def test_update_dict(self):
title = 'this'
fig = Figure()
fig.update(layout=Layout(title=title))
assert fig == Figure(layout=Layout(title=title))
fig['layout'].update(xaxis=XAxis())
assert fig == Figure(layout=Layout(title=title, xaxis=XAxis()))
def test_update_dict(self):
title = "this"
fig = Figure()
update_res1 = fig.update(layout=Layout(title=title))
assert fig == Figure(layout=Layout(title=title))
update_res2 = fig["layout"].update(xaxis=XAxis())
assert fig == Figure(layout=Layout(title=title, xaxis=XAxis()))
assert update_res1 is fig
assert update_res2 is fig.layout
def test_two_row():
expected = Figure(data=Data(),
layout=Layout(xaxis1=XAxis(domain=[0.0, 1.0],
anchor='y1'),
xaxis2=XAxis(domain=[0.0, 1.0],
anchor='y2'),
yaxis1=YAxis(domain=[0.0, 0.425],
anchor='x1'),
yaxis2=YAxis(domain=[0.575, 1.0],
anchor='x2')))
assert tls.get_subplots(2) == expected
def _createLayout(title=None,
xLabel="Date",
yLabel="Metric",
fontSize=12,
width=WIDTH,
height=HEIGHT):
"""Return plotly Layout object."""
layoutArgs = {
"title": title,
"font": {
"size": fontSize
},
"showlegend": False,
"width": width,
"height": height,
"xaxis": XAxis(title=xLabel, ),
"yaxis": YAxis(
title=yLabel,
domain=[0, 1],
autorange=True,
),
"barmode": "stack",
"bargap": 0
}
margins = {"l": 70, "r": 30, "b": 50, "t": 90, "pad": 4}
if title is None:
margins["t"] -= 70
if fontSize > 12:
margins["l"] += (fontSize - 12) * 3
margins["b"] += (fontSize - 12) * 3
layoutArgs["margin"] = margins
return Layout(**layoutArgs)
def plotConfusionMatrix(self, dataFrame, name):
"""
@param data (pandas DF) The confusion matrix.
@param name (str)
"""
labels = dataFrame.columns.values.tolist()
values = map(list, dataFrame.values)
data = Data(
[Heatmap(z=values, x=labels, y=labels, colorscale='YIGnBu')])
layout = Layout(
title='Confusion Matrix for ' + name,
xaxis=XAxis(title='',
side='top',
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f')),
yaxis=YAxis(title='',
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f'),
autorange='reversed'),
barmode='overlay',
autosize=True,
width=1000,
height=1000,
margin=Margin(l=80, r=80, b=120, t=140))
fig = Figure(data=data, layout=layout)
plot_url = py.plot(fig)
print "Confusion matrix URL: ", plot_url
def plot_explained_variance(pca):
explained_var = pca.explained_variance_ratio_
cum_var_exp = np.cumsum(explained_var)
py.iplot(Figure(
data=[
Bar(y=explained_var, name='individual explained variance'),
Scatter(y=cum_var_exp, name='cumulative explained variance'),
],
layout=\
Layout(xaxis=XAxis(title='Principal components'),
yaxis=YAxis(title='Explained variance ratio'),
shapes=[{
'type': 'line',
'xref': 'paper',
'x0': 0,
'y0': 1 / len(explained_var), # use absolute value or variable here
'x1': 1,
'y1': 1 / len(explained_var), # ditto
'line': {
'color': 'rgb(50, 171, 96)',
'width': 2,
'dash': 'dash',
},}]
)
))
def kamervragen_reply_time_per_year(years, kamervraag_durations):
fig = ff.create_distplot(
kamervraag_durations,
years,
# colors=colors,
bin_size=1,
show_curve=True,
show_hist=False,
show_rug=False)
xaxis = XAxis(range=[0, 60])
fig['layout'].update(xaxis=xaxis)
# fig['layout'].update(title="Kamervraag Antwoordtijd per Ministerie tijdens Rutte-II (KDE probability distributie)")
fig['layout'].update(xaxis=dict(title='Antwoordtijd [dagen]'))
# fig['layout'].update(yaxis=dict(title=''))
fig['layout'].update(height=700)
fig['layout'].update(margin=Margin(t=20))
legend = dict(
# x=0.01,
# y=1,
bordercolor='#E2E2E2',
bgcolor='#FFFFFF',
borderwidth=2)
fig['layout'].update(legend=legend)
return Plot.create_plot_html_default(figure_or_data=fig)
def init_layout(self, key, element, ranges):
l, b, zmin, r, t, zmax = self.get_extents(element, ranges)
xd, yd, zd = (element.get_dimension(i) for i in range(3))
xaxis = dict(range=[l, r], title=str(xd))
if self.logx:
xaxis['type'] = 'log'
yaxis = dict(range=[b, t], title=str(yd))
if self.logy:
yaxis['type'] = 'log'
zaxis = dict(range=[zmin, zmax], title=str(zd))
if self.logz:
zaxis['type'] = 'log'
opts = {}
if self.aspect == 'cube':
opts['aspectmode'] = 'cube'
else:
opts['aspectmode'] = 'manual'
opts['aspectratio'] = self.aspect
scene = Scene(xaxis=XAxis(xaxis),
yaxis=YAxis(yaxis),
zaxis=ZAxis(zaxis),
**opts)
return dict(width=self.width,
height=self.height,
title=self._format_title(key, separator=' '),
plot_bgcolor=self.bgcolor,
scene=scene)
def createLayout(title=None,
xLabel="Date",
yLabel="Metric",
fontSize=12,
width=800,
height=500):
"""Return plotly Layout object."""
layoutArgs = {
"title": title,
"font": {
"size": fontSize
},
"showlegend": False,
"width": width,
"height": height,
"xaxis": XAxis(title=xLabel, ),
"yaxis": YAxis(
title=yLabel,
domain=[0, 1],
autorange=True,
autotick=True,
),
"barmode": "stack",
"bargap": 0
}
margins = {"l": 70, "r": 30, "b": 50, "t": 90, "pad": 4}
layoutArgs["margin"] = margins
return Layout(**layoutArgs)
def results(symbol, trend1, trend2):
data = web.DataReader(symbol, data_source='yahoo')
#Guardamos la data en formato HDF5
# filename = 'data'+'_'+symbol+'_'+trend1+'_'+trend2+'.h5'
filename = 'data.h5'
store = {}
store['symbol'] = data
h5 = pd.HDFStore(filename, 'w')
h5['symbol'] = store['symbol']
h5.close()
#Mandamos la data al correo
#os.system("python send_email.py")
data['Tendencia 1'] = pd.rolling_mean(data['Adj Close'],
window=int(trend1))
data['Tendencia 2'] = pd.rolling_mean(data['Adj Close'],
window=int(trend2))
layout = Layout(xaxis=XAxis(showgrid=True,
gridcolor='#bdbdbd',
gridwidth=2),
yaxis=YAxis(showgrid=True,
gridcolor='#bdbdbd',
gridwidth=2))
fig = Figure(data=df_to_plotly(
data[['Adj Close', 'Tendencia 1', 'Tendencia 2']]),
layout=layout)
plot = ply.plot(fig, auto_open=False)
table = data.tail().to_html()
return render_template('plotly.html',
symbol=symbol,
plot=plot,
table=table)
def test_get_single_plot():
expected = Figure(data=Data(),
layout=Layout(xaxis1=XAxis(domain=[0.0, 1.0],
anchor='y1'),
yaxis1=YAxis(domain=[0.0, 1.0],
anchor='x1')))
assert tls.get_subplots() == expected
def plot_linear_prediction(results, data, filename):
x_transform = (add_month(data[0][-1]) - min(data[0])).days
x_val = add_month(data[0][-1])
prediction = results.params["x"] * x_transform + results.params["Intercept"]
trace1 = Scatter(x=data[0],
y=data[1],
mode='lines',
marker=Marker(color='rgb(255, 127, 14)'),
name='Data')
trace2 = Scatter(x=[x_val],
y=[prediction],
mode='markers',
marker=Marker(color='rgb(31, 119, 180)'),
name='Fit')
layout = Layout(title='Linear Fit in Python',
plot_bgcolor='rgb(229, 229, 229)',
xaxis=XAxis(zerolinecolor='rgb(255,255,255)',
gridcolor='rgb(255,255,255)'),
yaxis=YAxis(zerolinecolor='rgb(255,255,255)',
gridcolor='rgb(255,255,255)'))
to_scatter = [trace1, trace2]
plotly.offline.plot({"data": to_scatter, "layout": layout})
shutil.move("temp-plot.html", filename)
def init_plotly():
"""
Prepares authenticate tokens for each trace, prepares layout and streams with corresponding scatter graph traces.
:return: Returns initialized stream IDs for each trace
"""
logger = logging.getLogger(sys._getframe().f_code.co_name)
# pull in Plotly authentication data
plotly_creds = plotly.tools.get_credentials_file()
username = plotly_creds['username']
api_key = plotly_creds['api_key']
token_cpu, token_temp, token_humidity, token_pressure, token_wu = plotly_creds['stream_ids'][0:5]
plotly.plotly.sign_in(username, api_key)
# create Stream structures with proper tokens and maximum preserved graph points
my_stream_cpu = Stream(token=token_cpu, maxpoints=MAX_POINTS)
my_stream_temp = Stream(token=token_temp, maxpoints=MAX_POINTS)
my_stream_humidity = Stream(token=token_humidity, maxpoints=MAX_POINTS)
my_stream_pressure = Stream(token=token_pressure, maxpoints=MAX_POINTS)
my_stream_wu = Stream(token=token_wu, maxpoints=MAX_POINTS)
# create Scatter-type structures with appropriate names; don't provide sample data as we'll provide it live in
# Stream mode
my_scatter_cpu = Scatter(x=[], y=[], stream=my_stream_cpu, name='CPU temperature', mode=TRACE_MODE)
my_scatter_temp = Scatter(x=[], y=[], stream=my_stream_temp,
name='Environment temperature', mode=TRACE_MODE)
my_scatter_humidity = Scatter(x=[], y=[], stream=my_stream_humidity,
name='Environment humidity', mode=TRACE_MODE)
my_scatter_pressure = Scatter(x=[], y=[], stream=my_stream_pressure,
name='Barometric pressure', yaxis='y2',
mode=TRACE_MODE)
my_scatter_wu = Scatter(x=[], y=[], stream=my_stream_wu, name='Outdoor temperature (Weather Underground)',
mode=TRACE_MODE)
# prepare Data structure
my_data = Data([my_scatter_cpu, my_scatter_temp, my_scatter_humidity,
my_scatter_pressure, my_scatter_wu])
# create Layout structure where we have one shared X axis (time series) and two Y axis, one left side (temperature
# and humidity) and one right side (pressure)
my_layout = Layout(title='Raspberry PI Sensors',
xaxis=XAxis(title='Time'), yaxis=YAxis(title='Temperature [C] / Humidity [%]'),
yaxis2=YAxis(title='Pressure [hPa]',
overlaying='y', side='right',
titlefont=Font(color='rgb(148, 103, 189)'),
tickfont=Font(color='rgb(148, 103, 189)')))
# prepare Figure structure
my_fig = Figure(data=my_data, layout=my_layout)
try:
# overwrite existing data on creating the new figure
plotly.plotly.plot(my_fig, filename=PLOTLY_CHART_NAME, auto_open=False, fileopt=GRAPH_MODE)
except requests.exceptions.ConnectionError, e:
logger.error('Cannot connect to PlotLy to create chart: %s. Exiting...' % e)
sys.exit(1)
def setUp(self):
super(TestToDataframe, self).setUp()
self.fig = Figure(data=Data([
Scatter(x=[52698, 43117],
y=[53, 31],
mode='markers',
name='North America',
text=['United States', 'Canada'],
marker=Marker(color='rgb(164, 194, 244)',
size=12,
line=Line(color='white', width=0.5))),
Scatter(x=[
39317, 37236, 35650, 30066, 29570, 27159, 23557, 21046, 18007
],
y=[33, 20, 13, 19, 27, 19, 49, 44, 38],
mode='markers',
name='Europe',
text=[
'Germany', 'Britain', 'France', 'Spain', 'Italy',
'Czech Rep.', 'Greece', 'Poland'
],
marker=Marker(color='rgb(255, 217, 102)',
size=12,
line=Line(color='white', width=0.5))),
Scatter(x=[42952, 37037, 33106, 17478, 9813, 5253, 4692, 3899],
y=[23, 42, 54, 89, 14, 99, 93, 70],
mode='markers',
name='Asia/Pacific',
text=[
'Australia', 'Japan', 'South Korea', 'Malaysia',
'China', 'Indonesia', 'Philippines', 'India'
],
marker=Marker(color='rgb(234, 153, 153)',
size=12,
line=Line(color='white', width=0.5))),
Scatter(x=[19097, 18601, 15595, 13546, 12026, 7434, 5419],
y=[43, 47, 56, 80, 86, 93, 80],
mode='markers',
name='Latin America',
text=[
'Chile', 'Argentina', 'Mexico', 'Venezuela',
'Venezuela', 'El Salvador', 'Bolivia'
],
marker=Marker(color='rgb(142, 124, 195)',
size=12,
line=Line(color='white', width=0.5)))
]),
layout=Layout(title='Quarter 1 Growth',
autosize=False,
width=500,
height=500,
xaxis=XAxis(title='GDP per Capita',
showgrid=False,
zeroline=False),
yaxis=YAxis(title='Percent',
showline=False),
margin=Margin(l=65, r=50, b=65, t=90)))
def plot_df(df, title):
ax = ['x', 'y', 'z']
data = [Scatter(y=df[a], name=a) for a in ax]
layout = Layout(title=title,
xaxis=XAxis(title='Timestep'),
yaxis=YAxis(title='{x, y, z} coordinates in Gs'))
plot({'data': data, 'layout': layout}, show_link=False)
def scatter_df_plotly(df,
x,
y,
z,
text_fnct=hover_all,
streaming=True,
filename=None):
"""
a wrapper function for `plotlyi.graph_objs.Scatter` on `pandas.Dataframe`
usage:
=====
d
"""
layout = Layout(
title='colour map: %s' % z,
hovermode='closest', # (!) hover -> closest data pt
showlegend=False, # remove legend (info in hover)
autosize=False, # turn off autosize
width=650, # plot width
height=500, # plot height
xaxis=XAxis(title=x,
titlefont=Font(family='Courier New, monospace',
size=14,
color='#7f7f7f')),
yaxis=YAxis(title=y,
titlefont=Font(family='Courier New, monospace',
size=14,
color='#7f7f7f')))
# print('marker sizes:')
# print(rescale_marker(df[z], square = True))
SC = Scatter(x=df[x],
y=df[y],
mode='markers',
marker=Marker(size=rescale_marker(df[z], square=True),
color=df[z],
colorscale='Jet',
sizeref=1,
sizemode='area'))
FIG = Figure(data=Data([SC]), layout=layout)
#FIG['layout'].update( )
if text_fnct is not None:
FIG['data'][0].update(text = \
df.apply(lambda ds: text_fnct(ds, x=x, y=y,z=z), axis = 1).tolist() )
if not streaming:
if filename is not None:
return py.plot(FIG, filename=filename)
else:
return py.plot(FIG)
else:
return py.iplot(FIG)
def test_append_scatter():
expected = Figure(
data=Data([Scatter(x=[1, 2, 3], y=[2, 3, 4], xaxis="x5", yaxis="y5")]),
layout=Layout(
xaxis1=XAxis(domain=[0.0, 0.2888888888888889], anchor="y1"),
xaxis2=XAxis(domain=[0.35555555555555557, 0.6444444444444445],
anchor="y2"),
xaxis3=XAxis(domain=[0.7111111111111111, 1.0], anchor="y3"),
xaxis4=XAxis(domain=[0.0, 0.2888888888888889], anchor="y4"),
xaxis5=XAxis(domain=[0.35555555555555557, 0.6444444444444445],
anchor="y5"),
xaxis6=XAxis(domain=[0.7111111111111111, 1.0], anchor="y6"),
yaxis1=YAxis(domain=[0.575, 1.0], anchor="x1"),
yaxis2=YAxis(domain=[0.575, 1.0], anchor="x2"),
yaxis3=YAxis(domain=[0.575, 1.0], anchor="x3"),
yaxis4=YAxis(domain=[0.0, 0.425], anchor="x4"),
yaxis5=YAxis(domain=[0.0, 0.425], anchor="x5"),
yaxis6=YAxis(domain=[0.0, 0.425], anchor="x6"),
),
)
trace = Scatter(x=[1, 2, 3], y=[2, 3, 4])
fig = tls.make_subplots(rows=2, cols=3)
fig.append_trace(trace, 2, 2)
d1, d2 = strip_dict_params(fig["data"][0], expected["data"][0])
assert d1 == d2
d1, d2 = strip_dict_params(fig["layout"], expected["layout"])
assert d1 == d2
def test_append_scatter():
expected = Figure(
data=Data([Scatter(x=[1, 2, 3], y=[2, 3, 4], xaxis='x5', yaxis='y5')]),
layout=Layout(
xaxis1=XAxis(domain=[0.0, 0.2888888888888889], anchor='y1'),
xaxis2=XAxis(domain=[0.35555555555555557, 0.6444444444444445],
anchor='y2'),
xaxis3=XAxis(domain=[0.7111111111111111, 1.0], anchor='y3'),
xaxis4=XAxis(domain=[0.0, 0.2888888888888889], anchor='y4'),
xaxis5=XAxis(domain=[0.35555555555555557, 0.6444444444444445],
anchor='y5'),
xaxis6=XAxis(domain=[0.7111111111111111, 1.0], anchor='y6'),
yaxis1=YAxis(domain=[0.575, 1.0], anchor='x1'),
yaxis2=YAxis(domain=[0.575, 1.0], anchor='x2'),
yaxis3=YAxis(domain=[0.575, 1.0], anchor='x3'),
yaxis4=YAxis(domain=[0.0, 0.425], anchor='x4'),
yaxis5=YAxis(domain=[0.0, 0.425], anchor='x5'),
yaxis6=YAxis(domain=[0.0, 0.425], anchor='x6')))
trace = Scatter(x=[1, 2, 3], y=[2, 3, 4])
fig = tls.make_subplots(rows=2, cols=3)
fig.append_trace(trace, 2, 2)
d1, d2 = strip_dict_params(fig['data'][0], expected['data'][0])
assert d1 == d2
d1, d2 = strip_dict_params(fig['layout'], expected['layout'])
assert d1 == d2
def _createLayout(title):
"""Return plotly Layout object."""
return Layout(title=title,
showlegend=False,
width=1000,
height=600,
xaxis=XAxis(title="Date"),
yaxis=YAxis(title="Metric",
domain=[0, 1],
autorange=True,
autotick=True),
barmode="stack",
bargap=0)
def plot_explained_variance(pca):
import plotly
from plotly.graph_objs import Scatter, Marker, Layout, XAxis, YAxis, Bar, Line
plotly.offline.init_notebook_mode() # run at the start of every notebook
explained_var = pca.explained_variance_ratio_
cum_var_exp = np.cumsum(explained_var)
plotly.offline.iplot({
"data": [Bar(y=explained_var, name='individual explained variance'),
Scatter(y=cum_var_exp, name='cumulative explained variance')
],
"layout": Layout(xaxis=XAxis(title='Principal components'), yaxis=YAxis(title='Explained variance ratio'))
})
def results(symbol, trend1, trend2):
data = web.DataReader(symbol, data_source='yahoo')
data['Trend 1'] = pd.rolling_mean(data['Adj Close'], window=int(trend1))
data['Trend 2'] = pd.rolling_mean(data['Adj Close'], window=int(trend2))
layout = Layout(
xaxis=XAxis(showgrid=True, gridcolor='#bdbdbd', gridwidth=2),
yaxis=YAxis(showgrid=True, gridcolor='#bdbdbd', gridwidth=2)
)
fig = Figure(data=df_to_plotly(data[['Adj Close', 'Trend 1', 'Trend 2']]),
layout=layout)
plot = ply.plot(fig, auto_open=False)
table = data.tail().to_html()
return render_template('plotly.html', symbol=symbol,
plot=plot, table=table)
def fig1():
trace1 = Scatter(
x=df['LifeExpectancy'],
y=df['GNP'],
text=country_names,
mode='markers'
)
layout = Layout(
xaxis=XAxis(title='Life Expectancy'),
yaxis=YAxis(type='log', title='GNP')
)
data = Data([trace1])
fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='world GNP vs life expectancy')
def plotly_create_stream(data):
api_key = os.environ.get("PLOTLY_KEY_API")
stream_token = os.environ.get("PULSE_STREAM_TOKEN")
x, y = process_data_for_pulse(data)
py.sign_in('SergeyParamonov', api_key)
data = Data([Scatter(x=x, y=y, stream=dict(token=stream_token))])
layout = Layout(
title="Пульс Хабра — изменение просмотров статей в Новом (в минуту)",
xaxis=XAxis(title=u"Московское время"), # x-axis title
yaxis=YAxis(title=u"Просмотры"), # y-axis title
showlegend=False, # remove legend (info in hover)
hovermode='closest', # N.B hover -> closest data pt
)
plotly_fig = Figure(data=data, layout=layout)
unique_url = py.plot(plotly_fig, filename="pulse")
return unique_url
def biplot(pca, dat, title='', show_points=True, components=(0, 1)):
import plotly
from plotly.graph_objs import Scatter, Marker, Layout, XAxis, YAxis, Bar, Line
plotly.offline.init_notebook_mode() # run at the start of every notebook
pc1, pc2 = components
# 0,1 denote PC1 and PC2; change values for other PCs
xvector = pca.components_[pc1]
yvector = pca.components_[pc2]
tmp = pca.transform(dat.values)
xs = tmp[:, pc1]
ys = tmp[:, pc2]
if show_points:
annotations = [
Scatter(x=xs,
y=ys,
mode='markers',
marker=dict(size=1),
name='cumulative explained variance')
]
else:
annotations = []
for i in range(len(xvector)):
txt = list(dat.columns.values)[i]
annotations.append(
Scatter(
x=[0, xvector[i] * max(xs)],
y=[0, yvector[i] * max(ys)],
mode='lines+text',
text=['', txt],
name=txt,
))
plotly.offline.iplot({
"data":
annotations,
"layout":
Layout(xaxis=XAxis(title='Principal Component ' + str(pc1 + 1)),
yaxis=YAxis(title='Principal Component ' + str(pc2 + 1)),
title=title)
})
plt.show()
def visualize_shares_post(dates, vk, fb, tw, post_id, title):
locale.setlocale(locale.LC_ALL, 'en_US.utf8')
api_key = os.environ.get("PLOTLY_KEY_API")
py.sign_in('SergeyParamonov', api_key)
vk_trace = Scatter(x=dates, y=vk, mode='lines+markers', name=u"Вконтакте")
fb_trace = Scatter(x=dates, y=fb, mode='lines+markers', name=u"Facebook")
tw_trace = Scatter(x=dates, y=tw, mode='lines+markers', name=u"Twitter")
data = Data([vk_trace, fb_trace, tw_trace])
layout = Layout(
title=u"Репосты: " + title,
xaxis=XAxis(title=u"Московское время"), # x-axis title
yaxis=YAxis(title=u"Репосты"), # y-axis title
hovermode='closest', # N.B hover -> closest data pt
)
plotly_fig = Figure(data=data, layout=layout)
plotly_filename = "monitor_post_id_" + str(post_id) + "_" + "shares"
unique_url = py.plot(plotly_fig, filename=plotly_filename)
return unique_url
def visualize_post_plotly(x, y, field, post_id, title):
api_key = os.environ.get("PLOTLY_KEY_API")
py.sign_in('SergeyParamonov', api_key)
data = Data([Scatter(x=x, y=y)])
if field == "favorite":
ytitle = u"Избранное"
else:
ytitle = u"Просмотры"
layout = Layout(
title=ytitle + ": " + title,
xaxis=XAxis(title=u"Московское время"), # x-axis title
yaxis=YAxis(title=ytitle), # y-axis title
showlegend=False, # remove legend (info in hover)
hovermode='closest', # N.B hover -> closest data pt
)
plotly_fig = Figure(data=data, layout=layout)
plotly_filename = "monitor_post_id_" + str(post_id) + "_" + field
unique_url = py.plot(plotly_fig, filename=plotly_filename)
return unique_url
def init_plotly():
import locale
try:
locale.setlocale(locale.LC_ALL, 'de_DE')
except locale.Error:
locale.setlocale(locale.LC_ALL, 'deu_deu')
from sys import stdout
stdout.write("init plotly..")
global plot, Scatter, Layout, Bar, Xaxis, YAxis, Legend, Marker, Annotation
global bar, scatter, layout, data, last_bar, day_line, day_marker
from plotly.offline import plot
from plotly.graph_objs import Scatter, Layout, Bar, XAxis, YAxis, Legend, Marker, Annotation
# data
bar = Bar(x=[], y=[], name='Aktueller Verbrauch')
last_bar = Bar(x=[],
y=[],
name='Bisheriger Verbrauch<br>in aktueller Stunde',
marker=dict(color='rgb(159, 197, 232)'))
scatter = Scatter(x=[],
y=[],
name='Durchschnittlicher Verbrauch',
mode="lines+markers")
#layout
layout = Layout(
title="Stromverbrauch der letzten 24 Stunden",
barmode='stacked',
xaxis=XAxis(tickmode='array',
ticktext=[],
tickvals=[],
range=[-0.5, 1],
tickangle=-45),
yaxis=YAxis(title='Wh'),
legend=Legend(bordercolor='#FFFFFF',
borderwidth=3,
xanchor="right",
x=1.0,
y=1.11) # bgcolor='#E2E2E2'
)
data = {"data": [bar, scatter, last_bar], "layout": layout}
print(" done")
def plotBucketsMetrics(self, metricsDict, comboMethod, numIterations,
modelName):
"""
@param metricsDicts (dict) Arrays for the min, mean, and max of each
metric.
@param comboMethod (str) Concatenation method from the experiment.
@param numIterations (str) Number of inference steps run.
@param modelName (str) Name of tested model.
"""
xData = range(1, numIterations + 1)
for metricName, results in metricsDict.iteritems():
if metricName == "totalRanked": continue
minTrace = Scatter(x=xData,
y=results[0],
mode="lines+markers",
name="min")
meanTrace = Scatter(x=xData,
y=results[1],
mode="lines+markers",
name="mean")
maxTrace = Scatter(x=xData,
y=results[2],
mode="lines+markers",
name="max")
data = [minTrace, meanTrace, maxTrace]
layout = Layout(
title="Buckets Experiment for {} ('{}' concatenation) ".format(
modelName, comboMethod),
xaxis=XAxis(title="Number of samples queried",
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f'),
dtick=1),
yaxis=YAxis(title=metricName,
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f')))
fig = Figure(data=data, layout=layout)
plotUrl = py.plot(fig)
print "Plot URL for {}: {}".format(metricName, plotUrl)
def draw_graph(word, hypernym=False):
"""Draw graph of word hyponym, or hypernym if hypernym=True,
Displays in Jupyter notebook.
Requires pre-installed plotly apikey"""
G = visualize_word(word, not hypernym)
nxpos = nx.fruchterman_reingold_layout(G)
labels = []
pos = []
for k, v in nxpos.items():
labels.append(str(k()))
pos.append(v)
trace1 = scatter_edges(G, nxpos)
trace2 = scatter_nodes(pos, labels=labels)
axis = dict(
showline=False, # hide axis line, grid, ticklabels and title
zeroline=False,
showgrid=False,
showticklabels=False,
title='')
layout = Layout(
title='Graph for word "{}"'.format(word),
font=Font(),
showlegend=False,
autosize=True,
# width=width,
# height=height,
xaxis=XAxis(axis),
yaxis=YAxis(axis),
#margin=Margin(
# l=40,
# r=40,
# b=85,
# t=100,
# pad=0,),
# plot_bgcolor='#EFECEA', #set background color
hovermode='closest')
data = Data([trace1, trace2])
fig = Figure(data=data, layout=layout)
return plotly.iplot(fig, filename='networkx')
def make_report_plotly(counts, suffix, report):
for count in counts:
run_test( report, count )
for category, results in report.iteritems():
#formatter = results['formatter']
scale = results['scale']
unit = results['unit']
bars = []
for name, values in results.iteritems():
if name in ['title', 'scale', 'unit']:
continue
#values = map(formatter, map(lambda x: x * scale, values))
values = map(lambda x: x * scale, values)
bar = Bar( x=counts,
y=values,
name=name )
bars.append(bar)
layout = Layout(title=results['title'],
font=Font(family='Raleway, sans-serif'),
showlegend=True,
barmode='group',
bargap=0.15,
bargroupgap=0.1,
legend=Legend(x=0, y=1.0),
xaxis=XAxis(title='Num Sites', type='category'),
yaxis=YAxis(title=results['unit'])
)
data = Data(bars)
fig = Figure(data=data, layout=layout)
outpath = '../images/%s%s.png' % (category, suffix)
py.image.save_as(fig, outpath)
print "Wrote", outpath
def make_XAxis(x_in):
xaxis = XAxis(range=[x_in.min(),x_in.max()])
xaxis.update(axis_style)
return xaxis
