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 perf_std_discrete(ticks,
avgs,
stds,
legend=None,
std_width=0.3,
plot_width=1000,
plot_height=300,
color=BLUE,
alpha=1.0,
**kwargs):
plotting.rect(ticks,
avgs, [std_width for _ in stds],
2 * np.array(stds),
line_color=None,
fill_color=color,
fill_alpha=alpha * 0.5,
plot_width=plot_width,
plot_height=plot_height,
**kwargs)
plotting.hold(True)
plotting.line(ticks,
avgs,
line_color=color,
line_alpha=alpha,
legend=legend)
plotting.circle(ticks,
avgs,
line_color=None,
fill_color=color,
fill_alpha=alpha)
plotting.grid().grid_line_color = 'white'
plotting_axis()
plotting.hold(False)
def bokeh_plot(self):
import os
from bokeh.plotting import line
from bokeh.plotting import hold, figure, show, output_file
import numpy as np
import bokeh.embed as embed
figure()
hold()
for i in self.SensorDict:
line(
self.SensorDict[i]['dates'], self.SensorDict[i]['values']
)
print(len(self.SensorDict[i]['dates']))
#print(self.SensorDict[i]['dates'][0])
#print(self.SensorDict[i]['values'][0])
print('tjo')
os.chdir('..')
os.chdir('..')
output_file('plot.html', title='Plot22')
show()
def get_plottag(script_path):
""" Saves js in script_path and returns tag for embedding in html
"""
import numpy as np
import pandas as pd
import os
y_data = np.random.randn(100)
x_data = pd.date_range('31-Aug-2014', periods=len(y_data))
figure(x_axis_type='datetime',
tools='pan,wheel_zoom,box_zoom,reset,previewsave,crosshair',
name='test plot')
hold()
line(x_data, y_data,
line_color="#D95B43", line_width=4, alpha=0.7,
legend='random',
background_fill= '#cccccc')
circle(x_data, y_data,
color='red', fill_color=None, size=6,
legend='random')
curplot().title = 'Test Plot'
xaxis().axis_label='date'
yaxis().axis_label='some random numbers'
grid().grid_line_color='white'
grid().grid_line_alpha = 0.5
plotid = curplot()._id
script_path = os.path.join(script_path, plotid+'.js')
js, tag = autoload_static(curplot(), CDN, script_path=script_path)
with open(script_path, 'w') as f:
f.write(js)
return tag
def bokeh_quantiles(ticks,
avgs,
quantiles,
color='#000055',
alpha=1.0,
**kwargs):
plotting.line(ticks,
avgs,
color=color,
line_alpha=alpha,
title_text_font_size='6pt',
**kwargs)
plotting.hold(True)
if quantiles is not None:
print(quantiles)
x_std = list(ticks) + list(reversed(ticks))
y_std = ([q_min for q_min, q_max in quantiles] +
list(reversed([q_max for q_min, q_max in quantiles])))
plotting.patch(x_std,
y_std,
fill_color=color,
fill_alpha=alpha * 0.25,
line_color=None)
plotting.grid().grid_line_color = 'white'
plotting_axis()
plotting.hold(False)
def posture_signals(kin_env,
m_signals,
title='posture graphs',
color='#666666',
alpha=1.0,
radius_factor=1.0,
swap_xy=True,
x_range=[-1.0, 1.0],
y_range=[-1.0, 1.0],
**kwargs):
for m_signal in m_signals:
m_vector = kin_env.flatten_synergies(m_signal)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal['x{}'.format(i + 1)])
ys.append(s_signal['y{}'.format(i + 1)])
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths * kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs.update({
'x_range': x_range,
'y_range': y_range,
'line_color': color,
'line_alpha': alpha,
'fill_color': color,
'fill_alpha': alpha,
'title': title
})
if swap_xy:
xs, ys = ys, xs
plotting.line(xs, ys, line_width=2.0 * radius_factor, **kwargs)
plotting.hold(True)
plotting.grid().grid_line_color = None
plotting.ygrid().grid_line_color = None
plotting_axis()
plotting.circle(xs[:1], ys[:1], radius=radius_factor * 0.015, **kwargs)
plotting.circle(xs[1:-1],
ys[1:-1],
radius=radius_factor * 0.008,
**kwargs)
plotting.circle(xs[-1:],
ys[-1:],
radius=radius_factor * 0.01,
color='red',
alpha=alpha)
plotting.hold(False)
def build_timeplot(data):
output_file('distribution.html', title='distribution')#, js="relative", css="relative")
x,y=[],[]
if len(data)>0:
x,y=zip(*[(k, data[k]) for k in sorted(data)])
line([pd.DatetimeIndex([dt])[0] for dt in x] , [int(yt) for yt in y],x_axis_type='datetime', tools="pan,zoom,resize", width=400,height=300, title = 'voorkomens')
xaxis()[0].axis_label = "Datum"
yaxis()[0].axis_label = "# artikels"
# Create an HTML snippet of our plot.
snippet = curplot().create_html_snippet(embed_base_url='/static/plots/', embed_save_loc=plotdir)
# Return the snippet we want to place in our page.
return snippet
def posture_signals(
kin_env,
m_signals,
title="posture graphs",
color="#666666",
alpha=1.0,
radius_factor=1.0,
swap_xy=True,
x_range=[-1.0, 1.0],
y_range=[-1.0, 1.0],
**kwargs
):
for m_signal in m_signals:
m_vector = kin_env.flatten_synergies(m_signal)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal["x{}".format(i + 1)])
ys.append(s_signal["y{}".format(i + 1)])
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths * kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs.update(
{
"x_range": x_range,
"y_range": y_range,
"line_color": color,
"line_alpha": alpha,
"fill_color": color,
"fill_alpha": alpha,
"title": title,
}
)
if swap_xy:
xs, ys = ys, xs
plotting.line(xs, ys, line_width=2.0 * radius_factor, **kwargs)
plotting.hold(True)
plotting.grid().grid_line_color = None
plotting.ygrid().grid_line_color = None
plotting_axis()
plotting.circle(xs[:1], ys[:1], radius=radius_factor * 0.015, **kwargs)
plotting.circle(xs[1:-1], ys[1:-1], radius=radius_factor * 0.008, **kwargs)
plotting.circle(xs[-1:], ys[-1:], radius=radius_factor * 0.01, color="red", alpha=alpha)
plotting.hold(False)
def line(x_range, avg, std, color='#E84A5F', dashes=(4, 2), alpha=1.0):
plotting.line(x_range, [avg, avg],
line_color=color,
line_dash=list(dashes),
line_alpha=alpha)
plotting.hold(True)
plotting.rect([(x_range[0] + x_range[1]) / 2.0], [avg],
[x_range[1] - x_range[0]], [2 * std],
fill_color=color,
line_color=None,
fill_alpha=0.1 * alpha)
plotting.hold(False)
def perf_std_discrete(ticks, avgs, stds, legend=None,
std_width=0.3, plot_width=1000, plot_height=300,
color=BLUE, alpha=1.0, **kwargs):
plotting.rect(ticks, avgs, [std_width for _ in stds], 2*np.array(stds),
line_color=None, fill_color=color, fill_alpha=alpha*0.5,
plot_width=plot_width, plot_height=plot_height, **kwargs)
plotting.hold(True)
plotting.line(ticks, avgs, line_color=color, line_alpha=alpha, legend=legend)
plotting.circle(ticks, avgs, line_color=None, fill_color=color, fill_alpha=alpha)
plotting.grid().grid_line_color = 'white'
plotting_axis()
plotting.hold(False)
def line(x_range, avg, std, color="#E84A5F", dashes=(4, 2), alpha=1.0):
plotting.line(x_range, [avg, avg], line_color=color, line_dash=list(dashes), line_alpha=alpha)
plotting.hold(True)
plotting.rect(
[(x_range[0] + x_range[1]) / 2.0],
[avg],
[x_range[1] - x_range[0]],
[2 * std],
fill_color=color,
line_color=None,
fill_alpha=0.1 * alpha,
)
plotting.hold(False)
def bokeh_quantiles(ticks, avgs, quantiles, color="#000055", alpha=1.0, **kwargs):
plotting.line(ticks, avgs, color=color, line_alpha=alpha, title_text_font_size="6pt", **kwargs)
plotting.hold(True)
if quantiles is not None:
print(quantiles)
x_std = list(ticks) + list(reversed(ticks))
y_std = [q_min for q_min, q_max in quantiles] + list(reversed([q_max for q_min, q_max in quantiles]))
plotting.patch(x_std, y_std, fill_color=color, fill_alpha=alpha * 0.25, line_color=None)
plotting.grid().grid_line_color = "white"
plotting_axis()
plotting.hold(False)
def tweetsGraph():
logger.info("Drawing graphs to %s" % path_to_graphs+"Stats.html")
stat_db_cursor.execute('SELECT * FROM tweets')
tweets = stat_db_cursor.fetchall()
date, volume, cumulative, volumePast24h = zip(*[(datetime.datetime.strptime(t['date'], "%Y-%m-%dT%H"), t['current_hour'], t['cumulative'], t['past_24h']) for t in tweets])
hourly =zip([datetime.datetime(year=d.year, month=d.month, day=d.day) for d in date],volume)
hourly.sort()
days, dailyVolume = zip(*[(d, sum([v[1] for v in vol])) for d,vol in itertools.groupby(hourly, lambda i:i[0])])
bokeh_plt.output_file(path_to_graphs+"Stats.html")
bokeh_plt.hold()
bokeh_plt.quad(days, [d+datetime.timedelta(days=1) for d in days], dailyVolume, [0]*len(dailyVolume), x_axis_type="datetime", color='gray', legend="Daily volume")
bokeh_plt.line(date, volume, x_axis_type="datetime", color='red', legend="Hourly volume")
bokeh_plt.line(date, volumePast24h, x_axis_type="datetime", color='green', legend="Volume in the past 24 hours")
bokeh_plt.curplot().title = "Volume"
bokeh_plt.figure()
bokeh_plt.line(date, cumulative, x_axis_type="datetime")
bokeh_plt.curplot().title = "Cumulative volume"
fig, ax = matplotlib_plt.subplots()
f=DateFormatter("%Y-%m-%d")
ax.xaxis.set_major_formatter(f)
matplotlib_plt.plot(date, volume)
matplotlib_plt.plot(date, volumePast24h)
matplotlib_plt.plot(days, dailyVolume)
matplotlib_plt.xticks(np.concatenate((np.array(date)[range(0,len(date),24*7)],[date[-1]])), rotation=70)
matplotlib_plt.savefig(path_to_graphs+"volume.png", bbox_inches="tight")
stat_db_cursor.execute('SELECT * FROM users')
users = stat_db_cursor.fetchall()
date, nUsers, nUsersWithFriends = zip(*[(datetime.datetime.strptime(u['date'], "%Y-%m-%dT%H:%M:%S.%f"), u['total'], u['with_friends']) for u in users])
bokeh_plt.figure()
bokeh_plt.line(date, nUsers, x_axis_type="datetime", legend="Total")
bokeh_plt.line(date, nUsersWithFriends, x_axis_type="datetime", legend="Friendship collected")
bokeh_plt.legend().orientation = "top_left"
bokeh_plt.curplot().title = "Number of users"
bokeh_plt.save()
matplotlib_plt.figure()
fig, ax = matplotlib_plt.subplots()
f=DateFormatter("%Y-%m-%d")
ax.xaxis.set_major_formatter(f)
matplotlib_plt.plot(date, nUsers)
matplotlib_plt.plot(date, nUsersWithFriends)
matplotlib_plt.xticks(np.concatenate((np.array(date)[range(0,len(date),24*7)],[date[-1]])), rotation=70)
matplotlib_plt.savefig(path_to_graphs+"users.png", bbox_inches="tight")
def count_by_day_graph(collection_name):
# TODO: выделить на графике выходные дни
import numpy as np
from bokeh.plotting import output_file, hold, figure, line, curplot, grid, show
a = count_by_day(collection_name)
output_file("output/count_by_day_graph.html", title="count_by_day_graph")
hold()
figure(x_axis_type="datetime", tools="pan,wheel_zoom,box_zoom,reset,previewsave", plot_width=1800)
line(np.array(a["date"], 'M64'), a['count'], color='#A6CEE3', legend='Количество статей')
line(np.array(a["date"], 'M64'), [averange_count(collection_name)] * len(a["date"]), color='#ff0000', legend='Среднее количество статей')
curplot().title = "Количество статей по дням"
grid().grid_line_alpha=0.3
show()
def getHTML(self,params): df = self.getData(params) # get data bokeh_plot = line(df['Date'],df['close'], color='#1c2980', legend="close", x_axis_type = "datetime", title=self.company_name) bokeh_plot.line(df['Date'],df['high'], color='#80641c', legend="high") bokeh_plot.line(df['Date'],df['low'], color='#80321c', legend="low") script, div = components(bokeh_plot, CDN) html = "%s\n%s"%(script, div) return html
def create(cls):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
obj = cls()
obj.source = ColumnDataSource(data=dict(x=[], y=[]))
obj.text = TextInput(title="title", name='title', value='my sin wave')
obj.offset = Slider(title="offset",
name='offset',
value=0.0,
start=-5.0,
end=5.0,
step=0.1)
obj.amplitude = Slider(title="amplitude",
name='amplitude',
value=1.0,
start=-5.0,
end=5.0)
obj.phase = Slider(title="phase",
name='phase',
value=0.0,
start=0.0,
end=2 * np.pi)
obj.freq = Slider(title="frequency",
name='frequency',
value=1.0,
start=0.1,
end=5.1)
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
obj.plot = line('x',
'y',
tools=toolset,
source=obj.source,
plot_width=400,
plot_height=400,
line_width=3,
line_alpha=0.6,
title=obj.text.value,
x_range=[0, 4 * np.pi],
y_range=[-2.5, 2.5])
obj.update_data()
obj.inputs = VBoxForm(children=[
obj.text, obj.offset, obj.amplitude, obj.phase, obj.freq
])
obj.children.append(obj.inputs)
obj.children.append(obj.plot)
return obj
def build_scatter_tooltip(x, y, tt, add_line=True, radius=3, title='My Plot',
xlabel='Iteration number', ylabel='Score'):
bk.figure(title=title)
bk.hold()
bk.circle(
x, y, radius=radius, source=ColumnDataSource(tt),
fill_alpha=0.6, line_color=None, tools=TOOLS)
if add_line:
bk.line(x, y, line_width=2)
xax, yax = bk.axis()
xax.axis_label = xlabel
yax.axis_label = ylabel
cp = bk.curplot()
hover = cp.select(dict(type=HoverTool))
format_tt = [(s, '@%s' % s) for s in tt.columns]
hover.tooltips = OrderedDict([("index", "$index")] + format_tt)
def make_object():
source = ServerDataSource(data_url="IonXpress_004_rawlib.sam",
owner_username="******",
transform={'resample' : 'genomic_coverage',
'primary_column' : 'coverage',
'domain_name' : 'chr1'},
data={})
x_range = Range1d(start=0, end=249250621)
plot = line('chr1', 'counts', tools="pan,wheel_zoom,box_zoom,reset,previewsave",
source=source, x_range=x_range)
return plot
def bokeh_kin(kin_env, m_signal, color='#DF4949', alpha=1.0, **kwargs):
m_vector = tools.to_vector(m_signal, kin_env.m_channels)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal['x{}'.format(i + 1)])
ys.append(s_signal['y{}'.format(i + 1)])
xs, ys = ys, xs # we swap x and y for a more symmetrical look
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths * kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs = {
'plot_height': int(350 * 1.60),
'plot_width': int(350 * 1.60),
'x_range': [-1.0, 1.0],
'y_range': [-1.0, 1.0],
'line_color': color,
'line_alpha': alpha,
'fill_color': color,
'fill_alpha': alpha,
'title': ''
}
plotting.hold()
plotting.line(xs, ys, **kwargs)
plotting.grid().grid_line_color = None
plotting.xaxis().major_tick_in = 0
plotting.ygrid().grid_line_color = None
plotting.yaxis().major_tick_in = 0
plotting.circle(xs[:1], ys[:1], radius=0.015, **kwargs)
plotting.circle(xs[1:-1], ys[1:-1], radius=0.008, **kwargs)
plotting.circle(xs[-1:], ys[-1:], radius=0.01, color='red')
plotting.hold(False)
def make_object():
source = ServerDataSource(data_url="IonXpress_004_rawlib.sam",
owner_username="******",
transform={'resample' : 'genomic_coverage',
'primary_column' : 'coverage',
'domain_name' : 'chr5'},
data={})
x_range = Range1d(start=0, end=249250621)
plot1 = line('chr5', 'counts', tools="pan,wheel_zoom,box_zoom,reset,previewsave",
plot_width=800, plot_height=350,
source=source, x_range=x_range)
plot2 = make_plot(x_range)
return VBox(children=[plot1, plot2])
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 getHTML(self,params): df = self.getData(params) # get data try: bokeh_plot = plotting.line(df['Date'],df['close'], color='#1c2980', legend="close", x_axis_type = "datetime", title=self.company_name) except AttributeError: bokeh_plot = plotting.figure(x_axis_type='datetime', title=self.company_name) bokeh_plot.line(df['Date'],df['close'], color='#1c2980', legend="close") bokeh_plot.line(df['Date'],df['high'], color='#80641c', legend="high") bokeh_plot.line(df['Date'],df['low'], color='#80321c', legend="low") script, div = components(bokeh_plot, CDN) html = "%s\n%s"%(script, div) return html
def make_test_plot():
import numpy as np
from bokeh.plotting import output_server, line
N = 8000
x = np.linspace(0, 4 * np.pi, N)
y = np.sin(x)
output_server("line.py example")
l = line(x, y, color="#0000FF", plot_height=300, plot_width=300, tools="pan,resize")
return l
def bokeh_kin(kin_env, m_signal, color='#DF4949', alpha=1.0, **kwargs):
m_vector = tools.to_vector(m_signal, kin_env.m_channels)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal['x{}'.format(i+1)])
ys.append(s_signal['y{}'.format(i+1)])
xs, ys = ys, xs # we swap x and y for a more symmetrical look
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths*kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs ={'plot_height' : int(350*1.60),
'plot_width' : int(350*1.60),
'x_range' : [-1.0, 1.0],
'y_range' : [-1.0, 1.0],
'line_color' : color,
'line_alpha' : alpha,
'fill_color' : color,
'fill_alpha' : alpha,
'title':''
}
plotting.hold()
plotting.line(xs, ys, **kwargs)
plotting.grid().grid_line_color = None
plotting.xaxis().major_tick_in = 0
plotting.ygrid().grid_line_color = None
plotting.yaxis().major_tick_in = 0
plotting.circle(xs[ : 1], ys[ : 1], radius=0.015, **kwargs)
plotting.circle(xs[ 1:-1], ys[ 1:-1], radius=0.008, **kwargs)
plotting.circle(xs[-1: ], ys[-1: ], radius=0.01, color='red')
plotting.hold(False)
def create(cls):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
obj = cls()
obj.source = ColumnDataSource(data=dict(x=[], y=[]))
obj.text = TextInput(
title="title", name='title', value='my sin wave'
)
obj.offset = Slider(
title="offset", name='offset',
value=0.0, start=-5.0, end=5.0
)
obj.amplitude = Slider(
title="amplitude", name='amplitude',
value=1.0, start=-5.0, end=5.0
)
obj.phase = Slider(
title="phase", name='phase',
value=0.0, start=0.0, end=2*np.pi
)
obj.freq = Slider(
title="frequency", name='frequency',
value=1.0, start=0.1, end=5.1
)
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
obj.plot = line('x', 'y', tools=toolset, source=obj.source,
plot_width=400, plot_height=400,
line_width=3, line_alpha=0.6,
title=obj.text.value,
x_range=[0, 4*np.pi], y_range=[-2.5, 2.5]
)
obj.update_data()
obj.inputs = VBoxForm(
children=[
obj.text, obj.offset, obj.amplitude, obj.phase, obj.freq
]
)
obj.children.append(obj.inputs)
obj.children.append(obj.plot)
return obj
def perf_astd(ticks,
avgs,
astds,
color=BLUE,
alpha=1.0,
sem=1.0,
plot_width=1000,
plot_height=500,
**kwargs):
plotting.line(ticks,
avgs,
color=color,
line_alpha=alpha,
title_text_font_size='6pt',
plot_width=plot_width,
plot_height=plot_height,
**kwargs)
plotting.hold(True)
if astds is not None:
x_std = list(ticks) + list(reversed(ticks))
y_std = ([
a - s_min / math.sqrt(sem)
for a, (s_min, s_max) in zip(avgs, astds)
] + list(
reversed([
a + s_max / math.sqrt(sem)
for a, (s_min, s_max) in zip(avgs, astds)
])))
plotting.patch(x_std,
y_std,
fill_color=color,
fill_alpha=alpha * 0.25,
line_color=None)
plotting.grid().grid_line_color = 'white'
plotting_axis()
plotting.hold(False)
def perf_astd(ticks, avgs, astds, color=BLUE, alpha=1.0, sem=1.0, plot_width=1000, plot_height=500, **kwargs):
plotting.line(
ticks,
avgs,
color=color,
line_alpha=alpha,
title_text_font_size="6pt",
plot_width=plot_width,
plot_height=plot_height,
**kwargs
)
plotting.hold(True)
if astds is not None:
x_std = list(ticks) + list(reversed(ticks))
y_std = [a - s_min / math.sqrt(sem) for a, (s_min, s_max) in zip(avgs, astds)] + list(
reversed([a + s_max / math.sqrt(sem) for a, (s_min, s_max) in zip(avgs, astds)])
)
plotting.patch(x_std, y_std, fill_color=color, fill_alpha=alpha * 0.25, line_color=None)
plotting.grid().grid_line_color = "white"
plotting_axis()
plotting.hold(False)
def get(self, request, *args, **kwargs):
return self.render_to_json_response({'plot': u''})
# measurement_type_pk = kwargs.get('measurement_type_pk')
# measurement_type = MeasurementType.objects.get(pk=measurement_type_pk)
collection = Collection.objects.get(pk=kwargs.get('pk'))
units = Unit.objects.filter(measurements__match={'measurement_type': measurement_type.pk, 'active': True},
pk__in=[unit.pk for unit in collection.units], active=True)
if units:
bk.hold()
bk.figure(x_axis_type="datetime", tools="pan,wheel_zoom,box_zoom,reset,previewsave")
colors = self.get_colors(len(units))
for i, unit in enumerate(units):
measurements = [(measurement.created_at, measurement.value) for measurement in unit.measurements
if measurement.active and measurement.measurement_type == measurement_type]
measurements.sort(key=lambda measurement: measurement[0])
data = {
'date': [measurement[0] for measurement in measurements],
'value': [measurement[1] for measurement in measurements]
}
bk.line(np.array(data['date']), data['value'], color=colors[i], line_width=2, legend=unit.__unicode__())
bk.grid().grid_line_alpha = 0.3
xax, yax = bk.axis()
xax.axis_label = ugettext('Date')
yax.axis_label = ugettext('Values')
plot = bk.curplot()
bk.get_default_color()
plot.title = ugettext('Measurements type')
# plot.title = ugettext('Measurements type {}'.format(measurement_type.__unicode__()))
js, tag = autoload_static(plot, Resources(mode='server', root_url=settings.STATIC_URL), "")
return self.render_to_json_response({'plot': u'{}<script>{}</script>'.format(tag, js)})
return self.render_to_json_response(ugettext('Not found'), status=404)
def addData(self,
xvals, yvals,
xlabel, ylabel,
nplotted,
yerrors=None):
xxvals, yyvals = Stats.filterNone((xvals, yvals))
color, linestyle, marker = self.getFormat(nplotted)
self.plots.append(bk.line(
xxvals,
yyvals,
color=color,
line_width=2))
def create(self, doc):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
self.modelform = MyModel()
self.modelform.create_inputs(doc)
self.source = ColumnDataSource(data={'x':[], 'y':[]})
self.update_data()
self.plot = line('x', 'y', source=self.source,
plot_width=400, plot_height=400,
title=self.modelform.title
)
self.children.append(self.modelform)
self.children.append(self.plot)
def make_test_plot():
import numpy as np
from bokeh.plotting import output_server, line
N = 8000
x = np.linspace(0, 4*np.pi, N)
y = np.sin(x)
output_server("line.py example")
l = line(
x,y, color="#0000FF",
plot_height=300, plot_width=300,
tools="pan,resize")
return l
def create(self, doc):
"""
This function is called once, and is responsible for
creating all objects (plots, datasources, etc)
"""
self.modelform = MyModel()
self.modelform.create_inputs(doc)
self.source = ColumnDataSource(data={'x': [], 'y': []})
self.update_data()
self.plot = line('x',
'y',
source=self.source,
plot_width=400,
plot_height=400,
title=self.modelform.title)
self.children.append(self.modelform)
self.children.append(self.plot)
def getHTML(self, params):
ticker = params['ticker']
if ticker == 'empty':
ticker = params['custom_ticker'].upper()
df = self.getData(params) # get data
try:
bokeh_plot = plotting.line(
df.index, df['Close'], color='#1c2980',
legend="Close", x_axis_type="datetime", title=ticker
)
except AttributeError:
bokeh_plot = plotting.figure(x_axis_type='datetime', title=ticker)
bokeh_plot.line(df.index, df['Close'], color='#1c2980', legend="Close")
bokeh_plot.line(df.index, df['High'], color='#80641c', legend="High")
bokeh_plot.line(df.index, df['Low'], color='#80321c', legend="Low")
script, div = components(bokeh_plot, CDN)
html = "%s\n%s" % (script, div)
return html
def make_object():
source = ServerDataSource(data_url="IonXpress_004_rawlib.sam",
owner_username="******",
transform={
'resample': 'genomic_coverage',
'primary_column': 'coverage',
'domain_name': 'chr5'
},
data={})
x_range = Range1d(start=0, end=249250621)
plot1 = line('chr5',
'counts',
tools="pan,wheel_zoom,box_zoom,reset,previewsave",
plot_width=800,
plot_height=350,
source=source,
x_range=x_range)
plot2 = make_plot(x_range)
return VBox(children=[plot1, plot2])
def getHTML(self, params):
ticker = params['ticker']
if ticker == 'empty':
ticker = params['custom_ticker'].upper()
df = self.getData(params) # get data
try:
bokeh_plot = plotting.line(df.index,
df['Close'],
color='#1c2980',
legend="Close",
x_axis_type="datetime",
title=ticker)
except AttributeError:
bokeh_plot = plotting.figure(x_axis_type='datetime', title=ticker)
bokeh_plot.line(df.index,
df['Close'],
color='#1c2980',
legend="Close")
bokeh_plot.line(df.index, df['High'], color='#80641c', legend="High")
bokeh_plot.line(df.index, df['Low'], color='#80321c', legend="Low")
script, div = components(bokeh_plot, CDN)
html = "%s\n%s" % (script, div)
return html
def parse_line(line):
"""Find sequence and rtt -- return as int, float or return None"""
regex = re.compile(r'icmp_seq=(?P<seq>[0-9]+) .* time=(?P<time>[0-9.]+) ms')
m = regex.search(line)
if not m:
return None
return int(m.groupdict()["seq"]), float(m.groupdict()["time"])
# Use a deque to limit to 100 o entries
x_store = deque(maxlen=MAX_HISTORY)
y_store = deque(maxlen=MAX_HISTORY)
# Unfortuantely, deques can't be serialized -- change to np array
x = list(x_store)
y = list(y_store)
line(x,y, tools="", title = "Ping google.com")
xaxis()[0].axis_label = "Sequence Number"
yaxis()[0].axis_label = "RTT in ms"
save()
renderer = [r for r in curplot().renderers if isinstance(r, GlyphRenderer)][0]
ds = renderer.data_source
f = tail_generator()
for line_ in f:
data = parse_line(line_)
if data is None:
continue
TODO: Replace synthetic data with data streaming from a .csv file from different sensors.
"""
import numpy as np
import bokeh.plotting as bk
from bokeh.objects import Glyph
import time
t = 0
time_x = np.array([0])
pres_y = np.array([20])
bk.output_server('Pressure')
bk.line(time_x, pres_y, color='#0000FF',
tools='pan,wheel_zoom,box_zoom,reset,resize,crosshair,select,previewsave,embed',
width=1200,height=300)
bk.xaxis()[0].axis_label = 'Time'
bk.yaxis()[0].axis_label = 'Pressure'
renderer = [r for r in bk.curplot().renderers if isinstance(r, Glyph)][0]
ds = renderer.data_source
while True:
ds.data["x"] = time_x
ds.data["y"] = pres_y
ds._dirty = True
bk.session().store_obj(ds)
time.sleep(1)
import numpy as np
import bokeh.plotting as bk
from bokeh.objects import Glyph
import time
t = 0
time_x = np.array([0])
pres_y = np.array([20])
bk.output_server('Pressure')
bk.line(
time_x,
pres_y,
color='#0000FF',
tools=
'pan,wheel_zoom,box_zoom,reset,resize,crosshair,select,previewsave,embed',
width=1200,
height=300)
bk.xaxis()[0].axis_label = 'Time'
bk.yaxis()[0].axis_label = 'Pressure'
renderer = [r for r in bk.curplot().renderers if isinstance(r, Glyph)][0]
ds = renderer.data_source
while True:
ds.data["x"] = time_x
ds.data["y"] = pres_y
ds._dirty = True
bk.session().store_obj(ds)
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load deploys.csv into a numpy array
deploydata = np.loadtxt('sfr_dep.dat',delimiter=" ")
# provide handles for the x and y columns
time = deploydata[:,0]
deploys = deploydata[:,1]
lwrs = []
for v in deploydata[:,1] :
lwrs.append(100-int(v/3))
bp.output_file("sfr.html", title="bokeh_sfr.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time, deploys, x_axis_label="Time (m)", y_axis_label="Reactors (#)",
color='#1F78B4', legend='SFRs')
bp.hold()
bp.line(time, lwrs, x_axis_label="Time (m)", y_axis_label="Reactors (#)",
color="green", legend='LWRs')
bp.curplot().title = "SFRs"
bp.grid().grid_line_alpha=0.3
bp.show() # open a browser
# provide handles for the x and y columns
time = deploydata[:, 0]
deploys = deploydata[:, 1]
lwrs = []
for v in deploydata[:, 1]:
lwrs.append(100 - int(v / 3))
bp.output_file("sfr.html", title="bokeh_sfr.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time,
deploys,
x_axis_label="Time (m)",
y_axis_label="Reactors (#)",
color='#1F78B4',
legend='SFRs')
bp.hold()
bp.line(time,
lwrs,
x_axis_label="Time (m)",
y_axis_label="Reactors (#)",
color="green",
legend='LWRs')
bp.curplot().title = "SFRs"
bp.grid().grid_line_alpha = 0.3
def make_spectrogram():
plot_kw = dict(
tools="", min_border=1, h_symmetry=False, v_symmetry=False, toolbar_location=None
)
freq = VBox(
children=[
Paragraph(text="Freq Range"),
Slider(orientation="vertical", start=1, end=MAX_FREQ, value=MAX_FREQ, step=1, name="freq")
]
)
gain = VBox(
children=[
Paragraph(text="Gain"),
Slider(orientation="vertical", start=1, end=20, value=1, step=1, name="gain")
]
)
spec_source = ColumnDataSource(data=dict(image=[], x=[]))
spec = image_rgba(
x='x', y=0, image='image', dw=TILE_WIDTH, dh=MAX_FREQ,
cols=TILE_WIDTH, rows=SPECTROGRAM_LENGTH, title=None,
source=spec_source, plot_width=800, plot_height=300,
x_range=[0, NGRAMS], y_range=[0, MAX_FREQ],
dilate=True, name="spectrogram", **plot_kw)
spectrum_source = ColumnDataSource(data=dict(x=[], y=[]))
spectrum = line(
x="x", y="y", line_color="darkblue", title="Power Spectrum",
source=spectrum_source, plot_width=800, plot_height=250,
x_range=[0, MAX_FREQ], y_range=[10**(-4), 10**3], y_axis_type="log",
name="spectrum", **plot_kw)
signal_source = ColumnDataSource(data=dict(x=[], y=[]))
signal = line(
x="x", y="y", line_color="darkblue", title="Signal",
source=signal_source, plot_width=800, plot_height=250,
x_range=[0, TIMESLICE*1.01], y_range=[-0.1, 0.1],
name="signal", **plot_kw)
radial_source = ColumnDataSource(data=dict(
inner_radius=[], outer_radius=[], start_angle=[], end_angle=[], fill_alpha=[],
))
eq = annular_wedge(
x=0, y=0, fill_color="#688AB9", fill_alpha="fill_alpha", line_color=None,
inner_radius="inner_radius", outer_radius="outer_radius",
start_angle="start_angle", end_angle="end_angle", title=None,
source=radial_source, plot_width=500, plot_height=520,
x_range=[-20, 20], y_range=[-20, 20],
name="eq", **plot_kw)
grid().grid_line_color=None
lines = VBox(
children=[spectrum, signal]
)
layout = VBox(
children = [
HBox(children=[freq, gain, spec]),
HBox(children=[lines, eq])
]
)
return layout
color=data["Survived"].map(colormap),alpha=0.8,
xlabel="Fare")
pl.show()
if __name__=="__main__":
data = loadData("train.csv")
data,label = buildDataset(data,False)
data_to_plot = data["Age"]
data_to_plot = data_to_plot[-np.isnan(data_to_plot)]
hist,edges = np.histogram(data_to_plot,bins=20)
pl.output_file('histogram.html')
pl.figure()
pl.hold()
pl.quad(top=hist,bottom=0,left=edges[:-1],right=edges[1:],
fill_color="#036564", line_color="#033649",
title="Age distribution",xlabel="Age",ylabel="Number")
pl.line(np.average([edges[1:],edges[:-1]],axis=0),hist,
line_width=5,color="red",alpha=0.8)
pl.show()
import numpy as np
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load decays.csv into a numpy array
decaydata = np.loadtxt('decays.csv',delimiter=",",skiprows=1)
# provide handles for the x and y columns
time = decaydata[:,0]
decays = decaydata[:,1]
bp.output_file("decays.html", title="bokeh_decay.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time, decays, x_axis_label="Time (s)", y_axis_label="Decays (#)",
color='#1F78B4', legend='Decays per second')
bp.curplot().title = "Decays"
bp.grid().grid_line_alpha=0.3
bp.show() # open a browser
def make_spectrogram():
plot_kw = dict(tools="",
min_border=1,
h_symmetry=False,
v_symmetry=False,
toolbar_location=None)
freq = VBox(children=[
Paragraph(text="Freq Range"),
Slider(orientation="vertical",
start=1,
end=MAX_FREQ,
value=MAX_FREQ,
step=1,
name="freq")
])
gain = VBox(children=[
Paragraph(text="Gain"),
Slider(orientation="vertical",
start=1,
end=20,
value=1,
step=1,
name="gain")
])
spec_source = ColumnDataSource(data=dict(image=[], x=[]))
spec = image_rgba(x='x',
y=0,
image='image',
dw=TILE_WIDTH,
dh=MAX_FREQ,
cols=TILE_WIDTH,
rows=SPECTROGRAM_LENGTH,
title=None,
source=spec_source,
plot_width=800,
plot_height=300,
x_range=[0, NGRAMS],
y_range=[0, MAX_FREQ],
dilate=True,
name="spectrogram",
**plot_kw)
spectrum_source = ColumnDataSource(data=dict(x=[], y=[]))
spectrum = line(x="x",
y="y",
line_color="darkblue",
title="Power Spectrum",
source=spectrum_source,
plot_width=800,
plot_height=250,
x_range=[0, MAX_FREQ],
y_range=[10**(-4), 10**3],
y_axis_type="log",
name="spectrum",
**plot_kw)
signal_source = ColumnDataSource(data=dict(x=[], y=[]))
signal = line(x="x",
y="y",
line_color="darkblue",
title="Signal",
source=signal_source,
plot_width=800,
plot_height=250,
x_range=[0, TIMESLICE * 1.01],
y_range=[-0.1, 0.1],
name="signal",
**plot_kw)
radial_source = ColumnDataSource(data=dict(
inner_radius=[],
outer_radius=[],
start_angle=[],
end_angle=[],
fill_alpha=[],
))
eq = annular_wedge(x=0,
y=0,
fill_color="#688AB9",
fill_alpha="fill_alpha",
line_color=None,
inner_radius="inner_radius",
outer_radius="outer_radius",
start_angle="start_angle",
end_angle="end_angle",
title=None,
source=radial_source,
plot_width=500,
plot_height=520,
x_range=[-20, 20],
y_range=[-20, 20],
name="eq",
**plot_kw)
grid().grid_line_color = None
lines = VBox(children=[spectrum, signal])
layout = VBox(children=[
HBox(children=[freq, gain, spec]),
HBox(children=[lines, eq])
])
return layout
import numpy as np
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load decays.csv into a numpy array
decaydata = np.loadtxt("decays2.csv", delimiter=",", skiprows=1)
# provide handles for the x and y columns
time = decaydata[:, 0]
decays = decaydata[:, 1]
bp.output_file("decays.html", title="bokeh_decay.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time, decays, x_axis_label="Time (s)", y_axis_label="Decays (#)", color="#1F78B4", legend="Decays per second")
bp.curplot().title = "Decays"
bp.grid().grid_line_alpha = 0.3
bp.show() # open a browser
import numpy as np
# import the Bokeh plotting tools
from bokeh import plotting as bp
# as in the above example, load decays.csv into a numpy array
decaydata = np.loadtxt('decays.csv', delimiter=",", skiprows=1)
# provide handles for the x and y columns
time = decaydata[:, 0]
decays = decaydata[:, 1]
bp.output_file("decays.html", title="bokeh_decay.py example")
bp.figure(tools="pan,wheel_zoom,box_zoom,reset,previewsave")
bp.line(time,
decays,
x_axis_label="Time (s)",
y_axis_label="Decays (#)",
color='#1F78B4',
legend='Decays per second')
bp.curplot().title = "Decays"
bp.grid().grid_line_alpha = 0.3
bp.show() # open a browser
