def __call__(self, *args, **kwargs):
assert all([linename in kwargs.keys() for linename in self.linenames]), "Line names must be in dict of keyword " \
"arguments."
for linename in self.linenames:
# Fetch from kwargs
inp = kwargs[linename]
# Parse
if isinstance(inp, (tuple, list)):
assert len(inp) == 2
xval, yval = inp
else:
yval = inp
if self.xaxis == 'iterations':
xval = kwargs['iternum']
elif self.xaxis == 'epochs':
xval = kwargs['epoch']
elif self.xaxis in kwargs.keys():
# Easter egg
xval = kwargs[self.xaxis]
else:
raise NotImplementedError(
"Unknown token for x-axis: {}. Correct tokens are: "
"'iterations' and 'epochs'".format(self.xaxis))
self.datasources[linename].data["y"].append(yval)
self.datasources[linename].data["x"].append(xval)
# Send to server
bplt.cursession().store_objects(self.datasources[linename])
def __call__(self, *args, **kwargs):
# Note: there used to be an assert statement here about linenames not being in kwargs.keys(). These days, a
# linename is simply ignored (for the iteration) if it's not passed as a keyword arg.
for linename in self.linenames:
# Fetch from kwargs if it's in there (skip otherwise)
# Skip
if linename not in kwargs.keys():
continue
# Fetch
inp = kwargs[linename]
# Parse
if isinstance(inp, (tuple, list)):
assert len(inp) == 2
xval, yval = inp
else:
yval = inp
if self.xaxis == 'iterations':
xval = kwargs['iternum']
elif self.xaxis == 'epochs':
xval = kwargs['epoch']
elif self.xaxis in kwargs.keys():
# Easter egg
xval = kwargs[self.xaxis]
else:
raise NotImplementedError("Unknown token for x-axis: {}. Correct tokens are: "
"'iterations' and 'epochs'".format(self.xaxis))
self.datasources[linename].data["y"].append(yval)
self.datasources[linename].data["x"].append(xval)
# Send to server
bplt.cursession().store_objects(self.datasources[linename])
def update(self, r, theta):
renderer = self.plot.select(dict(name='scatter'))[0]
x = r * cos(theta)
y = r * sin(theta)
renderer.data_source.data['x'] = x
renderer.data_source.data['y'] = y
cursession().store_objects(renderer.data_source)
def set_data_images(self, data_img, img_list):
if len(data_img) != len(img_list):
print 'ERROR: Number of images incorrect. It should be: ' + str(len(data_img)) + ' and got: ' \
+ str(len(img_list))
exit()
else:
for i in xrange(len(data_img)):
data_img[i].data['image'] = [self.convert_image(img_list[i])]
cursession().store_objects(data_img[i])
def set_data_images(self, data_img, img_list):
if len(data_img) != len(img_list):
print 'ERROR: Number of images incorrect. It should be: ' + str(len(data_img)) + ' and got: ' \
+ str(len(img_list))
exit()
else:
for i in xrange(len(data_img)):
data_img[i].data['image'] = [self.convert_image(img_list[i])]
cursession().store_objects(data_img[i])
def run(self):
while not rospy.is_shutdown():
print "<<<<<< Updating plot >>>>>"
self.ds.data["x"] = self.x_data
self.ds.data["y"] = self.y_data
self.ds._dirty = True
cursession().store_objects(self.ds)
time.sleep(0.01)
def update(self, acq):
c1, c2 = acq.plot_code_window
dopp_min, dopp_max = acq.dopp_bins[0], acq.dopp_bins[-1]
img = self.make_image(acq.plot_corr)
self.renderer.data_source.data['image'] = img
self.renderer.data_source.data['x'] = [c1]
self.renderer.data_source.data['y'] = [c2]
self.renderer.data_source.data['dw'] = [c2 - c1]
self.renderer.data_source.data['dh'] = [dopp_max - dopp_min]
cursession().store_objects(self.renderer.data_source)
def update(self, acq):
renderer = self.plot.select(dict(name='line'))[0]
angles = unwrap(angle(acq.phases))
t = arange(len(angles)) * acq.block_length
print(t[0], t[-1], angles[0], angles[-1])
renderer.data_source.data['x'] = t
renderer.data_source.data['y'] = angles
renderer.data_source._dirty = True
cursession().store_objects(renderer.data_source)
def append_data_plot(self, values):
if self.n_lines_plot != len(values):
print 'ERROR: Number of values incorrect. It should be: ' + str(self.n_lines_plot) + ' and got: ' \
+ str(len(values))
exit()
else:
for i in xrange(len(self.data_plot)):
self.data_plot[i].data["x"].append(len(self.data_plot[i].data["x"]))
self.data_plot[i].data["y"].append(values[i])
cursession().store_objects(self.data_plot[i])
def append_data_plot(self, values):
if self.n_lines_plot != len(values):
print 'ERROR: Number of values incorrect. It should be: ' + str(self.n_lines_plot) + ' and got: ' \
+ str(len(values))
exit()
else:
for i in xrange(len(self.data_plot)):
self.data_plot[i].data["x"].append(
len(self.data_plot[i].data["x"]))
self.data_plot[i].data["y"].append(values[i])
cursession().store_objects(self.data_plot[i])
def update_plot(algo):
''' Extend this to update a live streaming plot (require bokeh server)
'''
# Dive into the 'history_container' how lookbacks are calculated
hist_df = algo.history(5, '1d', 'price')
hist_df = hist_df.fill_na(0)
for sid_id in algo.asset_finder.sids:
renderer = algo.figure.select(dict(name=str(sid_id)))[0]
ds = renderer.data_source
ds.data["y"] = hist_df[sid_id].values
cursession().store_objects(ds)
def background_thread(ds):
"""Plot animation, update data if play is True, otherwise stop"""
try:
while True:
for i in np.hstack((np.linspace(1, -1, 100), np.linspace(-1, 1, 100))):
if should_play():
ds.data["y"] = y * i
cursession().store_objects(ds)
time.sleep(0.05)
except:
logger.exception("An error occurred")
raise
def run(self):
while True:
priority, obj = self.queue.get()
if priority == PushThread.PUT:
cursession().store_objects(obj)
elif priority == PushThread.PUSH:
push()
# delete queued objects when training has finished
if obj == "after_training":
with self.queue.mutex:
del self.queue.queue[:]
break
self.queue.task_done()
def run(self):
while True:
priority, obj = self.queue.get()
if priority == PushThread.PUT:
cursession().store_objects(obj)
elif priority == PushThread.PUSH:
push()
# delete queued objects when training has finished
if obj == "after_training":
with self.queue.mutex:
del self.queue.queue[:]
break
self.queue.task_done()
def on_epoch_end(self, epoch, logs={}):
I = self.get_image()
if not hasattr(self, 'fig'):
self.fig = figure(title=self.fig_title,
x_range=[0, 1], y_range=[0, 1])
self.fig.image(image=[I], x=[0], y=[0], dw=[1], dh=[1],
name='weight')
renderer = self.fig.select({'name': 'weight'})
self.plots.append(renderer[0].data_source)
show(self.fig)
else:
self.plots[0].data['image'] = [I]
cursession().store_objects(self.plots[0])
push()
def __init__(self,
mode='arduino',
serialObject=None): #nof indicates number of json fields
self.mode = mode
self.modeset = True
if mode is 'arduino':
import serial
if serialObject is None:
n = 0
while True:
try:
self.ser = serial.Serial(SERIAL_PORT_DEV + str(n),
BAUDRATE)
break
except serial.serialutil.SerialException:
print 'Could not open any serial port on {0}{1}. Trying with {0}{2}'.format(
SERIAL_PORT_DEV, n, n + 1)
n += 1
else:
self.ser = serialObject
self.ser.open()
elif mode is 'rpi':
self.bmp, self.dht = BMP180Wrapper(), DHT22Wrapper()
self.data = {}
self.data["temp"], self.data["hum"], self.data["pres"], self.data[
"timenow"] = [], [], [], []
self.lastdata = ''
self.lastjson = None
#charts
self.tempchart, self.preschart, self.humchart = MeteoStation.MeteoChart(
'temp', 'Temperature',
'Temperature in *C'), MeteoStation.MeteoChart(
'pres', 'Pressure',
'Pressure in hPa'), MeteoStation.MeteoChart(
'hum', 'Humidity', 'Humidity in %RH')
self.charts = [self.tempchart, self.preschart, self.humchart]
self.figs = [chart.fig for chart in self.charts]
self.show_all()
cursession().publish()
#pymongo integration
try:
self.db = client['meteo_db']
self.collection = self.db.colletion['meteo_data_collection']
except: #retry with object-style formalism
self.db = client.meteo_db
self.collection = self.db.collection.meteo_data_collection
def update_plots(self, epoch, monitors):
"""
Given the calculated monitors (collapsed name and value tuple), add its datapoint to the appropriate figure
and update the figure in bokeh-server.
Parameters
----------
epoch : int
The epoch (x-axis value in the figure).
monitors : dict
The dictionary of monitors calculated at this epoch. The dictionary is of the form
{collapsed_monitor_name: value}. The name is the same that was used in the creation of the
figures in the plot, so it is used as the key to finding the appropriate figure to add the
data.
"""
if BOKEH_AVAILABLE:
for key, value in monitors.items():
if key in self.figure_indices:
if key not in self.plots:
# grab the correct figure by its index for the key (same with the color)
fig = self.figures[self.figure_indices[key]]
color_idx = self.figure_color_indices[
self.figure_indices[key]]
# split the channel from the monitor name
name = key.split(COLLAPSE_SEPARATOR, 1)
if len(name) > 1:
name = name[1]
else:
name = name[0]
# create a new line
fig.line([epoch], [value],
legend=name,
x_axis_label='iterations',
y_axis_label='value',
name=name,
line_color=self.colors[color_idx %
len(self.colors)])
color_idx += 1
# set the color index back in the figure list
self.figure_color_indices[
self.figure_indices[key]] = color_idx
# grab the render object and put it in the plots dictionary
renderer = fig.select(dict(name=name))
self.plots[key] = renderer[0].data_source
else:
self.plots[key].data['x'].append(epoch)
self.plots[key].data['y'].append(value)
cursession().store_objects(self.plots[key])
push()
def __init__(self, title, legends, n_images_train=0, n_images_test=0, n_cols_images=3, axisx="Epochs",
axisy="Values", line_width=4, alpha=0.8, line_dash=[4, 4], plot_width=800, plot_height=400):
# Save some parameters
self.n_lines_plot = len(legends)
self.n_images_train = n_images_train
self.n_images_test = n_images_test
# prepare output to server
output_server(title)
cursession().publish()
# Create the figure plot
if self.n_lines_plot > 0:
p_plot, self.data_plot = self.create_figure_plot(title, legends, axisx=axisx, axisy=axisy,
line_width=line_width, alpha=alpha, line_dash=line_dash,
plot_width=plot_width, plot_height=plot_height)
if n_images_train > 0:
# Create black images as initialization
img = np.zeros((64, 256, 3))
img_batch = []
for i in xrange(n_images_train):
img_batch.append(img)
# Create the training image grid
p_images_train, self.data_img_train = self.create_grid_images(img_batch, name='img_train', n_cols=n_cols_images)
if n_images_test > 0:
# Create black images as initialization
img = np.zeros((64, 256, 3))
img_batch = []
for i in xrange(n_images_test):
img_batch.append(img)
# Create the testing image grid
p_images_test, self.data_img_test = self.create_grid_images(img_batch, name='img_test', n_cols=n_cols_images)
# Create a vertical grid with the plot and the train and test images
if self.n_lines_plot > 0 and n_images_train > 0 and n_images_test > 0:
p = vplot(p_plot, p_images_train, p_images_test)
elif self.n_lines_plot > 0 and n_images_train > 0 and n_images_test <= 0:
p = vplot(p_plot, p_images_train)
else:
print 'ERROR: Not implemented combination. Please, do it!'
# Show the plot
show(p)
def main(document, server_url):
plots = {}
output_server(document, url=server_url)
# Create figures for each group of channels
p = []
p_indices = {}
for i, channel_set in enumerate(channels):
channel_set_opts = {}
if isinstance(channel_set, dict):
channel_set_opts = channel_set
channel_set = channel_set_opts.pop("channels")
channel_set_opts.setdefault("title", "{} #{}".format(document, i + 1))
p.append(figure(**channel_set_opts))
for channel in channel_set:
p_indices[channel] = i
files = [f for f in listdir(document) if isfile(join(document, f)) and "_log_" in f]
for file_name in sorted(files, key=lambda s: int(re.findall(r"\d+", s)[1])):
with open(join(document, file_name), "rb") as f:
log = pickle.load(f)
iteration = log.status["iterations_done"]
i = 0
for key, value in log.current_row.items():
if key in p_indices:
if key not in plots:
fig = p[p_indices[key]]
fig.line(
[iteration],
[value],
legend=key,
x_axis_label="iterations",
y_axis_label="value",
name=key,
line_color=colors[i % len(colors)],
)
i += 1
renderer = fig.select(dict(name=key))
plots[key] = renderer[0].data_source
else:
plots[key].data["x"].append(iteration)
plots[key].data["y"].append(value)
cursession().store_objects(plots[key])
push()
def animated():
from numpy import pi, cos, sin, linspace, zeros_like
N = 50 + 1
r_base = 8
theta = linspace(0, 2 * pi, N)
r_x = linspace(0, 6 * pi, N - 1)
rmin = r_base - cos(r_x) - 1
rmax = r_base + sin(r_x) + 1
colors = ["FFFFCC", "#C7E9B4", "#7FCDBB", "#41B6C4", "#2C7FB8",
"#253494", "#2C7FB8", "#41B6C4", "#7FCDBB", "#C7E9B4"] * 5
cx = cy = zeros_like(rmin)
output_server("animated_reveal")
figure(title="Animations")
hold()
annular_wedge(
cx, cy, rmin, rmax, theta[:-1], theta[1:],
x_range=[-11, 11],
y_range=[-11, 11],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
tools="pan,wheel_zoom,box_zoom,reset,previewsave"
)
return curplot(), cursession()
def update_plots(root, figures, types):
# for type, ids in types.iteritems():
for type, fig in figures.iteritems():
for id in types[type]:
renderer = fig.select(dict(name=id))
ds = renderer[0].data_source
y = get_data(root, id)
# if len(y) > fig.x_range.end-5:
# print "heeeeey"
# fig.x_range = Range1d(start=0, end=len(y)+20)
ds.data["y"] = y
ds.data["x"] = np.arange(len(y))
ds._dirty = True
cursession().store_objects(ds)
cursession().publish()
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma ** 2))) / 2
output_server("distribution_reveal")
hold()
figure(title="Interactive plots",
tools="pan, wheel_zoom, box_zoom, reset, previewsave",
background_fill="#E5E5E5")
quad(top=hist, bottom=np.zeros(len(hist)), left=edges[:-1], right=edges[1:],
fill_color="#333333", line_color="#E5E5E5", line_width=3)
# Use `line` renderers to display the PDF and CDF
line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
xgrid().grid_line_color = "white"
xgrid().grid_line_width = 3
ygrid().grid_line_color = "white"
ygrid().grid_line_width = 3
legend().orientation = "top_left"
return curplot(), cursession()
def draw_plot(all_freqs, all_segm_dict, uniq_id="no-id"):
#zbior nazw wszystkich komorek podczas symulacji
cells_types = set()
for i in range(len(all_freqs)):
for e in all_freqs[i].keys():
cells_types.add(e)
#dla kazdego typu komorek przypisywany jest inny kolor
col_dict = {}
from bokeh.palettes import brewer
colors_palette = brewer["Spectral"][11]
color = lambda: random.randint(0, 255)
for i, element in enumerate(cells_types):
if i < 11:
col_dict[element] = colors_palette[i]
else:
#losowy kolor
col_dict[element] = ('#%02X%02X%02X' % (color(), color(), color()))
#dodanie klucza od wartosci ktorych nie ma po pierwszej iteracji
for element in cells_types:
for i in range(len(all_freqs)):
if element not in all_freqs[i]:
all_freqs[i][element] = [0, 0, 0, 0]
output_server("stops3" + uniq_id)
bar, geny = plot_segments(all_freqs[0], col_dict)
plot = plot_environment(all_segm_dict[0])
push()
return bar, plot, cursession(), geny
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma ** 2))) / 2
output_server("distribution_reveal")
p = figure(title="Interactive plots",
background_fill="#E5E5E5")
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="#333333", line_color="#E5E5E5", line_width=3)
# Use `line` renderers to display the PDF and CDF
p.line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
p.line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
p.legend.orientation = "top_left"
p.xaxis.axis_label = 'x'
p.xgrid[0].grid_line_color = "white"
p.xgrid[0].grid_line_width = 3
p.yaxis.axis_label = 'Pr(x)'
p.ygrid[0].grid_line_color = "white"
p.ygrid[0].grid_line_width = 3
push()
return p, cursession()
def animated():
from numpy import pi, cos, sin, linspace
N = 50 + 1
r_base = 8
theta = linspace(0, 2 * pi, N)
r_x = linspace(0, 6 * pi, N - 1)
rmin = r_base - cos(r_x) - 1
rmax = r_base + sin(r_x) + 1
colors = ["FFFFCC", "#C7E9B4", "#7FCDBB", "#41B6C4", "#2C7FB8",
"#253494", "#2C7FB8", "#41B6C4", "#7FCDBB", "#C7E9B4"] * 5
output_server("animated_reveal")
p = figure(title="Animations", x_range=[-11, 11], y_range=[-11, 11])
p.annular_wedge(
0, 0, rmin, rmax, theta[:-1], theta[1:],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
)
push()
return p, cursession()
def create_plot(self):
output_server('animation')
source = ColumnDataSource(data=dict(x=[], y=[]))
p = figure(plot_width=800, plot_height=400)
p.line(x='x', y='y', source=source, name='line')
push()
return p, cursession()
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma ** 2))) / 2
output_server("distribution_reveal")
p = figure(title="Interactive plots",
background_fill="#E5E5E5")
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="#333333", line_color="#E5E5E5", line_width=3)
# Use `line` renderers to display the PDF and CDF
p.line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
p.line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
p.legend.orientation = "top_left"
p.xaxis.axis_label = 'x'
p.xgrid[0].grid_line_color = "white"
p.xgrid[0].grid_line_width = 3
p.yaxis.axis_label = 'Pr(x)'
p.ygrid[0].grid_line_color = "white"
p.ygrid[0].grid_line_width = 3
push()
return p, cursession()
def animated():
from numpy import pi, cos, sin, linspace
N = 50 + 1
r_base = 8
theta = linspace(0, 2 * pi, N)
r_x = linspace(0, 6 * pi, N - 1)
rmin = r_base - cos(r_x) - 1
rmax = r_base + sin(r_x) + 1
colors = ["FFFFCC", "#C7E9B4", "#7FCDBB", "#41B6C4", "#2C7FB8",
"#253494", "#2C7FB8", "#41B6C4", "#7FCDBB", "#C7E9B4"] * 5
output_server("animated_reveal")
p = figure(title="Animations", x_range=[-11, 11], y_range=[-11, 11])
p.annular_wedge(
0, 0, rmin, rmax, theta[:-1], theta[1:],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
)
push()
return p, cursession()
def __init__(self, name, fig_title, url):
"""
fig_title: Figure Title
url : str, optional
Url of the bokeh-server. Ex: when starting the bokeh-server with
``bokeh-server --ip 0.0.0.0`` at ``alice``, server_url should be
``http://alice:5006``. When not specified the default configured
by ``bokeh_server`` in ``.blocksrc`` will be used. Defaults to
``http://localhost:5006/``.
Reference: mila-udem/blocks-extras
"""
Callback.__init__(self)
self.name = name
self.fig_title = fig_title
self.plots = []
output_server(name, url=url)
cursession().publish()
def do(self, which_callback, *args):
log = self.main_loop.log
iteration = log.status["iterations_done"]
i = 0
for key, value in log.current_row.items():
if key in self.p_indices:
if key not in self.plots:
fig = self.p[self.p_indices[key]]
fig.line([iteration], [value], legend=key, name=key, line_color=self.colors[i % len(self.colors)])
i += 1
renderer = fig.select(dict(name=key))
self.plots[key] = renderer[0].data_source
else:
self.plots[key].data["x"].append(iteration)
self.plots[key].data["y"].append(value)
cursession().store_objects(self.plots[key])
push()
def bokeh_animated_colours(data_source):
"""
Creates a coloured map of London tube stations by zone where the size of the tube station changes
according to the number of people on the station
:param data_source:
:return:
"""
#create the map
data_file="/home/winterflower/programming_projects/python-londontube/src/data/londontubes.txt"
map=simulation_utils.Map(data_file)
map.initialise_map()
graph_object=map.graph_object
map=sim.initialise_commuters(map)
print "Please make sure the bokeh-server is running before you run the script"
output_server("bokeh_tube_stations")
TOOLS="resize,hover,save"
animated_figure=figure(title="London Underground", tools=TOOLS)
animated_figure.plot_height=1000
animated_figure.plot_width=1000
length=len(data_source.data["longitudes"])
########################################
#Get the sizes
#######################################
commuters=get_commuter_numbers(data_source.data["station_names"], map)
# scale the commuters
color=map_to_colours(commuters)
data_source.data["color"]=color
size=[10 for i in range(length)]
animated_figure.circle(data_source.data["longitudes"], data_source.data["latitudes"], size=data_source.data["size"], color=data_source.data["color"], alpha=0.8, name="circle")
show(animated_figure)
#obtain the glyph renderer
glyph_renderer=animated_figure.select((dict(name="circle")))
print glyph_renderer
figure_data_source=glyph_renderer[0].data_source
while True:
map=sim.simulate_one_cycle(map)
commuters=get_commuter_numbers(data_source.data["station_names"],map)
color=map_to_colours(commuters)
size=map_to_comuters(commuters)
figure_data_source.data["color"]=color
figure_data_source.data["size"]=size
cursession().store_objects(figure_data_source)
time.sleep(0.05)
def update_plots(self, epoch, monitors):
"""
Given the calculated monitors (collapsed name and value tuple), add its datapoint to the appropriate figure
and update the figure in bokeh-server.
Parameters
----------
epoch : int
The epoch (x-axis value in the figure).
monitors : dict
The dictionary of monitors calculated at this epoch. The dictionary is of the form
{collapsed_monitor_name: value}. The name is the same that was used in the creation of the
figures in the plot, so it is used as the key to finding the appropriate figure to add the
data.
"""
if BOKEH_AVAILABLE:
for key, value in monitors.items():
if key in self.figure_indices:
if key not in self.plots:
# grab the correct figure by its index for the key (same with the color)
fig = self.figures[self.figure_indices[key]]
color_idx = self.figure_color_indices[self.figure_indices[key]]
# split the channel from the monitor name
name = key.split(COLLAPSE_SEPARATOR, 1)
if len(name) > 1:
name = name[1]
else:
name = name[0]
# create a new line
fig.line([epoch], [value], legend=name,
x_axis_label='iterations',
y_axis_label='value', name=name,
line_color=self.colors[color_idx % len(self.colors)])
color_idx += 1
# set the color index back in the figure list
self.figure_color_indices[self.figure_indices[key]] = color_idx
# grab the render object and put it in the plots dictionary
renderer = fig.select(dict(name=name))
self.plots[key] = renderer[0].data_source
else:
self.plots[key].data['x'].append(epoch)
self.plots[key].data['y'].append(value)
cursession().store_objects(self.plots[key])
push()
def update_animation(plot, session):
from numpy import roll
renderer = plot.select(dict(type=GlyphRenderer))
ds = renderer[0].data_source
while True:
rmin = ds.data["inner_radius"]
rmin = roll(rmin, 1)
ds.data["inner_radius"] = rmin
rmax = ds.data["outer_radius"]
rmax = roll(rmax, -1)
ds.data["outer_radius"] = rmax
cursession().store_objects(ds)
time.sleep(0.1)
def update(self, svid, azimuth, elevation):
self.data[svid] = SkyCoordinates(azimuth, elevation)
azimuth = asarray([sky.azimuth for sky in self.data.values()]).flatten()
elevation = asarray([sky.elevation for sky in self.data.values()]).flatten()
r = 90 - elevation
theta = pi / 2 - radians(azimuth)
mask = r < 90
print(azimuth.shape, elevation.shape, npsum(mask))
r = r[mask]
theta = theta[mask]
x = r * cos(theta)
y = r * sin(theta)
print(x.shape, y.shape)
renderer = self.plot.select(dict(name='points'))[0]
renderer.data_source.data['x'] = x
renderer.data_source.data['y'] = y
cursession().store_objects(renderer.data_source)
def update(self):
i_corr_renderer = self.i_corr_plot.select(dict(name='i_corr'))[0]
q_corr_renderer = self.q_corr_plot.select(dict(name='q_corr'))[0]
phase_error_renderer = self.phase_error_plot.select(dict(name='phase_error'))[0]
delay_error_renderer = self.delay_error_plot.select(dict(name='delay_error'))[0]
i_corr_renderer.data_source.data['x'] = self.time.get()
i_corr_renderer.data_source.data['y'] = self.i_corr.get()
q_corr_renderer.data_source.data['x'] = self.time.get()
q_corr_renderer.data_source.data['y'] = self.q_corr.get()
phase_error_renderer.data_source.data['x'] = self.time.get()
phase_error_renderer.data_source.data['y'] = self.phase_error.get()
delay_error_renderer.data_source.data['x'] = self.time.get()
delay_error_renderer.data_source.data['y'] = self.delay_error.get()
q_corr_renderer.data_source._dirty = True
i_corr_renderer.data_source._dirty = True
phase_error_renderer.data_source._dirty = True
delay_error_renderer.data_source._dirty = True
cursession().store_objects(i_corr_renderer.data_source, q_corr_renderer.data_source, phase_error_renderer.data_source, delay_error_renderer.data_source)
def update_animation(plot, session):
from numpy import roll
renderer = plot.select(dict(type=GlyphRenderer))
ds = renderer[0].data_source
while True:
rmin = ds.data["inner_radius"]
rmin = roll(rmin, 1)
ds.data["inner_radius"] = rmin
rmax = ds.data["outer_radius"]
rmax = roll(rmax, -1)
ds.data["outer_radius"] = rmax
cursession().store_objects(ds)
time.sleep(0.1)
def call(self):
log = self.manager.main_loop.log
iteration = log.status.iterations_done
i = 0
for key, value in log.current_row:
if key in self.p_indices:
if key not in self.plots:
fig = self.p[self.p_indices[key]]
fig.line([iteration], [value], legend=key,
x_axis_label='iterations',
y_axis_label='value', name=key,
line_color=self.color_mapping[key])
i += 1
renderer = fig.select(dict(name=key))
self.plots[key] = renderer[0].data_source
else:
self.plots[key].data['x'].append(iteration)
self.plots[key].data['y'].append(value)
cursession().store_objects(self.plots[key])
def background_thread(ds, year_ds, month_ds, timeseries_ds):
"""Plot animation, update data if play is True, otherwise stop"""
try:
while True:
for year_index in np.arange(2000, 2015, 1):
year_ds.data["text"] = [str(year_index)]
for month_index in np.arange(1, 13, 1):
if should_play():
month_ds.data["text"] = [months_str[month_index-1]]
image = get_slice(t, year_index, month_index)
date = datetime.date(year_index, month_index, 1)
timeseries_ds.data["x"] = [date]
timeseries_ds.data["y"] = [df[df.datetime == date].moving_average]
ds.data["image"] = [image]
cursession().store_objects(ds, year_ds, month_ds, timeseries_ds)
time.sleep(0.5)
time.sleep(0.5)
except:
logger.exception("An error occurred")
raise
def bokeh_plotting(self, total):
if self.bokeh_server is not None:
x = range(len(total))
if not hasattr(self, 'fig'):
self.fig = bplt.figure(title='top.Optimizer')
self.fig.line(x,
total,
legend='cost',
x_axis_label='epoch number',
name='top_figure',
plot_width=100,
plot_height=100)
bplt.show(self.fig)
# Update cost function graph
renderer = self.fig.select(dict(name='top_figure'))
ds = renderer[0].data_source
ds.data['y'].append(total[-1])
ds.data['x'].append(ds.data['x'][-1] + 1)
bplt.cursession().store_objects(ds)
def on_selection_change(obj, attr, old, new):
inds = np.array(new)
if len(inds) == 0 or len(inds) == len(x):
hhist = hzeros
vhist = vzeros
hhist2 = hzeros
vhist2 = vzeros
else:
hhist, _ = np.histogram(x[inds], bins=hedges)
vhist, _ = np.histogram(y[inds], bins=vedges)
negative_inds = np.ones_like(x, dtype=np.bool)
negative_inds[inds] = False
hhist2, _ = np.histogram(x[negative_inds], bins=hedges)
vhist2, _ = np.histogram(y[negative_inds], bins=vedges)
ph_source.data["top"] = hhist
pv_source.data["right"] = vhist
ph_source2.data["top"] = -hhist2
pv_source2.data["right"] = -vhist2
cursession().store_objects(ph_source, pv_source, ph_source2, pv_source2)
def do(self, which_callback, *args):
log = self.main_loop.log
iteration = log.status['iterations_done']
i = 0
for key, value in log.current_row.items():
if key in self.p_indices:
if key not in self.plots:
fig = self.p[self.p_indices[key]]
fig.line([iteration], [value], legend=key,
x_axis_label='iterations',
y_axis_label='value', name=key,
line_color=self.colors[i % len(self.colors)])
i += 1
renderer = fig.select(dict(name=key))
self.plots[key] = renderer[0].data_source
else:
self.plots[key].data['x'].append(iteration)
self.plots[key].data['y'].append(value)
cursession().store_objects(self.plots[key])
push()
def draw_bokeh_plot(pop_mat,
grid_shape,
color_dict,
width=800,
uniq_id="no-id"):
#wykres na siatce heksagonalnej
output_server("stops2" + uniq_id)
p = figure(plot_width=width, title="STOPS")
p.ygrid.grid_line_color = "white"
p.xgrid.grid_line_color = "white"
m, n = grid_shape
beta = 0.5
alpha = beta / math.sqrt(3)
h = beta / math.sqrt(3)
points_x = []
points_y = []
y = 0
for i in range(n):
if i % 2 == 1:
x = -0.5
else:
x = -1
for j in range(m):
if i % 2 == 1:
x += 1
else:
x += 1
points = hexagon_points(x, y, alpha, beta)
points_x.append(points[0])
points_y.append(points[1])
y += 3 * h
colors, wektor = value_color(pop_mat, color_dict)
source = ColumnDataSource(data=dict(
points_x=points_x, points_y=points_y, wektor=wektor, colors=colors))
p.patches(xs="points_x",
ys="points_y",
source=source,
line_color="blue",
color="colors",
line_width=2,
fill_alpha=0.8)
hover = HoverTool(plot=p, tooltips=dict(wektor="@wektor"))
p.tools.append(hover)
push()
return p, cursession()
def on_selection_change(obj, attr, old, new):
inds = np.array(new)
if len(inds) == 0 or len(inds) == len(x):
hhist = hzeros
vhist = vzeros
hhist2 = hzeros
vhist2 = vzeros
else:
hhist, _ = np.histogram(x[inds], bins=hedges)
vhist, _ = np.histogram(y[inds], bins=vedges)
negative_inds = np.ones_like(x, dtype=np.bool)
negative_inds[inds] = False
hhist2, _ = np.histogram(x[negative_inds], bins=hedges)
vhist2, _ = np.histogram(y[negative_inds], bins=vedges)
ph_source.data["top"] = hhist
pv_source.data["right"] = vhist
ph_source2.data["top"] = -hhist2
pv_source2.data["right"] = -vhist2
cursession().store_objects(ph_source, pv_source, ph_source2, pv_source2)
def on_epoch_end(self, epoch, logs={}):
epoch = self.epochNo
self.x = np.append(self.x, epoch)
self.y = np.append(self.y, logs['val_loss'])
self.bx = np.append(self.bx, epoch)
self.by = np.append(self.by, self.loss.mean())
self.loss = np.array([])
self.cx = np.append(self.cx, epoch)
renderer = self.p.select(dict(name="line"))
ds = renderer[0].data_source
ds.data['x'] = self.x
ds.data['y'] = self.y
cursession().store_objects(ds)
# ds.push_notebook()
renderer = self.p.select(dict(name="batch_line"))
ds = renderer[0].data_source
ds.data['x'] = self.bx
ds.data['y'] = self.by
# ds.push_notebook()
cursession().store_objects(ds)
# if logs['val_loss'] < self.min_loss:
if self.predit_funct:
self.psnr = self.predit_funct(self.model, epoch)
print("psnr: {}".format(self.psnr))
self.psnrs = np.append(self.psnrs, self.psnr)
renderer = self.p2.select(dict(name="psnr"))
ds = renderer[0].data_source
ds.data['x'] = self.x
ds.data['y'] = self.psnrs
cursession().store_objects(ds)
self.min_loss = min(self.min_loss, logs['val_loss'])
self.epochNo += 1
def on_epoch_end(self, epoch, logs={}):
epoch = self.epochNo
self.x = np.append(self.x, epoch)
self.y = np.append(self.y, logs['val_loss'])
self.bx = np.append(self.bx, epoch)
self.by = np.append(self.by, self.loss.mean())
self.loss = np.array([])
self.cx = np.append(self.cx, epoch)
renderer = self.p.select(dict(name="line"))
ds = renderer[0].data_source
ds.data['x'] = self.x
ds.data['y'] = self.y
cursession().store_objects(ds)
# ds.push_notebook()
renderer = self.p.select(dict(name="batch_line"))
ds = renderer[0].data_source
ds.data['x'] = self.bx
ds.data['y'] = self.by
# ds.push_notebook()
cursession().store_objects(ds)
# if logs['val_loss'] < self.min_loss:
if self.predit_funct:
self.psnr = self.predit_funct(self.model, epoch)
print("psnr: {}".format(self.psnr))
self.psnrs = np.append(self.psnrs, self.psnr)
renderer = self.p2.select(dict(name="psnr"))
ds = renderer[0].data_source
ds.data['x'] = self.x
ds.data['y'] = self.psnrs
cursession().store_objects(ds)
self.min_loss = min(self.min_loss, logs['val_loss'])
self.epochNo += 1
def call(self):
log = self.manager.main_loop.log
iteration = log.status.iterations_done
i = 0
for key, value in log.current_row:
if key in self.p_indices:
if key not in self.plots:
fig = self.p[self.p_indices[key]]
fig.line([iteration], [value],
legend=key,
x_axis_label='iterations',
y_axis_label='value',
name=key,
line_color=self.color_mapping[key])
i += 1
renderer = fig.select(dict(name=key))
self.plots[key] = renderer[0].data_source
else:
self.plots[key].data['x'].append(iteration)
self.plots[key].data['y'].append(value)
cursession().store_objects(self.plots[key])
def __init__(self, mode = 'arduino',serialObject=None): #nof indicates number of json fields
self.mode = mode
self.modeset = True
if mode is 'arduino':
import serial
if serialObject is None:
n=0
while True:
try:
self.ser = serial.Serial(SERIAL_PORT_DEV + str(n), BAUDRATE)
break
except serial.serialutil.SerialException:
print 'Could not open any serial port on {0}{1}. Trying with {0}{2}'.format(SERIAL_PORT_DEV,n,n+1)
n += 1
else:
self.ser = serialObject
self.ser.open()
elif mode is 'rpi':
self.bmp, self.dht = BMP180Wrapper(), DHT22Wrapper()
self.data = {}
self.data["temp"], self.data["hum"], self.data["pres"], self.data["timenow"] = [],[],[],[]
self.lastdata = ''; self.lastjson = None
#charts
self.tempchart, self.preschart, self.humchart = MeteoStation.MeteoChart('temp', 'Temperature', 'Temperature in *C'), MeteoStation.MeteoChart('pres','Pressure', 'Pressure in hPa'), MeteoStation.MeteoChart('hum','Humidity', 'Humidity in %RH')
self.charts = [self.tempchart, self.preschart, self.humchart]
self.figs = [chart.fig for chart in self.charts]
self.show_all()
cursession().publish()
#pymongo integration
try:
self.db = client['meteo_db']
self.collection = self.db.colletion['meteo_data_collection']
except: #retry with object-style formalism
self.db = client.meteo_db
self.collection = self.db.collection.meteo_data_collection
def stream(name, width=800, height=500):
"""
Broken
"""
output_server(name)
p = figure(plot_width=width, plot_height=height)
p.line([1,2,3], [4,5,6], name='ex_line')
renderer = p.select({'name': 'ex_line'})
ds = renderer[0].data_source
while True:
x, y = yield
ds.data['x'] = x
ds.data['y'] = y
cursession().store_objects(ds)
show(p)
def background_thread(ds, year_ds, month_ds, timeseries_ds):
"""Plot animation, update data if play is True, otherwise stop"""
try:
while True:
for year_index in np.arange(2000, 2015, 1):
year_ds.data["text"] = [str(year_index)]
for month_index in np.arange(1, 13, 1):
if should_play():
month_ds.data["text"] = [months_str[month_index - 1]]
image = get_slice(t, year_index, month_index)
date = datetime.date(year_index, month_index, 1)
timeseries_ds.data["x"] = [date]
timeseries_ds.data["y"] = [
df[df.datetime == date].moving_average
]
ds.data["image"] = [image]
cursession().store_objects(ds, year_ds, month_ds,
timeseries_ds)
time.sleep(0.5)
time.sleep(0.5)
except:
logger.exception("An error occurred")
raise
def on_epoch_end(self, epoch, logs={}):
if not hasattr(self, 'fig'):
self.fig = figure(title=self.fig_title)
for i, v in enumerate(['loss', 'val_loss']):
if v == 'loss':
L = self.totals[v] / self.seen
else:
L = logs.get(v)
self.fig.line([epoch], [L], legend=v,
name=v, line_width=2,
line_color=self.colors[i % len(self.colors)])
renderer = self.fig.select({'name': v})
self.plots.append(renderer[0].data_source)
show(self.fig)
else:
for i, v in enumerate(['loss', 'val_loss']):
if v == 'loss':
L = self.totals[v] / self.seen
else:
L = logs.get(v)
self.plots[i].data['y'].append(L)
self.plots[i].data['x'].append(epoch)
cursession().store_objects(self.plots[i])
push()
def animated():
from numpy import pi, cos, sin, linspace, zeros_like
N = 50 + 1
r_base = 8
theta = linspace(0, 2 * pi, N)
r_x = linspace(0, 6 * pi, N - 1)
rmin = r_base - cos(r_x) - 1
rmax = r_base + sin(r_x) + 1
colors = [
"FFFFCC", "#C7E9B4", "#7FCDBB", "#41B6C4", "#2C7FB8", "#253494",
"#2C7FB8", "#41B6C4", "#7FCDBB", "#C7E9B4"
] * 5
cx = cy = zeros_like(rmin)
output_server("animated_reveal")
figure(title="Animations",
x_range=[-11, 11],
y_range=[-11, 11],
tools="pan,wheel_zoom,box_zoom,reset,previewsave")
hold()
annular_wedge(
cx,
cy,
rmin,
rmax,
theta[:-1],
theta[1:],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
)
return curplot(), cursession()
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu)**2 /
(2 * sigma**2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma**2))) / 2
output_server("distribution_reveal")
hold()
figure(title="Interactive plots",
tools="pan, wheel_zoom, box_zoom, reset, previewsave",
background_fill="#E5E5E5")
quad(top=hist,
bottom=np.zeros(len(hist)),
left=edges[:-1],
right=edges[1:],
fill_color="#333333",
line_color="#E5E5E5",
line_width=3)
# Use `line` renderers to display the PDF and CDF
line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
xgrid().grid_line_color = "white"
xgrid().grid_line_width = 3
ygrid().grid_line_color = "white"
ygrid().grid_line_width = 3
legend().orientation = "top_left"
return curplot(), cursession()
# set up callbacks
def on_selection_change(obj, attr, old, new):
inds = np.array(new)
if len(inds) == 0 or len(inds) == len(x):
hhist = hzeros
vhist = vzeros
hhist2 = hzeros
vhist2 = vzeros
else:
hhist, _ = np.histogram(x[inds], bins=hedges)
vhist, _ = np.histogram(y[inds], bins=vedges)
negative_inds = np.ones_like(x, dtype=np.bool)
negative_inds[inds] = False
hhist2, _ = np.histogram(x[negative_inds], bins=hedges)
vhist2, _ = np.histogram(y[negative_inds], bins=vedges)
ph_source.data["top"] = hhist
pv_source.data["right"] = vhist
ph_source2.data["top"] = -hhist2
pv_source2.data["right"] = -vhist2
cursession().store_objects(ph_source, pv_source, ph_source2, pv_source2)
scatter_ds.on_change('selected', on_selection_change)
layout = vbox(hbox(p, pv), hbox(ph, Paragraph()))
show(layout)
cursession().poll_document(curdoc(), 0.05)
p.xgrid.grid_line_color = 'white'
p.ygrid.grid_line_color = 'white'
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.minor_tick_line_color = None
p.yaxis.axis_label = 'Percent'
p.yaxis.axis_label_standoff = ylabel_standoff
p.xaxis.formatter = xformatter
p.yaxis.formatter = yformatter
p.xaxis.axis_label_standoff = 12
p.yaxis.major_label_text_font = 'courier'
p.xaxis.major_label_text_font = 'courier'
push()
ip = load(urlopen('http://jsonip.com'))['ip']
ssn = cursession()
ssn.publish()
tag = embed.autoload_server(p, ssn, public=True).replace('localhost', ip)
html = """
{%% extends "layout.html" %%}
{%% block bokeh %%}
%s
{%% endblock %%}
""" % tag
with open('templates/performance.html', 'w+') as f:
f.write(html)
renderer = p.select(dict(name='returns'))
ds_returns = renderer[0].data_source
config_root_logger()
# main()
from bokeh.plotting import figure, cursession, show, output_server
output_server("test doc")
# plot settings
plot = figure(title="Combined Streams")
plot.line([], [], color="blue", name='Sample Streaming Data 1')
plot.line([], [], color="red", name='Sample Streaming Data 2')
show(plot)
ds1 = plot.select(dict(name='Sample Streaming Data 1'))[0].data_source
ds2 = plot.select(dict(name='Sample Streaming Data 2'))[0].data_source
for i in range(100, 200):
# update data into ds
ds1.data["x"].append(i - 100)
ds1.data["y"].append(i)
cursession().store_objects(ds1)
for i in range(100, 400):
ds2.data["x"].append(i - 100)
ds2.data["y"].append(i - 20)
cursession().store_objects(ds2)
p.annular_wedge(
cx,
cy,
rmin,
rmax,
theta[:-1],
theta[1:],
inner_radius_units="data",
outer_radius_units="data",
fill_color=colors,
line_color="black",
)
show(p)
renderer = p.select(dict(type=GlyphRenderer))
ds = renderer[0].data_source
while True:
rmin = ds.data["inner_radius"]
rmin = roll(rmin, 1)
ds.data["inner_radius"] = rmin
rmax = ds.data["outer_radius"]
rmax = roll(rmax, -1)
ds.data["outer_radius"] = rmax
cursession().store_objects(ds)
time.sleep(.10)
