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ChartLayouts.py
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ChartLayouts.py
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import statistics
import pandas as pd
import Charts as Charts
from dash import html, dcc
from dash import dash_table
import SingleStudyCharts
import styles
import Helper
import Layouts
download_layouts = [{'label': 'Default plot format' + ' (Width: ' + str(
Helper.defaultPlotSettings.width) + ' Height: ' + str(Helper.defaultPlotSettings.height) + ' Font Size: ' + str(
Helper.defaultPlotSettings.fontSize) + ')', 'value': 'defaultPlot'},
{'label': 'Narrow plot format' + ' (Width: ' + str(
Helper.narrowPlotSettings.width) + ' Height: ' + str(
Helper.narrowPlotSettings.height) + ' Font Size: ' + str(
Helper.narrowPlotSettings.fontSize) + ')', 'value': 'narrowPlot'},
{'label': 'Wide plot format' + ' (Width: ' + str(Helper.widePlotSettings.width) + ' Height: ' + str(
Helper.widePlotSettings.height) + ' Font Size: ' + str(Helper.widePlotSettings.fontSize) + ')',
'value': 'widePlot'},
{'label': 'Custom format', 'value': 'customSize'}
]
sort_values = [{'label': 'Alphabetical order', 'value': 'alphabetically'},
{'label': 'Mean', 'value': 'mean'},
{'label': 'Median', 'value': 'median'}
]
def CreatePercentilePlotLayout(SUSData, systemList):
options = []
value = []
for system in systemList:
options.append({'label': system, 'value': system})
value.append(system)
fig = Charts.CreatePercentilePlot(SUSData, value)
tableContent = createPercentilePlotTable(SUSData)
graphContent = [
html.Div([
html.Div([
html.Div([
dcc.Graph(
id='percentilePlot',
figure=fig,
style=styles.graph_style
),
],
style=styles.graph_div_style),
html.Div([tableContent], id='percentile-plot-table-div', style=styles.tableStyle),
],
style=styles.main_content_style
),
html.Div([
html.Label([
Helper.percentileIntoText,
],
style={'display': 'block',
'padding': '10px 10px 0px 10px'
},
),
html.Label([
"Show in plot: ",
dcc.Checklist(id='systems-percentilePlot',
options=options,
value=value,
labelStyle={'display': 'block'},
style={'font-weight': 'normal',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px',
},
),
html.Label([
"Sort by: ",
dcc.Dropdown(id='sort-by-percentile',
options=sort_values,
value='alphabetically',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
Helper.imageDownloadLabelFactory('percentile'),
dcc.Download(id='download-image-percentile'),
dcc.Download(id='download-csv-percentile'),
html.Div([
html.Button('Download this chart', id='image-percentile-button', className='button1'),
],
style=styles.download_div_style
),
html.Div([html.Button('Download this data table', id='csv-percentile-button', className='button1'),
dcc.Download(id='download-csv-percentile')],
style=styles.download_div_style),
html.Div([
html.Button('Download complete analysis', id='download-all-percentile-button',
className='button1')],
style=styles.download_div_style
),
],
className='editor'
)
],
style=styles.graph_editor_container
)
]
return graphContent
def CreateMainPlotLayout(SUSData, systemList):
options = []
value = []
for system in systemList:
options.append({'label': system, 'value': system})
value.append(system)
fig = Charts.CreateMainplot(SUSData, 'outliers', 'adjectiveScale', 'vertical', 'mainplot', 'mean', "",
'default-colors')
#
tableContent = createMainplotTable(SUSData, 'adjectiveScale')
#
# conclusivenessFigure = Charts.CreateConclusivenessChart(SUSData)
graphContent = [
html.Div([
html.Div([
html.Div(
[
dcc.Graph(id='mainplot',
figure=fig,
style=styles.graph_style
)
],
style=styles.graph_div_style
),
html.Div([tableContent], id='mainplot-table-div', style=styles.tableStyle),
], style=styles.main_content_style
),
# html.Img(src='/assets/adjective_scale.JPG'),
html.Div([
html.Label([
html.P(children=[
'Each System Usability Scale (SUS) study score represents an average perceived usability score between 0 - 100 calculated based on the formula from ',
html.A('Brooke et al. 1996',
href='https://scholar.google.de/citations?view_op=view_citation&hl=de&user=qjAGPUcAAAAJ&citation_for_view=qjAGPUcAAAAJ:u5HHmVD_uO8C',
target="_blank"),
'. SUS study scores can be compared against each other and on contextualization scales.'
]),
],
style={'display': 'block',
'padding': '10px 10px 0px 10px'
},
),
html.Label([
html.Details([
html.Summary("Plot type: "),
html.P(id="plotstyle-info",
children=[Helper.plotStyleInfoTexts['mainplot']],
style=styles.editorInfoTextStyle)], open=True),
dcc.Dropdown(id='plotstyle-mainplot',
options=[{'label': 'Boxplot', 'value': 'mainplot'},
{'label': 'Notched Boxplot', 'value': 'notched'},
{'label': 'Bar chart', 'value': 'per-question-chart'}],
value='mainplot',
style={'font-weight': 'normal',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px',
},
),
html.Label([
html.Details([html.Summary("Contextualization scale: "),
html.P(id="scaletype-info",
children=[Helper.scaleInfoTexts['adjectiveScale']],
style=styles.editorInfoTextStyle)], open=True),
dcc.Dropdown(id='scale-mainplot',
options=[{'label': 'Adjective Scale', 'value': 'adjectiveScale'},
{'label': 'Grade Scale', 'value': 'gradeScale'},
{'label': 'Quartile Scale', 'value': 'quartileScale'},
{'label': 'Acceptability Scale', 'value': 'acceptabilityScale'},
{'label': 'Net Promoter Scale', 'value': 'promoterScale'},
{'label': 'Industry Benchmark Scale', 'value': 'industryBenchmarkScale'},
{'label': 'No Scale', 'value': 'none'}
# {'label': 'Background Adjective Scale', 'value': 'BGAdjectiveScale'}
],
value='adjectiveScale',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
'Colorize plot according to contextualization scale:',
dcc.Dropdown(id='colorize-by-scale',
options={'scale-colors': 'Colorize by scale',
'default-colors': 'Default Colors'},
value='default-colors',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Show in plot: ",
dcc.Checklist(id='systems-mainplot',
options=options,
value=value,
labelStyle={'display': 'block'},
style={'font-weight': 'normal',
'margin-top': '10px'}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px',
},
),
html.Label([
"Sort by: ",
dcc.Dropdown(id='sort-by-mainplot',
options=sort_values,
value='alphabetically',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Individual data points: ",
dcc.Dropdown(id='datapoints-mainplot',
options=[{'label': 'Show outliers', 'value': 'outliers'},
{'label': 'Show all datapoints', 'value': 'all'},
{'label': 'Show no datapoints', 'value': 'False'}],
value='outliers',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
id='datapoints-label',
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Mean and Standard Deviation: ",
dcc.Dropdown(id='mean_sd-mainplot',
options=[{'label': 'Show Mean', 'value': 'mean'},
{'label': 'Show Mean and SD', 'value': 'sd'},
{'label': 'None', 'value': 'neither'}],
value='mean',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
id='mean_sdValue-label',
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Standard Deviation Type: ",
dcc.Dropdown(id='sd_type-mainplot',
options=[
{'label': 'Sample standard deviation', 'value': 'sample'},
{'label': 'Population Standard-Deviation', 'value': 'population'},
],
value='sample',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
id='mean_sdValue-label',
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Plot orientation: ",
dcc.Dropdown(id='orientation-mainplot',
options=[{'label': 'Vertical', 'value': 'vertical'},
{'label': 'Horizontal', 'value': 'horizontal'}],
value='vertical',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"X-Axis label: ",
dcc.Input(id='axis-title-mainplot',
type="text",
debounce=True,
value="",
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
id='orientation-label',
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
Helper.imageDownloadLabelFactory('mainplot'),
dcc.Download(id='download-image-mainplot'),
dcc.Download(id='download-csv-mainplot'),
html.Div([
html.Button('Download this chart', id='image-mainplot-button', className='button1'),
],
style=styles.download_div_style
),
html.Div([
html.Button('Download this data table', id='csv-mainplot-button', className='button1'), ],
style=styles.download_div_style
),
html.Div([
html.Button('Download complete analysis', id='download-all-mainplot-button', className='button1')],
style=styles.download_div_style
),
],
className='editor'
)
],
style=styles.graph_editor_container),
]
return graphContent
def CreatePerQuestionChartLayout(SUSData, systemList):
options = []
value = []
for system in systemList:
options.append({'label': system, 'value': system})
value.append(system)
questionOptions = [{'label': 'Question 1', 'value': 'Question 1'},
{'label': 'Question 2', 'value': 'Question 2'},
{'label': 'Question 3', 'value': 'Question 3'},
{'label': 'Question 4', 'value': 'Question 4'},
{'label': 'Question 5', 'value': 'Question 5'},
{'label': 'Question 6', 'value': 'Question 6'},
{'label': 'Question 7', 'value': 'Question 7'},
{'label': 'Question 8', 'value': 'Question 8'},
{'label': 'Question 9', 'value': 'Question 9'},
{'label': 'Question 10', 'value': 'Question 10'}]
questionsTicked = ['Question 1', 'Question 2', 'Question 3', 'Question 4', 'Question 5', 'Question 6',
'Question 7', 'Question 8', 'Question 9',
'Question 10']
fig = Charts.CreatePerQuestionChart(SUSData, questionsTicked, value, 'vertical')
tableContent = createPerItemTable(SUSData, questionsTicked)
graphContent = [
html.Div([
html.Div([
html.Div([
html.Div([
Layouts.per_question_context,
],
style={'float': 'left',
'width': '40%'},
),
dcc.Graph(id='per-question-chart',
figure=fig,
style=styles.graph_style_per_item),
],
style=styles.graph_div_style
),
html.Div([tableContent], id='per-item-table-div', style=styles.tableStyle),
], style=styles.main_content_style
),
html.Div([
html.Label([
html.P(
'The per item chart visualizes the impact of participants answers to specific SUS questions. The per item values are normalized values between 0-10 representing their contribution to the SUS study scores and not the Likert scale values in the questionnaire where even numbered questions (2, 4, 6, 8, and 10) are formulated negatively.'),
],
style={'display': 'block',
'padding': '10px 10px 0px 10px'
},
),
html.Label([
"Show in plot: ",
dcc.RadioItems(id='systems-per-question-chart-radio',
options=options,
value=value[0],
labelStyle={'display': 'block'},
style={'font-weight': 'normal',
'margin-top': '10px'
}
),
],
id='systems-label-radio',
style={'display': 'none',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px',
},
),
html.Label([
"Show in plot: ",
dcc.Checklist(id='systems-per-question-chart',
options=options,
value=value,
labelStyle={'display': 'block'},
style={'font-weight': 'normal',
'margin-top': '10px'
}
),
],
id='systems-label',
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px',
},
),
html.Label([
"Show questionnaire items: ",
dcc.Checklist(id='questions-per-question-chart',
options=questionOptions,
value=questionsTicked,
labelStyle={'display': 'block'},
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Plot type: ",
dcc.Dropdown(id='plotstyle-per-question-chart',
options=[{'label': 'Bar chart', 'value': 'per-question-chart'},
{'label': 'Radar chart', 'value': 'radar'},
{'label': 'Stacked bar chart', 'value': 'likert'},
{'label': 'Boxplot', 'value': 'boxplot'}],
value='per-question-chart',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Plot orientation: ",
dcc.Dropdown(id='orientation-per-question-chart',
options=[{'label': 'Vertical', 'value': 'vertical'},
{'label': 'Horizontal', 'value': 'horizontal'}],
value='vertical',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
id='orientation-label',
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
Helper.imageDownloadLabelFactory('perquestion'),
dcc.Download(id='download-image-perquestion'),
dcc.Download(id='download-csv-perquestion'),
html.Div([
html.Button('Download this chart', id='image-perquestion-button', className='button1'),
],
style=styles.download_div_style
),
dcc.Download(id='download-csv-per-question'),
html.Div([
html.Button('Download this data table', id='csv-per-question-button', className='button1'),
],
style=styles.download_div_style
),
html.Div([
html.Button('Download complete analysis', id='download-all-per-question-button',
className='button1'), ],
style=styles.download_div_style
),
],
className='editor'
),
],
style=styles.graph_editor_container,
)
]
return graphContent
def CreateCocnlusivenessChartLayout(SUSData):
value = []
options = []
for study in SUSData.SUSStuds:
options.append({'label': study.name, 'value': study.name})
value.append(study.name)
fig = Charts.CreateConclusivenessChart(SUSData)
tableContent = CreateConclusivenessPlotTable(SUSData, SUSData.getAllStudNames())
graphContent = [
html.Div([
html.Div([
html.Div([
dcc.Graph(id='conclusivenessPlot',
figure=fig,
style=styles.graph_style)
],
style=styles.graph_div_style),
html.Div([tableContent], id='conclusiveness-plot-table-div', style=styles.tableStyle),
],
style=styles.main_content_style
),
# html.Img(src='/assets/adjective_scale.JPG'),
html.Div([
html.Label([
Helper.conclusivenessInfoText,
],
style={'display': 'block',
'padding': '10px 10px 0px 10px'
},
),
html.Label([
"Show in plot: ",
dcc.Checklist(id='systems-conclusivenessPlot',
options=options,
value=value,
labelStyle={'display': 'block'},
style={'font-weight': 'normal',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px',
},
),
Helper.imageDownloadLabelFactory('conclusiveness'),
dcc.Download(id='download-image-conclusiveness'),
dcc.Download(id='download-csv-conclusiveness'),
html.Div([
html.Button('Download this chart', id='image-conclusiveness-button', className='button1'),
],
style=styles.download_div_style
),
html.Div([
html.Button('Download this data table', id='csv-conclusiveness-button', className='button1'),
dcc.Download(id='download-csv-conclusiveness')],
style=styles.download_div_style
),
html.Div([
html.Button('Download complete analysis', id='download-all-conclusiveness-button',
className='button1')],
style=styles.download_div_style
),
],
className='editor'
),
],
style=styles.graph_editor_container),
]
return graphContent
def CreateSingleStudyChartLayout(SUSData):
singleStudy = SUSData.SUSStuds[0]
fig = SingleStudyCharts.singleStudyPresetDict['preset_1'](singleStudy)
questionTable = []
for i, questionScore in enumerate(singleStudy.avgScorePerQuestion):
questionTable.append(
html.Tr([html.Td('Question ' + str(i + 1) + ':'), html.Td(round(singleStudy.avgScorePerQuestion[i], 2))]))
graphContent = html.Div([
html.Div([
html.Div([
dcc.Graph(id='single-study-chart',
figure=fig,
style=styles.single_study_graph_style)
])
],
style=styles.single_study_main_content_style
),
html.Div([
html.Label([
"Plot type: ",
dcc.Dropdown(id='preset-single-study',
clearable=False,
options=[{'label': 'Preset 1', 'value': 'preset_1'},
{'label': 'Preset 2', 'value': 'preset_2'},
{'label': 'Preset 3', 'value': 'preset_3'},
{'label': 'Preset 4', 'value': 'preset_4'}],
value='preset_1',
style={'font-weight': 'normal',
'margin-top': '10px',
}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Score and Interpretation: ",
html.Table([
html.Tr([html.Td('SUS Study Score: '), html.Td(round(singleStudy.Score, 2))]),
html.Tr([html.Td('Median: '), html.Td(singleStudy.median)]),
html.Tr([html.Td('Standard Dev. '), html.Td(round(singleStudy.standardDevOverall, 2))]),
html.Tr([html.Td('Adjective: '), html.Td(Helper.getAdjectiveValue(singleStudy.Score))]),
html.Tr([html.Td('Grade: '), html.Td(Helper.getGradeScaleValue(singleStudy.Score))]),
html.Tr([html.Td('Acceptability: '), html.Td(Helper.getAcceptabilityValue(singleStudy.Score))]),
html.Tr([html.Td('Quartile: '), html.Td(Helper.getQuartileScaleValue(singleStudy.Score))]),
],
style={'cellspacing': '0',
'cellpadding': '0',
'font-weight': 'normal'}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Per Question Scores: ",
html.P(
children=[
'The per item values are normalized values between 0-10 representing their contribution to the SUS study score and not the Likert scale values in the questionnaire where even numbered questions are formulated negatively.'],
style=styles.editorInfoTextStyle),
html.Table(
questionTable
,
style={'cellspacing': '0',
'cellpadding': '0',
'font-weight': 'normal'}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Conclusiveness ",
html.Table([
html.Tr([html.Td('Conclusiveness: '), html.Td(Helper.getConclusiveness(singleStudy))]),
],
style={'cellspacing': '0',
'cellpadding': '0',
'font-weight': 'normal'}
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Label([
"Primary sources ",
html.Details([html.Summary(['Adjective Scale'], style={'font-size': 'medium',
'font-weight': 'normal'}),
html.P(Helper.scaleInfoTexts['adjectiveScale'],
style=styles.editorInfoTextStyle)],
),
html.Details([html.Summary(['Grade Scale'], style={'font-size': 'medium',
'font-weight': 'normal'}),
html.P(Helper.scaleInfoTexts['gradeScale'],
style=styles.editorInfoTextStyle)],
),
html.Details([html.Summary(['Acceptability Scale'], style={'font-size': 'medium',
'font-weight': 'normal'}),
html.P(Helper.scaleInfoTexts['acceptabilityScale'],
style=styles.editorInfoTextStyle)],
),
html.Details([html.Summary(['Percentile-Curve'], style={'font-size': 'medium',
'font-weight': 'normal'}),
html.P(Helper.percentileIntoText,
style=styles.editorInfoTextStyle)],
),
html.Details([html.Summary(['Conslusiveness Chart'], style={'font-size': 'medium',
'font-weight': 'normal'}),
html.P(Helper.conclusivenessInfoText,
style=styles.editorInfoTextStyle)],
),
],
style={'display': 'block',
'font-weight': 'bold',
'padding': '10px 10px 10px 10px'
},
),
html.Div(id="download-single-study-chart-content",
children=[
html.Button("Download Chart", id="download-single-study-chart-button",
className="button1"),
dcc.Download(id="download-single-study-chart"),
],
style=styles.download_div_style, ),
],
className='editor'
),
],
style=styles.graph_editor_container)
return graphContent
def createMainplotDataframe(SUSData, population_standard_deviation: bool = False):
mins = []
firstQuartiles = []
medians = []
thirdQuartiles = []
maxs = []
OverallScores = []
standardDeviation = []
adjectiveScale = []
grade = []
quartile = []
acceptability = []
systemList = []
nps = []
industryBenchmark = []
for study in SUSData.SUSStuds:
adjectiveScale.append(Helper.getAdjectiveValue(study.Score))
grade.append(Helper.getGradeScaleValue(study.Score))
quartile.append(Helper.getQuartileScaleValue(study.Score))
acceptability.append(Helper.getAcceptabilityValue(study.Score))
nps.append(Helper.getNPSValue(study.Score))
industryBenchmark.append(Helper.getIndustryBenchmarkValue(study.Score))
OverallScores.append(round(study.Score, 2))
susScores = study.getAllSUSScores()
mins.append(min(susScores))
systemList.append(study.name)
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))
if population_standard_deviation:
standardDeviation.append(round(study.popStandardDevOverall, 2))
else:
standardDeviation.append(round(study.standardDevOverall, 2))
data = {
'Variable ': systemList,
'SUS Score (mean) ': OverallScores,
'SD ': standardDeviation,
'Min ': mins,
'Max ': maxs,
'1. Quartile ': firstQuartiles,
'Median ': medians,
'3. Quartile ': thirdQuartiles,
'Adjective Scale ': adjectiveScale,
'Grade Scale ': grade,
'Quartile Scale ': quartile,
'Acceptability Scale ': acceptability,
'NPS Scale': nps,
'Industry Benchmark': industryBenchmark
}
# tableHeader = [" "].extend(systems)
df = pd.DataFrame(data)
df = df
return df
def createMainplotTable(SUSData, scaleType, population_standard_deviation: bool = False):
df = createMainplotDataframe(SUSData, population_standard_deviation)
dataframeConditions = Helper.dataFrameConditions[scaleType]
table = dash_table.DataTable(
id='main-plot-dataframe',
columns=[{"name": i, "id": i} for i in df.columns],
style_table={
'overflowX': 'auto'},
data=df.to_dict('records'),
style_cell={'textAlign': 'right',
},
style_data_conditional=dataframeConditions,
style_header={
'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'
}
)
return table
def createPerItemDataFrame(SUSData, questionsTicked=None):
if questionsTicked is None:
questionsTicked = []
questions = ['Question 1', 'Question 2', 'Question 3', 'Question 4', 'Question 5', 'Question 6',
'Question 7', 'Question 8', 'Question 9',
'Question 10']
standardDev = []
removeIdxs = []
for idx, question in enumerate(questions):
if question not in questionsTicked:
removeIdxs.append(idx)
filteredQuestions = [i for j, i in enumerate(questions) if j not in removeIdxs]
data = {'Items': filteredQuestions}
for study in SUSData.SUSStuds:
avgScorePerQuestion, scorePerQuestionValues = study.calcSUSScorePerQuestion(removeIdxs)
standardDeviations = []
for question in scorePerQuestionValues.values():
try:
standardDeviations.append(statistics.pstdev(question))
except statistics.StatisticsError:
standardDeviations.append(0)
data[study.name + ' Contribution (SD)'] = [str(round(score, 2)) + ' (' + str(round(stdDev, 2)) + ')'
for score, stdDev in
zip(avgScorePerQuestion, standardDeviations)]
df = pd.DataFrame(data)
df.set_index('Items', inplace=True)
df = df.transpose()
df.reset_index(inplace=True)
df.rename(columns={'index': 'Variable'}, inplace=True)
return df
def createPerItemTable(SUSData, questionsTicked=None):
if questionsTicked is None:
questionsTicked = []
df = createPerItemDataFrame(SUSData, questionsTicked)
table = dash_table.DataTable(
columns=[{"name": str(i), "id": str(i)} for i in df.columns],
data=df.to_dict('records'),
style_table={'overflowX': 'auto'
},
style_data_conditional=[
{
'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(248, 248, 248)'
}
],
style_header={
'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'
}
)
return table
def createPercentilePlotDataFrame(SUSData):
data = {}
studyScores = []
percentileValues = []
systemList = SUSData.getAllStudNames()
for study in SUSData.SUSStuds:
studyScores.append(round(study.Score, 2))
percentileValues.append(round(Charts.parametrizePercentile(study.Score), 2))
data['Variable'] = systemList
data['SUS Score'] = studyScores
data['Percentile'] = percentileValues
df = pd.DataFrame(data)
return df
def createPercentilePlotTable(SUSData):
df = createPercentilePlotDataFrame(SUSData)
table = dash_table.DataTable(
columns=[{"name": i, "id": i} for i in df.columns],
data=df.to_dict('records'),
style_cell={'textAlign': 'right',
'width': '17%'
},
style_data_conditional=[
{
'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(248, 248, 248)'
}
],
style_header={
'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'
}
)
return table
def CreateConclusivenessPlotDataFrame(SUSData, systems):
sampleSizes = []
conclusiveness = []
yValues = dict({0: '0 %',
6: '35 %',
7: '55 %',
8: '75 %',
9: '78 %',
10: '80 %',
11: '98 %',
12: '100 %',
13: '100 %',
14: '100 %'
})
for study in SUSData.SUSStuds:
sampleSize = len(study.Results)
sampleSizes.append(sampleSize)
if sampleSize < 6:
conclusiveness.append('0 %')
elif sampleSize < 15:
conclusiveness.append(yValues.get(sampleSize))
else:
conclusiveness.append('100 %')
data = {'Variable': list(systems),
'Sample Size': sampleSizes,
'Conclusiveness': conclusiveness
}
# tableHeader = [" "].extend(systems)
df = pd.DataFrame(data)
return df
def CreateConclusivenessPlotTable(SUSData, systems):
df = CreateConclusivenessPlotDataFrame(SUSData, systems)
table = dash_table.DataTable(
columns=[{"name": i, "id": i} for i in df.columns],
data=df.to_dict('records'),
style_cell={'textAlign': 'right',
'width': '17%'
},