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Charts.py
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Charts.py
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import statistics
from collections import Counter
import numpy as np
import plotly.graph_objects as go
import pandas as pd
import Annotations
import plotly.io as pio
import Helper
pio.templates.default = 'seaborn'
plotSize = {0: 500,
1: 500,
2: 600,
3: 700,
4: 800,
5: 900,
'horizontal': 900}
def CreateRadarChart(SUSData, questionsTicked, SUSIds):
fig = go.Figure()
set_PaperBGColor(fig)
questions = ['Question 1', 'Question 2', 'Question 3', 'Question 4', 'Question 5', 'Question 6',
'Question 7', 'Question 8', 'Question 9',
'Question 10']
hovertext = ['I think that I would like to use this system frequently.',
'I found the system unnecessarily complex.',
'I thought the system was easy to use.',
'I think that I would need the support of a technical person to be able to use this system.',
'I found the various functions in this system were well integrated.',
'I thought there was too much inconsistency in this system.',
'I would imagine that most people would learn to use this system very quickly.',
'I found the system very cumbersome to use.',
'I felt very confident using the system.',
'I needed to learn a lot of things before I could get going with this system.']
removeIdxs = []
for idx, question in enumerate(questions):
if question not in questionsTicked:
removeIdxs.append(idx)
for study in SUSData.SUSStuds:
if study.name in SUSIds:
plotData = []
for questionScore in study.avgScorePerQuestion:
plotData.append(questionScore)
filteredPlotData = [i for j, i in enumerate(plotData) if j not in removeIdxs]
filteredQuestions = [i for j, i in enumerate(questions) if j not in removeIdxs]
filteredHover = [i for j, i in enumerate(hovertext) if j not in removeIdxs]
# Have to add the data for the last point twice, so the radar chart appears closed
filteredQuestions.append(filteredQuestions[0])
filteredHover.append(filteredHover[0])
filteredPlotData.append(filteredPlotData[0])
fig.add_trace(go.Scatterpolar(
r0=90,
dr=10,
r=filteredPlotData,
theta=filteredQuestions,
fill='toself',
name=study.name,
hovertext=filteredHover,
))
fig.update_layout(
margin=dict(
l=12,
r=12,
b=40,
t=40,
),
polar=dict(
radialaxis=dict(
visible=True,
range=[0, 10]
),
angularaxis=dict(
direction="clockwise")
))
return fig
def CreateLikertChart(SUSData, questionsTicked, colorizeByMeaning='byMeaning'):
questions = ['Question 1', 'Question 2', 'Question 3', 'Question 4', 'Question 5', 'Question 6',
'Question 7', 'Question 8', 'Question 9',
'Question 10']
y_data = [{'question': 'I think that I would like to<br> use this system frequently.',
'positiveWording': True},
{'question': 'I found the system <br>unnecessarily complex.',
'positiveWording': False},
{'question': 'I thought the system was easy to use.',
'positiveWording': True},
{
'question': 'I think that I would need the support<br>of a technical person to be able to<br>use this system.',
'positiveWording': False},
{'question': 'I found the various functions in<br> this system were well integrated.',
'positiveWording': True},
{'question': 'I thought there was too much <br>inconsistency in this system.',
'positiveWording': False},
{'question': 'I would imagine that most people<br> would learn to use this system very quickly.',
'positiveWording': True},
{'question': 'I found the system very <br>cumbersome to use.',
'positiveWording': False},
{'question': 'I felt very confident<br> using the system.',
'positiveWording': True},
{'question': 'I needed to learn a lot of things before<br> I could get going with this system.',
'positiveWording': False}]
colors = ['#8FD14F', '#CEE741', '#FEF445', '#FAC710', '#F24726']
top_labels = ['Strongly<br>disagree', 'Disagree', 'Neutral', 'Agree', 'Strongly<br>agree']
x_data = [[],
[],
[],
[],
[],
[],
[],
[],
[],
[]]
removeIdxs = []
for idx, question in enumerate(questions):
if question not in questionsTicked:
removeIdxs.append(idx)
for i, questionResults in enumerate(SUSData.rawResultPerQuestion):
for j in range(1, 6):
x_data[i].append(questionResults.count(j) / len(SUSData.rawResultPerQuestion[0]) * 100)
# x_data_strings[i].append(round(questionResults.count(j)/len(questionResults)*100))
x_data = [i for j, i in enumerate(x_data) if j not in removeIdxs]
y_data = [i for j, i in enumerate(y_data) if j not in removeIdxs]
# x_data_strings.reverse()
x_data.reverse()
y_data.reverse()
fig = go.Figure()
for i in range(0, len(x_data[0])):
j = 0
for xd, yd in zip(x_data, y_data):
if yd['positiveWording'] != 0 or colorizeByMeaning == 'regular':
fig.add_trace(go.Bar(
hovertemplate='%{x:d}%, ' + '%{text}<extra></extra>',
text=[top_labels[i]],
x=[xd[i]], y=[yd['question']],
orientation='h',
textposition="none",
marker=dict(
color=colors[4-i],
line=dict(color='rgb(248, 248, 249)', width=1)
)
))
else:
fig.add_trace(go.Bar(
hovertemplate='%{x:d}%, ' + '%{text}<extra></extra>',
text=[top_labels[i]],
x=[xd[i]], y=[yd['question']],
textposition="none",
orientation='h',
marker=dict(
color=colors[i],
line=dict(color='rgb(248, 248, 249)', width=1)
)
))
j += 1
fig.update_layout(
xaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
zeroline=False,
domain=[0.15, 1],
range=[0, 100]
),
yaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
zeroline=False,
),
barmode='stack',
paper_bgcolor='#FAFAFA',
plot_bgcolor='#FAFAFA',
showlegend=False,
margin=dict(
l=150,
r=12,
b=12,
),
)
annotations = []
labelPos = 10
label_offset = 20
for yd, xd in zip(y_data, x_data):
# labeling the y-axis
annotations.append(dict(xref='paper', yref='y',
x=0.14, y=yd['question'],
xanchor='right',
text=str(yd['question']),
font=dict(family='Arial', size=10,
color='rgb(67, 67, 67)'),
showarrow=False, align='right'))
# labeling the first percentage of each bar (x_axis)
percentage = round(xd[0])
if percentage != 0:
annotations.append(dict(xref='x', yref='y',
x=xd[0] / 2, y=yd['question'],
text=str(round(xd[0])) + '%',
font=dict(family='Arial', size=14),
showarrow=False))
# labeling the first Likert scale (on the top)
if yd == y_data[-1]:
annotations.append(dict(xref='x', yref='paper',
x=labelPos, y=1.15,
text=top_labels[0],
font=dict(family='Arial', size=14,
color='rgb(67, 67, 67)'),
showarrow=False))
space = xd[0]
for i in range(1, len(xd)):
# labeling the rest of percentages for each bar (x_axis)
percentage = round(xd[i])
if percentage != 0:
annotations.append(dict(xref='x', yref='y',
x=space + (xd[i] / 2), y=yd['question'],
text=str(round(xd[i])) + '%',
font=dict(family='Arial', size=14),
showarrow=False))
# labeling the Likert scale
if yd == y_data[-1]:
labelPos += label_offset
annotations.append(dict(xref='x', yref='paper',
x=labelPos, y=1.15,
text=top_labels[i],
font=dict(family='Arial', size=14,
color='rgb(67, 67, 67)'),
showarrow=False))
space += xd[i]
fig.update_layout(annotations=annotations)
return fig
def CreateMainplot(SUSData, boxpoints, scaleValue, orientationValue, plotStyle, mean_sdValue, axis_title, colorizeByScale):
mean_sdValue = determineMean_sdValue(mean_sdValue)
fig = go.Figure()
set_PaperBGColor(fig)
if plotStyle == 'notched':
notchedValue = True
else:
notchedValue = False
if boxpoints == 'False':
boxpoints = False
if orientationValue == 'vertical':
for study in SUSData.SUSStuds:
if colorizeByScale == 'scale-colors' and scaleValue != 'none':
if plotStyle == 'mainplot' or plotStyle == 'notched':
fig.add_trace(
go.Box(y=study.getAllSUSScores(), name=study.name, boxpoints=boxpoints, boxmean=mean_sdValue,
notched=notchedValue, fillcolor=scaleColors[scaleValue](round(study.Score, 2)), line=dict(color='black')))
elif plotStyle == 'per-question-chart':
fig.add_trace(
go.Bar(y=[study.Score], name=study.name, x=[study.name],
error_y=dict(type='data', array=[study.standardDevOverall],), marker_color=scaleColors[scaleValue](round(study.Score, 2))))
else:
if plotStyle == 'mainplot' or plotStyle == 'notched':
fig.add_trace(
go.Box(y=study.getAllSUSScores(), name=study.name, boxpoints=boxpoints, boxmean=mean_sdValue,
notched=notchedValue))
elif plotStyle == 'per-question-chart':
fig.add_trace(
go.Bar(y=[study.Score], name=study.name, x=[study.name],
error_y=dict(type='data', array=[study.standardDevOverall])))
if scaleValue == 'none':
fig.update_layout(
yaxis_range=[0, 100],
xaxis_title=axis_title,
yaxis_title="SUS Score",
uniformtext=dict(mode="show", minsize=9),
margin=dict(
l=0,
r=0,
b=12,
t=12,
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
)
else:
fig.update_layout(
yaxis_range=[0, 100],
xaxis_title=axis_title,
yaxis_title="SUS Score",
uniformtext=dict(mode="show", minsize=9),
margin=dict(
l=0,
r=0,
b=12,
t=12,
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
barmode='stack',
xaxis=dict(
domain=[0, 0.9],
),
yaxis=dict(
domain=[0, 1],
range=[0, 100]
),
xaxis2=dict(
domain=[0.9, 1],
anchor='y2',
range=[-0.5, 0.5],
showgrid=False,
showticklabels=False
),
yaxis2=dict(
domain=[0, 1],
anchor='x2',
showgrid=False,
range=[0, 100],
showticklabels=False
),
)
fig.layout.yaxis.fixedrange = True
else:
for study in SUSData.SUSStuds:
if colorizeByScale == 'scale-colors' and scaleValue != 'none':
if plotStyle == 'mainplot' or plotStyle == 'notched':
fig.add_trace(
go.Box(x=study.getAllSUSScores(), name=study.name, boxpoints=boxpoints, boxmean=mean_sdValue,
notched=notchedValue, line=dict(color='black') ,fillcolor=scaleColors[scaleValue](round(study.Score, 2))))
elif plotStyle == 'per-question-chart':
fig.add_trace(
go.Bar(x=[study.Score], name=study.name, y=[study.name], orientation='h',
error_x=dict(type='data', array=[study.standardDevOverall]), marker_color=scaleColors[scaleValue](round(study.Score, 2))))
else:
if plotStyle == 'mainplot' or plotStyle == 'notched':
fig.add_trace(
go.Box(x=study.getAllSUSScores(), name=study.name, boxpoints=boxpoints, boxmean=mean_sdValue,
notched=notchedValue))
elif plotStyle == 'per-question-chart':
fig.add_trace(
go.Bar(x=[study.Score], name=study.name, y=[study.name], orientation='h',
error_x=dict(type='data', array=[study.standardDevOverall])))
if scaleValue == 'none':
fig.update_layout(
yaxis_title=axis_title,
xaxis_title="SUS Score",
xaxis_range=[0, 100],
barmode='stack',
margin=dict(
l=0,
r=0,
b=12,
t=12,
),
xaxis=dict(
domain=[0, 1],
range=[0, 100],
tickmode='linear',
tick0=0,
dtick=10,
)
)
else:
fig.update_layout(
yaxis_title=axis_title,
xaxis_title="SUS Score",
xaxis_range=[0, 100],
barmode='stack',
margin=dict(
l=0,
r=0,
b=12,
t=12,
),
yaxis=dict(
domain=[0, 0.8],
),
xaxis=dict(
domain=[0, 1],
range=[0, 100],
tickmode='linear',
tick0=0,
dtick=10,
),
yaxis2=dict(
domain=[0.8, 1],
anchor='x2',
range=[-0.5, 0.5],
showgrid=False,
showticklabels=False
),
xaxis2=dict(
domain=[0, 1],
anchor='y2',
showgrid=False,
range=[0, 100],
showticklabels=False
),
)
fig.layout.xaxis.fixedrange = True
# If enabled, add contextualization Scales
if scaleValue != 'none':
fig.add_traces(scales[scaleValue](orientationValue))
return fig
def CreatePerQuestionBoxPlot(SUSData, questionsTicked, SUSIds, orientationValue):
fig = go.Figure()
set_PaperBGColor(fig)
questions = ['Question 1', 'Question 2', 'Question 3', 'Question 4', 'Question 5', 'Question 6',
'Question 7', 'Question 8', 'Question 9',
'Question 10']
removeIdxs = []
for idx, question in enumerate(questions):
if question not in questionsTicked:
removeIdxs.append(idx)
# Filter questions based on the ones user chose
filteredQuestions = [i for j, i in enumerate(questions) if j not in removeIdxs]
if orientationValue == 'vertical':
for study in SUSData.SUSStuds:
if study.name in SUSIds:
x_axis_descriptions = []
# prepare x-axis description. for each score per sus result one entry specifying to which question this score relates is needed.
# very messy, but that's just how grouped boxplot work in plotly.
for question in filteredQuestions:
for result in study.Results:
x_axis_descriptions.append(question)
# dict with scores for each question
scoresPerQuestion = study.calcSUSScorePerQuestion(removeIdxs)[1]
plotdata = []
# all the ordered SUS values of this study are put in one list.
# again very messy, but just how plotly works here...
for questionValues in scoresPerQuestion.values():
plotdata.extend(questionValues)
# put it all together in the box plot
fig.add_trace(go.Box(name=study.name, y=plotdata, x=x_axis_descriptions, boxmean=True))
fig.update_layout(
yaxis_title='SUS Score',
boxmode='group',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
)
)
else:
# same thing with reversed axis
for study in SUSData.SUSStuds:
if study.name in SUSIds:
y_axis_descriptions = []
# prepare y-axis description. for each score per sus result one entry specifying to which question this score relates is needed.
# very messy, but that's just how grouped boxplot work in plotly.
for question in filteredQuestions:
for result in study.Results:
y_axis_descriptions.append(question)
# dict with scores for each question
scoresPerQuestion = study.calcSUSScorePerQuestion(removeIdxs)[1]
plotdata = []
# all the ordered SUS values of this study are put in one list.
# again very messy, but just how plotly works here...
for questionValues in scoresPerQuestion.values():
plotdata.extend(questionValues)
# put it all together in the box plot
fig.add_trace(go.Box(name=study.name, y=y_axis_descriptions, x=plotdata, boxmean=True))
fig.update_traces(orientation='h')
fig.update_layout(
xaxis=dict(title='SUS Score', zeroline=False),
boxmode='group',
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
)
)
return fig
def CreatePerQuestionChart(SUSData, questionsTicked, SUSIds, orientationValue):
fig = go.Figure()
set_PaperBGColor(fig)
questions = ['Question 1', 'Question 2', 'Question 3', 'Question 4', 'Question 5', 'Question 6',
'Question 7', 'Question 8', 'Question 9',
'Question 10']
hovertext = ['I think that I would like to use this system frequently.',
'I found the system unnecessarily complex.',
'I thought the system was easy to use.',
'I think that I would need the support of a technical person to be able to use this system.',
'I found the various functions in this system were well integrated.',
'I thought there was too much inconsistency in this system.',
'I would imagine that most people would learn to use this system very quickly.',
'I found the system very cumbersome to use.',
'I felt very confident using the system.',
'I needed to learn a lot of things before I could get going with this system.']
removeIdxs = []
for idx, question in enumerate(questions):
if question not in questionsTicked:
removeIdxs.append(idx)
if orientationValue == 'vertical':
# for study in SUSData.SUSStuds:
# if study.name in SUSIds:
# filteredQuestions = [i for j, i in enumerate(questions) if j not in removeIdxs]
# plotData = []
#
# for questionScore in study.avgScorePerQuestion:
# plotData.append(questionScore)
# print(plotData)
#
# for study in SUSData.SUSStuds:
# scoresPerQuestion = study.calcSUSScorePerQuestion(removeIdxs)[1]
# allStudyScoresPerQuestion.append(scoresPerQuestion)
#
# for studyScores in allStudyScoresPerQuestion:
# for scores in studyScores:
# plotData.extend(scores)
for study in SUSData.SUSStuds:
if study.name in SUSIds:
plotData = study.calcSUSScorePerQuestion(removeIdxs)[0]
filteredErrorBars = [i for j, i in enumerate(study.standardDevPerQuestion) if j not in removeIdxs]
filteredQuestions = [i for j, i in enumerate(questions) if j not in removeIdxs]
filteredHover = [i for j, i in enumerate(hovertext) if j not in removeIdxs]
fig.add_trace(go.Bar(name=study.name, y=plotData, x=filteredQuestions, hovertext=filteredHover,
error_y=dict(type='data', array=filteredErrorBars)))
fig.update_layout(
yaxis_title="SUS Score",
yaxis_range=[0, 10],
margin=dict(
l=12,
r=12,
b=12,
t=12,
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
)
)
else:
for study in SUSData.SUSStuds:
if study.name in SUSIds:
plotData = study.calcSUSScorePerQuestion(removeIdxs)[0]
filteredErrorBars = [i for j, i in enumerate(study.standardDevPerQuestion) if j not in removeIdxs]
filteredQuestions = [i for j, i in enumerate(questions) if j not in removeIdxs]
filteredHover = [i for j, i in enumerate(hovertext) if j not in removeIdxs]
fig.add_trace(
go.Bar(name=study.name, x=plotData, y=filteredQuestions, hovertext=filteredHover, orientation='h',
error_x=dict(type='data', array=filteredErrorBars)))
# fig.update_layout(
# xaxis_title="SUS Score",
# xaxis_range=[0, 10],
# margin=dict(
# l=12,
# r=12,
# b=12,
# t=12,
# ),
# legend=dict(
# orientation="h",
# yanchor="bottom",
# y=1.02,
# xanchor="right",
# x=1
# ),
# )
return fig
def CreateConclusivenessChart(SUSData):
yVal = [35, 75, 80, 100, 100]
xVal = [6, 8, 10, 12, 14]
studySampleSizes = dict()
for study in SUSData.SUSStuds:
if len(study.Results) > 14:
sampleSize = 14
else:
sampleSize = len(study.Results)
studySampleSizes[study.name] = sampleSize
studySamples = []
annotations = []
for idx, study in enumerate(studySampleSizes):
studySamples.append(studySampleSizes[study])
occurrences = Counter(studySamples)
annotations.append(Annotations.generateConclusivenessAnnotation(studySampleSizes[study], study,
occurrences[studySampleSizes[study]] * 5))
fig = go.Figure()
set_PaperBGColor(fig)
fig.add_trace(go.Scatter(x=xVal, y=yVal, hoverinfo='skip'))
fig.update_layout(
margin=dict(
l=0,
r=0,
b=12,
t=12,
),
annotations=annotations,
xaxis_title="Sample Size",
yaxis_title="Conclusiveness Percentage",
#yaxis=dict(
# tickmode='array',
# tickvals=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
# ticktext=['10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']
#)
)
return fig
def CreatePercentilePlot(SUSData, systems):
x = np.linspace(0, 100, 100)
y = parametrizePercentile(x)
fig = go.Figure()
set_PaperBGColor(fig)
defaultPlotlyColors = ['#1f77b4', ' #ff7f0e', '#2ca02c', ' #d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
'#bcbd22', '#17becf']
fig.update_layout(dict(
xaxis_title='SUS Score',
yaxis_title='Percentile Value',
margin=dict(
l=0,
r=0,
b=12,
t=12,
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1
),
colorway=['#1f77b4', ' #ff7f0e', '#2ca02c', ' #d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22',
'#17becf']
))
fig.layout.yaxis.fixedrange = True
fig.add_trace(go.Scatter(x=x, y=y, showlegend=False, hoverinfo='skip'))
for i, study in enumerate(SUSData.SUSStuds):
if study.name in systems:
fig.add_trace(go.Scatter(x=[study.Score], y=[parametrizePercentile(study.Score)],
marker=dict(size=12),
mode='markers',
name=study.name,
hovertemplate='%{y:d}th Percentile, ' + study.name + '<extra></extra>'))
return fig
def parametrizePercentile(x):
a1 = 106.3
b1 = 118
c1 = 46.32
a2 = 33.06
b2 = 81.58
c2 = 15.92
return a1 * np.exp(-((x - b1) / c1) ** 2) + a2 * np.exp(-((x - b2) / c2) ** 2)
def update_Datapoints(figure, datapointValues):
if datapointValues == 'False':
datapointValues = False
for element in figure['data']:
element['boxpoints'] = datapointValues
return figure
def set_PaperBGColor(fig):
fig.update_layout(
paper_bgcolor='#FAFAFA'
)
def determineMean_sdValue(mean_sdValue):
if mean_sdValue == 'neither':
return False
if mean_sdValue == 'mean':
return True
return mean_sdValue
def parseDataForTable(SUSData):
mins = ["Min"]
firstQuartiles = ["First Quartile"]
medians = ["Median"]
thirdQuartiles = ["Third Quartile"]
maxs = ["Max"]
OverallScores = ["Score (mean)"]
standardDeviation = ["Standard Dev."]
for study in SUSData.SUSStuds:
OverallScores.append(round(study.Score, 2))
susScores = study.getAllSUSScores()
mins.append(min(susScores))
try:
firstQuartiles.append(statistics.quantiles(susScores)[0])
except statistics.StatisticsError:
firstQuartiles.append('-')
try:
medians.append(statistics.median(susScores))
except statistics.StatisticsError:
medians.append('-')
try:
thirdQuartiles.append(statistics.quantiles(susScores)[2])
except statistics.StatisticsError:
thirdQuartiles.append('-')
maxs.append(max(susScores))
standardDeviation.append(round(study.standardDevOverall, 2))
data = {
'Min': mins,
'First Quartile': firstQuartiles,
'Median': medians,
'Overall Score (mean)': OverallScores,
'Standard Deviation': standardDeviation,
'Third Quartile': thirdQuartiles,
'Max': maxs,
}
df = pd.DataFrame(data)
return df
# noinspection PyTypeChecker
def getAdjectiveScaleTraces(orientation, xaxis='x2', yaxis='y2'):
if orientation == 'vertical':
traces = [
go.Bar(y=[25], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip',
text="Worst<br>Imagineable",
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(y=[26.7], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Poor',
marker_color='#FAC710', insidetextanchor='middle'),
go.Bar(y=[19.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='OK',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(y=[9.7], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Good',
marker_color='#CEE741', insidetextanchor='middle'),
go.Bar(y=[3.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Excellent',
marker_color='#8FD14F', insidetextanchor='middle'),
go.Bar(y=[16], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip',
text='Best<br>Imagineable',
marker_color='#008000', insidetextanchor='middle')
]
else:
traces = [
go.Bar(x=[25], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip',
text="Worst<br>Imagineable",
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(x=[26.7], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Poor',
marker_color='#FAC710', insidetextanchor='middle'),
go.Bar(x=[19.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='OK',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(x=[9.7], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Good',
marker_color='#CEE741', insidetextanchor='middle'),
go.Bar(x=[3.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Excellent',
marker_color='#8FD14F', insidetextanchor='middle'),
go.Bar(x=[16], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip',
text='Best<br>Imagineable',
marker_color='#008000', insidetextanchor='middle')
]
return traces
# noinspection PyTypeChecker
def getGradeScaleTraces(orientation, xaxis='x2', yaxis='y2'):
if orientation == 'vertical':
traces = [
go.Bar(y=[51.6], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='F',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(y=[11], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='D',
marker_color='#FAC710', insidetextanchor='middle'),
go.Bar(y=[9.9], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='C',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(y=[6.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='B',
marker_color='#CEE741', insidetextanchor='middle'),
go.Bar(y=[21.2], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='A',
marker_color='#8FD14F', insidetextanchor='middle')]
else:
traces = [
go.Bar(x=[51.6], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='F',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(x=[11], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='D',
marker_color='#FAC710', insidetextanchor='middle'),
go.Bar(x=[9.9], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='C',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(x=[6.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='B',
marker_color='#CEE741', insidetextanchor='middle'),
go.Bar(x=[21.2], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='A',
marker_color='#8FD14F', insidetextanchor='middle')
]
return traces
# noinspection PyTypeChecker
def getAcceptabilityScaleTraces(orientation, xaxis='x2', yaxis='y2'):
if orientation == 'vertical':
traces = [
go.Bar(y=[51.6], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip',
text='Not<br>Acceptable',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(y=[20.9], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Marginal',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(y=[27.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Acceptable',
marker_color='#8FD14F', insidetextanchor='middle'),
]
else:
traces = [
go.Bar(x=[51.6], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip',
text='Not<br>Acceptable',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(x=[20.9], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Marginal',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(x=[27.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Acceptable',
marker_color='#8FD14F', insidetextanchor='middle')
]
return traces
# noinspection PyTypeChecker
def getPromoterScaleTraces(orientation, xaxis='x2', yaxis='y2'):
if orientation == 'vertical':
traces = [
go.Bar(y=[62.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Detractor',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(y=[16.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Passive',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(y=[21.2], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Promoter',
marker_color='#8FD14F', insidetextanchor='middle')
]
else:
traces = [
go.Bar(x=[62.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Detractor',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(x=[16.3], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Passive',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(x=[21.2], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Promoter',
marker_color='#8FD14F', insidetextanchor='middle')
]
return traces
# noinspection PyTypeChecker
def getIndustryBenchmarkScale(orientation, xaxis='x2', yaxis='y2'):
if orientation == 'vertical':
traces = [
go.Bar(y=[68], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Below Average',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(y=[12], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Above Average',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(y=[20], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Above<br>Industry Benchmark',
marker_color='#8FD14F', insidetextanchor='middle')
]
else:
traces = [
go.Bar(x=[68], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Below Average',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(x=[12], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Above Average',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(x=[20], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='Above Industry Benchmark',
marker_color='#8FD14F', insidetextanchor='middle')
]
return traces
def getEmptyScaleTraces(orientation):
return []
# noinspection PyTypeChecker
def getQuartileScaleTraces(orientation, xaxis='x2', yaxis='y2'):
if orientation == 'vertical':
traces = [
go.Bar(y=[62.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='1st Quartile',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(y=[8.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='2nd Quartile',
marker_color='#FAC710', insidetextanchor='middle'),
go.Bar(y=[7], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='3nd Quartile',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(y=[22], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='4th Quartile',
marker_color='#CEE741', insidetextanchor='middle'),
]
else:
traces = [
go.Bar(x=[62.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='1st Quartile',
marker_color='#F24726', insidetextanchor='middle'),
go.Bar(x=[8.5], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='2nd Quartile',
marker_color='#FAC710', insidetextanchor='middle'),
go.Bar(x=[7], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='3nd Quartile',
marker_color='#FEF445', insidetextanchor='middle'),
go.Bar(x=[22], xaxis=xaxis, yaxis=yaxis, width=1, showlegend=False, hoverinfo='skip', text='4th Quartile',
marker_color='#CEE741', insidetextanchor='middle'),
]
return traces
def getAdjectiveScaleColors(SUSMean):
if SUSMean < 25.0:
return '#F24726'
elif SUSMean < 51.7:
return '#FAC710'
elif SUSMean < 71.1:
return '#FEF445'
elif SUSMean < 80.8:
return '#CEE741'
elif SUSMean < 84.1:
return '#8FD14F'
else:
return '#008000'
def getAcceptabilityScaleColors(SUSMean):
if SUSMean < 51.7:
return '#F24726'
elif SUSMean < 72.6:
return '#FEF445'
else:
return '#8FD14F'
def getQuartileScaleColors(SUSMean):
if SUSMean < 62.5:
return '#F24726'
elif SUSMean < 71.0:
return '#FAC710'
elif SUSMean < 78.0:
return '#FEF445'
else:
return '#CEE741'
def getGradeScaleColors(SUSMean):
if SUSMean < 51.6:
return '#F24726'