import pandas as pd


# ---------------------------------------------------------
# STEP 1 - Get user inputs
# ---------------------------------------------------------

spring_name = input(
    "Enter spring number (example: 1040_02): "
).strip()

threshold = float(
    input(
        "Enter peak detection threshold (mm): "
    )
)


# ---------------------------------------------------------
# STEP 2 - Open cleaned file
# ---------------------------------------------------------

input_file = f"Spring_{spring_name}_Cleaned.csv"

print(f"\nLoading {input_file}...")

df = pd.read_csv(input_file)


# ---------------------------------------------------------
# STEP 3 - Define important column names
# ---------------------------------------------------------

extension_column = "Extension from Preload (mm)"


# ---------------------------------------------------------
# STEP 4 - Create Above Threshold flag
#
# 1 = Extension is above threshold
# 0 = Extension is below threshold
# ---------------------------------------------------------

df["Above Threshold"] = (
    df[extension_column] >= threshold
).astype(int)


# ---------------------------------------------------------
# STEP 5 - Create Previous Above Threshold column
#
# Shift the Above Threshold column down by one row
# ---------------------------------------------------------

df["Previous Above Threshold"] = (
    df["Above Threshold"]
    .shift(1)
    .fillna(0)
    .astype(int)
)


# ---------------------------------------------------------
# STEP 6 - Create Rising Edge column
#
# Rising Edge occurs when:
#
# Previous = 0
# Current  = 1
# ---------------------------------------------------------

df["Rising Edge"] = (
    (df["Above Threshold"] == 1)
    &
    (df["Previous Above Threshold"] == 0)
).astype(int)


# ---------------------------------------------------------
# STEP 7 - Create Falling Edge column
#
# Falling Edge occurs when:
#
# Previous = 1
# Current  = 0
# ---------------------------------------------------------

df["Falling Edge"] = (
    (df["Above Threshold"] == 0)
    &
    (df["Previous Above Threshold"] == 1)
).astype(int)


# ---------------------------------------------------------
# STEP 8 - Create Cycle Number
#
# Every Rising Edge starts a new cycle.
#
# Example:
#
# Rising Edge:
# 0 0 1 0 0 1 0
#
# Cycle Number:
# 0 0 1 1 1 2 2
# ---------------------------------------------------------

df["Cycle Number"] = (
    df["Rising Edge"]
    .cumsum()
)


# ---------------------------------------------------------
# STEP 9 - Save analyzed file
# ---------------------------------------------------------

output_file = f"Spring_{spring_name}_Analyzed.csv"

df.to_csv(output_file, index=False)


# ---------------------------------------------------------
# STEP 10 - Report results
# ---------------------------------------------------------

total_cycles = int(df["Cycle Number"].max())

print("\nAnalysis Complete")
print(f"Total Cycles Found: {total_cycles}")
print(f"Output File: {output_file}")

# ---------------------------------------------------------
# STEP 11 - Create Peak Summary
# ---------------------------------------------------------

peak_summary = []

# Get all valid cycle numbers
cycle_numbers = sorted(
    df[df["Cycle Number"] > 0]["Cycle Number"].unique()
)

for cycle in cycle_numbers:

    # Get only rows for this cycle that are above threshold
    cycle_data = df[
        (df["Cycle Number"] == cycle)
        &
        (df["Above Threshold"] == 1)
    ]

    # Skip empty cycles just in case
    if len(cycle_data) == 0:
        continue

    # Find row with maximum load
    peak_index = cycle_data["Load (lb)"].idxmax()

    peak_row = df.loc[peak_index]

    peak_summary.append({
        "Cycle Number": cycle,
        "Test": peak_row["Test"],
        "Peak Load (lb)": peak_row["Load (lb)"],
        "Peak Extension (mm)": peak_row["Extension from Preload (mm)"],
        "Peak Time (s)": peak_row["Continuous Time (s)"],
        "Peak Row": peak_index
    })