practice.py

import csv
from datetime import datetime

DEGREE_SYMBOL = u"\N{DEGREE SIGN}C"


def format_temperature(temp):
    """Takes a temperature and returns it in string format with the degrees
        and Celcius symbols.

    Args:
        temp: A string representing a temperature.
    Returns:
        A string contain the temperature and "degrees Celcius."
    """
    return f"{temp}{DEGREE_SYMBOL}"


def convert_date(iso_string):
    """Converts and ISO formatted date into a human-readable format.

    Args:
        iso_string: An ISO date string.
    Returns:
        A date formatted like: Weekday Date Month Year e.g. Tuesday 06 July 2021
    """
    # %A	Weekday, full version -> Wednesday	
    # %d	Day of month 01-31 -> 31	
    # %B	Month name, full version -> December	
    # %Y	Year, full version -> 2018

    date = datetime.fromisoformat(iso_string)
    return date.strftime("%A %d %B %Y") # strftime changes the actual date instance to a different format


def convert_f_to_c(temp_in_fahrenheit):
    """Converts a temperature from Fahrenheit to Celcius.

    Args:
        temp_in_fahrenheit: float representing a temperature.
    Returns:
        A float representing a temperature in degrees Celcius, rounded to 1 decimal place.
    """
    # round(3.2) -> Output: 3
    # round(3.8) -> Output: 4
    # round(3.14159, 2) -> Output: 3.14
    # round(2.71882, 3) -> Output: 2.719

    return round(((float(temp_in_fahrenheit) - 32) * 5/9), 1) # Formula: (32°F − 32) × 5/9 = 0°C



def calculate_mean(weather_data):
    """Calculates the mean value from a list of numbers.

    Args:
        weather_data: a list of numbers.
    Returns:
        A float representing the mean value.
    """
    # sum(iterable, start - optional) -> sum([1, 2, 3]) = 0 + 1 + 2 + 3 = 6 or sum([1, 2, 3], 10) = 10 + 1 + 2 + 3 = 16

    return sum(weather_data)/len(weather_data) 



def load_data_from_csv(csv_file):
    """Reads a csv file and stores the data in a list.

    Args:
        csv_file: a string representing the file path to a csv file.
    Returns:
        A list of lists, where each sublist is a (non-empty) line in the csv file. 
    """
    lst = []
    with open(csv_file, encoding="utf-8") as data:
        file = csv.reader(data)
        for row in file:
            if len(row) > 0: # I think sublist should have exactly 3 elements not just non-empty for the best practice..
                lst.append(row)
    return lst


def find_min(weather_data):
    """Calculates the minimum value in a list of numbers.

    Args:
        weather_data: A list of numbers.
    Returns:
        The minimum value and its position in the list. (In case of multiple matches, return the index of the *last* example in the list.)
    """
    
    min = weather_data[0]
    index = 0
    for i, val in enumerate(weather_data):
        if val <= min:
            min = val
            index = i
    return min, index # Both saved in a tuple


def find_max(weather_data):
    """Calculates the maximum value in a list of numbers.

    Args:
        weather_data: A list of numbers.
    Returns:
        The maximum value and its position in the list. (In case of multiple matches, return the index of the *last* example in the list.)
    """
    max = weather_data[0]
    index = 0
    for i, val in enumerate(weather_data):
        if val >= max:
            max = val
            index = i
    return max, index # Both saved in a tuple


def generate_summary(weather_data):
    """Outputs a summary for the given weather data.

    Args:
        weather_data: A list of lists, where each sublist represents a day of weather data.
    Returns:
        A string containing the summary information.
    """

    # ─────────────────────────────
    # 🪐 PLANS 🪐
    # ─────────────────────────────

    # ─────────────────────────────
    # 💜 Function call order
    # - load_data_from_csv(file_path) -> maybe not coz we're given the data to work with as an argument?
    # - find_min(min_temps)
    # - find_max(max_temps)
    # - calculate_mean(temp_list)
    # - convert_date(iso_date)
    # - convert_f_to_c(temp_in_fahrenheit)
    # - format_temperature(temp)
    # ─────────────────────────────

    # ─────────────────────────────
    # 💜 Overall structure of the summary output
    # Line 1: <N> Day Overview
    #   -> N = number of lists (rows) in the CSV that have exactly 3 elements (date with 2 temps) in it
    # Line 2: The lowest temperature will be <lowest temp>, and will occur on <lowest date>. 
    #   -> lowest temp = a return value of find_min()
    #   -> find_min() requires a list of temps so this should be a list of all the min temps and that means I need to put all 3rd element of each list together
    #   -> ❓❓❓ where would I use the index returned from find_min() though?!
    #       -> Oh!!! because the temp is associated with the date!!!
    #       -> but then how am I going to get the respective date? All the dates should be collected in a list as well?
    #   -> date = first element of a list that has lowest temp among all the min temps.
    # Line 3: The highest temperature will be <highest temp>, and will occur on Saturday <highest date>. 
    #   -> Same logic as Line 2. 
    # Line 4: The average low this week is <avg low>.
    #   -> sum of min temps list devided by length of the list => use calculate_mean()
    # Line 5: The average high this week is <avg high>.
    #   -> Same logic as Line 4.
    # Line 6: empty
    # ─────────────────────────────

    # ─────────────────────────────
    # 🤔⁉️ Questions
    # - When should I convert the dates?
    # - When should I convert temps from f to t?
    #   -> After getting it 
    # - When should I format with the celcius sign?
    # ─────────────────────────────

    # For Line 1
    n_day = len(weather_data) - 1

    dates = [lst[0] for lst in weather_data[1:]] # Collect the element of index [0] (i.e. date) from each list
    min_temps = [float(lst[1]) for lst in weather_data[1:]]
    max_temps = [float(lst[2]) for lst in weather_data[1:]]

    # For Line 2
    min_and_index = find_min(min_temps)
    lowest_temp = format_temperature(convert_f_to_c(min_and_index[0])) # Assigning value of lowest temp
    # lowest_date = dates[min_and_index[1]] # Accessing the date of lowest temp using the index number of lowest temp
    # lowest_date = convert_date(lowest_date)
    lowest_date = convert_date(dates[min_and_index[1]])

    # For Line 3
    max_and_index = find_max(max_temps)
    highest_temp = format_temperature(convert_f_to_c(max_and_index[0]))
    highest_date = convert_date(dates[max_and_index[1]])

    # For Line 4
    avg_low = format_temperature(convert_f_to_c(calculate_mean(min_temps)))

    # For Line 5
    avg_high = format_temperature(convert_f_to_c(calculate_mean(max_temps)))

    return f"""{n_day} Day Overview
  The lowest temperature will be {lowest_temp}, and will occur on {lowest_date}.
  The highest temperature will be {highest_temp}, and will occur on {highest_date}.
  The average low this week is {avg_low}.
  The average high this week is {avg_high}.
"""

# file_paths = [
#     "./tests/data/example_one.csv",
#     "./tests/data/example_two.csv",
#     "./tests/data/example_three.csv"
# ]

# for path in file_paths:
#     print(generate_summary(load_data_from_csv(path)))
#     print()


def generate_daily_summary(weather_data):
    """Outputs a daily summary for the given weather data.

    Args:
        weather_data: A list of lists, where each sublist represents a day of weather data.
    Returns:
        A string containing the summary information.
    """
    
    # ─────────────────────────────
    # 🪐 PLANS 🪐
    # ─────────────────────────────

    # ─────────────────────────────
    # 💜 Function call order
    # - convert_date(iso_date)
    # - convert_f_to_c(temp_in_fahrenheit)
    # - format_temperature(temp)
    # ─────────────────────────────

    # ─────────────────────────────
    # 💜 Overall structure of the summary output
    # Line 1: ---- <date> ----
    # Line 2:   Minimum Temperature: <min_temp>
    # Line 3:   Maximum Temperature: <max_temp>
    # Line 4: empty 
    # -> Repeat until the last sublist in weather_data.
    # ────────────────────────

    for date, min, max in weather_data[1:]:
        date = convert_date(date)
        min = format_temperature(convert_f_to_c(float(min)))
        max = format_temperature(convert_f_to_c(float(max)))
        print(f"""---- {date} ----
  Minimum Temperature: {min}
  Maximum Temperature: {max}

""")


######## Fuction Calls ########
file_paths = [
    "./tests/data/example_one.csv",
    "./tests/data/example_two.csv",
    "./tests/data/example_three.csv"
]

for path in file_paths:
    print(generate_summary(load_data_from_csv(path)))
    print("=" * 50)

for path in file_paths:
    generate_daily_summary(load_data_from_csv(path))
    print("=" * 50)