def compareFileExtViaBarPlots(dictParam, fileNameParam, listParam):
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font
import numpy as np
xList = []
yList = []
for it_ in listParam:
xList.append(it_)
yList.append(np.mean(dictParam[it_]))
dataToPlot = [go.Bar(x=xList, y=yList)]
layoutToUse = Layout(
title='Distribution of File Types (Average) : ' + fileNameParam,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Values',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
#plot_url = py.plot(dataToPlot, filename=fileNameParam)
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
fileNameParam = "plot-session-info/" + fileNameParam + ".png"
py.image.save_as(figToPlot, fileNameParam)
def plotSharpVSOthers(dictParam, fileNameParam, extOfInterest="cSharpCount"):
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font
cSharpList = dictParam[extOfInterest]
otherList = [100.0 - float(x) for x in cSharpList]
cSharpTrace_ = go.Box(y=cSharpList, name="C# Files")
otherTrace_ = go.Box(y=otherList, name="Other Files")
dataToPlot = [cSharpTrace_, otherTrace_]
layoutToUse = Layout(
title='Distribution of File Types : ' + fileNameParam,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Values',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
#plot_url = py.plot(dataToPlot, filename=fileNameParam)
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
fileNameParam = "plot-session-info/" + fileNameParam + ".png"
py.image.save_as(figToPlot, fileNameParam)
def compareViaBoxPlot(posGroupParam, zeroGroupParam, negGroupParam,
fileNameParam):
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font
#from plotly.graph_objs import Bar, Marker, Data, Layout, XAxis, YAxis, Figure, Margin, Font
posGroup = go.Box(y=posGroupParam, name='Corr. >= 0.50')
zeroGroup = go.Box(y=zeroGroupParam, name='Corr. within 0.0 & 0.499')
negGroup = go.Box(y=negGroupParam, name='Corr. < 0.0')
layoutToUse = Layout(
title='Distribution of Three Groups: ' + fileNameParam,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Values',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
dataToPlot = [posGroup, zeroGroup, negGroup]
#plot_url = py.plot(dataToPlot, filename=fileNameParam)
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
fileNameParam = "plots/" + fileNameParam + ".png"
py.image.save_as(figToPlot, fileNameParam)
def plotTwoGroups(group1Param, group2Param, yAxisParam, titleParam,
fileNameParam):
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font
group1Trace_ = go.Box(y=group1Param, name="High Productivity Sessions")
group2Trace_ = go.Box(y=group2Param, name="Low Productivity Sessions")
dataToPlot = [group1Trace_, group2Trace_]
layoutToUse = Layout(
title='Summary of 84030 Sessions : ' + titleParam,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
yaxis=YAxis(
showgrid=True,
showline=True,
title=yAxisParam,
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
fileNameParam = "plot-session-info/" + fileNameParam + ".png"
py.image.save_as(figToPlot, fileNameParam)
return "DONE!"
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 compareFileExtViaBoxPlots(dictParam, fileNameParam, extList):
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font
#from plotly.graph_objs import Bar, Marker, Data, Layout, XAxis, YAxis, Figure, Margin, Font
dataToPlot = []
for it_ in extList:
trace_ = go.Box(y=dictParam[it_], name=it_)
dataToPlot.append(trace_)
layoutToUse = Layout(
title='Distribution of File Types : ' + fileNameParam,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Values',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
#plot_url = py.plot(dataToPlot, filename=fileNameParam)
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
fileNameParam = "plot-session-info/" + fileNameParam + ".png"
py.image.save_as(figToPlot, fileNameParam)
def plotDistForThreeGroups(group1, group2, group3, fileNameParam):
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font
#from plotly.graph_objs import Bar, Marker, Data, Layout, XAxis, YAxis, Figure, Margin, Font
dataToPlot = []
g1Trace_ = go.Box(y=group1, name="Corr >=0.50")
g2Trace_ = go.Box(y=group2, name="Corr >=0.0 AND Corr <=0.499")
g3Trace_ = go.Box(y=group3, name="Corr < 0.0")
dataToPlot = [g1Trace_, g2Trace_, g3Trace_]
layoutToUse = Layout(
title='Distribution of : ' + fileNameParam,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Values',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
#plot_url = py.plot(dataToPlot, filename=fileNameParam)
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
fileNameParam = "plot-session-info/" + fileNameParam + ".png"
py.image.save_as(figToPlot, fileNameParam)
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 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 new_canvas(self,
figure=None,
row=1,
col=1,
projection='2d',
xlabel=None,
ylabel=None,
zlabel=None,
title=None,
xlim=None,
ylim=None,
zlim=None,
**kwargs):
#if 'filename' not in kwargs:
# print('PlotlyWarning: filename was not given, this may clutter your plotly workspace')
# filename = None
#else:
# filename = kwargs.pop('filename')
if figure is None:
figure = self.figure(is_3d=projection == '3d')
figure.layout.font = Font(family="Raleway, sans-serif")
if projection == '3d':
figure.layout.legend.x = .5
figure.layout.legend.bgcolor = '#DCDCDC'
return (figure, row, col), kwargs
def plot_ly(subgraph, title, data, measure, colorscale='Hot'):
'''
Function to get a plotly figure object to be plotted
Args:
subgraph (NetworkX Graph): the graph to be plotted
title (str): the title for the plot
data (dict): the measured data to be plotted
measure (str): the measure being plotted
colorscale (str): colorscale for the data measure
ColorScale options
'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' |
Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' |
'YIGnBu'
'''
pos = nx.spring_layout(subgraph, k=.12)
nodes = list(subgraph.node.keys())
nodes.sort()
labels = []
for node in subgraph.nodes():
author_name = subgraph.node[node]['data']['author']['name'].title()
label = "{}<br>{} = {}".format(author_name, measure, str(data[node]))
labels.append(label)
trace1 = scatter_edges(subgraph, pos)
trace2 = scatter_nodes(pos=pos,
labels=labels,
title=title,
data=data,
nodes=nodes,
colorscale=colorscale)
width = 800
height = 600
axis = dict(showline=False,
zeroline=False,
showgrid=False,
showticklabels=False,
title='')
layout = Layout(title=title,
font=Font(),
showlegend=False,
autosize=False,
width=width,
height=height,
xaxis=dict(title='',
titlefont=dict(size=14, color='#7f7f7f'),
showline=False,
showticklabels=False,
zeroline=False),
yaxis=YAxis(axis),
margin=Margin(
l=40,
r=40,
b=85,
t=100,
pad=0,
),
hovermode='closest',
plot_bgcolor='#EFECEA')
data = Data([trace1, trace2])
fig = Figure(data=data, layout=layout)
return fig
def make_sbplt_anno(sbplt_in, x_in, y_in, name):
return Annotation(x=np.mean(x_in),
y=y_in[-1],
text=name,
xanchor='center',
align='center',
font=Font(size=14),
showarrow=False,
xref='x{}'.format(sbplt_in),
yref='y{}'.format(sbplt_in))
def make_score_anno(sbplt_in, x_in, y_in, score):
return Annotation(
x=x_in.max() - 0.95, # had to tweak these from
y=y_in.min() + 0, # from original
text=('%.2f' % score).lstrip('0'),
align='right',
font=Font(size=15),
showarrow=False,
xref='x{}'.format(sbplt_in),
yref='y{}'.format(sbplt_in))
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 plotConfusionMatrix(self, data, normalize=True):
"""
Plots the confusion matrix of the input dataframe.
@param data (pandas DF) The confusion matrix.
@param normalize (bool) True will normalize the confusion matrix
values for the total number of actual classifications per label. Thus
the cm values are 0.0 to 1.0.
"""
xyzData = self.interpretConfusionMatrixData(data, normalize)
data = Data([
Heatmap(z=xyzData["z"],
x=xyzData["x"],
y=xyzData["y"],
colorscale='YIGnBu')
])
layout = Layout(
title='Confusion matrix for ' + self.experimentName,
xaxis=XAxis(title='Predicted label',
side='top',
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f')),
yaxis=YAxis(title='True label',
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f'),
autorange='reversed'),
barmode='overlay',
autosize=True,
width=1000,
height=1000,
margin=Margin(l=200, r=80, b=80, t=450))
fig = Figure(data=data, layout=layout)
plot_url = py.plot(fig)
print "Confusion matrix URL: ", plot_url
def plotCumulativeAccuracies(self, classificationAccuracies, trainSize):
"""
Creates scatter plots that show the accuracy for each category at a
certain training size
@param classificationAccuracies (dict) Maps a category label to a list of
lists of accuracies. Each item in the key is a list of accuracies for
a specific training size, ordered by increasing training size.
@param trainSize (list) Sizes of training sets for trials.
"""
# Convert list of list of accuracies to list of means
classificationSummaries = [
(label, map(numpy.mean, acc))
for label, acc in classificationAccuracies.iteritems()
]
data = []
sizes = sorted(set(trainSize))
for label, summary in classificationSummaries:
data.append(Scatter(x=sizes, y=summary, name=label))
data = Data(data)
layout = Layout(
title='Cumulative Accuracies for ' + self.experimentName,
xaxis=XAxis(title='Training size',
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f')),
yaxis=YAxis(title='Accuracy',
titlefont=Font(family='Courier New, monospace',
size=18,
color='#7f7f7f')))
fig = Figure(data=data, layout=layout)
plot_url = py.plot(fig)
print "Cumulative Accuracies URL: ", plot_url
def create_annotations():
timestamp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
anno_text = 'time: {0}'.format(timestamp)
annotations = [
dict(
text=anno_text, # set annotation text
showarrow=False, # remove arrow
xref='paper', # use paper coords
yref='paper', # for both coordinates
x=0.95, # position's x-coord
y=1.10, # and y-coord
font=Font(size=16), # increase font size (default is 12)
bgcolor='#FFFFFF', # white background
borderpad=4 # space bt. border and text (in px)
)
]
return annotations
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
prediction = slope * (xi[-1]) + intercept
# Creating the dataset, and generating the plot
trace1 = Scatter(x=xi,
y=y,
mode='lines',
marker=Marker(color='rgb(255, 127, 14)'),
name='Data')
trace2 = Scatter(x=[xi[-1] + 1],
y=[prediction],
mode='markers',
marker=Marker(color='rgb(31, 119, 180)'),
name='Fit')
annotation = Annotation(x=3.5,
y=23.5,
text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
showarrow=False,
font=Font(size=16))
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)'),
annotations=[annotation])
to_scatter = [trace1, trace2]
plotly.offline.plot({"data": to_scatter, "layout": layout})
def draw_igraph(self, title, colors=None):
(Xn, Yn, Zn), (Xe, Ye, Ze) = self.get_3d_position()
trace1 = Scatter3d(x=Xe,
y=Ye,
z=Ze,
mode='lines',
line=Line(color='rgb(125,125,125)',
width=1,
dash=True),
hoverinfo='none')
trace2 = Scatter3d(
x=Xn,
y=Yn,
z=Zn,
mode='markers',
name='callers',
marker=Marker(
symbol='dot',
size=[100 * x + 5 for x in list(self.betweenness.values())],
color=self.colors,
colorscale='Rainbow',
opacity=0.5),
text=self.nodes,
hoverinfo='text')
axis = dict(showbackground=False,
showline=False,
zeroline=False,
showgrid=False,
showticklabels=False,
title='')
layout = Layout(
title=title,
width=1000,
height=1000,
showlegend=False,
scene=Scene(
xaxis=XAxis(axis),
yaxis=YAxis(axis),
zaxis=ZAxis(axis),
),
margin=Margin(t=100),
hovermode='closest',
annotations=Annotations([
Annotation(showarrow=False,
text="Data source: ???",
xref='paper',
yref='paper',
x=0,
y=0.1,
xanchor='left',
yanchor='bottom',
font=Font(size=14))
]),
)
data = Data([trace1, trace2])
fig = Figure(data=data, layout=layout)
iplot(fig, filename='Call Network')
import plotly.plotly as py
import plotly.tools as tls
from plotly.graph_objs import Heatmap, Layout, Font
stream_id = tls.get_credentials_file()['stream_ids'][3]
stream = py.Stream(stream_id)
stream.open()
anno_text = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
layout = Layout(annotations=[
dict(
text=anno_text, # set annotation text
showarrow=False, # remove arrow
xref='paper', # use paper coords
yref='paper', # for both coordinates
x=0.95, # position's x-coord
y=1.05, # and y-coord
font=Font(size=14), # increase font size (default is 12)
bgcolor='#FFFFFF', # white background
borderpad=4 # space bt. border and text (in px)
)
])
x = ['a', 'b', 'c', 'd']
y = ['x', 'y', 'z']
z = np.random.randn(3, 4)
trace = Heatmap(z=z, x=x, y=y)
stream.write(trace, layout=layout)
stream.close()
def draw3Dnx(graph=None,
out_path=None,
perc_threshold=None,
positions_array=None,
positions_dict=None,
plot_title='',
plot_description='',
colorscale='Set3',
notebook_mode=True,
auto_open=False):
"""Draws a given graph in 3D"""
if graph is None or nx.is_empty(graph):
raise ValueError('input graph can not be empty!')
# graph = nx.random_geometric_graph(200, 0.05)
if notebook_mode:
init_notebook_mode()
marker_size = 7
marker_edge_width = 2
link_width = 2
colorbar_title = 'Node Connections'
hover_description = '# connections: '
position_attr = ['x', 'y', 'z']
if positions_array is not None:
for node in graph.nodes():
for ix, attr in enumerate(position_attr):
graph.nodes[node][attr] = positions_array[node][ix]
elif positions_dict is not None:
for node in graph.nodes():
for attr in position_attr:
graph.nodes[node][attr] = positions_array[node][attr]
for attr in position_attr:
if not nx.get_node_attributes(graph, attr):
raise ValueError(
'Position attribute {} missing. '
'Add it to graph or supply with one of the position inputs'.
format(attr))
edge_threshold = -np.Inf
if perc_threshold is not None:
eval_distr = np.array(
list(nx.get_edge_attributes(graph, 'weight').values()))
try:
edge_threshold = np.percentile(eval_distr, perc_threshold)
except:
print('threshold to prune edges can not be determined.')
traceback.print_exc()
return
edge_trace = Scatter3d(
x=[],
y=[],
z=[],
mode='lines',
line=Line(width=marker_edge_width, color='#888'),
hoverinfo='none',
)
def get_position(gnode):
"""Helper to retun the x, y, z coords of a node"""
return gnode['x'], gnode['y'], gnode['z']
for src, dest in graph.edges():
# adding only the strongest edges
if perc_threshold is None or graph.get_edge_data(
src, dest)['weight'] > edge_threshold:
x0, y0, z0 = get_position(graph.nodes[src]) # ['position']
x1, y1, z1 = get_position(graph.nodes[dest]) # ['position']
edge_trace['x'].extend([x0, x1, None])
edge_trace['y'].extend([y0, y1, None])
edge_trace['z'].extend([z0, z1, None])
# empty lists here will be appended with data to be plotted
node_trace = Scatter3d(x=[],
y=[],
z=[],
text=[],
mode='markers',
hoverinfo='text',
marker=Marker(
symbol='dot',
showscale=True,
colorscale=colorscale,
reversescale=True,
color=[],
size=marker_size,
colorbar=dict(thickness=15,
title=colorbar_title,
xanchor='left',
titleside='right'),
))
# setting nodal positions and info
for ix, node in enumerate(graph.nodes()):
x, y, z = get_position(graph.nodes[node])
node_trace['x'].append(x)
node_trace['y'].append(y)
node_trace['z'].append(z)
node_trace['text'].append(node)
node_trace['marker']['color'].append(ix)
axis = dict(showbackground=False,
showline=False,
zeroline=False,
showgrid=False,
showticklabels=False,
title='')
scene = Scene(xaxis=XAxis(axis), yaxis=YAxis(axis), zaxis=ZAxis(axis))
annotations = Annotations([
Annotation(
showarrow=False,
text=plot_description,
xref='paper',
yref='paper',
x=0,
y=0.1, # z=0.05,
xanchor='left',
yanchor='bottom', # zanchor='bottom',
font=Font(size=14))
])
layout = Layout(
title=plot_title,
titlefont=dict(size=16),
# width=1000,
# height=1000,
showlegend=False,
hovermode='closest',
scene=scene,
margin=Margin(t=100),
# margin=dict(b=20,l=5,r=5,t=40),
annotations=annotations,
)
fig_data = Data([edge_trace, node_trace])
fig = Figure(data=fig_data, layout=layout)
if out_path is None and auto_open is False:
auto_open = True
if notebook_mode:
iplot(fig, filename=out_path)
else:
plot(fig, filename=out_path, auto_open=auto_open)
return fig
showlegend=False,
scene=Scene(
xaxis=XAxis(axis),
yaxis=YAxis(axis),
zaxis=ZAxis(axis),
),
margin=Margin(
t=100
),
hovermode='closest',
annotations=Annotations([
Annotation(
showarrow=False,
text="",
xref='paper',
yref='paper',
x=0,
y=0.1,
xanchor='left',
yanchor='bottom',
font=Font(
size=14
)
)
]), )
data=Data([trace1, trace2])
fig=Figure(data=data, layout=layout)
py.plot(fig, filename='Crawler')
def Generate_3DModel(idList, labelList, percentage):
network = pd.read_csv("network.csv")
L = len(network['TF'])
#Values=[network['importance'][k] for k in range(L)]
#G=ig.Graph(Edges, directed=False)
#layt=G.layout('kk', dim=3)
G = Create_Graph(idList, labelList, percentage, netthreshold)
N = len(list(G.node())) #--> Numero de nodos
V = list(G.node()) # lista con todos los nodos
#Edges=[(network['TF'][k], network['target'][k]) for k in range(L)]
Edges = list(G.edges())
#layt=nx.spectral_layout(G,dim=3)
#layt=nx.spring_layout(G, dim=3)
#layt=nx.fruchterman_reingold_layout(G,dim=3)
#layt=laytdict.values()
#g=nx.Graph()
#g.add_nodes_from(V)
#g.add_edges_from(Edges)
layt = nx.fruchterman_reingold_layout(G, dim=3)
#layt = nx.circular_layout(G,scale=10,dim=3)
#layt=nx.spring_layout(G,dim=3)
laytN = list(layt.values())
Xn = [laytN[k][0] for k in range(N)] # x-coordinates of nodes
Yn = [laytN[k][1] for k in range(N)] # y-coordinates
Zn = [laytN[k][2] for k in range(N)] # z-coordinates
Xe = []
Ye = []
Ze = []
for e in Edges:
Xe += [layt[e[0]][0], layt[e[1]][0],
None] # x-coordinates of edge ends
Ye += [layt[e[0]][1], layt[e[1]][1], None]
Ze += [layt[e[0]][2], layt[e[1]][2], None]
trace1 = Scatter3d(x=Xe,
y=Ye,
z=Ze,
mode='lines',
line=Line(color='rgb(125,125,125)', width=1),
hoverinfo='none')
trace2 = Scatter3d(x=Xn,
y=Yn,
z=Zn,
mode='markers+text',
textposition='top',
name='genes',
marker=Marker(symbol='dot',
size=6,
color='#6959CD',
colorscale='Viridis',
line=Line(color='rgb(50,50,50)',
width=1)),
text=V,
hoverinfo='text')
#for node, adjacencies in enumerate(G.adjacency()):
#trace2['marker']['color'].append(len(adjacencies))
#node_info = 'Number of connections: '+str(len(adjacencies))
#trace2['text'].append(node_info)
axis = dict(showbackground=False,
showline=False,
zeroline=False,
showgrid=False,
showticklabels=False,
title='')
fig = Figure(data=Data([trace1, trace2]),
layout=Layout(
title="Network (3D visualization)",
width=1000,
height=1000,
showlegend=False,
scene=Scene(
xaxis=XAxis(axis),
yaxis=YAxis(axis),
zaxis=ZAxis(axis),
),
margin=Margin(t=100),
hovermode='closest',
annotations=Annotations([
Annotation(showarrow=False,
text="",
xref='paper',
yref='paper',
x=0,
y=0.1,
xanchor='left',
yanchor='bottom',
font=Font(size=14))
]),
))
py.iplot(fig, filename='networkx3D')
layout=Layout(width=640,
height=480,
autosize=False,
margin=Margin(l=80, r=63, b=47, t=47,
pad=0),
hovermode='closest',
showlegend=False,
annotations=Annotations([
Annotation(x=0.000997987927565,
y=0.996414507772,
text='top-left',
xref='paper',
yref='paper',
showarrow=False,
align='left',
font=Font(size=12.0,
color='#000000'),
opacity=1,
xanchor='left',
yanchor='top'),
Annotation(x=0.000997987927565,
y=0.00358549222798,
text='bottom-left',
xref='paper',
yref='paper',
align='left',
showarrow=False,
font=Font(size=12.0,
color='#000000'),
opacity=1,
xanchor='left',
yanchor='bottom'),
def plotCorrelations(fileNameP, listP):
import plotly.plotly as py
#import plotly
from plotly.graph_objs import Bar, Marker, Data, Layout, XAxis, YAxis, Figure, Margin, Font
#import plotly.graph_objs as gObjLib
### authentication stuff ....
#py = plotly.plotly(username='******', key='9ebth53cdo')
#barSize = 5
fileTrace = [listP[0], listP[1], listP[2]]
projectTrace = [listP[3], listP[4], listP[5]]
stateTrace = [listP[6], listP[7], listP[8]]
namespaceTrace = [listP[9], listP[10], listP[11]]
fileCorrTrace = Bar(
x=['Corr. >= 0.50', 'Corr. within 0.0 & 0.499', 'Corr. < 0.0'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=fileTrace,
name='FileInterleaving')
projCorrTrace = Bar(
x=['Corr. >= 0.50', 'Corr. within 0.0 & 0.499', 'Corr. < 0.0'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=projectTrace,
name='ProjetInterleaving')
nspaceCorrTrace = Bar(
x=['Corr. >= 0.50', 'Corr. within 0.0 & 0.499', 'Corr. < 0.0'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=namespaceTrace,
name='NamespaceInterleaving')
stateTrace = Bar(
x=['Corr. >= 0.50', 'Corr. within 0.0 & 0.499', 'Corr. < 0.0'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=stateTrace,
name='StateInterleaving')
#fileCorrTrace['marker'] = Marker(color='blue', size=barSize)
#projCorrTrace['marker'] = Marker(color='green', size=barSize)
#nspaceCorrTrace['marker'] = Marker(color='yellow', size=barSize)
#stateTrace['marker'] = Marker(color='red', size=barSize)
fileCorrTrace['marker'] = Marker(color='blue')
projCorrTrace['marker'] = Marker(color='green')
nspaceCorrTrace['marker'] = Marker(color='yellow')
stateTrace['marker'] = Marker(color='red')
dataToPlot = Data(
[fileCorrTrace, projCorrTrace, nspaceCorrTrace, stateTrace])
layoutToUse = Layout(
title='Distribution of Correlation for 339 Datasets : ' + fileNameP,
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
barmode='group',
xaxis=XAxis(showgrid=True,
showline=True,
title='Interleaving Correlations',
titlefont=Font(family='Times New Roman',
size=12,
color='#000000'),
tickwidth=1.5),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Count',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
fileToSave = 'plots/' + fileNameP + '.png'
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
py.image.save_as(figToPlot, fileToSave)
def plotLinesForBaseline(fileNameParam):
import csv
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import Layout, YAxis, Figure, Margin, Font, XAxis
xList = []
baselineAct = []
optimizedAct = []
baselinePass = []
optimizedPass = []
fileToRead = open(fileNameParam, "rU")
try:
fileReader = csv.reader(fileToRead,
delimiter=',',
dialect=csv.excel_tab)
next(fileReader, None)
for rowVar in fileReader:
xList.append(rowVar[0])
baselineAct.append(rowVar[1])
optimizedAct.append(rowVar[3])
baselinePass.append(rowVar[2])
optimizedPass.append(rowVar[4])
finally:
fileToRead.close()
baselineActTrace = go.Scatter(x=xList,
y=baselineAct,
mode='lines+markers',
name='Baseline-Undetected Active Errors')
optimizedActTrace = go.Scatter(x=xList,
y=optimizedAct,
mode='lines+markers',
name='Optimized-Undetected Active Errors')
baselinePassTrace = go.Scatter(x=xList,
y=baselinePass,
mode='lines+markers',
name='Baseline-Undetected Passive Errors')
optimizedPassTrace = go.Scatter(x=xList,
y=optimizedPass,
mode='lines+markers',
name='Optimized-Undetected Passive Errors')
ActTraces = [baselineActTrace, optimizedActTrace]
PassTraces = [baselinePassTrace, optimizedPassTrace]
layout_ActError = Layout(
title=
'Comparing Optimized Values with Baseline Values : Undetected Active Errors',
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
xaxis=XAxis(showgrid=True,
showline=True,
title='Iterations of DE',
autorange=False,
range=[0, 1100],
titlefont=Font(family='Times New Roman',
size=12,
color='#000000')),
yaxis=YAxis(showgrid=True,
showline=True,
title='Optimized Value',
range=[0, 1500],
autorange=False,
titlefont=Font(family='Times New Roman',
size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
layout_PassError = Layout(
title=
'Comparing Optimized Values with Baseline Values : Undetected Passive Errors',
titlefont=Font(family='Times New Roman', size=18, color='#000000'),
xaxis=XAxis(showgrid=True,
showline=True,
title='Iterations of DE',
autorange=False,
range=[0, 1100],
titlefont=Font(family='Times New Roman',
size=12,
color='#000000')),
yaxis=YAxis(showgrid=True,
showline=True,
title='Optimized Value',
range=[0, 5],
autorange=False,
titlefont=Font(family='Times New Roman',
size=12,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
fig_UnActErr = Figure(data=ActTraces, layout=layout_ActError)
fileNameParam = "UnActError.png"
py.image.save_as(fig_UnActErr, fileNameParam)
fig_UnPassErr = Figure(data=PassTraces, layout=layout_PassError)
fileNameParam = "UnPassError.png"
py.image.save_as(fig_UnPassErr, fileNameParam)
return "DONE!"
layout=Layout(width=640,
height=480,
autosize=False,
margin=Margin(l=80, r=63, b=47, t=47,
pad=0),
hovermode='closest',
showlegend=False,
xaxis1=XAxis(domain=[0.0, 1.0],
range=[0.0, 5.0],
type='linear',
showline=True,
ticks='inside',
nticks=6,
showgrid=False,
zeroline=False,
tickfont=Font(size=12.0),
anchor='y1',
side='bottom',
mirror='ticks'),
yaxis1=YAxis(domain=[0.0, 1.0],
range=[0.0, 200.0],
type='linear',
showline=True,
ticks='inside',
nticks=5,
showgrid=False,
zeroline=False,
tickfont=Font(size=12.0),
anchor='x1',
side='left',
mirror='ticks')))
def plotAllStates(fileNameP, dictP):
import plotly.plotly as py
from plotly.graph_objs import Bar, Marker, Data, Layout, XAxis, YAxis, Figure, Margin, Font
#import plotly.graph_objs as gObjLib
#####
dirToSave = "plotsesscat/"
#barSize = 6
listForNormalTrace = []
listForHeavyTrace = []
listForSuperHeavyTrace = []
allCatList = ['None', 'Idle', 'System', 'Code', 'Debug', 'Build', 'Navi']
for it_ in allCatList:
#print "ZZZZZZZ", dictP[it_]
listForNormalTrace.append(dictP[it_][0])
listForHeavyTrace.append(dictP[it_][1])
listForSuperHeavyTrace.append(dictP[it_][2])
#print "length - Normal ... ", listForNormalTrace
#print "length - Heavy ... ", listForHeavyTrace
#print "length - SuperHeavy ... ", listForSuperHeavyTrace
normalTrace = Bar(
x=['None', 'Idle', 'System', 'Code', 'Debug', 'Build', 'Navi'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=listForNormalTrace,
name='Normal')
heavyTrace = Bar(
x=['None', 'Idle', 'System', 'Code', 'Debug', 'Build', 'Navi'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=listForHeavyTrace,
name='Heavy')
superHeavyTrace = Bar(
x=['None', 'Idle', 'System', 'Code', 'Debug', 'Build', 'Navi'],
#x=[ 'Idle', 'System', 'Code', 'Debug', 'Build'],
y=listForSuperHeavyTrace,
name='Super Heavy')
normalTrace['marker'] = Marker(color='green')
heavyTrace['marker'] = Marker(color='yellow')
superHeavyTrace['marker'] = Marker(color='red')
dataToPlot = Data([normalTrace, heavyTrace, superHeavyTrace])
layoutToUse = Layout(
title='Distribution of Different Session Categories',
titlefont=Font(family='Times New Roman', size=22, color='#000000'),
barmode='group',
xaxis=XAxis(showgrid=True,
showline=True,
title='Types of Sessions',
titlefont=Font(family='Times New Roman',
size=18,
color='#000000'),
tickwidth=1.5),
yaxis=YAxis(
showgrid=True,
showline=True,
title='Count',
#range=[0, 35],
autorange=True,
titlefont=Font(family='Times New Roman', size=18,
color='#000000')),
width=800,
height=600,
margin=Margin(l=140, r=40, b=50, t=80),
)
fileToSave = dirToSave + fileNameP + '.png'
figToPlot = Figure(data=dataToPlot, layout=layoutToUse)
#plot_url = py.plot(figToPlot, filename=fileToSave)
py.image.save_as(figToPlot, fileToSave)