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Merge pull request #174 from KTH-dESA/crg/jordan_integrated_scenario
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Cleaned callbacks in Jordan app
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@camiloramirezgo
camiloramirezgo authored May 31, 2021
2 parents f96d430 + 696083b commit 91958f9
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6 changes: 1 addition & 5 deletions Jordan model/Softlinking.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@
"metadata": {},
"outputs": [],
"source": [
"scenario = 'Increased Water Productivity'\n",
"scenario = 'Reference'\n",
"climate = 'Historical Trend'\n",
"data_file = os.path.join('data', 'WEAP results', 'March 2021', f'WEAPResults - {scenario} - {climate}.xlsx')\n",
"data = pd.ExcelFile(data_file)"
Expand Down Expand Up @@ -332,13 +332,9 @@
"\n",
"sf.save_dataframe(desalination, 2020, 2050, output_folder, 'desalination.gz')\n",
"sf.save_dataframe(wwtp_inflow, 2020, 2050, output_folder, 'wwtp_inflow.gz')\n",
"# sf.save_dataframe(surface_water, 2020, 2050, output_folder, 'surface_water_supply.csv')\n",
"sf.save_dataframe(gw_supply, 2020, 2050, output_folder, 'groundwater_supply.gz')\n",
"sf.save_dataframe(pl_flow, 2020, 2050, output_folder, 'pipelines_flow.gz')\n",
"\n",
"output_folder = os.path.join('dashboard', 'data', scenario, climate)\n",
"os.makedirs(output_folder, exist_ok=True)\n",
"\n",
"sf.save_dataframe(crop_production, 2020, 2050, output_folder, 'crop_production.gz')\n",
"sf.save_dataframe(delivered_demand, 2020, 2050, output_folder, 'water_delivered.gz')\n",
"sf.save_dataframe(required_demand, 2020, 2050, output_folder, 'water_requirements.gz')"
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112 changes: 71 additions & 41 deletions Jordan model/dashboard/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,12 +114,10 @@ def title_info(title, info_id, info_title, modal_content):
[
title_info(title='Select scenario', info_id='scenario-info', info_title='Scenario information',
modal_content=[
html.P('Four scenarios have been analysed to date in order to explore nexus interactions and '
html.P('Four scenarios were analysed in order to explore nexus interactions and '
'the impact any given nexus solution or policy measure targeted to one of the systems '
'(i.e. water, energy or agriculture), may have upon the other systems. '
'The scenarios evaluated so far are a reference (Business as Usual), an improved '
'agricultural efficiency and a new water resources scenario '
'(i.e. New desalinated water), and are run for 30 years from 2020 to 2050.'),
'The scenarios evaluated run for 30 years from 2020 to 2050.'),
html.H6('Reference scenario'),
html.P('This scenario takes a Business as Usual approach where the main trends '
'(in terms of demand, supply and growth) are kept unchanged. It assumes that '
Expand All @@ -135,7 +133,12 @@ def title_info(title, info_id, info_title, modal_content):
'achieve by year 2050.'),
html.H6('New water resources scenario'),
html.P('This scenario assumes the realization of the Red Sea-Dead Sea project and new '
'desalination plant Red-Dead (110 MCM/yr).')]),
'desalination plant Red-Dead (110 MCM/yr).'),
html.H6('Integrated strategies scenario'),
html.P('This scenario considers combined strategies of augmenting water supplies '
'through seawater desalination, reducing non-revenue and mitigating irrigation water '
'demands through increased water productivity.')
]),
html.Div(
dbc.RadioItems(
id="rb-scenario",
Expand All @@ -145,6 +148,7 @@ def title_info(title, info_id, info_title, modal_content):
{"label": "New water resources", "value": 'New Resources'},
{"label": "Reduce non-revenue water", "value": 'Reduce NRW to 20 percent'},
{"label": "Increased water productivity", "value": 'Increased Water Productivity'},
{"label": "Integrated strategies", "value": 'Integrated NRW 20'},
],
value='Reference',
className='checklist-selected-style',
Expand Down Expand Up @@ -237,7 +241,6 @@ def title_info(title, info_id, info_title, modal_content):
# hidden=True,
)


save_scenario = html.Div(
[
title_info(title='Save scenario', info_id='save-info',
Expand Down Expand Up @@ -414,28 +417,32 @@ def title_info(title, info_id, info_title, modal_content):
dcc.Loading(
id="loading-map",
type="default",
children=dcc.Graph(id="map",
config=dict(showSendToCloud=True,
toImageButtonOptions=dict(format='png',
filename='map',
height=700,
width=700, scale=2)))
children=[html.Div(dcc.Graph(id="map",
config=dict(showSendToCloud=True,
toImageButtonOptions=dict(format='png',
filename='map',
height=700,
width=700, scale=2))),
id='map-container'),
dcc.Store(id='current')
]
)

],
id='map-div',
className='col-xl-7 col-lg-12',
)

graphs = html.Div([results_header,
html.Div(dbc.Col(dcc.Loading(id='graphs', style=dict(height='10px'))), id='graphs-container'),
html.Div(dcc.Loading(id='graphs'), id='graphs-container'),
footer_results
],
id='results-container',
className='col-xl-5 col-lg-12')

content = html.Div([map, graphs], id="page-content")

app.layout = html.Div([dcc.Store(id='current'), dcc.Store(id='compare', data={}), sidebar, content])
app.layout = html.Div([dcc.Store(id='compare', data={}), sidebar, content])


# Helper funtions
Expand Down Expand Up @@ -493,7 +500,12 @@ def get_graphs(data, water_delivered, water_required, gw_pumped, pl_flow,
dff_energy = dff_energy.append(dff, sort=False)
# dff_energy.rename(columns={'pa_e': 'value'}, inplace=True)

data['WaterDelivered'] = plotting.water_delivered(dff_delivered, layout, 'Water delivered (Mm3)')
dff_agri_delivered = water_delivered.loc[water_delivered['type'] == 'Agriculture']
dff_agri_delivered = dff_agri_delivered.groupby(['Year'])[['sswd']].sum() / 1000000
dff_agri_delivered['crop_per_drop'] = df_production.groupby('Year')['production'].sum() / dff_agri_delivered['sswd']
dff_agri_delivered.reset_index(inplace=True)

data['WaterDelivered'] = plotting.water_delivered(dff_delivered, layout, 'Water delivered (Mm<sup>3</sup>)')
data['UnmetDemand'] = plotting.unmet_demand(dff_unmet.reset_index(), layout, 'Unmet water demand (%)')

dff_wtd = gw_pumped.copy()
Expand All @@ -505,22 +517,23 @@ def get_graphs(data, water_delivered, water_required, gw_pumped, pl_flow,
dff['point'] = [x[1] for x in dff['point'].str.split('_')]
dff = dff.groupby(['Year', 'point'])['sswd'].sum() / 1000000
dff = dff.reset_index()
data['WaterExtraction'] = plotting.groundwater_extraction(dff, layout, 'Groundwater extraction (Mm3)')
data['WaterExtraction'] = plotting.groundwater_extraction(dff, layout, 'Groundwater extraction (Mm<sup>3</sup>)')

data['CropProduction'] = plotting.plot_production(df_production, layout,
'Annual cropland production by type (kton)',
'variable')

data['CropPerDrop'] = plotting.crop_per_drop(dff_agri_delivered, layout,
'Agricultural water productivity (kg/m<sup>3</sup>)')

data['EnergyDemand'] = plotting.energy_demand(dff_energy,
layout, 'Energy demand (GWh)')

return data


##### need to change the compare scenarios logic to the calback of the compare modal
#### also add logic to the save button and modal
@app.callback(
[Output("current", "data"), Output('map', 'selectedData')],
Output("current", "data"),
[
Input("button-apply", "n_clicks"),
# Input("popover-map-target", "n_clicks"),
Expand All @@ -546,7 +559,7 @@ def update_current_data(n_1, scenario, eto, efficiency):
# map = plot_map(water_delivered, water_required, gw_pumped, pl_flow, wwtp_data, desal_data)
# graphs = {}
# data_dict = dict(map=map, graphs=graphs, scenario=scenario, level=level, eff_end=eff_end, eff_init=eff_init)
return data_dict, None
return data_dict


@app.callback(
Expand All @@ -573,8 +586,8 @@ def toggle_collapse(n, is_open):

@app.callback(
[Output("graphs", "children"), Output('resultsTitle', 'children')],
[Input('map', 'selectedData'), Input('map-selection', 'value')],
[State('current', 'data')],
[Input('map', 'selectedData')],
[State('map-selection', 'value'), State('current', 'data')],
prevent_initial_call=True
)
def update_results(selection, map_type, data_current):
Expand All @@ -596,27 +609,27 @@ def update_results(selection, map_type, data_current):
df = dff_delivered.groupby(['Year', 'Governorate'])[['sswd']].sum() / 1000000
df.reset_index(inplace=True)


data['WaterDeliveredGov'] = plotting.plot_water_delivered_by_gov(df, layout,
'Annual water delivered by Governorate (Mm3)',)
'Annual water delivered by Governorate (Mm3)', )

df = crop_production.groupby(['Year', 'Governorate'])[['production']].sum() / 1000000
df.reset_index(inplace=True)
df_production = crop_production.groupby(['Year', 'Governorate'])[['production']].sum() / 1000000
df_production.reset_index(inplace=True)

df_production['crop_per_drop'] = df_production['production'] / df['sswd']

data['CropProduction'] = plotting.plot_production(df, layout,
data['CropProduction'] = plotting.plot_production(df_production, layout,
'Annual cropland production by Governorate (kton)',
'Governorate')
data[f'{name}CropPerDrop'] = plotting.crop_per_drop_governorate(df_production, layout,
'Agricultural water productivity (kg/m<sup>3</sup>)')

df = crop_production.groupby(['Governorate', 'Year', 'variable'])[['production']].sum() / 1000000
df.reset_index(inplace=True)
df = df.groupby(['Governorate', 'variable'])[['production']].mean().reset_index()
data['CropProductionByType'] = plotting.plot_production_by_gov(df, layout,
'Annual average cropland production (kton)',
'variable', 'Governorate')

# data['CropProductionByGovernorate'] = plotting.plot_production_by_gov(df, layout,
# 'Total cropland production (Mton)',
# 'Governorate', 'variable')
data['CropProductionByType'] = plotting.plot_production_by_gov(df, layout,
'Annual average cropland production (kton)',
'variable', 'Governorate')

elif selection['points'][0]['customdata'][0] in ['Municipality', 'Industry', 'Agriculture']:
name = selection['points'][0]['customdata'][1]
Expand All @@ -637,9 +650,15 @@ def update_results(selection, map_type, data_current):
df = df.groupby(['Year', 'variable'])[['production']].sum() / 1000000
df.reset_index(inplace=True)

df_productivity = df.groupby('Year')[['production']].sum()
df_productivity['crop_per_drop'] = df_productivity['production'] / dff.groupby('Year')['sswd'].sum()
df_productivity.reset_index(inplace=True)

data[f'{name}CropProduction'] = plotting.plot_production(df, layout,
'Annual cropland production by type (kton)',
'variable')
data[f'{name}CropPerDrop'] = plotting.crop_per_drop(df_productivity, layout,
'Agricultural water productivity (kg/m<sup>3</sup>)')

elif selection['points'][0]['customdata'][0] in ['Groundwater supply']:
name = selection['points'][0]['customdata'][1]
Expand Down Expand Up @@ -727,21 +746,29 @@ def update_results(selection, map_type, data_current):
name = selection['points'][0]['customdata'][0]

dff_delivered, crop_production = scripts.read_data.load_data(data_current['scenario'], data_current['eto'],
data_current['efficiency'], ['water_delivered.gz', 'crop_production.gz'])
data_current['efficiency'],
['water_delivered.gz', 'crop_production.gz'])

df = dff_delivered.loc[dff_delivered['Governorate']==name]
df = dff_delivered.loc[dff_delivered['Governorate'] == name]
df = df.groupby(['Year', 'type'])['sswd'].sum() / 1000000
df = df.reset_index()

data[f'{name}WaterDelivered'] = plotting.water_delivered(df, layout,
'Annual water delivered (Mm3)')

df = crop_production.loc[crop_production['Governorate']==name]
df = df.groupby(['Year', 'variable'])[['production']].sum() / 1000000
df.reset_index(inplace=True)
data[f'{name}CropProduction'] = plotting.plot_production(df, layout,
df_production = crop_production.loc[crop_production['Governorate'] == name]
df_production = df_production.groupby(['Year', 'variable'])[['production']].sum() / 1000000
df_production.reset_index(inplace=True)

df_productivity = df_production.groupby('Year')[['production']].sum()
df_productivity['crop_per_drop'] = df_productivity['production'] / df.groupby('Year')['sswd'].sum()
df_productivity.reset_index(inplace=True)

data[f'{name}CropProduction'] = plotting.plot_production(df_production, layout,
'Annual cropland production by type (kton)',
'variable')
data[f'{name}CropPerDrop'] = plotting.crop_per_drop(df_productivity, layout,
'Agricultural water productivity (kg/m<sup>3</sup>)')

plots = []
for key, value in data.items():
Expand Down Expand Up @@ -795,13 +822,15 @@ def update_scenario_title(ts, data):


@app.callback(
[Output("map", "figure"), Output("loading-map", "children")],
[Output("map", "figure"), Output("map-container", "children")],
[
Input('map-background', 'value'),
Input('map-selection', 'value'),
Input('current', 'modified_timestamp'),
],
prevent_initial_call=True
)
def update_map(background, map_type):
def update_map(background, map_type, ts):
map = plot_map(background, map_type)
return {}, dcc.Graph(figure=map, id="map",
config=dict(showSendToCloud=True,
Expand All @@ -811,6 +840,7 @@ def update_map(background, map_type):
width=700, scale=2)))



# @app.callback(
# [Output('page-1', 'active'), Output('page-2', 'active'),
# Output('tools', 'hidden'), Output('visual-tools', 'hidden')],
Expand Down
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