| # https://ffmpeg.org/ffmpeg-filters.html#il | |
| import ffmpeg | |
| try: | |
| top = ffmpeg.input("top/%d.jpg") | |
| bot = ffmpeg.input("bottom/%d.jpg") | |
| s = ( | |
| ffmpeg | |
| .filter([top, bot], 'vstack') | |
| .filter('il', l='i', c='i') |
| import time | |
| import logging | |
| class Timer: | |
| def __init__(self, timed, logger=None): | |
| self.logger = logger | |
| self.timed = timed | |
| def __enter__(self): | |
| self.start = time.perf_counter() |
| <html> | |
| <head> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js" type="text/javascript"></script> | |
| <script> | |
| function GetRandomBackground() | |
| { | |
| var app_id = 'YOUR_APPLICATION_ID' | |
| var url = 'https://api.unsplash.com/photos/random?client_id=' + app_id; | |
| $.ajax({ | |
| url: url, |
I hereby claim:
To claim this, I am signing this object:
| Day | Pulse Ox [^automatically detected] | Charge 2 [^manual trigger] | |
|---|---|---|---|
| 1 | 12 min / 165 cals / 2.27 km | 13 min / 164 cals / 1.91 km | |
| 2 | - | - | |
| 3 | 17 min / 175 cals / 2.76 km | 13 min / 154 cals / 1.93 km | |
| 4 | - | - | |
| 5 | - | - | |
| 6 | - | - | |
| 7 | 42 min / 161 cals / 4.5 km | 41 min / 328 cals / 3.68 km |
| Day | Pulse Ox | Charge 2 | |
|---|---|---|---|
| 1 | 7.475 | 9.689 | |
| 2 | 7.048 | 8.805 | |
| 3 | 5.692 | 6.689 | |
| 4 | 7.910 | 9.583 | |
| 5 | 6.404 | 9.540 | |
| 6 | 7.514 | 9.350 | |
| 7 | 5.688 | 7.516 |
| Day | Pulse Ox [^manual] | Charge 2 [^automatically detected] | |
|---|---|---|---|
| 1 | 7h41 | 7h21 | |
| 2 | 7h30 | 7h20 | |
| 3 | 7h40 | 7h37 | |
| 4 | 8h19 | 8h33 | |
| 5 | 7h34 | 7h58 (+ 1h54 [^a nap]) | |
| 6 | 8h33 | 8h23 | |
| 7 | 7h45 | 7h16 |
| import glob | |
| import pandas as pd | |
| files = glob.glob('*.xlsx') | |
| df = pd.DataFrame() | |
| for f in files: | |
| data = pd.read_excel(f,'Sheet1') | |
| df = df.append(data) |
| from pyspark import SparkConf, SparkContext | |
| from sklearn.datasets import make_classification | |
| from sklearn.ensemble import ExtraTreesClassifier | |
| import pandas as pd | |
| import numpy as np | |
| conf = (SparkConf() | |
| .setMaster("local[*]") | |
| .setAppName("My app") | |
| .set("spark.executor.memory", "1g")) |
