# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# %%
# data_path = '../../data_import/exports/raw_data.csv'
data_path = '../../data_preprocess/exports/preprocessed_data.csv'
df = pd.read_csv(data_path)

# %%
df = df[df['MDM']].reset_index(drop=True)

# %%
# we want to print the string length

# print summary stats
def summary_stats(arr):
    return {
        "Mean": np.mean(arr),
        "Median": np.median(arr),
        "Standard Deviation": np.std(arr),
        "Variance": np.var(arr),
        "Min": np.min(arr),
        "Max": np.max(arr),
        "Range": np.ptp(arr),
        "25th Percentile": np.percentile(arr, 25),
        "75th Percentile": np.percentile(arr, 75),
        "Sum": np.sum(arr),
    }

# %%
ship_domain_data = df['tag_description'] + df['unit'].fillna('')

ship_domain_array = np.array([len(item) for item in ship_domain_data])

stats = summary_stats(ship_domain_array)

for key, value in stats.items():
    print(f"{key}: {value}")


# %%
plt.hist(ship_domain_array, bins=50)
# %%

# %%
platform_domain_data = df['thing'] + df['property']

platform_domain_array = np.array([len(item) for item in platform_domain_data])

stats = summary_stats(platform_domain_array)

for key, value in stats.items():
    print(f"{key}: {value}")


# %%