def test_handle_plot_inputs():
mock_graph_data = create_mock_graph_data()
time = ("2016-01-01-00", "2016-12-31-23", "utc", "H")
zone_list, graph_data = handle_plot_inputs("Western", time, None, None,
mock_graph_data)
assert zone_list == ZONES["Western"]
assert graph_data == mock_graph_data
def plot_pie(
interconnect,
time,
scenario_ids=None,
scenario_names=None,
custom_data=None,
min_percentage=0,
):
"""Plot any number of scenarios as pie charts with two columns per scenario -
defaults to generation and capacity.
:param str interconnect: either 'Western' or 'Texas'.
:param tuple time: time related parameters.
1st element is the starting date.
2nd element is the ending date (left out).
3rd element is the timezone, only *'utc'*, *'US/Pacific'* and *'local'* are
possible.
4th element is the frequency, which can be *'H'* (hour), *'D'* (day), *'W'*
(week) or *'auto'*.
:param list scenario_ids: list of scenario ids, defaults to None.
:param list scenario_names: list of scenario names of same length as scenario ids,
defaults to None.
:param dict custom_data: hand-generated data, defaults to None.
:param float min_percentage: roll up small pie pieces into an Other category,
defaults to 0.
.. note::
if you want to plot scenario data and custom data together, custom data MUST be
in TWh for generation and GW for capacity. We may add a feature to check for
and convert to equal units but it's not currently a priority.
"""
zone_list, graph_data = handle_plot_inputs(interconnect, time,
scenario_ids, scenario_names,
custom_data)
for zone in zone_list:
ax_data_list = _construct_pie_ax_data(zone, graph_data, min_percentage)
_construct_pie_visuals(zone, ax_data_list)
print("\nDone\n")
def plot_hbar(
interconnect,
time,
scenario_ids=None,
scenario_names=None,
custom_data=None,
resource_types=None,
):
"""Plot any number of scenarios as horizontal bar charts with two columns per
scenario - defaults to generation and capacity.
:param str interconnect: either 'Western' or 'Texas'.
:param tuple time: time related parameters.
1st element is the starting date.
2nd element is the ending date (left out).
3rd element is the timezone, only *'utc'*, *'US/Pacific'* and *'local'* are
possible.
4th element is the frequency, which can be *'H'* (hour), *'D'* (day), *'W'*
(week) or *'auto'*.
:param list scenario_ids: list of scenario ids, defaults to None.
:param list scenario_names: list of scenario names of same len as scenario ids,
defaults to None
:param custom_data: hand-generated data as returned by
:func:`postreise.plot.multi.plot_helpers.handle_plot_inputs`, defaults to None.
:param list resource_types: list of resource types to show, defaults to None.
.. note::
If you want to plot scenario data and custom data together, custom data MUST be
in TWh for generation and GW for capacity. We may add a feature to check for
and convert to equal units but it's not currently a priority.
"""
zone_list, graph_data = handle_plot_inputs(
interconnect, time, scenario_ids, scenario_names, custom_data
)
for zone in zone_list:
ax_data_list = _construct_bar_ax_data(zone, graph_data, resource_types)
_construct_hbar_visuals(zone, ax_data_list)
print("\nDone\n")
def test_handle_plot_inputs_throws_valueError_if_not_enough_scenario_ids_or_custom_data(
):
time = ("2016-01-01-00", "2016-12-31-23", "utc", "H")
with pytest.raises(ValueError):
handle_plot_inputs("Western", time, ["123"], None, custom_data=None)
def test_handle_plot_inputs_throws_valueError_when_wrong_number_of_scenario_names(
):
time = ("2016-01-01-00", "2016-12-31-23", "utc", "H")
with pytest.raises(ValueError):
handle_plot_inputs("Western", time, ["123", "456"],
["Hist. Moon Cheese"], None)
def test_handle_plot_inputs_throws_valueError_for_incorrect_interconnect():
time = ("2016-01-01-00", "2016-12-31-23", "utc", "H")
with pytest.raises(ValueError):
handle_plot_inputs("The Moon", time, None, None,
create_mock_graph_data())
def plot_shortfall(
interconnect,
time,
scenario_ids=None,
scenario_names=None,
custom_data=None,
is_match_CA=False,
has_collaborative_scenarios=None,
baselines=None,
targets=None,
demand=None,
):
"""Plot a stacked bar chart of generation shortfall for any number of scenarios.
:param str interconnect: currently only 'Western' works. We include this param
to keep a consistent API with all plotting functions.
:param tuple time: time related parameters.
1st element is the starting date.
2nd element is the ending date (left out).
3rd element is the timezone, only *'utc'*, *'US/Pacific'* and *'local'* are
possible.
4th element is the frequency, which can be *'H'* (hour), *'D'* (day), *'W'*
(week) or *'auto'*.
:param list scenario_ids: list of scenario ids.
:param list scenario_names: list of scenario names of same len as scenario ids.
:param dict custom_data: hand-generated data, defaults to None.
:param bool is_match_CA: calculate shortfall using special rules that apply when
all zones match California goals, defaults to False
:param list has_collaborative_scenarios: list of scenario ids where all zones
collaborate to meet goals. Affects results for interconnect, defaults to None.
:param dict baselines: baseline renewables generation for each zone, defaults to
None.
:param dict targets: target renewables renewable generation for each zone, defaults
to None.
:param dict demand: total demand for each zone, defaults to None
.. note::
If you want to plot scenario data and custom data together, custom data MUST be
in TWh for generation and GW for capacity. We may add a feature to check for
and convert to equal units but it's not currently a priority.
"""
zone_list, graph_data = handle_plot_inputs(interconnect, time,
scenario_ids, scenario_names,
custom_data)
baselines, targets, demand = handle_shortfall_inputs(
is_match_CA, baselines, targets, demand)
for zone in zone_list:
if zone == "Western":
ax_data, shortfall_pct_list = _construct_shortfall_data_for_western(
graph_data,
is_match_CA,
has_collaborative_scenarios,
baselines[zone],
targets,
demand[zone],
)
else:
ax_data, shortfall_pct_list = _construct_shortfall_ax_data(
zone,
graph_data,
is_match_CA,
baselines[zone],
targets[zone],
demand[zone],
)
_construct_shortfall_visuals(zone, ax_data, shortfall_pct_list,
is_match_CA, targets[zone], demand[zone])
print("\nDone\n")
