def main():
file_csv = []
for f in os.listdir("."):
if f.endswith('.csv'):
file_csv.append(f)
menu_list = [
'Home', 'Category Management', 'Contract Management', 'Procure-to-Pay',
'Strategic Sourcing'
]
# Display options in Sidebar
st.sidebar.title('Navigation')
menu_sel = st.sidebar.radio('', menu_list, index=0, key=None)
# Display text in Sidebar
about.display_sidebar()
# Selecting About Menu
if menu_sel == 'Home':
about.display_about()
if menu_sel == 'Category Management':
# st.markdown('# Category Management')
html_temp = """
<div style="background-color:#8E1047;padding:10px">
<h2 style="color:white;text-align:center;">Category Management </h2>
</div>
"""
st.markdown(html_temp, unsafe_allow_html=True)
cat_level1 = [
'Category Analytics', 'Spend Analytics',
'Savings Lifecycle Analytics'
]
a = st.radio('', cat_level1, index=0, key=None)
if a == 'Category Analytics':
cat_level2 = ['Classification', 'Consumption Analysis']
b = st.selectbox('Select Sublevel', cat_level2, index=1)
st.write('You selected `%s`' % b)
if b == 'Consumption Analysis':
st.header('Demand Forecasting')
st.markdown('''
For the monthly demand for each product in different central warehouse
- Products are manufactured in different loaction all over the world
- Takes more than one month to ship products via ocean to different central ware houses
The task is to do a **Demand Forecast** across multiple warehouses
''')
demand_forecast(file_csv)
if a == 'Spend Analytics':
cat_level3 = ['Spend Classification', 'Spend Forecasting']
st.selectbox('Select Sublevel', cat_level3, index=0)
if a == 'Savings Lifecycle Analytics':
cat_level4 = ['Cost-Savings', 'Spend vs Budget']
st.selectbox('Select Sublevel', cat_level4, index=0)
return 0
def main():
menu = ['About', 'Mushroom data', 'Upload dataset']
menuSelection = st.sidebar.radio('', menu, index=0, key=None)
if menuSelection == 'About':
header()
about.display_about()
about.display_contribute()
if st.sidebar.button("Many thanks"):
st.balloons()
if menuSelection == 'Upload dataset':
st.success("# _Model Hints_Web App_ `version0.0.1` ")
st.markdown(
"<h1 style = 'text-align: center; color: green;' > 3-Models of Binary Classification </ h1>",
unsafe_allow_html=True)
st.markdown(
"<h3 style = 'text-align: center; color: Blue;' > 📁Uplaod CSV file </ h3>",
unsafe_allow_html=True)
uploadFile()
if menuSelection == 'Mushroom data':
header()
mushromm.mushroomSetup()
def main():
st.markdown(
'<style> body {background-color: white; color: black}</style>',
unsafe_allow_html=True)
st.markdown('<style> h1 {color: sandybrown; text-align: center }</style>',
unsafe_allow_html=True)
#st.sidebar.header("sidebar")
st.sidebar.title('Select a recommendation method ')
# add_selectbox = st.sidebar.radio(
# "->",
# ("FastAI", "xDeepFM")
# )
add_selectbox = st.sidebar.selectbox('', ('', 'FastAI', 'xDeepFM'))
st.sidebar.text(" \n")
st.sidebar.text(" \n")
st.sidebar.markdown('---')
st.sidebar.text(" \n")
st.sidebar.text(" \n")
if add_selectbox == 'FastAI':
st.title("FastAI")
st.text(" \n")
st.text(" \n")
st.header("*The recommendations for this user are : * :arrow_forward:")
user_input = st.sidebar.number_input("Please Enter User ID",
min_value=0,
max_value=1000,
value=0,
step=1)
user_in = str(user_input)
print(type(user_in))
if st.sidebar.button('Get Recommeded Products'):
result = get_dataset(user_in)
# print(result['predictions ']['productId'])
pred = result['predictions ']
out = list(
zip(pred['productId'].values(), pred['Product Name'].values(),
pred['Price'].values(), pred['Prediction'].values(),
pred['img'].values()))
productIds = list(pred['productId'].values())
productNames = list(pred['Product Name'].values())
productPrice = list(pred['Price'].values())
preds = list(pred['Prediction'].values())
image = list(pred['img'].values())
print(len(productIds))
print(len(preds))
df = pd.DataFrame({
'ProductID': productIds,
'ProductName': productNames,
'Productprice': productPrice,
'Predictions': preds,
'image': image
})
df = df.sort_values(['Predictions'], ascending=False)
dfcheck = df.loc[0:4, [
'ProductID', 'ProductName', 'Productprice', 'Predictions',
'image'
]] #.assign( Table ='').set_index('Table')
dfcheck = dfcheck.reset_index(drop=True)
dfcheck2 = dfcheck.loc[0:4, :]
dfcheck2.set_index("ProductID", inplace=True)
st.write(dfcheck2.to_html(
escape=False, formatters=dict(image=path_to_image_html)),
unsafe_allow_html=True)
#st.write(dfcheck.ProductID)
#st.write(dfcheck.image
# colors = ['rgb(255,218,185)', 'rgb(255,228,181)', 'rgb(255,239,213)',
# 'rgb(250,250,210)', 'rgb(255,250,205)']
# dfn = pd.DataFrame({'ProductID': productIds, 'ProductName': productNames, 'Productprice':productPrice, 'Predictions': preds, 'image': image})
# dfn = dfn.sort_values(['Predictions'], ascending=False)
# dfnn2= dfn.loc[0:4,['ProductID' , 'ProductName','Productprice','Predictions','image']].assign( hack ='').set_index('hack')
# dfnn2['Color'] = colors
# dfnn3 = st.write(dfnn2.to_html(escape=False ,formatters=dict(image=path_to_image_html)), unsafe_allow_html=True)
# print(dfnn3.ProductID)
# fig = go.Figure(data=[go.Table(header=dict(
# values=["<b>Product ID<b>", "<b>Product Name</b>", "<b>Product Price</b>","<b>Predicted Rating</b>","<b>Product Image</b>"],
# line_color='white', fill_color='olive',
# align='center',font=dict(color='white', size=16),height=50
# ),
# cells=dict(
# values=[dfnn3.ProductID, dfnn3.ProductName, dfnn3.Productprice, dfnn3.Predictions, dfnn3.image],
# line_color=[dfnn2.Color], fill_color=[dfnn2.Color],
# align='center',font=dict(color='saddlebrown', size=20),height=50
# ))
# ],layout=layout)
# #fig.show()python -m pip install -r .\requirements.txt
# image = PIL.Image.open('my_table3.png')
# st.image(image, use_column_width=True)
if add_selectbox == 'xDeepFM':
st.title("xDeepFM")
st.text(" \n")
st.text(" \n")
st.header("*The recommendations for this user are : * :arrow_forward:")
user_input2 = st.sidebar.number_input("Please Enter User ID",
min_value=0,
max_value=1000,
value=0,
step=1)
user_in2 = str(user_input2)
if st.sidebar.button('Get Recommeded Products'):
result2 = get_dataset1(user_in2)
# print(result['predictions ']['productId'])
pred = result2['Like Prob']
out = list(
zip(pred['productId'].values(), pred['Product Name'].values(),
pred['Price'].values(), pred['Like Probability'].values(),
pred['img'].values()))
productIds = list(pred['productId'].values())
productNames = list(pred['Product Name'].values())
productPrice = list(pred['Price'].values())
preds = list(pred['Like Probability'].values())
image = list(pred['img'].values())
df = pd.DataFrame({
'Product ID': productIds,
'Product Name': productNames,
'Product price': productPrice,
'Like Probability': preds,
'image': image
})
dfcheck = df.loc[0:4, [
'Product ID', 'Product Name', 'Product price',
'Like Probability', 'image'
]] #.assign( Table ='').set_index('Table')
dfcheck.set_index("Product ID", inplace=True)
st.write(dfcheck.to_html(
escape=False, formatters=dict(image=path_to_image_html)),
unsafe_allow_html=True)
# colors = ['rgb(255,218,185)', 'rgb(255,228,181)', 'rgb(255,239,213)',
# 'rgb(250,250,210)', 'rgb(255,250,205)']
# dfn = pd.DataFrame({'ProductID': productIds, 'Predictions': preds})
# dfnn2= dfn.loc[0:4,['ProductID' , 'Predictions']].assign( hack ='').set_index('hack')
# dfnn2['Color'] = colors
# fig = go.Figure(data=[go.Table(header=dict(
# values=["<b>Product ID<b>", "<b>Like Probability</b>"],
# line_color='white', fill_color='olive',
# align='center',font=dict(color='white', size=16),height=50
# ),
# cells=dict(
# values=[dfnn2.ProductID, dfnn2.Predictions],
# line_color=[dfnn2.Color], fill_color=[dfnn2.Color],
# align='center',font=dict(color='saddlebrown', size=20),height=50
# ))
# ],layout=layout)
# #fig.show()
# fig.write_image("my_table3.png")
# image = PIL.Image.open('my_table3.png')
# st.image(image, caption='Sunrise by the mountains', use_column_width=True)
menu_list = [
'Execute JMeter Test Plan', 'Analyze JMeter Test Results', 'Home'
]
# Display options in Sidebar
st.sidebar.title('Load Testing Using Jmeter')
menu_sel = st.sidebar.radio('', menu_list, index=2, key=None)
# Display text in Sidebar
about.display_sidebar()
# Selecting About Menu
if menu_sel == 'Home':
about.display_about()
# Selecting Execute Menu
if menu_sel == 'Execute JMeter Test Plan':
#jmeter_run = st.radio('Select',('Default','Execute','Analyze'))
#if jmeter_run == 'Execute':
st.title('Execute JMeter Test Plan')
jmeter_execute_load()
#if jmeter_run == 'Analyze':
if menu_sel == 'Analyze JMeter Test Results':
st.title('Analyze JMeter Test Results')
filename = jmeter_analyze()
st.write('You selected `%s`' % filename)
#DATA_URL = ('C:\\Users\\Navee\\OneDrive\\Documents\\Tools\\apache-jmeter-5.2\\bin\\Run2.csv')
DATA_URL = filename
st.markdown('')
# Show Graphs Checkbox
show_graphs = st.checkbox('Show Graphs')
# Show Profiling Report
profile_report = st.button('Generate Profiling Report')
# Generate Profiling Report
if profile_report:
st.write('Generating Report for ', filename)
pd_profile(filename)
st.title('Apache JMeter Load Test Results')
data = pd.read_csv(DATA_URL)
#Display Start Time
startTime = data['timeStamp'].iloc[0] / 1000
startTime = datetime.datetime.fromtimestamp(startTime).strftime(
'%Y-%m-%d %H:%M:%S')
st.write('Start Time ', startTime)
endTime = data['timeStamp'].iloc[-1] / 1000
endTime = datetime.datetime.fromtimestamp(endTime).strftime(
'%Y-%m-%d %H:%M:%S')
st.write('End Time ', endTime)
FMT = '%Y-%m-%d %H:%M:%S'
delta = datetime.datetime.strptime(
endTime, FMT) - datetime.datetime.strptime(startTime, FMT)
st.write('Total duration of the test (HH:MM:SS) is ', delta)
st.subheader('Summary Report - Response Time')
st.write(
data.groupby('label')['elapsed'].describe(
percentiles=[0.75, 0.95, 0.99]))
st.subheader('Error Count')
errCount = data.groupby(['label',
'responseCode'])['responseCode'].count()
st.write(errCount)
if show_graphs:
chart_data = pd.DataFrame(data,
columns=[
'timeStamp', 'Latency', 'label',
'responseCode', 'elapsed', 'Connect',
'bytes'
])
st.subheader("Graph between Timestamp and Latency")
st.vega_lite_chart(
chart_data, {
"mark": {
"type": "bar",
"color": "maroon"
},
"selection": {
"grid": {
"type": "interval",
"bind": "scales"
}
},
'encoding': {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "Latency",
"type": "quantitative"
}],
'x': {
'field': 'timeStamp',
'type': 'temporal'
},
'y': {
'field': 'Latency',
'type': 'quantitative'
},
},
})
st.subheader("Graph between Timestamp and Response Code")
st.vega_lite_chart(
chart_data, {
"mark": {
"type": "bar",
"color": "aqua"
},
"selection": {
"grid": {
"type": "interval",
"bind": "scales"
}
},
'encoding': {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "responseCode",
"type": "quantitative"
}],
'x': {
'field': 'timeStamp',
'type': 'temporal'
},
'y': {
'field': 'responseCode',
'type': 'quantitative'
},
},
})
st.subheader("Graph between Timestamp and Response Time")
st.vega_lite_chart(
chart_data, {
"mark": {
"type": "bar",
"color": "orange"
},
"selection": {
"grid": {
"type": "interval",
"bind": "scales"
}
},
'encoding': {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "elapsed",
"type": "quantitative"
}],
'x': {
'field': 'timeStamp',
'type': 'temporal'
},
'y': {
'field': 'elapsed',
'type': 'quantitative'
},
},
})
st.subheader("Graph between Timestamp and Connect Time")
st.vega_lite_chart(
chart_data, {
"mark": {
"type": "bar",
"color": "darkgreen"
},
"selection": {
"grid": {
"type": "interval",
"bind": "scales"
}
},
'encoding': {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "Connect",
"type": "quantitative"
}],
'x': {
'field': 'timeStamp',
'type': 'temporal'
},
'y': {
'field': 'Connect',
'type': 'quantitative'
},
},
})
st.subheader("Graph between Timestamp and bytes")
st.vega_lite_chart(
chart_data, {
"mark": {
"type": "bar",
"color": "darkblue"
},
"selection": {
"grid": {
"type": "interval",
"bind": "scales"
}
},
'encoding': {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "bytes",
"type": "quantitative"
}],
'x': {
'field': 'timeStamp',
'type': 'temporal'
},
'y': {
'field': 'bytes',
'type': 'quantitative'
},
},
})
st.subheader(
"Graph between Timestamp and Response Time - Line Chart")
st.vega_lite_chart(
chart_data, {
"mark": "line",
"encoding": {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "elapsed",
"type": "quantitative"
}],
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "elapsed",
"type": "quantitative"
},
"color": {
"field": "label",
"type": "nominal"
}
},
})
st.subheader(
"Graph between Timestamp and Response Time - Bar Chart")
st.vega_lite_chart(
chart_data, {
"mark": "bar",
"encoding": {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "elapsed",
"type": "quantitative"
}],
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "elapsed",
"type": "quantitative"
},
"color": {
"field": "label",
"type": "nominal"
}
},
})
st.subheader("Histogram")
st.vega_lite_chart(
chart_data, {
"transform": [{
"filter": {
"and": [{
"field": "timeStamp",
"valid": True
}, {
"field": "elapsed",
"valid": True
}]
}
}],
"mark":
"rect",
"width":
300,
"height":
200,
"encoding": {
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "elapsed",
"type": "quantitative"
},
"color": {
"aggregate": "count",
"type": "quantitative"
}
},
"config": {
"view": {
"stroke": "transparent"
}
}
})
st.subheader("Histogram")
st.vega_lite_chart(
chart_data, {
"transform": [{
"filter": {
"and": [{
"field": "timeStamp",
"valid": True
}, {
"field": "Connect",
"valid": True
}]
}
}],
"mark":
"rect",
"width":
300,
"height":
200,
"encoding": {
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "Connect",
"type": "quantitative"
},
"color": {
"aggregate": "count",
"type": "quantitative"
}
},
"config": {
"view": {
"stroke": "transparent"
}
}
})
st.subheader("Scatter Plot between Timestamp and Response Time")
st.vega_lite_chart(
chart_data, {
"selection": {
"grid": {
"type": "interval",
"bind": "scales"
}
},
"mark": "circle",
"encoding": {
"tooltip": [{
"field": "timeStamp",
"type": "temporal"
}, {
"field": "label",
"type": "nominal"
}, {
"field": "elapsed",
"type": "quantitative"
}],
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "elapsed",
"type": "quantitative"
},
"size": {
"field": "label",
"type": "nominal"
}
},
})
def OnAbout(self, event): # wxGlade: MainFrame.<event_handler>
from about import display_about
display_about()
def main():
menu_list = ['Execute JMeter Test Plan','Analyze JMeter Test Results', 'Home']
# Display options in Sidebar
st.sidebar.title('Navigation')
menu_sel = st.sidebar.radio('', menu_list, index=2, key=None)
# Display text in Sidebar
about.display_sidebar()
# Selecting About Menu
if menu_sel == 'Home':
about.display_about()
# Selecting Execute Menu
if menu_sel == 'Execute JMeter Test Plan':
#jmeter_run = st.radio('Select',('Default','Execute','Analyze'))
#if jmeter_run == 'Execute':
st.title('Execute JMeter Test Plan')
jmeter_execute_load()
#if jmeter_run == 'Analyze':
if menu_sel == 'Analyze JMeter Test Results':
st.title('Analyze JMeter Test Results')
filename = jmeter_analyze()
st.write('You selected `%s`' % filename)
#DATA_URL = ('C:\\Users\\Navee\\OneDrive\\Documents\\Tools\\apache-jmeter-5.2\\bin\\Run2.csv')
DATA_URL = filename
st.markdown('')
# Show Graphs Checkbox
show_graphs = st.checkbox('Show Graphs')
# Show Profiling Report
profile_report = st.button('Generate Profiling Report')
# Generate Profiling Report
if profile_report:
st.write('Generating Report for ', filename)
pd_profile(filename)
st.title('Apache JMeter Load Test Results')
data = pd.read_csv(DATA_URL)
#Display Start Time
startTime = data['timeStamp'].iloc[0]/1000
startTime = datetime.datetime.fromtimestamp(startTime).strftime('%Y-%m-%d %H:%M:%S')
st.write('Start Time ', startTime)
endTime = data['timeStamp'].iloc[-1]/1000
endTime = datetime.datetime.fromtimestamp(endTime).strftime('%Y-%m-%d %H:%M:%S')
st.write('End Time ', endTime)
FMT = '%Y-%m-%d %H:%M:%S'
delta = datetime.datetime.strptime(endTime, FMT) - datetime.datetime.strptime(startTime, FMT)
st.write('Total duration of the test (HH:MM:SS) is ', delta)
st.subheader('Summary Report - Response Time')
st.write(data.groupby('label')['elapsed'].describe(percentiles=[0.75,0.95,0.99]))
st.subheader('Error Count')
errCount = data.groupby(['label','responseCode'])['responseCode'].count()
st.write(errCount)
if show_graphs:
chart_data = pd.DataFrame(data,columns=['timeStamp','Latency','label','responseCode','elapsed','Connect','bytes'])
st.subheader("Graph between Timestamp and Latency")
st.vega_lite_chart(chart_data, {
"mark": {"type": "bar", "color": "maroon"},
"selection": {
"grid": {
"type": "interval", "bind": "scales"
}
},
'encoding': {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "Latency", "type": "quantitative"}
],
'x': {'field': 'timeStamp', 'type': 'temporal'},
'y': {'field': 'Latency', 'type': 'quantitative'},
},
})
st.subheader("Graph between Timestamp and Response Code")
st.vega_lite_chart(chart_data, {
"mark": {"type": "bar", "color": "aqua"},
"selection": {
"grid": {
"type": "interval", "bind": "scales"
}
},
'encoding': {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "responseCode", "type": "quantitative"}
],
'x': {'field': 'timeStamp', 'type': 'temporal'},
'y': {'field': 'responseCode', 'type': 'quantitative'},
},
})
st.subheader("Graph between Timestamp and Response Time")
st.vega_lite_chart(chart_data, {
"mark": {"type": "bar", "color": "orange"},
"selection": {
"grid": {
"type": "interval", "bind": "scales"
}
},
'encoding': {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "elapsed", "type": "quantitative"}
],
'x': {'field': 'timeStamp', 'type': 'temporal'},
'y': {'field': 'elapsed', 'type': 'quantitative'},
},
})
st.subheader("Graph between Timestamp and Connect Time")
st.vega_lite_chart(chart_data, {
"mark": {"type": "bar", "color": "darkgreen"},
"selection": {
"grid": {
"type": "interval", "bind": "scales"
}
},
'encoding': {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "Connect", "type": "quantitative"}
],
'x': {'field': 'timeStamp', 'type': 'temporal'},
'y': {'field': 'Connect', 'type': 'quantitative'},
},
})
st.subheader("Graph between Timestamp and bytes")
st.vega_lite_chart(chart_data, {
"mark": {"type": "bar", "color": "darkblue"},
"selection": {
"grid": {
"type": "interval", "bind": "scales"
}
},
'encoding': {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "bytes", "type": "quantitative"}
],
'x': {'field': 'timeStamp', 'type': 'temporal'},
'y': {'field': 'bytes', 'type': 'quantitative'},
},
})
st.subheader("Graph between Timestamp and Response Time - Line Chart")
st.vega_lite_chart(chart_data, {
"mark": "line",
"encoding": {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "elapsed", "type": "quantitative"}
],
"x": {"field": "timeStamp", "type": "temporal"},
"y": {"field": "elapsed", "type": "quantitative"},
"color": {"field": "label", "type": "nominal"}
},
})
st.subheader("Graph between Timestamp and Response Time - Bar Chart")
st.vega_lite_chart(chart_data, {
"mark": "bar",
"encoding": {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "elapsed", "type": "quantitative"}
],
"x": {"field": "timeStamp", "type": "temporal"},
"y": {"field": "elapsed", "type": "quantitative"},
"color": {"field": "label", "type": "nominal"}
},
})
st.subheader("Histogram")
st.vega_lite_chart(chart_data, {
"transform": [{
"filter": {"and": [
{"field": "timeStamp", "valid": True},
{"field": "elapsed", "valid": True}
]}
}],
"mark": "rect",
"width": 300,
"height": 200,
"encoding": {
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "elapsed",
"type": "quantitative"
},
"color": {
"aggregate": "count",
"type": "quantitative"
}
},
"config": {
"view": {
"stroke": "transparent"
}
}
})
st.subheader("Histogram")
st.vega_lite_chart(chart_data, {
"transform": [{
"filter": {"and": [
{"field": "timeStamp", "valid": True},
{"field": "Connect", "valid": True}
]}
}],
"mark": "rect",
"width": 300,
"height": 200,
"encoding": {
"x": {
"field": "timeStamp",
"type": "temporal"
},
"y": {
"field": "Connect",
"type": "quantitative"
},
"color": {
"aggregate": "count",
"type": "quantitative"
}
},
"config": {
"view": {
"stroke": "transparent"
}
}
})
st.subheader("Scatter Plot between Timestamp and Response Time")
st.vega_lite_chart(chart_data, {
"selection": {
"grid": {
"type": "interval", "bind": "scales"
}
},
"mark": "circle",
"encoding": {
"tooltip": [
{"field": "timeStamp", "type": "temporal"},
{"field": "label", "type": "nominal"},
{"field": "elapsed", "type": "quantitative"}
],
"x": {
"field": "timeStamp", "type": "temporal" },
"y": {
"field": "elapsed", "type": "quantitative" },
"size": {"field": "label", "type": "nominal"}
},
})