import numpy as np
import pandas as pd
from piel.types import SignalDCCollection
from .transfer.metrics import get_out_min_max, get_out_response_in_transition_range
from .transfer.power import get_power_metrics
[docs]
def compile_dc_min_max_metrics_from_dc_collection(
collections: list[SignalDCCollection],
label_list: list[str],
label_column_name: str = "label",
threshold_kwargs: dict = None,
debug: bool = False,
**kwargs,
) -> pd.DataFrame:
"""
Compiles DC analysis metrics from a list of SignalDCCollection instances into a final DataFrame.
Args:
collections (List[SignalDCCollection]): List of SignalDCCollection instances to analyze.
label_list (List[str]): List of labels corresponding to each SignalDCCollection.
threshold_kwargs (dict, optional): Threshold kwargs for the transition transmission. Defaults to None.
label_column_name (str, optional): How the label column should be called. Defaults to "label".
debug (bool, optional): If True, raises exceptions during processing. Defaults to False.
**kwargs: Additional keyword arguments for pd.DataFrame.
Returns:
pd.DataFrame: A DataFrame containing the compiled metrics with combined min-max columns.
"""
if not (len(collections) == len(label_list)):
raise ValueError("Length of collections and label_list must be equal.")
if threshold_kwargs is None:
threshold_kwargs = {}
data = []
for idx, (collection, label) in enumerate(zip(collections, label_list)):
try:
# Apply analysis functions
vout_metrics = get_out_min_max(collection, **threshold_kwargs)
vin_metrics = get_out_response_in_transition_range(
collection, **threshold_kwargs
)
power_metrics = get_power_metrics(collection, **threshold_kwargs)
# Extract metrics
min_vin = vin_metrics.min
max_vin = vin_metrics.max
vout_min = vout_metrics.min
vout_max = vout_metrics.max
power_max = power_metrics.max / 1e-3 # Convert to mW
power_delta = (
power_metrics.max - power_metrics.min
) / 1e-3 # Convert to mW
# Format metrics to three decimal places
formatted_vin = f"{min_vin:.3f}-{max_vin:.3f}"
formatted_vout = f"{vout_min:.3f}-{vout_max:.3f}"
formatted_power_max = f"{power_max:.3f}"
formatted_power_delta = f"{power_delta:.3f}"
# Append to data
data.append(
{
label_column_name: label,
r"$V_{out}$ $V$": formatted_vout,
r"$V_{tr,in}$ $V$": formatted_vin,
r"$P_{dd,max}$ $mW$": formatted_power_max,
r"$\Delta P_{dd}$ $mW$": formatted_power_delta,
}
)
except Exception as e:
print(
f"Error processing collection at index {idx} with label '{label}': {e}"
)
# Optionally, append NaNs or skip
data.append(
{
label_column_name: label,
r"$V_{out}$ $V$": np.nan,
r"$V_{tr,in}$ $V$": np.nan,
r"$P_{dd,max}$ $mW$": np.nan,
r"$\Delta P_{dd}$ $mW$": np.nan,
}
)
if debug:
raise e
# Create DataFrame
final_df = pd.DataFrame(data, **kwargs)
return final_df
[docs]
def compile_dc_transition_metrics_from_dc_collection(
collections: list[SignalDCCollection],
label_list: list[str],
label_column_name: str = "label",
threshold_kwargs: dict = None,
debug: bool = False,
**kwargs,
) -> pd.DataFrame:
"""
Compiles DC analysis metrics from a list of SignalDCCollection instances into a final DataFrame.
Args:
collections (List[SignalDCCollection]): List of SignalDCCollection instances to analyze.
label_list (List[str]): List of labels corresponding to each SignalDCCollection.
threshold_kwargs (dict, optional): Threshold kwargs for the transition transmission. Defaults to None.
label_column_name (str, optional): How the label column should be called. Defaults to "label".
debug (bool, optional): If True, raises exceptions during processing. Defaults to False.
**kwargs: Additional keyword arguments for pd.DataFrame.
Returns:
pd.DataFrame: A DataFrame containing the compiled metrics with combined min-max columns.
"""
if not (len(collections) == len(label_list)):
raise ValueError("Length of collections and label_list must be equal.")
if threshold_kwargs is None:
threshold_kwargs = {}
data = []
for idx, (collection, label) in enumerate(zip(collections, label_list)):
try:
# Apply analysis functions
vout_metrics = get_out_min_max(collection, **threshold_kwargs)
vin_metrics = get_out_response_in_transition_range(
collection, **threshold_kwargs
)
# Extract metrics
min_vin = vin_metrics.min
max_vin = vin_metrics.max
vout_min = vout_metrics.min
vout_max = vout_metrics.max
# Format metrics to three decimal places
formatted_vin = f"{min_vin:.3f}-{max_vin:.3f}"
formatted_vout = f"{vout_min:.3f}-{vout_max:.3f}"
# Append to data
data.append(
{
label_column_name: label,
r"$V_{out}$ $V$": formatted_vout,
r"$V_{tr,in}$ $V$": formatted_vin,
}
)
except Exception as e:
print(
f"Error processing collection at index {idx} with label '{label}': {e}"
)
# Optionally, append NaNs or skip
data.append(
{
label_column_name: label,
r"$V_{out}$ $V$": np.nan,
r"$V_{tr,in}$ $V$": np.nan,
}
)
if debug:
raise e
# Create DataFrame
final_df = pd.DataFrame(data, **kwargs)
return final_df
