trying to get peak simulation to work

LoadProfile.py

@@ -1,5 +1,5 @@
 import numpy as np
-from Utilities.Time import generate_timestrings
+from Utilities.Time import generate_timestrings, index_peak_times
 
 
 def get_no_of_peaks(peak_bounds):
@@ -15,10 +15,31 @@ def generate_peak_info(c, dt):
         dt,
     )
     peak_times = np.random.choice(operating_hours, no_of_peaks, replace=False)
+    peak_durations = np.random.randint(
+        c["site_info"]["peak_duration"]["min"],
+        c["site_info"]["peak_duration"]["max"],
+        no_of_peaks,
+    )
 
-    print(f"Peak times: {peak_times}")
+    return peak_times, peak_durations
-    return peak_times
 
 
-def get_load_profile(c, dt):
+def get_load_profile(c, dt, batch_start_time, batch_process_duration):
-    return generate_peak_info(c, dt)
+    # Generate load profile for each site
+
+    # c is the configuration dictionary
+    # dt is the time step in seconds
+    # batch_start_time is the start time for the batch process in seconds since the epoch
+    # batch_process_duration is the duration of the batch process in seconds
+
+    # start with indexing all the peak occurences
+    # generate timeseries from start to end time
+    start_time = batch_start_time
+    end_time = start_time + batch_process_duration
+    timestamps = np.arange(start_time, end_time, dt)
+
+    # Generate peak times and durations
+    peak_times, peak_durations = generate_peak_info(c, dt)
+
+    # Generate peak times and durations
+    peak_indices = index_peak_times(timestamps, peak_times, peak_durations)

Utilities/Time.py

@@ -1,5 +1,7 @@
 import time
 from datetime import datetime, timedelta
+import numpy as np
+import matplotlib.pyplot as pl
 
 
 def get_current_time():
@@ -19,3 +21,34 @@ def generate_timestrings(start_time_str, end_time_str, dt):
         current_time += timedelta(seconds=dt)
     timestrings = [dt.strftime("%H:%M") for dt in timestamps]
     return timestrings
+
+
+def index_peak_times(timestamps, peak_times, peak_durations):
+    """Converts peak times from HH:MM format to seconds since epoch."""
+    # start_time is the start time of the batch process in seconds since the epoch
+    # peak_times is a list of strings in HH:MM format
+    dt = timestamps[1] - timestamps[0]  # time step in seconds
+    peak_indices = np.zeros(len(timestamps), dtype=int)
+    start_datetime = datetime.fromtimestamp(timestamps[0])
+    processed_times = []
+    peak_occurence_no = 1
+    for time_str, duration in zip(peak_times, peak_durations):
+        # convert HH:MM to a datetime object
+        time_obj = datetime.strptime(time_str, "%H:%M")
+        full_datetime = start_datetime.replace(
+            hour=time_obj.hour, minute=time_obj.minute, second=0, microsecond=0
+        )
+        peak_start = int(full_datetime.timestamp())
+        peak_end = peak_start + duration * 60  # duration in minutes to seconds
+        peak_timestamps = np.arange(peak_start, peak_end, dt, dtype=int)
+
+        common, indices_ts, _ = np.intersect1d(
+            timestamps, peak_timestamps, return_indices=True
+        )
+        peak_indices[indices_ts] = peak_occurence_no
+        peak_occurence_no += 1
+
+    pl.plot(peak_indices, label="Peak Indices")
+    pl.show()
+
+    return processed_times

YAMLs/site_info.yaml

@@ -30,5 +30,5 @@ no_of_peaks:
   max: 100
 peak_duration:
   unit: minutes
-  min: 0.5
+  min: 1
-  max: 2
+  max: 4

main.py

@@ -14,8 +14,11 @@ dt = c["sim_time"]["time_step_minutes"] * 60
 duration = c["sim_time"]["duration_days"] * 24 * 60 * 60
 c["sim_end_time"] = c["sim_start_time"] + duration
 
+# batch process hours in seconds
+c["sim_time"]["batch_process_seconds"] = c["sim_time"]["batch_process_hours"] * 60 * 60
+
 # load site info
 c["site_info"] = yaml.safe_load(open(c["paths"]["site_info"]))
 
 # generate load profiles
-get_load_profile(c, dt)
+get_load_profile(c, dt, c["sim_start_time"], c["sim_time"]["batch_process_seconds"])