def plot(self):
plot_scatter(None,
self.data_x,
self.data_y,
size=20,
pch='x',
colour="red",
title=self.name)
def display(self):
plot_hist(list(self.knockouts_hist),
title="Knockout size distribution",
colour="blue")
lines = [
"%s, %s" % (x, y)
for x, y in zip(self.solution.solutions['Size'],
self.solution.solutions['Fitness'])
]
plot_scatter(
lines, None, None, 20, "*", "blue",
"Correlation between number of knockouts and fitness")
def handle(self) -> None:
g2v = Garm2Vec()
filepath = self.argument("filepath")
full_filepath = os.path.abspath(os.path.expanduser(filepath))
description = self.argument("description")
vector = g2v.get_one([full_filepath, description])
data = [f"{x},{y}" for x, y in enumerate(vector)]
plot_scatter(
f=data,
xs=None,
ys=None,
title="Garm2Vec",
pch="o",
size=40,
colour="default",
)
def scatter(x_data, y_data, title, n_points=None):
"""
x_data: iterable of floats
X data to plot
y_data: iterable of floats
Y data to plot
title: string
Title of the graph
n_points: int
How many of points (most recent) to plot. If None, plot all.
"""
# bashplotlib.scatter.plot_scatter only accepts **files** (well, the pypi
# release only accepts files. [PR43](https://github.com/glamp/bashplotlib/pull/43)
# was merged but not released on pypi.
# So we fake it. The code for `plot_scatter` will call `open(xs)`, so we just
# write things to a temp file. /shrug.
x_stream = tempfile.NamedTemporaryFile()
y_stream = tempfile.NamedTemporaryFile()
for i, ts in enumerate(x_data[-n_points:]):
# timestamps are pd.datetime64 objects which store the number of
# nanoseconds since the Unix epoch in the `value` attribute.
ns = ts.value
ms = ts.value / 1000
x_stream.write((str(i) + "\n").encode("utf-8"))
x_stream.seek(0)
for yval in y_data[-n_points:]:
y_stream.write((str(yval) + "\n").encode("utf-8"))
y_stream.seek(0)
plot_scatter(
f=None,
xs=x_stream.name,
ys=y_stream.name,
size=20,
pch="x",
colour="red",
title=title,
)
# bashplotlib does not close files...
x_stream.close()
y_stream.close()
def updater_routine(self, net_version, in_line_testing = False):
for ii in tqdm(range(1+net_version, 1+net_version+self.n_epochs)):
if ii < self.sequential_after_n_epochs:
loss, val_loss = self.update_1step()
else:
loss, val_loss = self.update_sequence()
new_loss = np.array([loss, val_loss])[np.newaxis,:]
self.loss_history = np.append(self.loss_history, new_loss, axis = 0)
if ii >= self.plot_last_n and not ii % self.visualize_every_n:
print(f'iteration = {ii}')
print(f'training loss = {loss}')
print(f'validation loss = {val_loss}')
loss_array = self.loss_history[-self.plot_last_n:,0]
val_loss_array = self.loss_history[-self.plot_last_n:,1]
np.savetxt("loss_hist.csv", np.concatenate((np.arange(1,loss_array.shape[0]+1)[:,np.newaxis],loss_array[:,np.newaxis]), axis = 1), delimiter=",")
np.savetxt("val_loss_hist.csv", np.concatenate((np.arange(1,val_loss_array.shape[0]+1)[:,np.newaxis],val_loss_array[:,np.newaxis]), axis = 1), delimiter=",")
try:
plot_scatter(f = "loss_hist.csv",xs = None, ys = None, size = 30, colour = 'white',pch = '*', title = 'training loss')
plot_scatter(f = "val_loss_hist.csv",xs = None, ys = None, size = 30, colour = 'yellow',pch = '*', title = 'validation loss')
except Exception:
pass
if not ii % self.save_every_n:
net_name = self.net_name +'_' +str(ii)
self.obs_net.save_net_params(net_name = net_name)
# inline testing for debugging only
if in_line_testing:
self.test_net(reset_every = self.training_sequence_max_length, save_test_result = True, save_name = net_name)
loss_array = self.loss_history[1:,0]
val_loss_array = self.loss_history[1:,1]
tr_loss_filename = self.net_name + "_tr_loss.csv"
val_loss_filename =self.net_name + "_val_loss.csv"
np.savetxt(tr_loss_filename, np.concatenate((np.arange(1,loss_array.shape[0]+1)[:,np.newaxis],loss_array[:,np.newaxis]), axis = 1), delimiter=",")
np.savetxt(val_loss_filename, np.concatenate((np.arange(1,val_loss_array.shape[0]+1)[:,np.newaxis],val_loss_array[:,np.newaxis]), axis = 1), delimiter=",")
#! /usr/bin/env python # -*- coding: utf-8 -*- # vim:fenc=utf-8 # # Copyright © 2019 red <red@red-Swift-SF113-31> # # Distributed under terms of the MIT license. """ """ from bashplotlib.scatterplot import plot_scatter x_coords = [-10, 20, 30] y_coords = [-10, 20, 30] width = 10 char = 'x' color = 'default' title = 'My Test Graph' plot_scatter(None, x_coords, y_coords, width, char, color, title)
def display(self):
plot_hist(list(self.knockouts_hist), title="Knockout size distribution", colour="blue")
lines = ["%s, %s" % (x, y) for x, y in
zip(self.solution.solutions['Size'], self.solution.solutions['Fitness'])]
plot_scatter(lines, None, None, 20, "*", "blue", "Correlation between number of knockouts and fitness")
def plot_production_envelope_cli(envelope, objective, key, grid=None, width=None, height=None, title=None,
points=None, points_colors=None, axis_font_size=None, color="blue"):
scatterplot.plot_scatter(None, envelope[key], envelope["objective_upper_bound"], "*")
method = 'ticker'
buy_value = 50650 #Set the buy value
interval = 5 #Minutes
xy_file = "xy.txt"
count = 0
file = open(xy_file, 'w')
file.close()
while (True):
file = open(xy_file, 'a')
try:
page = requests.get("https://www.mercadobitcoin.net/api/" + search +
"/" + method + "/")
except Exception as e:
continue
count += 1
data_json = json.loads(str(BeautifulSoup(page.content, 'html.parser')))
percent = (abs(float(data_json['ticker']['last']) - buy_value) /
buy_value) * 100
file.write(str(count) + "," + data_json['ticker']['last'] + "\n")
file.close()
if float(data_json['ticker']['last']) >= buy_value:
symb = u"\u2191"
else:
symb = u"\u2193"
plot_scatter(
xy_file, "", "", 30, "*", "red",
data_json['ticker']['last'] + " " + symb + " " + str(percent) + "%")
time.sleep((interval - int(percent)) * 60)
print chr(27) + "[2J"
