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Fix Generate_results_figures for Model B outputs and overlapping columns #4

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Fix Generate_results_figures for Model B outputs and overlapping columns #4

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78 changes: 62 additions & 16 deletions Generate_results_figures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import plotly.graph_objects as go
import plotly.express as px
import os
import sys
from Core_Models.Price_Forecasting.helper_functions import parse_CLI_arguments

# Select the project folder in the Simulation_Results directory
Expand All @@ -19,13 +20,49 @@ def repo_path(*parts: str) -> str:
# Import and format the data
##############################################

result_dir = repo_path("Simulation_Results", project)
# Julia may write result CSVs to result_dir or to result_dir/<case_id>/ (e.g. "1")
def _find_result_dir(result_dir: str, project: str) -> str:
"""Return result_dir or first subdir that contains all three dispatch CSVs."""
if not os.path.isdir(result_dir):
return result_dir
base = result_dir
for candidate in [base] + [os.path.join(base, d) for d in sorted(os.listdir(base)) if os.path.isdir(os.path.join(base, d))]:
hydro = os.path.join(candidate, f"ALEAF_HydroBoost_{project}__hydro_dispatch.csv")
plant = os.path.join(candidate, f"ALEAF_HydroBoost_{project}__plant_dispatch.csv")
storage = os.path.join(candidate, f"ALEAF_HydroBoost_{project}__storage_dispatch.csv")
if os.path.isfile(hydro) and os.path.isfile(plant) and os.path.isfile(storage):
return candidate
return base

result_dir = _find_result_dir(repo_path("Simulation_Results", project), project)
figures_dir = os.path.join(result_dir, "Figures")
os.makedirs(figures_dir, exist_ok=True)

df_hydro = pd.read_csv(os.path.join(result_dir, f"ALEAF_HydroBoost_{project}__hydro_dispatch.csv"))
df_plant = pd.read_csv(os.path.join(result_dir, f"ALEAF_HydroBoost_{project}__plant_dispatch.csv"))
df_storage = pd.read_csv(os.path.join(result_dir, f"ALEAF_HydroBoost_{project}__storage_dispatch.csv"))
hydro_file = os.path.join(result_dir, f"ALEAF_HydroBoost_{project}__hydro_dispatch.csv")
plant_file = os.path.join(result_dir, f"ALEAF_HydroBoost_{project}__plant_dispatch.csv")
storage_file = os.path.join(result_dir, f"ALEAF_HydroBoost_{project}__storage_dispatch.csv")
required = [("hydro_dispatch", hydro_file), ("plant_dispatch", plant_file), ("storage_dispatch", storage_file)]
missing = [name for name, p in required if not os.path.isfile(p)]
if missing:
print(f"Result CSV(s) not found: {', '.join(missing)}. Skipping figure generation.")
print("Run the Julia optimization (Step 2) first to produce simulation results.")
sys.exit(0)

df_hydro = pd.read_csv(hydro_file)
df_plant = pd.read_csv(plant_file)
df_storage = pd.read_csv(storage_file)

# Require one row per hour (8760) so index assignment is valid
EXPECTED_LEN = 8760
if len(df_hydro) == 0 or len(df_plant) == 0 or len(df_storage) == 0:
print("One or more result CSVs have no data rows. Skipping figure generation.")
print("This can happen when the optimization had no feasible solution or no units of a given type.")
sys.exit(0)
if len(df_hydro) != EXPECTED_LEN or len(df_plant) != EXPECTED_LEN or len(df_storage) != EXPECTED_LEN:
print(f"Result CSVs have unexpected length (expected {EXPECTED_LEN} rows per file).")
print(f" hydro: {len(df_hydro)}, plant: {len(df_plant)}, storage: {len(df_storage)}")
print("Figure generation expects one row per hour (365*24). Skipping.")
sys.exit(0)

dates = pd.date_range(start='2025-01-01', periods=8760, freq='h')

Expand All @@ -49,15 +86,17 @@ def repo_path(*parts: str) -> str:
"u_4_jht": "Water Discharge from Piecewise Block 4 (ft^3/s)",
})

df_plant = df_plant.rename(columns={'p_B_DT_ht': 'Battery Discharged (MW)',
'p_B_CT_ht': 'Battery Charging (MW)',
'p_GB_ht': 'Grid to Battery (MW)',
'p_HT_ht': 'Total Hydropower (MW)',
'p_HB_ht': 'Hydropower to Battery (MW)',
df_plant = df_plant.rename(columns={'p_B_DT_ht': 'Battery Discharged (MW)',
'p_B_CT_ht': 'Battery Charging (MW)',
'p_GB_ht': 'Grid to Battery (MW)',
'p_HT_ht': 'Total Hydropower (MW)',
'p_G_ht': 'Total Hydropower (MW)', # Model B / A naming
'p_HB_ht': 'Hydropower to Battery (MW)',
'p_HG_ht': 'Hydropower to Grid',
's_ht': 'Spillage (ft^3/s)',
'u_ht': 'Total Water Discharge (ft^3/s)',
'e_H_ht': 'Volume of Reservoir (Acre*feet)',
'p_G_Gr_ht': 'Hydropower to Grid', # Model B aggregate to grid
's_ht': 'Spillage (ft^3/s)',
'u_ht': 'Total Water Discharge (ft^3/s)',
'e_H_ht': 'Volume of Reservoir (Acre*feet)',
'I_ht': 'Water Inflow to Reservoir (ft^3/s)',
})

Expand All @@ -76,7 +115,14 @@ def repo_path(*parts: str) -> str:
'r_SR_iht': 'Reserve Spinning Market Sell (MW)'
})

df = df_hydro.join(df_plant.join(df_storage.drop(columns=['unit_id', 'UnitGroup', 'Unit_Category', 'Unit_Type'])))
# Plant + hydro CSVs can both expose the same column name (e.g. q_G_jht) with different semantics;
# join requires unique names. Merge plant+storage first, then align names before joining hydro.
_stor = df_storage.drop(columns=['unit_id', 'UnitGroup', 'Unit_Category', 'Unit_Type'])
_inner = df_plant.join(_stor, rsuffix='_storage')
_overlap = df_hydro.columns.intersection(_inner.columns)
if len(_overlap) > 0:
_inner = _inner.rename(columns={c: f"{c}_plant" for c in _overlap})
df = df_hydro.join(_inner)
df.index.name = 'Datetime'

# Create revenue columns based on results and price
Expand Down Expand Up @@ -110,8 +156,6 @@ def repo_path(*parts: str) -> str:
# Line plot
###########################################

print(df.columns)

fig = px.line(df.drop(columns=["unit_id", "UnitGroup", "Unit_Category", "Unit_Type"]))
fig.write_html(os.path.join(figures_dir, "All_data.html"))

Expand All @@ -123,7 +167,7 @@ def repo_path(*parts: str) -> str:
# Create a figure
fig = go.Figure()

monthly_data = df[revenue_columns].resample('M').sum()
monthly_data = df[revenue_columns].resample('ME').sum()

# Create month labels
month_names = ['January', 'February', 'March', 'April', 'May', 'June',
Expand Down Expand Up @@ -447,3 +491,5 @@ def get_cell_color(value, is_total_row=False, column=None):

fig.write_html(os.path.join(figures_dir, "Monthly_Revenue.html"))
table_fig.write_html(os.path.join(figures_dir, "Monthly_Summary_Table.html"))

print(f"Figures written under: {figures_dir}")