print("test")

#Original
def output_json_log_data_to_file(filename, record_dictionary_list):
    with open(filename, 'w') as outputFile:
        for record in record_dictionary_list:
            json.dump(record, outputFile)
            outputFile.write('\n')


#Atomic
def output_json_log_data_to_file(filename, record_dictionary_list):
    # Use atomic file operations to prevent race conditions with readers
    # Write to temporary file first, then atomically rename to target file
    tmp_filename = filename + '.tmp'
    with open(tmp_filename, 'w') as outputFile:
        for record in record_dictionary_list:
            json.dump(record, outputFile)
            outputFile.write('\n')
    # Atomic rename - this prevents readers from seeing partial writes
    shutil.move(tmp_filename, filename)

def calculate_values():
    value1 = 10
    value2 = 20
    return value1, value2

result1, result2 = calculate_values()
print("Result 1:", result1)
print("Result 2:", result2)

"""
Take screenshots at x interval - make a movie of doings on a computer.
"""

import time
from datetime import datetime

import ffmpeg
import pyautogui

while True:
    epoch_time = int(time.time())
    today = datetime.now().strftime("%Y_%m_%d")
    filename = str(epoch_time) + ".png"

    print("taking screenshot: {0}".format(filename))
    myScreenshot = pyautogui.screenshot()

    myScreenshot.save(today + "/" + filename)

    time.sleep(5)

#
# and then tie it together with: https://github.com/kkroening/ffmpeg-python/blob/master/examples/README.md#assemble-video-from-sequence-of-frames
#

"""
import ffmpeg
(
    ffmpeg
    .input('./2021_01_22/*.png', pattern_type='glob', framerate=25)
    .filter('deflicker', mode='pm', size=10)
    .filter('scale', size='hd1080', force_original_aspect_ratio='increase')
    .output('movie.mp4', crf=20, preset='slower', movflags='faststart', pix_fmt='yuv420p')
    .run()
)
"""

import pandas as pd



x = pd.read_excel(FILE_NAME)

print(x)

from collections import defaultdict

def collect_dictionary(obj):
  inv_obj = defaultdict(list)
  for key, value in obj.items():
    inv_obj[value].append(key)
  return dict(inv_obj)
  
ages = {
  'Peter': 10,
  'Isabel': 10,
  'Anna': 9,
}
collect_dictionary(ages) # { 10: ['Peter', 'Isabel'], 9: ['Anna'] }