def create_graph(locID, height='520px', width='100%'):
df = getAllData(location_id=locID)
latitude, longiude = getLatLong(locID)
months = df['month'].to_list()
prcp = df['prcp'].to_list()
avgTemp = df['tave'].to_list()
minTemp = df['tmin'].to_list()
maxTemp = df['tmax'].to_list()
avgTemp = [(x * 9/5) + 32 for x in avgTemp]
minTemp = [(x * 9/5) + 32 for x in minTemp]
maxTemp = [(x * 9/5) + 32 for x in maxTemp]
prcp = [(x/25.4) for x in prcp]
for i in range(len(prcp)):
if i != 0:
prcp[i] = prcp[i] + prcp[i-1]
data = [go.Scatter(x=months, y=avgTemp, name="Avg Temperature"), go.Scatter(x=months, y=minTemp, name="Min Temperature"), go.Scatter(x=months, y=maxTemp, name="Max Temperature")]
layout = {
'title': 'Tepmerature Data for {0}, {1}'.format(latitude, longiude),
'xaxis': {'title': 'Time'},
'yaxis': {'title': 'Temperature (F)'}
}
figure = {'data': data, 'layout': layout}
temperature_plot = PlotlyView(figure, height=height, width=width)
data = [go.Scatter(x=months, y=prcp, name="Precipitation")]
layout = {
'title': 'Cumulative Precipitation for {0}, {1}'.format(latitude, longiude),
'xaxis': {'title': 'Time'},
'yaxis': {'title': 'Precipitation (in)'}
}
figure = {'data': data, 'layout': layout}
precipitation_plot = PlotlyView(figure, height=height, width=width)
return temperature_plot, precipitation_plot
def create_revenue_piechart(rev_scenario):
height = '520px'
width = '100%'
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
revenue_list = session.query(Revenue).all()
df = pd.DataFrame(columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
for entry in revenue_list:
if entry.scenario == rev_scenario:
df = df.append({'Scenario': entry.scenario, 'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)), 'Year': entry.year},
ignore_index=True)
# Build up Plotly plot
piechart_go = go.Figure(go.Pie(
name="",
values=df["Monetary Value"],
labels=df["Source"],
text=df["Source"],
customdata=df["Year"],
hovertemplate="Source: %{label} <br> Scenario: %{text} <br> Year: %{customdata} <br>Revenue: %{value}"
))
piechart_plot = PlotlyView(piechart_go, divid="pie-plot-rev-div", height=height, width=width)
session.close()
return piechart_plot
def create_capital_costs_bargraph(height='520 px', width='100%'):
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
df = pd.DataFrame(columns=['Construction Year', 'Projected Cost', 'Facility ID', 'Project', 'Category'])
for project in projects:
if project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost) - 1):
df = df.append({'Construction Year': int(project.const_year) + y,
'Projected Cost': int(json.loads(project.const_cost[x + 1])),
'Facility ID': project.facility_id, 'Project': project.project,
'Category': project.category}, ignore_index=True)
y = y + 1
else:
df = df.append(
{'Construction Year': int(project.const_year), 'Projected Cost': int(json.loads(project.const_cost[0])),
'Facility ID': project.facility_id, 'Project': project.project, 'Category': project.category},
ignore_index=True)
# Build up Plotly plot
bargraph_px = px.bar(
df,
hover_data=["Facility ID", "Category", "Project", "Projected Cost"],
x="Construction Year",
y="Projected Cost",
color='Category',
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#001f5b",
"Facilities": "#074768",
"Golf": "#ac162c",
"Transportation": "#232525",
"Storm": "#F78F07"}
# colors="black"
# name='Category',
# title="Future Project Costs"
)
bargraph_px.update_layout(
yaxis=dict(title='Construction Cost (USD)',),
xaxis=dict(title='Construction Year'),
legend=dict(title='Category')
)
bargraph_plot = PlotlyView(bargraph_px, height=height, width=width)
session.close()
return bargraph_plot
def create_revenue_vs_requirements_piechart(height='520 px', width='100%'):
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
dfrevreq = pd.DataFrame(columns=['Construction Year', 'Projected Cost', 'Facility ID', 'Project', 'Category'])
for project in projects:
if project.debt_checkbox_val == "true":
z = 1
for c in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append({'Construction Year': int(project.const_year) + z,
'Projected Cost': int(json.loads(project.const_cost[c + 1])),
'Facility ID': project.facility_id, 'Project': project.project,
'Category': project.category}, ignore_index=True)
z = z + 1
elif project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append({'Construction Year': int(project.const_year) + y,
'Projected Cost': int(json.loads(project.const_cost[x + 1])),
'Facility ID': project.facility_id, 'Project': project.project,
'Category': project.category}, ignore_index=True)
y = y + 1
else:
dfrevreq = dfrevreq.append(
{'Construction Year': int(project.const_year), 'Projected Cost': int(json.loads(project.const_cost[0])),
'Facility ID': project.facility_id, 'Project': project.project, 'Category': project.category},
ignore_index=True)
print("cost data ready")
# Build up Plotly plot
piechart_px = px.pie(
df,
values="Projected Cost",
labels="Category",
names="Category",
hovertemplate=["Facility ID", "Project", "Construction Year", "Cost"],
color="Category",
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#001f5b",
"Facilities": "#074768",
"Golf": "#ac162c",
"Transportation": "#232525",
"Storm": "#F78F07"}
)
print("piechart done")
piechart_plot = PlotlyView(piechart_px, height=height, width=width)
print("Plot rendered")
session.close()
return piechart_plot
def cq_event_plot(event_id, sub_event, height='520px', width='100%'):
# Build up Plotly plot
time, flow, concentration, segments = get_conc_flow_seg(event_id)
if len(segments) == 0:
d = {}
d['start'] = 0
d['end'] = len(flow)
segments.append(d)
if sub_event >= len(segments):
sub_event = len(segments) - 1
if sub_event < 0:
sub_event = 0
event_flow = flow[segments[sub_event]['start']:segments[sub_event]['end']]
event_concentration = concentration[
segments[sub_event]['start']:segments[sub_event]['end']]
hysteresis_go = go.Scatter(
x=event_flow,
y=event_concentration,
mode='lines',
name='Hysteresis',
line=dict(
width=10,
color='#1f77b4',
),
)
data = [hysteresis_go]
layout = dict(
width=400,
height=400,
autosize=False,
title='Hysteresis for event {0}'.format(str(sub_event)),
scene=dict(xaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)',
title='Time (T)'),
yaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)',
title='Discharge (Q)'),
aspectratio=dict(x=1, y=1),
aspectmode='manual'),
)
figure = {'data': data, 'layout': layout}
hydrograph_plot = PlotlyView(figure, height='200px', width='100%')
return hydrograph_plot
def create_revenue_vs_requirements_sunburst(height='520 px', width='100%'):
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
dfrevreq = pd.DataFrame(columns=['Construction Year', 'Projected Cost', 'Facility ID', 'Project', 'Category'])
for project in projects:
if project.debt_checkbox_val == "true":
z = 1
for c in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append({'Construction Year': int(project.const_year) + z,
'Projected Cost': float(json.loads(project.const_cost[c + 1])),
'Facility ID': project.facility_id, 'Project': project.project,
'Category': project.category}, ignore_index=True)
z = z + 1
elif project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append({'Construction Year': int(project.const_year) + y,
'Projected Cost': float(json.loads(project.const_cost[x + 1])),
'Facility ID': project.facility_id, 'Project': project.project,
'Category': project.category}, ignore_index=True)
y = y + 1
else:
dfrevreq = dfrevreq.append(
{'Construction Year': int(project.const_year), 'Projected Cost': float(json.loads(project.const_cost[0])),
'Facility ID': project.facility_id, 'Project': project.project, 'Category': project.category},
ignore_index=True)
# Build up Plotly plot
sunburst_px = px.sunburst(
dfrevreq,
path=['Category', 'Facility ID','Project'],
values='Projected Cost',
color='Category',
labels=dfrevreq["Category"],
# text=df["Project"],
# customdata=df["Construction Year"],
# hovertemplate="Facility ID: %{label} <br>Project: %{path[2]} <br>Construction Year: %{customdata} <br>Cost: %{value}"
)
sunburst_plot = PlotlyView(sunburst_px, height=height, width=width)
session.close()
return sunburst_plot
def create_revenue_sunburst(height='520 px', width='100%'):
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
revenue_list = session.query(Revenue).all()
dfhigh = pd.DataFrame(columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
dfmed = pd.DataFrame(columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
dflow = pd.DataFrame(columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
for entry in revenue_list:
if entry.scenario == "high":
dfhigh = dfhigh.append({'Scenario': entry.scenario, 'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)), 'Year': entry.year},
ignore_index=True)
elif entry.scenario == "medium":
dfmed = dfmed.append({'Scenario': entry.scenario, 'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)), 'Year': entry.year},
ignore_index=True)
elif entry.scenario == "low":
dflow = dflow.append({'Scenario': entry.scenario, 'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)), 'Year': entry.year},
ignore_index=True)
# Build up Plotly plot
sunburst_px = px.sunburst(
dfmed,
path=['Source', 'Year','Monetary Value'],
values='Monetary Value',
color='Source',
labels=dfmed["Source"],
# text=df["Project"],
# customdata=df["Construction Year"],
# hovertemplate="Facility ID: %{label} <br>Project: %{path[2]} <br>Construction Year: %{customdata} <br>Cost: %{value}"
)
sunburst_plot = PlotlyView(sunburst_px, divid="sun-plot-rev-div",height=height, width=width)
session.close()
return sunburst_plot
def create_capital_costs_sunburst(height='520 px', width='100%'):
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
df = pd.DataFrame(columns=['Construction Year', 'Projected Cost', 'Facility ID', 'Project', 'Category'])
for project in projects:
if project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost)-1):
df = df.append({'Construction Year': int(project.const_year) + y, 'Projected Cost': int(json.loads(project.const_cost[x + 1])), 'Facility ID': project.facility_id, 'Project': project.project, 'Category': project.category}, ignore_index=True)
y = y+1
else:
df = df.append({'Construction Year': int(project.const_year), 'Projected Cost': int(json.loads(project.const_cost[0])), 'Facility ID': project.facility_id, 'Project': project.project, 'Category': project.category}, ignore_index=True)
# Build up Plotly plot
sunburst_px = px.sunburst(
df,
path=['Category', 'Facility ID','Project'],
values='Projected Cost',
color='Category',
labels=df["Category"],
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#001f5b",
"Facilities": "#074768",
"Golf": "#ac162c",
"Transportation": "#232525",
"Storm": "#F78F07"}
# text=df["Project"],
# customdata=df["Construction Year"],
# hovertemplate="Facility ID: %{label} <br>Project: %{path[2]} <br>Construction Year: %{customdata} <br>Cost: %{value}"
)
sunburst_plot = PlotlyView(sunburst_px, height=height, width=width)
session.close()
return sunburst_plot
def create_ozone_graph(ozone_graph_id, height='520px', width='100%'):
"""
Generates a plotly view of a ozone graph.
"""
# Get objects from database
Session = app.get_persistent_store_database('sensor_db',
as_sessionmaker=True)
session = Session()
ozone_graph = session.query(OzoneGraph).get(ozone_graph_id)
ozone_points = session.query(OzonePoint).filter(
OzonePoint.ozone_graph == ozone_graph,
OzonePoint.time >= ozone_graph.updatets - timedelta(days=7))
time = []
ppb = []
error = []
for point in ozone_points:
time.append(point.time)
ppb.append(point.ppb)
error.append(point.std)
# Build up Plotly plot
ozone_graph_go = go.Scatter(
x=time,
y=ppb,
name='Ozone Graph for Sensor {0}'.format(ozone_graph_id),
mode='markers',
marker={
'color': '#0080ff',
'size': 10
},
)
data = [ozone_graph_go]
layout = {
'title': 'Ozone Graph for Sensor {0}'.format(ozone_graph_id),
'xaxis': {
'title': 'Time',
'range': [max(time) - timedelta(days=7),
max(time)]
},
'yaxis': {
'title': 'ppb'
},
}
figure = {'data': data, 'layout': layout}
ozone_graph_plot = PlotlyView(figure, height=height, width=width)
session.close()
return ozone_graph_plot
def create_temperature_graph(temperature_graph_id,
height='520px',
width='100%'):
"""
Generates a plotly view of a temperature graph.
"""
# Get objects from database
Session = app.get_persistent_store_database('sensor_db',
as_sessionmaker=True)
session = Session()
temperature_graph = session.query(TemperatureGraph).get(
int(temperature_graph_id))
sensor = temperature_graph.sensor
time = []
temp = []
for point in temperature_graph.points:
time.append(point.time.hour)
temp.append(point.temperature)
# Build up Plotly plot
temperature_graph_go = go.Scatter(
x=time,
y=temp,
name='Temperature Graph for Sensor {0}'.format(sensor.id),
line={
'color': '#0080ff',
'width': 4,
'shape': 'spline'
},
)
data = [temperature_graph_go]
layout = {
'title': 'Temperature Graph for Sensor {0}'.format(sensor.id),
'xaxis': {
'title': 'Time (min)',
'range': [max(time) - timedelta(days=30),
max(time)]
},
'yaxis': {
'title': 'Temp (F)'
},
}
figure = {'data': data, 'layout': layout}
temperature_graph_plot = PlotlyView(figure, height=height, width=width)
session.close()
return temperature_graph_plot
def create_revenue_bargraph(rev_scenario):
height = '520px'
width = '100%'
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db', as_sessionmaker=True)
session = Session()
revenue_list = session.query(Revenue).all()
df = pd.DataFrame(columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
for entry in revenue_list:
if entry.scenario == rev_scenario:
df = df.append({'Scenario': entry.scenario, 'Source': entry.revenue_source, 'Monetary Value': int(json.loads(entry.monetary_Value)), 'Year': entry.year}, ignore_index=True)
# Build up Plotly plot
bargraph_px = px.bar(
df,
hover_data=["Source", "Monetary Value", "Year"],
x="Year",
y="Monetary Value",
color="Source"
# title="Revenue"
)
bargraph_px.update_layout(
yaxis=dict(title='Revenue (USD)'),
xaxis=dict(title='Year'),
legend=dict(title='Source of Revenue')
)
bargraph_plot = PlotlyView(bargraph_px, height=height, width=width)
session.close()
return bargraph_plot
def create_hydrograph(hydrograph_id, height='520px', width='100%'):
"""
Generates a plotly view of a hydrograph.
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db',
as_sessionmaker=True)
session = Session()
hydrograph = session.query(Hydrograph).get(int(hydrograph_id))
dam = hydrograph.dam
time = []
flow = []
for hydro_point in hydrograph.points:
time.append(hydro_point.time)
flow.append(hydro_point.flow)
# Build up Plotly plot
hydrograph_go = go.Scatter(
x=time,
y=flow,
name='Hydrograph for {0}'.format(dam.name),
line={
'color': '#0080ff',
'width': 4,
'shape': 'spline'
},
)
data = [hydrograph_go]
layout = {
'title': 'Hydrograph for {0}'.format(dam.name),
'xaxis': {
'title': 'Time (hr)'
},
'yaxis': {
'title': 'Flow (cfs)'
},
}
figure = {'data': data, 'layout': layout}
hydrograph_plot = PlotlyView(figure, height=height, width=width)
session.close()
return hydrograph_plot
def cqt_event_plot(event_id, sub_event, height='520px', width='100%'):
# Build up Plotly plot
time, flow, concentration, segments = get_conc_flow_seg(event_id)
if len(segments) == 0:
d = {}
d['start'] = 0
d['end'] = len(flow)
segments.append(d)
if sub_event >= len(segments):
sub_event = len(segments) - 1
if sub_event < 0:
sub_event = 0
event_flow = flow[segments[sub_event]['start']:segments[sub_event]['end']]
event_concentration = concentration[
segments[sub_event]['start']:segments[sub_event]['end']]
event_time = time[segments[sub_event]['start']:segments[sub_event]['end']]
trajectory_go = go.Scatter3d(
x=event_time,
y=event_flow,
z=event_concentration,
mode='lines',
name='Trajectory',
line=dict(
width=10,
color=np.arange(0, len(event_flow)),
colorscale='Viridis',
),
)
data = [trajectory_go]
layout = dict(
width=400,
height=400,
autosize=False,
title='Trajectory for event {0}'.format(str(sub_event)),
scene=dict(xaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)',
title='Time (T)'),
yaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)',
title='Discharge (Q)'),
zaxis=dict(gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)',
title='Concentration (C)'),
camera=dict(up=dict(x=0, y=0, z=1),
eye=dict(
x=-1.7428,
y=1.0707,
z=0.7100,
)),
aspectratio=dict(x=1, y=1, z=1),
aspectmode='manual'),
)
figure = {'data': data, 'layout': layout}
hydrograph_plot = PlotlyView(figure, height='200px', width='100%')
return hydrograph_plot
def create_revenue_vs_req_compare_bargraph(rev_scenario):
height = '520px'
width = '100%'
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db',
as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
revenue_list = session.query(Revenue).all()
dfrevreq = pd.DataFrame(columns=[
'Construction Year',
'Projected Cost',
])
print("difference data started")
for project in projects:
if project.debt_checkbox_val == "true":
z = 1
for c in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append(
{
'Construction Year':
int(project.const_year) + z,
'Projected Cost':
float(json.loads(project.const_cost[c + 1]))
},
ignore_index=True)
z = z + 1
elif project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append(
{
'Construction Year':
int(project.const_year) + y,
'Projected Cost':
float(json.loads(project.const_cost[x + 1])),
},
ignore_index=True)
y = y + 1
else:
dfrevreq = dfrevreq.append(
{
'Construction Year': int(project.const_year),
'Projected Cost': float(json.loads(project.const_cost[0])),
'Facility ID': project.facility_id,
'Project': project.project,
'Category': project.category
},
ignore_index=True)
dfrev = pd.DataFrame(
columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
for entry in revenue_list:
if entry.scenario == rev_scenario:
dfrev = dfrev.append(
{
'Scenario': entry.scenario,
'Source': entry.revenue_source,
'Monetary Value': float(json.loads(entry.monetary_Value)),
'Year': int(entry.year)
},
ignore_index=True)
dfrevreq_rnm = dfrevreq.rename(columns={"Construction Year": "Year"})
dfrev_sum = dfrev.groupby("Year").agg(total_rev=('Monetary Value', 'sum'))
dfrevreq_sum = dfrevreq_rnm.groupby("Year").agg(
total_cost=('Projected Cost', 'sum'))
minyr = min(int(dfrevreq_rnm["Year"].min()), int(dfrev["Year"].min()))
maxyr = max(int(dfrevreq_rnm["Year"].max()), int(dfrev["Year"].max()))
year_range = []
for i in range(maxyr - minyr + 1):
year_range.append(minyr + i)
dfdiff = pd.DataFrame(columns=["Year", "Difference"])
for year in year_range:
dfdiff = dfdiff.append({
'Year': year,
"Difference": 0
},
ignore_index=True)
dfdiff = dfdiff.merge(dfrev_sum, how="left", on="Year")
dfdiff = dfdiff.merge(dfrevreq_sum, how="left", on="Year")
dfdiff["total_rev"] = dfdiff["total_rev"].fillna(0)
dfdiff["total_cost"] = dfdiff["total_cost"].fillna(0)
dfdiff["Difference"] = (dfdiff["total_rev"] - dfdiff["total_cost"])
dfdiff["Loss Or Gain"] = [
"Gain" if difval >= 0 else "Loss" for difval in dfdiff["Difference"]
]
print("difference data finished")
# Build up Plotly plot
bargraph_px = px.bar(
dfdiff,
hover_data=["Year", "total_rev", "total_cost", "Difference"],
x="Year",
y="Difference",
color="Loss Or Gain",
color_discrete_map={
"Loss": "#ac162c",
"Gain": "#001f5b"
}
# title="Revenue"
)
bargraph_px.update_layout(yaxis=dict(title='Revenue (USD)'),
xaxis=dict(title='Year'),
legend=dict(title='Source of Revenue'))
bargraph_plot = PlotlyView(bargraph_px, height=height, width=width)
session.close()
return bargraph_plot
def create_revenue_vs_requirements_bargraph(rev_scenario):
height = '520px'
width = '100%'
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db',
as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
revenue_list = session.query(Revenue).all()
dfrevreq = pd.DataFrame(columns=[
'Construction Year', 'Projected Cost', 'Facility ID', 'Project',
'Category'
])
for project in projects:
if project.debt_checkbox_val == "true":
z = 1
for c in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append(
{
'Construction Year':
int(project.const_year) + z,
'Projected Cost':
float(json.loads(project.const_cost[c + 1])),
'Facility ID':
project.facility_id,
'Project':
project.project,
'Category':
project.category
},
ignore_index=True)
z = z + 1
elif project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append(
{
'Construction Year':
int(project.const_year) + y,
'Projected Cost':
float(json.loads(project.const_cost[x + 1])),
'Facility ID':
project.facility_id,
'Project':
project.project,
'Category':
project.category
},
ignore_index=True)
y = y + 1
else:
dfrevreq = dfrevreq.append(
{
'Construction Year': int(project.const_year),
'Projected Cost': float(json.loads(project.const_cost[0])),
'Facility ID': project.facility_id,
'Project': project.project,
'Category': project.category
},
ignore_index=True)
print("bargraph data ready")
# Build up Plotly plot
bargraph_px = px.bar(
dfrevreq,
hover_data=["Facility ID", "Category", "Project", "Projected Cost"],
x="Construction Year",
y="Projected Cost",
color='Category',
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#001f5b",
"Facilities": "#074768",
"Golf": "#ac162c",
"Transportation": "#232525",
"Storm": "#F78F07"
}
# title="Future Project Costs"
)
bargraph_px.update_layout(yaxis=dict(title='Construction Cost (USD)', ),
xaxis=dict(title='Construction Year'),
legend=dict(title='Category'))
print("bargraphdone")
dfrev = pd.DataFrame(
columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
for entry in revenue_list:
if entry.scenario == rev_scenario:
dfrev = dfrev.append(
{
'Scenario': entry.scenario,
'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)),
'Year': int(entry.year)
},
ignore_index=True)
print("rev done")
dfrev_sum = dfrev.groupby("Year").agg(Revenue=('Monetary Value', 'sum'))
dfrevfinal = pd.DataFrame(columns=["Year"])
minyr = dfrev["Year"].min()
maxyr = dfrev["Year"].max()
year_range = []
for i in range(maxyr - minyr + 1):
year_range.append(minyr + i)
for year in year_range:
dfrevfinal = dfrevfinal.append({'Year': year}, ignore_index=True)
dfrevfinal = dfrevfinal.merge(dfrev_sum, how="left", on="Year")
print("groupby done")
bargraph_px.add_scatter(y=dfrevfinal["Revenue"],
x=dfrevfinal["Year"],
mode="lines",
line=dict(width=3, color="Red"),
name=rev_scenario)
print("scatter done")
bargraph_plot = PlotlyView(bargraph_px, height=height, width=width)
print("graph generated")
session.close()
return bargraph_plot
def candq_event_plot(event_id, sub_event, height='520px', width='100%'):
# Build up Plotly plot
time, flow, concentration, segments = get_conc_flow_seg(event_id)
if len(segments) == 0:
d = {}
d['start'] = 0
d['end'] = len(flow)
segments.append(d)
if sub_event >= len(segments):
sub_event = len(segments) - 1
if sub_event < 0:
sub_event = 0
event_flow = flow[segments[sub_event]['start']:segments[sub_event]['end']]
event_concentration = concentration[
segments[sub_event]['start']:segments[sub_event]['end']]
flow_go = go.Scatter(
x=np.arange(0, len(event_flow)),
y=event_flow,
mode='lines',
name='Discharge',
line=dict(
width=5,
color='#1f77b4',
),
)
concentration_go = go.Scatter(
x=np.arange(0, len(event_flow)),
y=event_concentration,
mode='lines',
name='concentration',
yaxis="y2",
line=dict(
width=5,
color="#ff7f0e",
),
)
data = []
data.append(flow_go)
data.append(concentration_go)
layout = dict(xaxis=dict(domain=[0.3, 0.7]),
yaxis=dict(title="Discharge (Q)",
titlefont=dict(color="#1f77b4"),
tickfont=dict(color="#1f77b4")),
yaxis2=dict(title="Concentration (C)",
titlefont=dict(color="#ff7f0e"),
tickfont=dict(color="#ff7f0e"),
overlaying="y",
side="right"))
figure = {'data': data, 'layout': layout}
hydrograph_plot = PlotlyView(figure, height='520px', width='100%')
return hydrograph_plot
def cqt_cq_event_plot(event_id, sub_event, height='800px', width='100%'):
# Build up Plotly plot
time, flow, concentration, segments = get_conc_flow_seg(event_id)
if len(segments) == 0:
d = {}
d['start'] = 0
d['end'] = len(flow)
segments.append(d)
if sub_event >= len(segments):
sub_event = len(segments) - 1
if sub_event < 0:
sub_event = 0
event_flow = flow[segments[sub_event]['start']:segments[sub_event]['end']]
event_concentration = concentration[
segments[sub_event]['start']:segments[sub_event]['end']]
event_time = time[segments[sub_event]['start']:segments[sub_event]['end']]
# Initialize figure with subplots
fig = make_subplots(
rows=2,
cols=2,
#Scott, column width and row with as you pointed out are ratio, we have two columns and two rows..
column_widths=[0.6, 0.4],
row_heights=[0.4, 0.6],
#Scott - horizontal spacing, is used to remove the gap between C-Q and 3-D plot..
horizontal_spacing=0.05,
specs=[[{
"type": "xy",
"secondary_y": True,
"colspan": 2
}, None], [{
"type": "scatter"
}, {
"type": "scatter3d"
}]])
# Add traces
fig.add_trace(
go.Scatter(
x=event_time,
y=event_flow,
name="Discharge (Q)",
line=dict(
width=4,
color='blue',
),
),
row=1,
col=1,
secondary_y=False,
)
fig.add_trace(
go.Scatter(
x=event_time,
y=event_concentration,
name="Concentration (C)",
line=dict(
width=4,
color='orange',
),
),
row=1,
col=1,
secondary_y=True,
)
# Add scattergeo globe map of volcano locations
fig.add_trace(go.Scatter(x=event_flow,
y=event_concentration,
mode='lines+markers',
name='Hysteresis',
fill='toself',
line=dict(
width=6,
color='#1f77b4',
),
marker=dict(
size=10,
color=np.arange(0, len(event_flow)),
colorscale='Viridis',
)),
row=2,
col=1)
# Add 3d surface of volcano
fig.add_trace(go.Scatter3d(x=event_time,
y=event_flow,
z=event_concentration,
mode='lines',
name='trajectory',
line=dict(
width=10,
color=np.arange(0, len(event_flow)),
colorscale='Viridis',
)),
row=2,
col=2)
#Scott - height 800 and width 950. I am using these to adjust height and width. I think if you want height to be greater or small, i would also change the height in line "PlotlyView(fig, height='800px', width='100%')" to match.
fig['layout'].update(height=800, width=950)
# Set theme, margin, and annotation in layout
fig.update_layout(
template=
"plotly_dark", #ggplot2, plotly_dark, seaborn, plotly, plotly_white, presentation, xgridoff
margin=dict(r=10, t=25, b=40, l=60),
autosize=True,
#automargin=True,
annotations=[
go.layout.Annotation(text="Source: CAUHSI - HEDA Tool",
showarrow=False,
xref="paper",
yref="paper",
x=0,
y=0)
],
scene1=dict(xaxis_title='Time',
yaxis_title='Discharge',
zaxis_title='Concentration'),
#showlegend=False,
#Scott - I moved the legend to top because it was messing with space utilization of figure.
#orientation "h" makes it a horizontal legend.
legend_orientation="h",
#location coordinates of legend.
legend=dict(x=-.1, y=1.1),
)
fig.update_yaxes(title_text="Concentration", row=2, col=1)
fig.update_xaxes(title_text="Discharge", row=2, col=1)
fig.update_xaxes(title_text="Time", row=1, col=1)
fig.update_yaxes(title_text="Discharge", row=1, col=1, secondary_y=False)
fig.update_yaxes(title_text="Concentration",
row=1,
col=1,
secondary_y=True)
hydrograph_plot = PlotlyView(fig, height='800px', width='100%')
return hydrograph_plot
def create_hydrograph(event_id, height='520px', width='100%'):
# Build up Plotly plot
time, flow, concentration, segments = get_conc_flow_seg(event_id)
if len(segments) == 0:
d = {}
d['start'] = 0
d['end'] = len(flow)
segments.append(d)
fig = make_subplots(specs=[[{"secondary_y": True}]])
#add all the flow
fig.add_trace(
go.Scatter(
x=time,
y=flow,
name='Hydrograph',
mode='lines',
line={
'color': 'blue',
'width': 1
},
),
secondary_y=False,
)
fig.add_trace(
go.Scatter(
x=time,
y=concentration,
name='Concentration graph',
mode='lines',
line={
'color': 'orange',
'width': 1
},
),
secondary_y=True,
)
flow = np.asarray(flow)
event_counter = 1
for segment in segments:
event_flow = flow[segment['start']:segment['end']]
event_time = time[segment['start']:segment['end']]
#event_concentration = concentration[segment['start']:concentration['end']]
fig.add_trace(
go.Scatter(
x=event_time,
y=event_flow,
mode='lines',
line={
'color': 'red',
'width': 4,
'shape': 'spline'
},
name="",
),
secondary_y=False,
)
event_counter = event_counter + 1
#this is a comment for syncing
if event_id != '1':
title = 'Number of events segmented: {0} '.format(str(len(segments)))
else:
title = 'Hydrograph to validate segmentation'
fig.update_layout(
xaxis=go.layout.XAxis(rangeselector=dict(buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all")
])),
rangeslider=dict(visible=True),
type="date"),
showlegend=False,
title_text=title,
)
# Set x-axis title
fig.update_xaxes(title_text="Time")
# Set y-axes titles
fig.update_yaxes(title_text="Flow (cfs)", secondary_y=False)
fig.update_yaxes(title_text="Concentration",
showgrid=False,
secondary_y=True)
hydrograph_plot = PlotlyView(fig, height='520px', width='100%')
return hydrograph_plot
def revenue_vs_requirements(request):
"""
Controller for the Plots Page.
"""
height = '520px'
width = '100%'
"""
Generates a plotly view of projects
"""
# Get objects from database
Session = app.get_persistent_store_database('primary_db',
as_sessionmaker=True)
session = Session()
projects = session.query(Project).all()
revenue_list = session.query(Revenue).all()
dfrevreq = pd.DataFrame(columns=[
'Construction Year', 'Projected Cost', 'Facility ID', 'Project',
'Category'
])
for project in projects:
if project.debt_checkbox_val == "true":
z = 1
for c in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append(
{
'Construction Year':
int(project.const_year) + z,
'Projected Cost':
float(json.loads(project.const_cost[c + 1])),
'Facility ID':
project.facility_id,
'Project':
project.project,
'Category':
project.category
},
ignore_index=True)
z = z + 1
elif project.recur_checkbox_val == "true":
y = 0
for x in range(len(project.const_cost) - 1):
dfrevreq = dfrevreq.append(
{
'Construction Year':
int(project.const_year) + y,
'Projected Cost':
float(json.loads(project.const_cost[x + 1])),
'Facility ID':
project.facility_id,
'Project':
project.project,
'Category':
project.category
},
ignore_index=True)
y = y + 1
else:
dfrevreq = dfrevreq.append(
{
'Construction Year': int(project.const_year),
'Projected Cost': float(json.loads(project.const_cost[0])),
'Facility ID': project.facility_id,
'Project': project.project,
'Category': project.category
},
ignore_index=True)
# Build up Plotly plot
bargraph_high_px = px.bar(
dfrevreq,
hover_data=["Facility ID", "Category", "Project", "Projected Cost"],
x="Construction Year",
y="Projected Cost",
color='Category',
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#8065ba",
"Facilities": "#074768",
"Golf": "#749f34",
"Transportation": "#ac162c",
"Stormwater": "#F78F07"
}
# title="Future Project Costs"
)
bargraph_high_px.update_layout(yaxis=dict(
title='Revenue/Revenue Required (USD)', ),
xaxis=dict(title='Year'),
legend=dict(title='Category'))
bargraph_med_px = px.bar(
dfrevreq,
hover_data=["Facility ID", "Category", "Project", "Projected Cost"],
x="Construction Year",
y="Projected Cost",
color='Category',
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#001f5b",
"Facilities": "#074768",
"Golf": "#ac162c",
"Transportation": "#232525",
"Stormwater": "#F78F07"
}
# title="Future Project Costs"
)
bargraph_med_px.update_layout(yaxis=dict(
title='Revenue/Revenue Required (USD)', ),
xaxis=dict(title='Year'),
legend=dict(title='Category'))
bargraph_low_px = px.bar(
dfrevreq,
hover_data=["Facility ID", "Category", "Project", "Projected Cost"],
x="Construction Year",
y="Projected Cost",
color='Category',
color_discrete_map={
"Water": "#056eb7",
"Wastewater": "#b3c935",
"Existing Debt": "#001f5b",
"Facilities": "#074768",
"Golf": "#ac162c",
"Transportation": "#232525",
"Stormwater": "#F78F07"
}
# title="Future Project Costs"
)
bargraph_low_px.update_layout(yaxis=dict(
title='Revenue/Revenue Required (USD)', ),
xaxis=dict(title='Year'),
legend=dict(title='Category'))
dfrevhigh = pd.DataFrame(
columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
dfrevmed = pd.DataFrame(
columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
dfrevlow = pd.DataFrame(
columns=['Scenario', 'Source', 'Monetary Value', 'Year'])
for entry in revenue_list:
if entry.scenario == "high":
dfrevhigh = dfrevhigh.append(
{
'Scenario': entry.scenario,
'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)),
'Year': int(entry.year)
},
ignore_index=True)
elif entry.scenario == "medium":
dfrevmed = dfrevmed.append(
{
'Scenario': entry.scenario,
'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)),
'Year': int(entry.year)
},
ignore_index=True)
elif entry.scenario == "low":
dfrevlow = dfrevlow.append(
{
'Scenario': entry.scenario,
'Source': entry.revenue_source,
'Monetary Value': int(json.loads(entry.monetary_Value)),
'Year': int(entry.year)
},
ignore_index=True)
dfrevhigh_sum = dfrevhigh.groupby("Year").agg(
HighRevenue=('Monetary Value', 'sum'))
dfrevmed_sum = dfrevmed.groupby("Year").agg(MedRevenue=('Monetary Value',
'sum'))
dfrevlow_sum = dfrevlow.groupby("Year").agg(LowRevenue=('Monetary Value',
'sum'))
dfrevfinal = pd.DataFrame(columns=["Year"])
minhyr = dfrevhigh["Year"].min()
maxhyr = dfrevhigh["Year"].max()
minmyr = dfrevmed["Year"].min()
maxmyr = dfrevmed["Year"].max()
minlyr = dfrevlow["Year"].min()
maxlyr = dfrevlow["Year"].max()
maxyr = max(maxlyr, maxmyr, maxhyr)
minyr = min(minlyr, minmyr, minhyr)
year_revrange = []
for i in range(maxyr - minyr + 1):
year_revrange.append(minyr + i)
for yearrev in year_revrange:
dfrevfinal = dfrevfinal.append({'Year': yearrev}, ignore_index=True)
dfrevfinal = dfrevfinal.merge(dfrevlow_sum, how="left", on="Year")
dfrevfinal = dfrevfinal.merge(dfrevmed_sum, how="left", on="Year")
dfrevfinal = dfrevfinal.merge(dfrevhigh_sum, how="left", on="Year")
bargraph_high_px.add_scatter(y=dfrevfinal["HighRevenue"],
x=dfrevfinal["Year"],
mode="lines",
line=dict(width=3, color="Red"),
name="High Revenue Scenario")
bargraph_med_px.add_scatter(y=dfrevfinal["MedRevenue"],
x=dfrevfinal["Year"],
mode="lines",
line=dict(width=3, color="Red"),
name="Medium Revenue Scenario")
bargraph_low_px.add_scatter(y=dfrevfinal["LowRevenue"],
x=dfrevfinal["Year"],
mode="lines",
line=dict(width=3, color="Red"),
name="Low Revenue Scenario")
bargraph_high_plot = PlotlyView(bargraph_high_px,
height=height,
width=width)
bargraph_med_plot = PlotlyView(bargraph_med_px, height=height, width=width)
bargraph_low_plot = PlotlyView(bargraph_low_px, height=height, width=width)
dfrevreq_rnm = dfrevreq.rename(columns={"Construction Year": "Year"})
dfrevreq_sum = dfrevreq_rnm.groupby("Year").agg(
total_cost=('Projected Cost', 'sum'))
minfyr = min(int(dfrevreq_rnm["Year"].min()), int(minyr))
maxfyr = max(int(dfrevreq_rnm["Year"].max()), int(maxyr))
year_range = []
for i in range(maxfyr - minfyr + 1):
year_range.append(minfyr + i)
dfdiff = pd.DataFrame(columns=[
"Year", "Difference High", "Difference Medium", "Difference Low"
])
for yearf in year_range:
dfdiff = dfdiff.append(
{
'Year': yearf,
"Difference High": 0,
"Difference Medium": 0,
"Difference Low": 0
},
ignore_index=True)
dfdiff = dfdiff.merge(dfrevfinal, how="left", on="Year")
dfdiff = dfdiff.merge(dfrevreq_sum, how="left", on="Year")
dfdiff["HighRevenue"] = dfdiff["HighRevenue"].fillna(0)
dfdiff["MedRevenue"] = dfdiff["MedRevenue"].fillna(0)
dfdiff["LowRevenue"] = dfdiff["LowRevenue"].fillna(0)
dfdiff["total_cost"] = dfdiff["total_cost"].fillna(0)
dfdiff["Difference High"] = (dfdiff["HighRevenue"] - dfdiff["total_cost"])
dfdiff["Difference Medium"] = (dfdiff["MedRevenue"] - dfdiff["total_cost"])
dfdiff["Difference Low"] = (dfdiff["LowRevenue"] - dfdiff["total_cost"])
dfdiff["High Deficit Or Surplus"] = [
"Surplus" if difvalh >= 0 else "Deficit"
for difvalh in dfdiff["Difference High"]
]
dfdiff["Medium Deficit Or Surplus"] = [
"Surplus" if difvalm >= 0 else "Deficit"
for difvalm in dfdiff["Difference Medium"]
]
dfdiff["Low Deficit Or Surplus"] = [
"Surplus" if difvall >= 0 else "Deficit"
for difvall in dfdiff["Difference Low"]
]
bargraphc_high_px = px.bar(
dfdiff,
hover_data=["Year", "HighRevenue", "total_cost", "Difference High"],
x="Year",
y="Difference High",
color="High Deficit Or Surplus",
color_discrete_map={
"Deficit": "#ac162c",
"Surplus": "#001f5b"
}
# title="Revenue"
)
bargraphc_high_px.update_layout(yaxis=dict(title='Monetary Value (USD)'),
xaxis=dict(title='Year'),
legend=dict(title='Deficit or Surplus'))
bargraphc_med_px = px.bar(
dfdiff,
hover_data=["Year", "MedRevenue", "total_cost", "Difference Medium"],
x="Year",
y="Difference Medium",
color="Medium Deficit Or Surplus",
color_discrete_map={
"Deficit": "#ac162c",
"Surplus": "#001f5b"
}
# title="Revenue"
)
bargraphc_med_px.update_layout(yaxis=dict(title='Monetary Value (USD)'),
xaxis=dict(title='Year'),
legend=dict(title='Deficit or Surplus'))
bargraphc_low_px = px.bar(
dfdiff,
hover_data=["Year", "LowRevenue", "total_cost", "Difference Low"],
x="Year",
y="Difference Low",
color="Low Deficit Or Surplus",
color_discrete_map={
"Deficit": "#ac162c",
"Surplus": "#001f5b"
}
# title="Revenue"
)
bargraphc_low_px.update_layout(yaxis=dict(title='Monetary Value (USD)'),
xaxis=dict(title='Year'),
legend=dict(title='Deficit or Surplus'))
bargraph_compare_high_plot = PlotlyView(bargraphc_high_px,
height=height,
width=width)
bargraph_compare_med_plot = PlotlyView(bargraphc_med_px,
height=height,
width=width)
bargraph_compare_low_plot = PlotlyView(bargraphc_low_px,
height=height,
width=width)
context = {
'bargraph_low_plot': bargraph_low_plot,
'bargraph_med_plot': bargraph_med_plot,
'bargraph_high_plot': bargraph_high_plot,
'bargraph_compare_low_plot': bargraph_compare_low_plot,
'bargraph_compare_med_plot': bargraph_compare_med_plot,
'bargraph_compare_high_plot': bargraph_compare_high_plot,
# 'can_add_projects': has_permission(request, 'add_projects')
}
session.close()
return render(request, 'project_inventory/revenue_vs_requirements.html',
context)
def create_hydrograph_old(event_id, height='520px', width='100%'):
# Build up Plotly plot
print(event_id)
time, flow, concentration, segments = get_conc_flow_seg(event_id)
if len(segments) == 0:
d = {}
d['start'] = 0
d['end'] = len(flow)
segments.append(d)
event_segments = []
flow_go = go.Scatter(
x=time,
y=flow,
name='Hydrograph',
mode='lines',
line={
'color': 'blue',
'width': 1
},
)
event_segments.append(flow_go)
flow = np.asarray(flow)
for segment in segments:
event_flow = flow[segment['start']:segment['end']]
event_time = time[segment['start']:segment['end']]
event_go = go.Scatter(
x=event_time,
y=event_flow,
mode='lines',
line={
'color': 'red',
'width': 4,
'shape': 'spline'
},
)
event_segments.append(event_go)
data = event_segments
if event_id != '1':
title = 'Hydrograph with {0} events'.format(str(len(segments)))
else:
title = 'Hydrograph to validate segmentation'
layout = {
'title':
title,
'xaxis': {
'title': 'Time'
},
'yaxis': {
'title': 'Flow (cfs)'
},
'showlegend':
False,
'xaxis_range': [time[0], time[-1]],
'xaxis':
go.layout.XAxis(rangeselector=dict(buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all")
])),
rangeslider=dict(visible=True),
type="date")
}
figure = {'data': data, 'layout': layout}
hydrograph_plot = PlotlyView(figure, height='520px', width='100%')
return hydrograph_plot
