Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

A first batch of Sonar fixes #449

Merged
merged 3 commits into from
Feb 16, 2025
Merged

A first batch of Sonar fixes #449

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
08c465f
A first batch of fixes
davidusb-geek Feb 5, 2025
f08b4e9
A first batch of fixes
davidusb-geek Feb 5, 2025
206f614
Merge branch 'davidusb-geek/fix/sonar_fixes1' of https://github.com/d…
davidusb-geek Feb 5, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion docs/_static/css/custom.css
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@

div {
border-radius: 7px;
box-align: start !important;
align-items: start;
text-align: left;
}
Expand Down
87 changes: 1 addition & 86 deletions scripts/load_clustering.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,9 +23,6 @@
pio.renderers.default = "browser"
pd.options.plotting.backend = "plotly"

# from skopt.space import Categorical, Real, Integer
# from tslearn.clustering import TimeSeriesKMeans

# the root folder
root = pathlib.Path(str(get_root(__file__, num_parent=2)))
emhass_conf = {}
Expand Down Expand Up @@ -191,86 +188,4 @@ def load_forecast(data, forecast_date, freq, template):

# Call the forecasting method
data.columns = ["load"]
forecast, used_days = load_forecast(data, forecast_date, freq, template)

# data_lag = pd.concat([data, data.shift()], axis=1)
# data_lag.columns = ["power_load y(t)", "power_load y(t+1)"]
# data_lag = data_lag.dropna()

# fig2 = data_lag.plot.scatter(
# x="power_load y(t)", y="power_load y(t+1)", c="DarkBlue"
# )
# fig2.layout.template = template
# fig2.show()

# Elbow method to check how many clusters
# distortions = []
# K = range(1,12)

# for cluster_size in K:
# kmeans = KMeans(n_clusters=cluster_size, init='k-means++')
# kmeans = kmeans.fit(data_lag)
# distortions.append(kmeans.inertia_)

# df = pd.DataFrame({'Clusters': K, 'Distortions': distortions})
# fig = (px.line(df, x='Clusters', y='Distortions', template=template)).update_traces(mode='lines+markers')
# fig.show()

# The silouhette method
# silhouette_scores = []
# K = range(2, 12)

# for cluster_size in K:
# kmeans = KMeans(n_clusters=cluster_size, init="k-means++", random_state=200)
# labels = kmeans.fit(data_lag).labels_
# silhouette_score_tmp = silhouette_score(
# data_lag, labels, metric="euclidean", sample_size=1000, random_state=200
# )
# silhouette_scores.append(silhouette_score_tmp)

# df = pd.DataFrame({"Clusters": K, "Silhouette Score": silhouette_scores})
# fig = (
# px.line(df, x="Clusters", y="Silhouette Score", template=template)
# ).update_traces(mode="lines+markers")
# fig.show()

# The clustering
# kmeans = KMeans(n_clusters=6, init="k-means++")
# kmeans = kmeans.fit(data_lag)
# data_lag["cluster_group"] = kmeans.labels_

# fig = px.scatter(
# data_lag,
# x="power_load y(t)",
# y="power_load y(t+1)",
# color="cluster_group",
# template=template,
# )
# fig.show()

# km = TimeSeriesKMeans(n_clusters=6, verbose=True, random_state=200)
# y_pred = km.fit_predict(data_lag)
# data_lag["cluster_group_tslearn_euclidean"] = y_pred

# fig = px.scatter(
# data_lag,
# x="power_load y(t)",
# y="power_load y(t+1)",
# color="cluster_group_tslearn_euclidean",
# template=template,
# )
# fig.show()

# dba_km = TimeSeriesKMeans(n_clusters=6, n_init=2, metric="dtw", verbose=True, max_iter_barycenter=10, random_state=200)
# y_pred = dba_km.fit_predict(data_lag)
# data_lag['cluster_group_tslearn_dba'] = y_pred

# fig = px.scatter(data_lag, x='power_load y(t)', y='power_load y(t+1)', color='cluster_group_tslearn_dba', template=template)
# fig.show()

# sdtw_km = TimeSeriesKMeans(n_clusters=6, metric="softdtw", metric_params={"gamma": .01}, verbose=True, random_state=200)
# y_pred = sdtw_km.fit_predict(data_lag)
# data_lag['cluster_group_tslearn_sdtw'] = y_pred

# fig = px.scatter(data_lag, x='power_load y(t)', y='power_load y(t+1)', color='cluster_group_tslearn_sdtw', template=template)
# fig.show()
forecast, used_days = load_forecast(data, forecast_date, freq, template)
13 changes: 7 additions & 6 deletions scripts/load_forecast_sklearn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -108,10 +108,11 @@ def neg_r2_score(y_true, y_pred):

data = copy.deepcopy(rh.df_final)

y_axis_title = "Power (W)"
logger.info(data.describe())
fig = data.plot()
fig.layout.template = template
fig.update_yaxes(title_text="Power (W)")
fig.update_yaxes(title_text=y_axis_title)
fig.update_xaxes(title_text="Time")
fig.show()
fig.write_image(
Expand Down Expand Up @@ -169,7 +170,7 @@ def neg_r2_score(y_true, y_pred):
df["pred"] = predictions
fig = df.plot()
fig.layout.template = template
fig.update_yaxes(title_text="Power (W)")
fig.update_yaxes(title_text=y_axis_title)
fig.update_xaxes(title_text="Time")
fig.update_xaxes(range=[date_train + pd.Timedelta("10days"), data_exo.index[-1]])
fig.show()
Expand Down Expand Up @@ -200,7 +201,7 @@ def neg_r2_score(y_true, y_pred):
df["pred"] = predictions_backtest
fig = df.plot()
fig.layout.template = template
fig.update_yaxes(title_text="Power (W)")
fig.update_yaxes(title_text=y_axis_title)
fig.update_xaxes(title_text="Time")
fig.show()
fig.write_image(
Expand Down Expand Up @@ -273,7 +274,7 @@ def search_space(trial):
df["pred_optim"] = predictions_loaded
fig = df.plot()
fig.layout.template = template
fig.update_yaxes(title_text="Power (W)")
fig.update_yaxes(title_text=y_axis_title)
fig.update_xaxes(title_text="Time")
fig.update_xaxes(range=[date_train + pd.Timedelta("10days"), data_exo.index[-1]])
fig.show()
Expand Down Expand Up @@ -326,7 +327,7 @@ def search_space(trial):

# Let's perform a naive load forecast for comparison
retrieve_hass_conf, optim_conf, plant_conf = get_yaml_parse(
emhass_conf, use_secrets=True
params, logger
)
fcst = Forecast(
retrieve_hass_conf, optim_conf, plant_conf, params, emhass_conf, logger
Expand Down Expand Up @@ -365,7 +366,7 @@ def search_space(trial):
df["pred_prod"] = predictions_prod
fig = df.plot()
fig.layout.template = template
fig.update_yaxes(title_text="Power (W)")
fig.update_yaxes(title_text=y_axis_title)
fig.update_xaxes(title_text="Time")
fig.show()
fig.write_image(
Expand Down
7 changes: 4 additions & 3 deletions scripts/optim_results_analysis.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -135,14 +135,15 @@ def get_forecast_optim_objects(
template = "presentation"

# Let's plot the input data
y_axis_title = "Power (W)"
fig_inputs1 = df_input_data[
[
retrieve_hass_conf["sensor_power_photovoltaics"],
str(retrieve_hass_conf["sensor_power_load_no_var_loads"] + "_positive"),
]
].plot()
fig_inputs1.layout.template = template
fig_inputs1.update_yaxes(title_text="Powers (W)")
fig_inputs1.update_yaxes(title_text=y_axis_title)
fig_inputs1.update_xaxes(title_text="Time")
if show_figures:
fig_inputs1.show()
Expand All @@ -155,7 +156,7 @@ def get_forecast_optim_objects(

fig_inputs_dah = df_input_data_dayahead.plot()
fig_inputs_dah.layout.template = template
fig_inputs_dah.update_yaxes(title_text="Powers (W)")
fig_inputs_dah.update_yaxes(title_text=y_axis_title)
fig_inputs_dah.update_xaxes(title_text="Time")
if show_figures:
fig_inputs_dah.show()
Expand Down Expand Up @@ -192,7 +193,7 @@ def get_forecast_optim_objects(
]
].plot() # 'P_def_start_0', 'P_def_start_1', 'P_def_bin2_0', 'P_def_bin2_1'
fig_res_dah.layout.template = template
fig_res_dah.update_yaxes(title_text="Powers (W)")
fig_res_dah.update_yaxes(title_text=y_axis_title)
fig_res_dah.update_xaxes(title_text="Time")
# if show_figures:
fig_res_dah.show()
Expand Down
13 changes: 7 additions & 6 deletions scripts/save_pvlib_module_inverter_database.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,9 @@
cec_inverters_emhass = pvlib.pvsystem.retrieve_sam(
path=str(emhass_conf["data_path"] / "emhass_inverters.csv")
)
logger.info("=================")
logger.info("=================")
strait_str = "================="
logger.info(strait_str)
logger.info(strait_str)

logger.info("Updating and saving databases")

Expand Down Expand Up @@ -91,8 +92,8 @@
tablefmt="psql",
)
)
logger.info("=================")
logger.info("=================")
logger.info(strait_str)
logger.info(strait_str)

# Inverters
cols_to_keep_inverters = [
Expand Down Expand Up @@ -124,8 +125,8 @@
tablefmt="psql",
)
)
logger.info("=================")
logger.info("=================")
logger.info(strait_str)
logger.info(strait_str)
logger.info("Modules databases")
print(tabulate(cec_modules.head(20).iloc[:, :5], headers="keys", tablefmt="psql"))
logger.info("Inverters databases")
Expand Down
9 changes: 5 additions & 4 deletions scripts/special_config_analysis.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -282,6 +282,7 @@ def get_forecast_optim_objects(
)

template = "presentation"
y_axis_title = "Power (W)"

# Let's plot the input data
fig_inputs1 = df_input_data[
Expand All @@ -291,7 +292,7 @@ def get_forecast_optim_objects(
]
].plot()
fig_inputs1.layout.template = template
fig_inputs1.update_yaxes(title_text="Powers (W)")
fig_inputs1.update_yaxes(title_text=y_axis_title)
fig_inputs1.update_xaxes(title_text="Time")
fig_inputs1.show()

Expand All @@ -303,7 +304,7 @@ def get_forecast_optim_objects(

fig_inputs_dah = df_input_data_dayahead.plot()
fig_inputs_dah.layout.template = template
fig_inputs_dah.update_yaxes(title_text="Powers (W)")
fig_inputs_dah.update_yaxes(title_text=y_axis_title)
fig_inputs_dah.update_xaxes(title_text="Time")
fig_inputs_dah.show()

Expand All @@ -313,7 +314,7 @@ def get_forecast_optim_objects(
opt_res_dah = opt.perform_dayahead_forecast_optim(df_input_data_dayahead, P_PV_forecast, P_load_forecast)
fig_res_dah = opt_res_dah[['P_deferrable0', 'P_deferrable1', 'P_grid']].plot()
fig_res_dah.layout.template = template
fig_res_dah.update_yaxes(title_text = "Powers (W)")
fig_res_dah.update_yaxes(title_text = y_axis_title)
fig_res_dah.update_xaxes(title_text = "Time")
fig_res_dah.show()"""

Expand Down Expand Up @@ -351,6 +352,6 @@ def get_forecast_optim_objects(
)
fig_res_mpc = opt_res_dayahead[["P_batt", "P_grid"]].plot()
fig_res_mpc.layout.template = template
fig_res_mpc.update_yaxes(title_text="Powers (W)")
fig_res_mpc.update_yaxes(title_text=y_axis_title)
fig_res_mpc.update_xaxes(title_text="Time")
fig_res_mpc.show()
13 changes: 7 additions & 6 deletions scripts/use_cases_analysis.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -119,6 +119,7 @@ def get_forecast_optim_objects(
df_input_data = fcst.get_prod_price_forecast(df_input_data)

template = "presentation"
y_axis_title = "Power (W)"

# Let's plot the input data
fig_inputs1 = df_input_data[
Expand All @@ -128,7 +129,7 @@ def get_forecast_optim_objects(
]
].plot()
fig_inputs1.layout.template = template
fig_inputs1.update_yaxes(title_text="Powers (W)")
fig_inputs1.update_yaxes(title_text=y_axis_title)
fig_inputs1.update_xaxes(title_text="Time")
fig_inputs1.show()
if save_figures:
Expand All @@ -152,7 +153,7 @@ def get_forecast_optim_objects(

fig_inputs_dah = df_input_data_dayahead.plot()
fig_inputs_dah.layout.template = template
fig_inputs_dah.update_yaxes(title_text="Powers (W)")
fig_inputs_dah.update_yaxes(title_text=y_axis_title)
fig_inputs_dah.update_xaxes(title_text="Time")
fig_inputs_dah.show()
if save_figures:
Expand All @@ -166,7 +167,7 @@ def get_forecast_optim_objects(
opt_res = opt.perform_perfect_forecast_optim(df_input_data, days_list)
fig_res = opt_res[["P_deferrable0", "P_deferrable1", "P_grid"]].plot()
fig_res.layout.template = template
fig_res.update_yaxes(title_text="Powers (W)")
fig_res.update_yaxes(title_text=y_axis_title)
fig_res.update_xaxes(title_text="Time")
fig_res.show()
if save_figures:
Expand All @@ -190,7 +191,7 @@ def get_forecast_optim_objects(
)
fig_res_dah = opt_res_dah[["P_deferrable0", "P_deferrable1", "P_grid"]].plot()
fig_res_dah.layout.template = template
fig_res_dah.update_yaxes(title_text="Powers (W)")
fig_res_dah.update_yaxes(title_text=y_axis_title)
fig_res_dah.update_xaxes(title_text="Time")
fig_res_dah.show()
if save_figures:
Expand Down Expand Up @@ -220,7 +221,7 @@ def get_forecast_optim_objects(
)
fig_res_dah = opt_res_dah[["P_deferrable0", "P_deferrable1", "P_grid"]].plot()
fig_res_dah.layout.template = template
fig_res_dah.update_yaxes(title_text="Powers (W)")
fig_res_dah.update_yaxes(title_text=y_axis_title)
fig_res_dah.update_xaxes(title_text="Time")
fig_res_dah.show()
if save_figures:
Expand Down Expand Up @@ -253,7 +254,7 @@ def get_forecast_optim_objects(
["P_deferrable0", "P_deferrable1", "P_grid", "P_batt"]
].plot()
fig_res_dah.layout.template = template
fig_res_dah.update_yaxes(title_text="Powers (W)")
fig_res_dah.update_yaxes(title_text=y_axis_title)
fig_res_dah.update_xaxes(title_text="Time")
fig_res_dah.show()
if save_figures:
Expand Down
Loading
Loading