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Merge pull request #165 from agoenergy/bordered_containers
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Element grouping with bordered containers
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@markushal
markushal authored Dec 8, 2023
2 parents 51694e2 + c7c11be commit 0426fa6
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Showing 7 changed files with 263 additions and 237 deletions.
32 changes: 20 additions & 12 deletions app/tab_certification_schemes.py
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Expand Up @@ -4,19 +4,8 @@
import streamlit as st


def content_certification_schemes(context_data: dict):
with st.expander("What is this?"):
st.markdown(
"""
**Get supplementary information on H2-relevant certification frameworks**
This sheet provides you with an overview of current governmental regulations
and voluntary standards for H2 products.
"""
)
def _render_scheme_info(context_data, scheme_name):
df = context_data["certification_schemes"]
helptext = "Select the certification scheme you want to know more about."
scheme_name = st.selectbox("Select scheme:", df["name"], help=helptext)
data = df.loc[df["name"] == scheme_name].iloc[0].to_dict()

# replace na with "not specified":
Expand Down Expand Up @@ -75,3 +64,22 @@ def content_certification_schemes(context_data: dict):

with st.expander("**Sources**"):
st.markdown(data["sources"])


def content_certification_schemes(context_data: dict):
with st.expander("What is this?"):
st.markdown(
"""
**Get supplementary information on H2-relevant certification frameworks**
This sheet provides you with an overview of current governmental regulations
and voluntary standards for H2 products.
"""
)

helptext = "Select the certification scheme you want to know more about."
scheme_name = st.selectbox(
"Select scheme:", context_data["certification_schemes"]["name"], help=helptext
)
with st.container(border=True):
_render_scheme_info(context_data=context_data, scheme_name=scheme_name)
59 changes: 30 additions & 29 deletions app/tab_country_fact_sheets.py
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Expand Up @@ -6,32 +6,6 @@
def _create_fact_sheet_demand_country(context_data: dict):
# select country:
country_name = st.session_state["country"]
with st.expander("What is this?"):
st.markdown(
"""
**Country fact sheets**
This sheet provides you with additional information on the production and import of
hydrogen and derivatives in all PTX BOA supply and demand countries.
For each selected supply and demand country pair, you will find detailed
country profiles.
For demand countries, we cover the following aspects:
country-specific projected hydrogen demand,
target sectors for hydrogen use,
hydrogen-relevant policies and competent authorities,
certification and regulatory frameworks,
and country-specific characteristics as defined in the demand countries'
hydrogen strategies.
For the supplying countries, we cover the country-specific technical potential
for renewables (based on various data sources),
LNG export and import infrastructure,
CCS potentials,
availability of an H2 strategy
and wholesale electricity prices.
"""
)
df = context_data["demand_countries"]
data = df.loc[df["country_name"] == country_name].iloc[0].to_dict()

Expand Down Expand Up @@ -122,6 +96,33 @@ def _create_fact_sheet_supply_country(context_data: dict):


def content_country_fact_sheets(context_data):
_create_fact_sheet_demand_country(context_data)
st.divider()
_create_fact_sheet_supply_country(context_data)
with st.expander("What is this?"):
st.markdown(
"""
**Country fact sheets**
This sheet provides you with additional information on the production and import of
hydrogen and derivatives in all PTX BOA supply and demand countries.
For each selected supply and demand country pair, you will find detailed
country profiles.
For demand countries, we cover the following aspects:
country-specific projected hydrogen demand,
target sectors for hydrogen use,
hydrogen-relevant policies and competent authorities,
certification and regulatory frameworks,
and country-specific characteristics as defined in the demand countries'
hydrogen strategies.
For the supplying countries, we cover the country-specific technical potential
for renewables (based on various data sources),
LNG export and import infrastructure,
CCS potentials,
availability of an H2 strategy
and wholesale electricity prices.
"""
)
with st.container(border=True):
_create_fact_sheet_demand_country(context_data)
with st.container(border=True):
_create_fact_sheet_supply_country(context_data)
83 changes: 44 additions & 39 deletions app/tab_dashboard.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,51 +36,56 @@ def content_dashboard(
"""
)

c_1, c_2 = st.columns([2, 1])
with st.container(border=True):
c_1, c_2 = st.columns([2, 1])

with c_1:
fig_map = plot_costs_on_map(
api, costs_per_region, scope="world", cost_component="Total"
)
st.plotly_chart(fig_map, use_container_width=True)
with c_1:
fig_map = plot_costs_on_map(
api, costs_per_region, scope="world", cost_component="Total"
)
st.plotly_chart(fig_map, use_container_width=True)

with c_2:
# create box plot and bar plot:
fig1 = create_box_plot(costs_per_region)
filtered_data = costs_per_region[
costs_per_region.index == st.session_state["region"]
]
fig2 = create_bar_chart_costs(filtered_data)
doublefig = make_subplots(rows=1, cols=2, shared_yaxes=True)
with c_2:
# create box plot and bar plot:
fig1 = create_box_plot(costs_per_region)
filtered_data = costs_per_region[
costs_per_region.index == st.session_state["region"]
]
fig2 = create_bar_chart_costs(filtered_data)
doublefig = make_subplots(rows=1, cols=2, shared_yaxes=True)

for trace in fig1.data:
trace.showlegend = False
doublefig.add_trace(trace, row=1, col=1)
for trace in fig2.data:
doublefig.add_trace(trace, row=1, col=2)
for trace in fig1.data:
trace.showlegend = False
doublefig.add_trace(trace, row=1, col=1)
for trace in fig2.data:
doublefig.add_trace(trace, row=1, col=2)

doublefig.update_layout(barmode="stack")
doublefig.update_layout(title_text="Cost distribution and details:")
st.plotly_chart(doublefig, use_container_width=True)
doublefig.update_layout(barmode="stack")
doublefig.update_layout(title_text="Cost distribution and details:")
st.plotly_chart(doublefig, use_container_width=True)

st.button(
"More Info on Supply Region and Demand Country",
on_click=move_to_tab,
args=("Country fact sheets",),
)
st.button(
"More Info on Supply Region and Demand Country",
on_click=move_to_tab,
args=("Country fact sheets",),
)

display_costs(
remove_subregions(api, costs_per_region, st.session_state["country"]),
"region",
"Costs by region:",
)
with st.container(border=True):
display_costs(
remove_subregions(api, costs_per_region, st.session_state["country"]),
"region",
"Costs by region:",
)

display_costs(costs_per_scenario, "scenario", "Costs by data scenario:")
with st.container(border=True):
display_costs(costs_per_scenario, "scenario", "Costs by data scenario:")

display_costs(
costs_per_res_gen, "res_gen", "Costs by renewable electricity source:"
)
with st.container(border=True):
display_costs(
costs_per_res_gen, "res_gen", "Costs by renewable electricity source:"
)

display_costs(
costs_per_chain, "chain", "Costs by supply chain:", output_unit="USD/MWh"
)
with st.container(border=True):
display_costs(
costs_per_chain, "chain", "Costs by supply chain:", output_unit="USD/MWh"
)
98 changes: 51 additions & 47 deletions app/tab_deep_dive_countries.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,54 +42,58 @@ def content_deep_dive_countries(api: PtxboaAPI, costs_per_region: pd.DataFrame)
"Select country:", ["Argentina", "Morocco", "South Africa"], horizontal=True
)

fig_map = plot_costs_on_map(
api, costs_per_region, scope=ddc, cost_component="Total"
)
st.plotly_chart(fig_map, use_container_width=True)

display_costs(
select_subregions(costs_per_region, ddc),
key="region",
titlestring="Costs per subregion",
key_suffix=ddc,
)
with st.container(border=True):
fig_map = plot_costs_on_map(
api, costs_per_region, scope=ddc, cost_component="Total"
)
st.plotly_chart(fig_map, use_container_width=True)

st.subheader("Full load hours of renewable generation")
st.divider()

# in order to keep the figures horizontally aligned, we create two st.columns pairs
# the columns are identified by c_{row}_{column}, zero indexed
c_0_0, c_0_1 = st.columns([2, 1], gap="large")
c_1_0, c_1_1 = st.columns([2, 1], gap="large")
with c_0_0:
st.markdown("**Map**")
map_parameter = st.selectbox(
"Show Parameter on Map",
[
"Wind Onshore",
"Wind Offshore",
"PV tilted",
"Wind-PV-Hybrid",
],
key="ddc_flh_map_parameter",
)
with c_1_0:
fig = plot_input_data_on_map(
api=api,
data_type="full load hours",
color_col=map_parameter,
scope=ddc,
)
st.plotly_chart(fig, use_container_width=True)
with st.expander("**Data**"):
df = display_and_edit_input_data(
api,
data_type="full load hours",
scope=ddc,
key="input_data_editor_full_load_hours_ddc",
display_costs(
select_subregions(costs_per_region, ddc),
key="region",
titlestring="Costs per subregion",
key_suffix=ddc,
)

with c_0_1:
st.markdown("**Regional Distribution**")
with c_1_1:
fig = px.box(df)
st.plotly_chart(fig, use_container_width=True)
with st.container(border=True):
st.subheader("Full load hours of renewable generation")

# in order to keep the figures horizontally aligned, we create two st.columns
# pairs, the columns are identified by c_{row}_{column}, zero indexed
c_0_0, c_0_1 = st.columns([2, 1], gap="large")
c_1_0, c_1_1 = st.columns([2, 1], gap="large")
with c_0_0:
st.markdown("**Map**")
map_parameter = st.selectbox(
"Show Parameter on Map",
[
"Wind Onshore",
"Wind Offshore",
"PV tilted",
"Wind-PV-Hybrid",
],
key="ddc_flh_map_parameter",
)
with c_1_0:
fig = plot_input_data_on_map(
api=api,
data_type="full load hours",
color_col=map_parameter,
scope=ddc,
)
st.plotly_chart(fig, use_container_width=True)
with st.expander("**Data**"):
df = display_and_edit_input_data(
api,
data_type="full load hours",
scope=ddc,
key="input_data_editor_full_load_hours_ddc",
)

with c_0_1:
st.markdown("**Regional Distribution**")
with c_1_1:
fig = px.box(df)
st.plotly_chart(fig, use_container_width=True)
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