68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import pandas as pd
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def format_dataframe(
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bess_soc_for_cycle, bess_data, load_profiles_since_start, swap_time, current_time
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):
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"""Formats the DataFrame for display in the dashboard."""
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# Create a DataFrame for sites
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# columns = ["Site Name", "MBESS Unit", "Current Load (kW)", "SoC (%)", "Predicted Swap Time"]
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status_df = pd.DataFrame(
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columns=[
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"Site Name",
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"MBESS Unit",
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"Current Load (kW)",
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"SoC (%)",
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"Predicted Swap Time",
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"Estimated Time To Swap",
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"Cycle Discharge Profile",
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"Load Profile Since Start",
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]
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)
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for site in load_profiles_since_start.keys():
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index = next(i for i, d in enumerate(bess_data["units"]) if d["site"] == site)
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soc = bess_data["units"][index]["SoC"]
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current_load = bess_data["units"][index]["current_load_kW"]
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unit_name = bess_data["units"][index]["name"]
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predicted_swap_time = swap_time.get(unit_name, "N/A")
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# calculate estimated time to swap
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if isinstance(predicted_swap_time, float):
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estimated_time_to_swap = predicted_swap_time - current_time
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estimated_time_to_swap = pd.to_timedelta(estimated_time_to_swap, unit="s")
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else:
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estimated_time_to_swap = "N/A"
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# convert predicted_swap_time to a readable format
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if isinstance(predicted_swap_time, float):
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predicted_swap_time = pd.to_datetime(
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predicted_swap_time, unit="s"
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).strftime("%Y-%m-%d %H:%M:%S")
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status_df = pd.concat(
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[
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status_df,
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pd.DataFrame(
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[
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{
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"Site Name": site,
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"MBESS Unit": unit_name,
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"Current Load (kW)": current_load,
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"SoC (%)": soc * 100, # Convert to percentage
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"Predicted Swap Time": predicted_swap_time,
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"Estimated Time To Swap": estimated_time_to_swap,
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"Cycle Discharge Profile": bess_soc_for_cycle[unit_name][
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"SoC"
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].tolist(),
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"Load Profile Since Start": load_profiles_since_start[
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site
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].tolist(),
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}
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]
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),
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],
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ignore_index=True,
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)
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return status_df
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