def historic_run():
"""Run the model."""
logger.info(f'Version: {VERSION}')
logger.info('Initialising cloud storage client ...')
client = init_azure_storage()
download_hydrology(client)
logger.info('Starting model run ...')
# Run the model
if not os.path.exists(OUT_DIR):
os.makedirs(OUT_DIR)
model = Model.load(MODEL_FILENAME)
# Add a storage recorder
TablesRecorder(model,
os.path.join(OUT_DIR, 'thames_output.h5'),
parameters=[p for p in model.parameters])
ProgressRecorder(model)
# Run the model
stats = model.run()
logger.info(stats)
# Upload the results
logger.info('Uploading outputs ...')
upload_outputs(client)
# Print stats
stats_df = stats.to_dataframe()
logger.info(stats_df)
def load_pywr_model(self, solver=None):
""" Create a Pywr model from the exported data. """
pywr_data = self.get_pywr_data()
model = Model.load(pywr_data, solver=solver)
self.model = model
return pywr_data
def main(filename):
base, ext = os.path.splitext(filename)
m = Model.load(filename, solver='glpk-dcopf')
gen1 = NumpyArrayNodeRecorder(m, m.nodes['gen1'])
pv2 = NumpyArrayNodeRecorder(m, m.nodes['pv2'])
ProgressRecorder(m)
CSVRecorder(m, f'{base}.csv')
m.setup()
stats = m.run()
print(stats.to_dataframe())
df = pandas.concat({'gen1': gen1.to_dataframe(), 'pv2': pv2.to_dataframe()}, axis=1)
fig, ax = plt.subplots(figsize=(8, 4))
df.plot(ax=ax)
df.resample('D').mean().plot(ax=ax, color='black')
ax.set_ylabel('MW')
fig.savefig(f'{base}.png', dpi=300)
fig, ax = plt.subplots(figsize=(8, 4))
df.resample('M').sum().plot(ax=ax)
ax.set_ylabel('MWh per month')
fig.savefig(f'{base}-monthly.png', dpi=300)
plt.show()
def test_basic_losses():
m = Model.load(os.path.join(TEST_FOLDER, 'models', 'basic-losses.json'),
solver='glpk-dcopf')
m.setup()
m.run()
np.testing.assert_allclose(m.nodes['gen1'].flow, [50])
np.testing.assert_allclose(m.nodes['gen2'].flow, [100])
np.testing.assert_allclose(m.nodes['load1'].flow, [135])
def test_export(db_with_pywr_network, logged_in_client):
client = logged_in_client
pywr_network_id, pywr_scenario_id, pywr_json_filename = db_with_pywr_network
exporter = PywrHydraExporter.from_network_id(client, pywr_network_id, pywr_scenario_id)
pywr_data_exported = exporter.get_pywr_data()
# Check transformed data is about right
with open(pywr_json_filename) as fh:
pywr_data = json.load(fh)
assert_identical_pywr_data(pywr_data, pywr_data_exported)
m = Model.load(pywr_data)
m.run()
def test_simple():
m = Model.load(os.path.join(TEST_FOLDER, 'models', 'simple.json'),
solver='glpk-dcopf')
m.setup()
m.run()
np.testing.assert_allclose(m.nodes['gen1'].flow, [100.0])
np.testing.assert_allclose(m.nodes['gen2'].flow, [50.0])
np.testing.assert_allclose(m.nodes['load3'].flow, [150.0])
np.testing.assert_allclose(m.nodes['line12'].flow, [50 / 3])
np.testing.assert_allclose(m.nodes['line13'].flow, [100.0 - 50 / 3])
np.testing.assert_allclose(m.nodes['line23'].flow, [50.0 + 50 / 3])
def test_export(db_with_pywr_network, logged_in_client):
client = logged_in_client
pywr_network_id, pywr_scenario_id, pywr_json_filename = db_with_pywr_network
exporter = PywrHydraExporter.from_network_id(client, pywr_network_id,
pywr_scenario_id)
pywr_data_exported = exporter.get_pywr_data()
# Check transformed data is about right
with open(pywr_json_filename) as fh:
pywr_data = json.load(fh)
assert_identical_pywr_data(pywr_data, pywr_data_exported)
m = Model.load(pywr_data)
m.run()
def test_simple_line_constraints():
m = Model.load(os.path.join(TEST_FOLDER, 'models',
'simple-line-constraints.json'),
solver='glpk-dcopf')
m.setup()
m.run()
np.testing.assert_allclose(m.nodes['gen1'].flow, [25.0])
np.testing.assert_allclose(m.nodes['gen2'].flow, [100.0])
np.testing.assert_allclose(m.nodes['load3'].flow, [125.0])
np.testing.assert_allclose(m.nodes['line12'].flow, [-25.0])
np.testing.assert_allclose(m.nodes['line13'].flow, [50.0])
np.testing.assert_allclose(m.nodes['line23'].flow, [75.0])
def test_from_model():
json_path = os.path.join(os.path.dirname(__file__), "models",
"river1.json")
model = Model.load(json_path)
json_dict = pywr_model_to_d3_json(model, attributes=True)
assert "nodes" in json_dict.keys()
assert "links" in json_dict.keys()
node_names = ["catchment1", "river1", "abs1", "link1", "term1", "demand1"]
for node in json_dict["nodes"]:
assert node["name"] in node_names
catchment = get_node(json_dict["nodes"], "catchment1")
catchment_max_flow = get_node_attribute(catchment, "max_flow")
assert catchment_max_flow["value"] == "5.0"
def test_pv_generator():
m = Model.load(os.path.join(TEST_FOLDER, 'models', 'pv-generator.json'),
solver='glpk-dcopf')
gen1 = NumpyArrayNodeRecorder(m, m.nodes['gen1'])
pv2 = NumpyArrayNodeRecorder(m, m.nodes['pv2'])
m.setup()
m.run()
df = pandas.concat({
'gen1': gen1.to_dataframe(),
'pv2': pv2.to_dataframe()
},
axis=1)
assert df.shape[0] == 745
def test_simple_battery():
m = Model.load(os.path.join(TEST_FOLDER, 'models', 'simple-battery.json'),
solver='glpk-dcopf')
gen1 = NumpyArrayNodeRecorder(m, m.nodes['gen1'])
pv2 = NumpyArrayNodeRecorder(m, m.nodes['pv2'])
battery1 = NumpyArrayStorageRecorder(m, m.nodes['battery1'])
m.setup()
m.run()
df = pandas.concat(
{
'gen1': gen1.to_dataframe(),
'pv2': pv2.to_dataframe(),
'battery1': battery1.to_dataframe()
},
axis=1)
assert df.shape[0] == 745
def test_simple_losses():
m = Model.load(os.path.join(TEST_FOLDER, 'models', 'simple-losses.json'),
solver='glpk-dcopf')
m.setup()
m.run()
# Gen1 losses
line12_losses = 0.1 * 100 / 9
line13_losses = 0.1 * (100 - 100 / 9)
line23_losses = 0.1 * (100 / 9 - line12_losses)
gen1_losses = line12_losses + line13_losses + line23_losses
gen2 = (150 - 100 + gen1_losses) / (1 - 0.1)
np.testing.assert_allclose(m.nodes['gen1'].flow, [100.0])
np.testing.assert_allclose(m.nodes['gen2'].flow, [gen2])
np.testing.assert_allclose(m.nodes['load3'].flow, [150.0])
np.testing.assert_allclose(m.nodes['line12'].flow, [100 / 9])
np.testing.assert_allclose(m.nodes['line13'].flow, [100.0 - 100 / 9])
np.testing.assert_allclose(m.nodes['line23'].flow,
[gen2 + 100 / 9 - line12_losses])
def load_pywr_model(self):
""" Create a Pywr model from the exported data. """
pywr_data = self.get_pywr_data()
model = Model.load(pywr_data)
self.model = model
def load_pywr_model(self):
""" Create a Pywr model from the exported data. """
pywr_data = self.get_pywr_data()
model = Model.load(pywr_data)
self.model = model
def run():
# Run the model
model = Model.load(MODEL_FILENAME)
# Add a storage recorder
TablesRecorder(model,
'thames_output.h5',
parameters=[p for p in model.parameters])
# Run the model
stats = model.run()
print(stats)
stats_df = stats.to_dataframe()
print(stats_df)
keys_to_plot = (
'time_taken_before',
'solver_stats.bounds_update_nonstorage',
'solver_stats.bounds_update_storage',
'solver_stats.objective_update',
'solver_stats.lp_solve',
'solver_stats.result_update',
'time_taken_after',
)
keys_to_tabulate = (
'timesteps',
'time_taken',
'solver',
'num_scenarios',
'speed',
'solver_name'
'solver_stats.total',
'solver_stats.number_of_rows',
'solver_stats.number_of_cols',
'solver_stats.number_of_nonzero',
'solver_stats.number_of_routes',
'solver_stats.number_of_nodes',
)
values = []
labels = []
explode = []
solver_sub_total = 0.0
for k in keys_to_plot:
v = stats_df.loc[k][0]
values.append(v)
label = k.split('.', 1)[-1].replace('_', ' ').capitalize()
explode.append(0.0)
if k.startswith('solver_stats'):
labels.append('Solver - {}'.format(label))
solver_sub_total += v
else:
labels.append(label)
values.append(stats_df.loc['solver_stats.total'][0] - solver_sub_total)
labels.append('Solver - Other')
explode.append(0.0)
values.append(stats_df.loc['time_taken'][0] - sum(values))
values = np.array(values) / sum(values)
labels.append('Other')
explode.append(0.0)
fig, (ax1, ax2) = plt.subplots(figsize=(12, 4),
ncols=2,
sharey='row',
gridspec_kw={'width_ratios': [2, 1]})
print(values, labels)
ax1.pie(values,
explode=explode,
labels=labels,
autopct='%1.1f%%',
startangle=90)
ax1.axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
cell_text = []
for index, value in stats_df.iterrows():
if index not in keys_to_tabulate:
continue
v = value[0]
if isinstance(v, (float, np.float64, np.float32)):
v = f'{v:.2f}'
cell_text.append([index, v])
tbl = ax2.table(cellText=cell_text,
colLabels=['Statistic', 'Value'],
loc='center')
tbl.scale(1.5, 1.5) # may help
tbl.set_fontsize(14)
ax2.axis('off')
fig.savefig('run_statistics_w_tables.png', dpi=300)
fig.savefig('run_statistics_w_tables.eps')
plt.show()
def run():
# Run the model
model = Model.load(MODEL_FILENAME)
# Add a storage recorder
TablesRecorder(model, "thames_output.h5", parameters=[p for p in model.parameters])
# Run the model
stats = model.run()
print(stats)
stats_df = stats.to_dataframe()
print(stats_df)
keys_to_plot = (
"time_taken_before",
"solver_stats.bounds_update_nonstorage",
"solver_stats.bounds_update_storage",
"solver_stats.objective_update",
"solver_stats.lp_solve",
"solver_stats.result_update",
"time_taken_after",
)
keys_to_tabulate = (
"timesteps",
"time_taken",
"solver",
"num_scenarios",
"speed",
"solver_name" "solver_stats.total",
"solver_stats.number_of_rows",
"solver_stats.number_of_cols",
"solver_stats.number_of_nonzero",
"solver_stats.number_of_routes",
"solver_stats.number_of_nodes",
)
values = []
labels = []
explode = []
solver_sub_total = 0.0
for k in keys_to_plot:
v = stats_df.loc[k][0]
values.append(v)
label = k.split(".", 1)[-1].replace("_", " ").capitalize()
explode.append(0.0)
if k.startswith("solver_stats"):
labels.append("Solver - {}".format(label))
solver_sub_total += v
else:
labels.append(label)
values.append(stats_df.loc["solver_stats.total"][0] - solver_sub_total)
labels.append("Solver - Other")
explode.append(0.0)
values.append(stats_df.loc["time_taken"][0] - sum(values))
values = np.array(values) / sum(values)
labels.append("Other")
explode.append(0.0)
fig, (ax1, ax2) = plt.subplots(
figsize=(12, 4), ncols=2, sharey="row", gridspec_kw={"width_ratios": [2, 1]}
)
print(values, labels)
ax1.pie(values, explode=explode, labels=labels, autopct="%1.1f%%", startangle=90)
ax1.axis("equal") # Equal aspect ratio ensures that pie is drawn as a circle.
cell_text = []
for index, value in stats_df.iterrows():
if index not in keys_to_tabulate:
continue
v = value[0]
if isinstance(v, (float, np.float64, np.float32)):
v = f"{v:.2f}"
cell_text.append([index, v])
tbl = ax2.table(cellText=cell_text, colLabels=["Statistic", "Value"], loc="center")
tbl.scale(1.5, 1.5) # may help
tbl.set_fontsize(14)
ax2.axis("off")
fig.savefig("run_statistics_w_tables.png", dpi=300)
fig.savefig("run_statistics_w_tables.eps")
plt.show()
