'] = 'timesteps in s'
labels['title'] = ''
kwargs = {
'linestyle': 'solid',
'label': '',
'marker': 'o',
'lw': '1.0',
'color': 'blue'
}
self.fig = pc.Plot(self.data, labels, **kwargs)
def draw_map(out_path, dataset, metric, ts, format, dots, file_formats):
tm = datetime.fromtimestamp(ts, timezone.utc)
decorations = format not in ['bare', 'overlay']
basemaps = format not in ['overlay']
plt = plot.Plot(metric, tm, decorations=decorations, basemaps=basemaps)
if metric == 'mufd':
plt.scale_mufd()
elif metric == 'fof2':
plt.scale_fof2()
else:
plt.scale_generic()
zi = dataset['/maps/' + metric][:]
plt.draw_contour(zi)
dotjson, dot_df = None, None
if dots == 'curr':
dotjson = str(dataset['/stationdata/curr'][...])
elif dots == 'pred':
dotjson = str(dataset['/stationdata/pred'][...])
if dotjson is not None:
dot_df = pd.read_json(dotjson)
dot_df = filter_data(dot_df, metric, ts)
plt.draw_dots(dot_df, metric)
if decorations:
plt.draw_title(
metric, 'eSFI: %.1f, eSSN: %.1f' %
(dataset['/essn/sfi'][...], dataset['/essn/ssn'][...]))
if 'svg' in file_formats:
plt.write(out_path + '.svg')
subprocess.run(
['/usr/local/bin/svgo', '--multipass', out_path + '.svg'],
check=True)
if 'png' in file_formats:
plt.write(out_path + '.png')
if 'jpg' in file_formats:
plt.write(out_path + '.jpg')
if 'station_json' in file_formats and dotjson is not None:
with open(out_path + '_station.json', 'w') as f:
dot_df.to_json(f, orient='records')
def __init__(self):
# Config parameters
self.EDGE_SIZE = 500
self.NUM_POINTS = 200
self.LOOKAHEAD = 1
self.STOPPING_LOOKBACK = 10
self.LINE_OPACITY = .03
self.PADDING = 10
self.RANDOM_INTERVAL = None
self.CACHE_REFRESH = 10
self.PLOT_INTERVAL = 50
self.LAMBDA = 0
self.original_img = get_image('/Users/delbalso/Downloads/dave.jpg')
self.original_weights = get_image(
'/Users/delbalso/Downloads/dave-mask.jpg')
self.points = self.config_circle()
self.ref_lines = {}
for a, b in list(itertools.combinations(xrange(self.NUM_POINTS), 2)):
if a > b: a, b = b, a
self.ref_lines[a, b] = list(
bresenham(self.points[a][0], self.points[a][1],
self.points[b][0], self.points[b][1]))
# Set up main images
self.img = normalize_image(
(self.original_img - self.original_img.min()) /
self.original_img.max() * 255, self.EDGE_SIZE)
self.weights = normalize_image(self.original_weights, self.EDGE_SIZE)
self.p = plot.Plot(self)
# Set up derivative images
# Raster is the image we're drawing to simulate thread
self.raster = np.zeros((self.EDGE_SIZE, self.EDGE_SIZE)) + 255
assert self.raster.shape == self.img.shape
self.l1_errors = []
self.l2_errors = []
self.loss_delta = []
self.update_diff()
# start with a random point
self.points_log = [randint(0, self.NUM_POINTS - 1)]
self.process_start_time = time.strftime("%Y-%m-%d %H:%M:%S",
time.gmtime())
self.wheelOptimizer = WheelOptimizer(self)
def run_net_1(training_data, test_data):
""" Runs net 1 """
n_input, n_hidden, n_out = 784, 30, 10
epochs, mini_batch_size, eta = 30, 10, 3.0
net = network_test_1.NetworkTest1([n_input, n_hidden, n_out])
net.stochastic_gradient_descent(training_data,
epochs,
mini_batch_size,
eta,
test_data=test_data,
stdout=True)
x, y = net.get_accuracy_per_epoch()
plt = plot.Plot(x, 'Epoch', y, 'Accuracy (%)', 'Net 1 Accuracy',
'net_1_accuracy')
plt.plot()
def redraw(self, agg):
self.canvas.Destroy()
self.toolbar.Destroy()
plt = plot.Plot()
kind = self.frame.plot_type.GetItemLabel(
self.frame.plot_type.GetSelection())
errkind = self.frame.err_type.GetItemLabel(
self.frame.err_type.GetSelection())
plt.plot(agg, kind=kind, errkind=errkind.lower()) #, within='rows')
self.canvas = FigureCanvas(self, -1, plt.fig)
self.toolbar = NavigationToolbar(self.canvas)
self.sizer.Add(self.toolbar, 0, wx.EXPAND)
self.sizer.Add(self.canvas, 1, wx.LEFT | wx.TOP | wx.GROW)
self.Layout()
def __init__(self, *args, **kwargs):
wx.Panel.__init__(self, *args, **kwargs)
self.subplots = None
self.rows = None
self.cols = None
self.yerr = None
self.values = None
self.sizer = wx.BoxSizer(wx.VERTICAL)
self.SetSizer(self.sizer)
plt = plot.Plot()
self.canvas = FigureCanvas(self, -1, plt.fig)
self.toolbar = NavigationToolbar(self.canvas)
self.sizer.Add(self.toolbar, 0, wx.EXPAND)
self.sizer.Add(self.canvas, 1, wx.GROW)
self.Fit()
def do_simulation(self, arg):
ticker, startdate = arg.split()
calc = stk.StockCalcIndex(self.stk_data_coll)
sd = stk.StockData()
sd.load(ticker, startdate)
port = des.DecisionCollection(ticker, 50000)
decision = des.DecisionSimpleSMA(ticker, (sd.Cs, sd.dates), port)
decision.looper()
print ticker, ":", str(port)
port2 = des.DecisionCollection(ticker, 50000)
decision2 = des.DecisionSimpleStopSMA(
ticker,
(sd.Cs, sd.dates),
port2,
risk_factor=0.01,
)
decision2.looper()
print ticker, ":", str(port2)
port2.print_all()
a_plot = plot.Plot(plot.PlotCell((sd.Cs, sd.dates)))
a_plot.addSimple(
plot.PlotCell(calc.sma((sd.Cs, sd.dates), 200), overlay=True))
a_plot.addSimple(
plot.PlotCell(calc.sma((sd.Cs, sd.dates), 50), overlay=True))
a_plot.addSimple(
plot.PlotCell(calc.llv((sd.Cs, sd.dates), 100), overlay=True))
a_plot.addSimple(
plot.PlotCell(port2.get_enter_plot_cell(),
overlay=True,
color='go'))
a_plot.addSimple(
plot.PlotCell(port2.get_leave_plot_cell(),
overlay=True,
color='ro'))
a_plot.addSimple(plot.PlotCell(port2.get_value_plot_cell()))
a_plot.plot()
def plot(self, refFlag=True, title='', xyPath=[]):
"""
Plot solution
@param refFlag set to True if reference solution, False for specimen
@param title add title to plot
@param xyPath show Newton covnergence
"""
import plot
data = self.refDic
if not refFlag:
data = self.spcDic
root = Tk()
bxsize = self.boxsize()
plot.Plot(root, data['grid'], width=800, height=800, \
title=title, boxsize=bxsize).draw(data['rho'], \
data['the'], xyPath=xyPath)
root.mainloop()
def make_plot(settings, config):
basename = 'plot{}'.format(hashargs(settings))
name = os.path.join(config['plotdir'], basename).replace('\\', '/')
# try to get plot from cache
if not config['debug'] and config['cachedir'] and os.path.isfile(name +
'.png'):
return [
dict([(e, name + '.' + e) for e in ['png', 'svg', 'pdf']]), None
]
else:
# lock long running plot creation
with plot_lock:
valid, errors = validate_settings(settings)
if not valid:
return [None, errors]
p = plot.Plot(config, **settings)
return [p.save(name), None]
def __init__(self, max_count, *layers):
self.count = 0 # count to change input
self.max_count = max_count
self.image_idx = 0
self.training_data = None
self.test_data = None
self.currentNumber = None
self.dictOfAttributeAdjustments = {}
self.count_to_five = 0
self.middle_layers_idx = []
self.input = []
self.neurons = [[]]
self.layers = layers
self.image_dir = None
self.label_dir = None
self.plot_reference = plot.Plot()
def do_plot_collection(self, arg):
calc = stk.StockCalcIndex(self.stk_data_coll)
sd = stk.StockData()
ticker, startdate = arg.split()
sd.load(ticker, startdate)
a_plot = plot.Plot(plot.PlotCell((sd.Cs, sd.dates)))
a_plot.addSimple(
plot.PlotCell(calc.sma((sd.Cs, sd.dates), 200), overlay=True))
a_plot.addSimple(
plot.PlotCell(calc.sma((sd.Cs, sd.dates), 50), overlay=True))
a_plot.addSimple(
plot.PlotCell(calc.llv((sd.Cs, sd.dates), 100), overlay=True))
a_plot.addSimple(plot.PlotCell(calc.sma((sd.Vs, sd.dates), 20)))
a_plot.addSimple(plot.PlotCell(calc.obv((sd.Cs, sd.Vs, sd.dates))))
a_plot.addSimple(plot.PlotCell(calc.correlation_adj(
(sd.Cs, sd.dates))))
a_plot.plot()
def do_plot_ticker_indexes(self, arg):
calc = stk.StockCalcIndex(self.stk_data_coll)
sd = stk.StockData()
ticker, indexes, startdate = arg.split()
indexes = indexes.split(',', 1)
sd.load(ticker, startdate)
a_plot = plot.Plot(plot.PlotCell((sd.Cs, sd.dates)))
a_plot.addSimple(
plot.PlotCell(calc.sma((sd.Cs, sd.dates), 200), overlay=True))
a_plot.addSimple(
plot.PlotCell(calc.sma((sd.Cs, sd.dates), 50), overlay=True))
for index in indexes:
p = plot.PlotCellIndex(index)
p.truncate(startdate)
a_plot.addSimple(plot.PlotCell((p.data, p.dates)))
a_plot.addSimple(plot.PlotCell(calc.sma((p.data, p.dates), 20)))
a_plot.addSimple(
plot.PlotCell(calc.sma((p.data, p.dates), 50), overlay=True))
a_plot.plot()
def main_pi():
# This code is obsolete
# This code needs to be changed with functions from laser.py if the actual laser tower is used
my_plot = plot.Plot(name='main plot', range=[0, 10])
laser_p = [0, 0, 180]
with camera.init_picamera() as cam:
input()
motor.start_motor()
temp = 0
while (True):
pic = camera.take_one_picture_pi(cam)
# print(pic)
ang = -motor.get_angle_motor()
x, y = camera.find_red_dot(pic)
if ang > temp:
break
floor_matrix = Matrix(angles=[0, 0, ang])
cam_matrix = Matrix(pos=laser_p, angles=[0, 0, 0.26])
result_matrix = mult([floor_matrix, cam_matrix])
p2 = result_matrix.get_pos()
v2 = result_matrix.get_vector_in_referential([0, 1, 0])
p1, v1 = camera.get_red_dot_point_vector_in_world(ang, x, y)
m, l = intersect(p1, v1, p2, v2)
point = m + [l]
my_plot.add_point(point)
# print(m, l, sep=' ')
temp = ang
# k = input()
# if k == 'q':
# break
motor.restart_motor()
input()
my_plot.close()
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
History = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=EPOCHS,
batch_size=BATCH_SIZE)
print(History.history.keys())
plot = plot.Plot(History)
plot.accuracy()
plot.loss()
