from timeit import timeit
from math import floor

# 1. Can you implement another optimised function (sum_even_numbers2) using purely native python?
#    Can you timeit to show it's faster and describe why it is.
#    Can you also state if there are any pros or cons to how it is implemented?

# 2. Can you implement another optimised function (sum_even_numbers3) using third party packages?
#    Can you timeit to show it's faster and describe why it is.
#    Can you also state if there are any pros or cons to how it is implemented?

def sum_even_numbers1(numbers: list[int]) -> int:
    total = 0
    for num in numbers:
        if floor(num/2) == num/2:
            total += num
    return total

# Floating point calculation is not required
# Only one division required
# float required 32byte, memory heavy
def sum_even_numbers2(numbers: list[int]) -> int:
    total = 0
    for num in numbers:
        if num%2 == 0:
            total += num
    return total

# needs to call that function from memory
def sum_even_numbers3(numbers: list[int]) -> int:
    return sum(filter(lambda x: x > 0 and x%2 == 0, numbers))
    

"""Test equipment positions."""
from __future__ import annotations
from collections import Counter, defaultdict
import datetime as dt
import math
import numpy as np
import pandas as pd
from typing import Any


def data_dict() -> defaultdict[str, Any]:
    """Return all equipment positions."""
    d = defaultdict(list)
    d["T123"].append({"position": {"x": 42, "y": 24, "z": 0.42}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=0)})
    d["T456"].append({"position": {"x": 21.0, "y": 34, "z": 0.289}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=0)})
    d["T789"].append({"position": {"x": 17, "y": 39, "z": 0.789}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=0)})
    d["T456"].append({"position": {"x": 91.0, "y": 114, "z": 0.489}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=3)})
    d["T123"].append({"position": {"x": 43, "y": 25, "z": 0.43}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=1)})
    d["T789"].append({"position": {"x": 19., "y": 79, "z": 0.991}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=6)})
    d["T123"].append({"position": {"x": 46, "y": 29, "z": 0.44}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=2)})
    d["T456"].append({"position": {"x": 24.0, "y": 37, "z": 0.297}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=4)})
    d["T123"].append({"position": {"x": 49.0, "y": 32, "z": 0.451}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=3)})
    d["T789"].append({"position": {"x": 23., "y": 81, "z": 1.103}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=7)})
    return d


def latest_snapshot() -> dict[str, Any]:
    """Return a snapshot of latest equipment."""
    return {
        "T123": {"position": {"x": 49.0, "y": 32, "z": 0.451}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=3)},
        "T456": {"position": {"x": 24.0, "y": 37, "z": 0.297}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=4)},
        "T789": {"position": {"x": 23.0, "y": 81, "z": 1.103}, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=7)},
    }


def counts() -> dict[str, int]:
    """Return counts per equipment."""
    return {
        "T123": 4,
        "T456": 3,
        "T789": 3
    }


def speeds() -> defaultdict[str, Any]:
    """Return speeds of equipment."""
    d = defaultdict(list)
    d["T123"].append({"speed": 4.242654947082074, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=3)})
    d["T123"].append({"speed": 5.00000999999, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=2)})
    d["T123"].append({"speed": 1.4142489172702237, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=1)})
    d["T123"].append({"speed": None, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=0)})
    d["T456"].append({"speed": 102.0687849638664, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=4)})
    d["T456"].append({"speed": 35.43388209045123, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=3)})
    d["T456"].append({"speed": None, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=0)})
    d["T789"].append({"speed": 4.473538196997986, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=7)})
    d["T789"].append({"speed": 6.6750796998987205, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=6)})
    d["T789"].append({"speed": None, "timestamp": dt.datetime(2022, 1, 1, hour=12, minute=0, second=0)})
    return d

  
def find_furthest_west(d: defaultdict) -> str:
    """
    Find the name of the truck that is furthest west. That is,
    the truck with the smallest easting position component.
    """
    latest = get_latest_snapshot(d)
    truck_furthest_west = ""
    x = latest[list(latest.keys())[0]]['position']['x']
    for item in latest:
        if latest[item]['position']['x'] < x:
            x = latest[item]['position']['x']
            truck_furthest_west = item
    return truck_furthest_west

def get_latest_snapshot(d: defaultdict) -> dict[str, Any]:
    """
    Return a snapshot of the latest positional updates for the
    equipment.
    """
    temp = defaultdict(list)
    
    for item in d:
        df = pd.DataFrame(d[item])
        temp[item] = df.iloc[-1].to_dict()
    return temp

def get_counts(d: defaultdict) -> dict[str, int]:
    """Return a dict of trucks and the times they have provided updates."""
    temp = defaultdict(list)
    for item in d:
        temp[item] = len(d[item])
    return temp

def calculate_speeds(d: defaultdict) -> defaultdict[str, Any]:
    """Return a dict of equipment and the speeds they are travelling at."""
    df = pd.json_normalize(d, sep='_')
    temp = defaultdict(list)
    for item in d:
        df = pd.json_normalize(d[item], sep='_')
        diff = df.diff()
        coords = [c for c in df.columns if not 'timestamp' in c]
        df['speed'] = np.linalg.norm(diff[coords], axis=1)/diff['timestamp'].dt.seconds
        temp[item] = df[['timestamp', 'speed']]
    print(temp)
    pass