def main(args=None):
"""
Parse the arguments, get the data in a df, and create the plot
:return: if requested, return the plot as JSON object
"""
if args is None:
args = sys.argv[1:]
parsed_args = parse_arguments(arguments=args)
conn = connect_to_database(db_path=parsed_args.database)
c = conn.cursor()
scenario_id, scenario = get_scenario_id_and_name(
scenario_id_arg=parsed_args.scenario_id,
scenario_name_arg=parsed_args.scenario,
c=c,
script="energy_plot")
tech_colors = get_tech_colors(c)
tech_plotting_order = get_tech_plotting_order(c)
energy_unit = get_unit(c, "energy")
plot_title = "{}Energy by Period - {} - Stage {}".format(
"{} - ".format(scenario) if parsed_args.scenario_name_in_title else "",
parsed_args.load_zone, parsed_args.stage)
plot_name = "EnergyPlot-{}-{}".format(parsed_args.load_zone,
parsed_args.stage)
df = get_plotting_data(conn=conn,
scenario_id=scenario_id,
load_zone=parsed_args.load_zone,
stage=parsed_args.stage)
source, x_col_reordered = process_stacked_plot_data(
df=df, y_col="energy_mwh", x_col=["period"], category_col="technology")
# Multi-level index in CDS will be joined into one column with "_" separator
x_col_cds = "_".join(x_col_reordered)
x_col_label = ", ".join([x.capitalize() for x in x_col_reordered])
plot = create_stacked_bar_plot(source=source,
x_col=x_col_cds,
x_label=x_col_label,
y_label="Energy ({})".format(energy_unit),
category_label="Technology",
category_colors=tech_colors,
category_order=tech_plotting_order,
title=plot_title,
ylimit=parsed_args.ylimit)
# Show plot in HTML browser file if requested
if parsed_args.show:
show_plot(plot=plot,
plot_name=plot_name,
plot_write_directory=parsed_args.plot_write_directory,
scenario=scenario)
# Return plot in json format if requested
if parsed_args.return_json:
return json_item(plot, plot_name)
def main(args=None):
"""
Parse the arguments, get the data in a df, and create the plot
:return: if requested, return the plot as JSON object
"""
if args is None:
args = sys.argv[1:]
parsed_args = parse_arguments(arguments=args)
conn = connect_to_database(db_path=parsed_args.database)
tech_colors = get_tech_colors(conn.cursor())
tech_plotting_order = get_tech_plotting_order(conn.cursor())
power_unit = get_unit(conn.cursor(), "power")
plot_title = "Total Capacity by Scenario - {} - Subproblem {} - Stage {}"\
.format(
parsed_args.load_zone,
parsed_args.subproblem,
parsed_args.stage
)
plot_name = "TotalCapacityPlot-{}-{}-{}"\
.format(
parsed_args.load_zone,
parsed_args.subproblem,
parsed_args.stage
)
df = get_plotting_data(conn=conn,
period=parsed_args.period,
load_zone=parsed_args.load_zone,
subproblem=parsed_args.subproblem,
stage=parsed_args.stage)
source, x_col_reordered = process_stacked_plot_data(
df=df,
y_col="capacity_mw",
x_col=["period", "scenario_id"],
category_col="technology")
# Multi-level index in CDS will be joined into one column with "_" separator
x_col_cds = "_".join(x_col_reordered)
x_col_label = ", ".join([x.capitalize() for x in x_col_reordered])
plot = create_stacked_bar_plot(
source=source,
x_col=x_col_cds,
x_label=x_col_label,
y_label="Total Capacity ({})".format(power_unit),
category_label="Technology",
category_colors=tech_colors,
category_order=tech_plotting_order,
title=plot_title,
ylimit=parsed_args.ylimit)
# Show plot in HTML browser file if requested
if parsed_args.show:
show_plot(plot=plot,
plot_name=plot_name,
plot_write_directory=parsed_args.plot_write_directory)
# Return plot in json format if requested
if parsed_args.return_json:
return json_item(plot, plot_name)
def update_plots(attr, old, new):
# TODO: could separate out updating the data sources from re-drawing the
# plots. Data source could be a DataProvier attribute that gets updated
# whenever user selections change.
# Get the current selection values
scenario = scenario_select.value
stage = stage_select.value
period = period_select.value
zone = zone_select.value
capacity_metric = capacity_select.value
# Get data sources
cap_src, cap_x_col = data.get_cap_src(scenario=scenario,
stage=stage,
period=period,
zone=zone,
capacity_metric=capacity_metric)
energy_src, energy_x_col = data.get_energy_src(scenario=scenario,
stage=stage,
period=period,
zone=zone)
cost_src, cost_x_col = data.get_cost_src(scenario=scenario,
stage=stage,
period=period,
zone=zone)
summary_src = data.get_summary_src(scenario=scenario,
stage=stage,
period=period,
zone=zone)
objective_src = data.get_objective_src(scenario=scenario, stage=stage)
# Create Bokeh Plots and Tables
title = PreText(
text="Results: {} - {} - {}".format(scenario, period, zone))
summary_table = create_datatable(summary_src)
objective_table = create_datatable(objective_src)
cap_plot = create_stacked_bar_plot(source=cap_src,
x_col=cap_x_col,
title="Capacity by Technology",
category_label="Technology",
y_label="Capacity (MW)")
energy_plot = create_stacked_bar_plot(
source=energy_src,
x_col=energy_x_col,
title="Energy by Technology",
category_label="Technology",
y_label="Energy (MWh)" # TODO: link to units
)
cost_plot = create_stacked_bar_plot(
source=cost_src,
x_col=cost_x_col,
title="Cost by Component",
category_label="Cost Component",
y_label="Cost (million USD)" # TODO: link to units
)
# Update layout with new plots
layout.children[0] = title
top_row.children[1] = summary_table
top_row.children[2] = objective_table
middle_row.children[0] = cap_plot
middle_row.children[1] = energy_plot
bottom_row.children[0] = cost_plot
