#!/usr/bin/env python
# coding: utf-8

# <img src="../images/headers/nb_2.svg"  width="1080" height="220">
# 
# In this notebook, the data will be visualized and analyzed

# # Import Modules and Data

# In[1]:


import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from bokeh.io import output_notebook


from roaf import visualization

plt.style.use("dark_background")


# In[2]:


df_by_person = pd.read_parquet("../data/processed/df_by_person.parquet")
df_by_accident = pd.read_parquet("../data/processed/df_by_accident.parquet")


# # Setup of notebook parameters

# In[3]:


PLOT_DIR = "../images/"
PLOT_FILE_FORMATS = ["png"]


# # Daytime distribution of accidents

# In[4]:


plot_df = pd.DataFrame(
    {
        "is_weekend": df_by_accident["is_weekend"],
        "time": df_by_accident["date"].apply(lambda x: int(x.strftime("%H%M"))),
    }
)


# In[5]:


day_time_ticks = (0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400)
day_time_tick_labels = (
    "0:00",
    "03:00",
    "06:00",
    "09:00",
    "12:00",
    "15:00",
    "18:00",
    "21:00",
    "24:00",
)
fig = plt.figure()
plot_df = df_by_accident[["is_weekend", "hhmm"]].astype("int")
weights = plot_df["is_weekend"].apply(lambda x: 0.5 if x == 1 else 0.2)
sns.histplot(
    data=plot_df,
    x="hhmm",
    hue="is_weekend",
    weights=weights,
    stat="frequency",
    bins=24,
    binrange=(0, 2400),
    common_norm=False,
    palette="Dark2",
)
plt.xticks(ticks=day_time_ticks, labels=day_time_tick_labels)
plt.xlabel("Time of Day")
plt.xlim((0, 2400))
plt.legend(["Weekends", "Weekdays"])
plt.title("Distribution of Accidents by Daytime")

visualization.savefig(
    basename="daytime_weekdays", filepath=PLOT_DIR, formats=PLOT_FILE_FORMATS
)


# The plot shows the distribution of accidents during the day. Most accidents happen around 6 pm
# both on weekends and during the week. Mon-Fri there is also a peak before 9 a.m. (when people
# go to work). At the weekend, there are more accidents after midnight (when people return from
# parties).

# # Age, sex, and role in accident

# In[6]:


ax = sns.violinplot(
    data=df_by_person,
    x="role",
    y="age",
    hue="sex",
    bw=0.1,
    scale="count",
    split=True,
    palette="Dark2",
    inner=None,
    cut=0,
)
plt.title("Age of People Involved in Road Accidents")
plt.ylim((0, df_by_person["age"].max()))
ax.set_xticklabels(["driver", "passenger", "pedestrian"])


# Add some horizontal lines for important ages
def add_ageline(age, text, linestyle="--", y_offset=-1.5):
    """Plot a horizontal line for a specified age with annotation"""
    plt.axhline(y=age, linestyle=linestyle, linewidth=1)
    plt.annotate(
        text=text, xy=(1.01, age + y_offset), xycoords=("axes fraction", "data")
    )


# Minimum ages for drvinig license
add_ageline(age=18, text="minimum age for driving license (car)", y_offset=1)
add_ageline(
    age=16,
    text="minimum age for driving license (motorcycle)",
    linestyle="dotted",
    y_offset=-3,
)

# People stop working with 60.6 years, according to the
# OECD (https://www.oecd.org/fr/france/PAG2021-FRA.pdf)
add_ageline(age=60.6, text="mean retirement age")

ax.legend(handles=ax.legend_.legend_handles, labels=["Male", "Female"])

visualization.savefig(
    basename="age_sex_role", filepath=PLOT_DIR, formats=PLOT_FILE_FORMATS
)


# The plot shows a huge difference for the sex of drivers involved in accidents.
# To interpret this figure correctly, one would need more information about the distribution of
# drivers, as men might simply be more often the person who drives.
# 
# Another interesting observation is that at higher age, there are virtually no male passengers
# involved. A possible explanation is, that men drive themselves while their wives are their
# passengers.
# 
# I expected to see a greater effect of the retirement age.
# 
# It might be important for the understanding that a 'driver' can also simply drive a bike, which
# explains the drivers under 16

# # Geodata

# In[7]:


output_notebook()
visualization.plot_geodata(
    df_by_accident,
    n_plot_max=10_000,
    figsize=int(500),
    return_html=False,
)


# In[8]:


plot_data = df_by_accident.sample(1_000)
m = visualization.plot_geo_markers(df=plot_data)
visualization.plot_geo_heatmap(df_by_accident, m=m)


# The heatmap does not provide meaningful information at first glance:
# One can see that there are more accidents in densly populated areas, but that is obviuos.
# It gets interesting when zooming in. Then, it is possible to see the local hotspots for
# accidents.
# 
# Unfortunately, plotting Folium markers is quite slow, and only a small number of accidents
# are plotted on the map. The whole dataset was used for the heatmap, however.