def test_plot_not_exist_data(self):
gpx = self.setUp(1)
points = gpx.track.track_segment
plot = Plotter(points)
result = plot.setup()
self.assertFalse(result)
def getPlotter():
global myPlotter
if myPlotter is None:
config = Config().getConfig()
myPlotter = Plotter(config, 100, 70)
myPlotter.init(False)
return myPlotter
def plot_graph(self, index):
gpx = self.get_gpx(index)
points = gpx.track.track_segment
plot = Plotter(points)
result = plot.setup()
if result:
plot.plot()
else:
self.show_warning("Нехватает данных о высоте!")
def __init__(self, image_size=128, conv_dim=16):
self.plotter = Plotter()
self.iter = 0
super(Discriminator, self).__init__()
# self.conv1 = conv()
self.conv1 = nn.Sequential(
conv(3, conv_dim, 4, bn=False)) # , conv(conv_dim, conv_dim * 2, 1, stride=1, pad=0))
# self.conv2 = nn.Sequential(conv(conv_dim*2, conv_dim*2, 4, bn=False), conv(conv_dim * 2, conv_dim * 4, 1, stride=1, pad=0))
# self.conv3 = nn.Sequential(conv(conv_dim*4, conv_dim*4, 4, bn=False), conv(conv_dim * 4, conv_dim * 8, 1, stride=1, pad=0))
# self.conv4 = nn.Sequential(conv(conv_dim*8, conv_dim*8, 4, bn=False), conv(conv_dim * 8, conv_dim * 16, 1, stride=1, pad=0))
# self.conv5 = nn.Sequential(conv(conv_dim*16, conv_dim*16, 4, bn=False), conv(conv_dim * 16, conv_dim * 32, 1, stride=1, pad=0))
self.conv2 = conv(conv_dim, conv_dim * 2, 4)
self.conv3 = conv(conv_dim * 2, conv_dim * 4, 4)
self.conv4 = conv(conv_dim * 4, conv_dim * 8, 4)
self.conv5 = conv(conv_dim * 8, conv_dim * 16, 4)
# self.fc = conv(conv_dim * 16, 1, conv_dim * 16, 1, 0, False)
self.fc1 = nn.Linear(conv_dim * 16 * 4 * 2, 32)
# self.fc1 = nn.Linear(4608, 1)
self.fc2 = nn.Linear(32, 1)
def doStep(self, data):
dir = data['dir']
steps = int(data['steps'])
self.isPlottingInProgress = True
logger.info("Starting to Step")
config = Config().getConfig()
plotter = Plotter(config, 0, 0)
plotter.init(False)
plotter.enableSteppers()
plotter.movePen(PenDirection.Up)
if dir == "leftUp":
plotter.moveLeft(CordDirection.Backward, steps)
if dir == "leftDown":
plotter.moveLeft(CordDirection.Forward, steps)
if dir == "rightUp":
plotter.moveRight(CordDirection.Backward, steps)
if dir == "rightDown":
plotter.moveRight(CordDirection.Forward, steps)
plotter.disableSteppers()
self.isPlottingInProgress = False
logger.info("Done Stepping")
return 'done'
def __init__(self, config, data_loader):
self.generator = None
self.discriminator = None
self.g_optimizer = None
self.d_optimizer = None
self.g_conv_dim = config.g_conv_dim
self.d_conv_dim = config.d_conv_dim
self.z_dim = config.z_dim
self.beta1 = config.beta1
self.beta2 = config.beta2
self.image_size = config.image_size
self.data_loader = data_loader
self.num_epochs = config.num_epochs
self.batch_size = config.batch_size
self.sample_size = config.sample_size
self.lr = config.lr
self.log_step = config.log_step
self.sample_step = config.sample_step
self.sample_path = config.sample_path
self.model_path = config.model_path
self.epoch = config.epoch
self.build_model()
self.plotter = Plotter()
def doMove(self, data):
fromX = int(data['fromX'])
fromY = int(data['fromY'])
toX = int(data['toX'])
toY = int(data['toY'])
p = int(data['pen'])
pen = PenDirection.Down if p == 0 else PenDirection.Up
logger.info("Starting to Move to {},{} from {},{}".format(
toX, toY, fromX, fromY))
config = Config().getConfig()
plotter = Plotter(config, fromX, fromY)
plotter.init(False)
plotter.enableSteppers()
plotter.moveTo(toX, toY, pen)
plotter.disableSteppers()
self.isPlottingInProgress = False
logger.info("Done Stepping")
return {'atX': toX, 'atY': toY}
# training = 'all_channels_200523_15h_3m'
# training = 'all_channels_200523_15h_16m'
plotter = Plotter (channel = ch,
base_dir = '/Users/manzoni/Documents/HNL/ntuples/20may20', #env['NTUPLE_DIR'],
post_fix = 'HNLTreeProducer_%s/tree.root' %ch,
selection_data = selection,
selection_mc = selection_mc,
selection_tight = selection_tight,
pandas_selection = pandas_selection,
lumi = 59700.,
model = '/'.join([env['NN_DIR'], 'trainings', training, 'net_model_weighted.h5' ]),
transformation = '/'.join([env['NN_DIR'], 'trainings', training, 'input_tranformation_weighted.pck']),
features = '/'.join([env['NN_DIR'], 'trainings', training, 'input_features.pck' ]),
process_signals = False, # switch off for control regions
mini_signals = False, # process only the signals that you'll plot
plot_signals = False,
blinded = False,
datacards = ['hnl_m_12_lxy_lt_0p5', 'hnl_m_12_lxy_0p5_to_1p5', 'hnl_m_12_lxy_1p5_to_4p0', 'hnl_m_12_lxy_mt_4p0'], # FIXME! improve this to accept wildcards / regex
mc_subtraction = True,
dir_suffix = 'check_alt_prompt_estimate',
relaxed_mc_scaling = 0.05,
)
if __name__ == '__main__':
plotter.plot()
def doPlot(self, data):
self.isPlottingInProgress = True
self.progress.clear()
logger.info("Starting to Plot")
orgX = int(data['orgX'])
orgY = int(data['orgY'])
cords = data['cords']
config = Config().getConfig()
plotter = Plotter(config, orgX, orgY)
plotter.init(False)
plotter.enableSteppers()
minX = min(cords['x'])
maxX = max(cords['x'])
minY = min(cords['y'])
maxY = max(cords['y'])
ax = [] # additional coordinates
ay = [] # additional coordinates
ap = []
# move to origin, even if we are already there
ax.append(orgX)
ay.append(orgY)
ap.append(0) # PenDirection.Up
#plotter.moveTo(minX, minY, PenDirection.Up)
ax.append(minX)
ay.append(minY)
ap.append(0) # PenDirection.Up
# top left corner horizontal line
#plotter.moveTo(minX+10, minY, PenDirection.Down)
ax.append(minX + 10)
ay.append(minY)
ap.append(1) # PenDirection.Down
#plotter.moveTo(maxX-10, minY, PenDirection.Up)
ax.append(maxX - 10)
ay.append(minY)
ap.append(0) # PenDirection.up
# top Right
# top right corner horizontal line
#plotter.moveTo(maxX, minY, PenDirection.Down)
ax.append(maxX)
ay.append(minY)
ap.append(1)
# top right corner vertical line
#plotter.moveTo(maxX, minY+10, PenDirection.Down)
ax.append(maxX)
ay.append(minY + 10)
ap.append(1)
#plotter.moveTo(maxX, maxY-10, PenDirection.Up)
ax.append(maxX)
ay.append(maxY - 10)
ap.append(0)
# bottom Right
# bottom right corner vertical line
#plotter.moveTo(maxX, maxY, PenDirection.Down)
ax.append(maxX)
ay.append(maxY)
ap.append(0)
# bottom right corner horizontal line
#plotter.moveTo(maxX-10, maxY, PenDirection.Down)
ax.append(maxX - 10)
ay.append(maxY)
ap.append(1)
#plotter.moveTo(minX+10, maxY, PenDirection.Up)
ax.append(minX + 10)
ay.append(maxY)
ap.append(0)
# bottom left
# bottom left corner horizontal line
#plotter.moveTo(minX, maxY, PenDirection.Down)
ax.append(minX)
ay.append(maxY)
ap.append(1)
# bottom left corner vertical line
#plotter.moveTo(minX, maxY-10, PenDirection.Down)
ax.append(minX)
ay.append(maxY - 10)
ap.append(1)
ax.append(minX)
ay.append(minY)
ap.append(0)
cords['x'] = ax + cords['x']
cords['y'] = ay + cords['y']
cords['p'] = ap + cords['p']
total = len(cords['x'])
for index in range(0, total - 1):
x = int(cords['x'][index])
y = int(cords['y'][index])
pen = PenDirection.Down if cords['p'][
index] == 0 else PenDirection.Up
perComplete = round(index / total * 100, 2)
self.progress.append((x, y, perComplete))
plotter.moveTo(x, y, pen)
logger.debug("Plotting {}%%".format(perComplete))
plotter.finalize()
self.progress.append((plotter.orgX, plotter.orgY, 100))
logger.info("Done Plotting")
self.isPlottingInProgress = False
return "Complete"
unlabelled_data_store = DataStore(
unlabelled_options.identity,
data_set_csv_path=unlabelled_options.data_path,
data_set_output_dir=labelled_options.output_dir,
data_groups=unlabelled_options.groups,
select_columns=unlabelled_options.cols,
shuffle=unlabelled_options.shuffle,
random_state=unlabelled_options.random_state,
persist=unlabelled_options.persist)
# Get the three split sets of data as configured with labelled_options.groups
training_data = labelled_data_store.get_data('train')
testing_data = labelled_data_store.get_data('test')
verification_data = labelled_data_store.get_data('ver')
print(training_data)
# Create an example plot using training data
labelled_plotter = Plotter(labelled_options.identity,
labelled_options.plot_path)
labelled_plotter.display_plot(0,
'Survived vs Passenger ID',
True,
training_data['PassengerId'],
AxisOptions('Passenger ID'),
training_data['Survived'],
AxisOptions('Survived'),
style='g.',
size=[7.2, 5.76],
legend=False)
