2.04 Working with the OS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with the OS\n",
    "\n",
    "A notebook for working with the operating system. To work with your computers operating system there are several libraries available. We will use some of the most common ones.\n",
    "\n",
    "## Contents\n",
    "0. Platform\n",
    "1. Glob\n",
    "2. Pathlib\n",
    "3. OS Library\n",
    "4. Socket\n",
    "5. Environmental variable (to store credentials)\n",
    "6. Dropbox - in progress\n",
    "9. Copying files with shutil\n",
    "10. PSUtil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Platform\n",
    "\n",
    "Platform is a python built-in module, which gives you access to underlying platform’s identifying data.\n",
    "Source: https://docs.python.org/3/library/platform.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'x86_64'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first example\n",
    "import platform\n",
    "platform.machine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macOS-10.16-x86_64-i386-64bit\n",
      "i386\n"
     ]
    }
   ],
   "source": [
    "print(platform.platform())\n",
    "print(platform.processor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('10.16', ('', '', ''), 'x86_64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#depending on your computer you can get more information\n",
    "platform.mac_ver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Glob\n",
    "The glob module finds all the pathnames matching a specified pattern according to the rules used by the Unix shell, although results are returned in arbitrary order.\n",
    "\n",
    "source: https://docs.python.org/3/library/glob.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['buik3.jpg', 'claxon_1m.jpg', 'claxon_1m_log.jpg', 'claxon_close.jpg', 'claxon_close_log.jpg', 'claxon_colorbar_log.jpg', 'claxon_iphone_log.jpg', 'claxon_michiel_log.jpg', 'newimage.jpg', 'output.jpg', 'pixellib_example.jpg', 'road.jpg', 'scatter.jpg', 'thuis.jpg']\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "files = glob.glob('*.jpg') # retrieve all the .json files in home dir and assign to a list\n",
    "# Take the files from the last two weeks only.\n",
    "files = sorted(files)[-14:] # sort the files of the last 14 days\n",
    "type(files)\n",
    "print(files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: https://stackoverflow.com/questions/14798220/how-can-i-search-sub-folders-using-glob-glob-module\n",
    "\n",
    "There's a lot of confusion on this topic. Let me see if I can clarify it (Python 3.7):\n",
    "\n",
    "- glob.glob('*.txt')                  :matches all files ending in '.txt' in current directory\n",
    "- glob.glob('*/*.txt')                :same as 1\n",
    "- glob.glob('**/*.txt')               :matches all files ending in '.txt' in the immediate subdirectories only, but not in the current directory\n",
    "- glob.glob('*.txt',recursive=True)   :same as 1\n",
    "- glob.glob('*/*.txt',recursive=True) :same as 3\n",
    "- glob.glob('**/*.txt',recursive=True):matches all files ending in '.txt' in the current directory and in all subdirectories\n",
    "\n",
    "So it's best to always specify recursive=True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/michielbontenbal/Documents/GitHub/Notebooks'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['my-audio.wav', 'new_file.wav', 'out.wav', 'file.wav', 'loudness.wav', 'test2.wav', 'claxon_iphone.wav', 'test3.wav', 'my_wav.wav', 'mywav_reduced_noise2.wav', 'piano_c.wav', 'claxon_michiel.wav', 'claxon_close.wav', 'recordedFile.wav', 'miaow_16k.wav', 'tone_220.wav', 'welcome.wav', 'silence.wav', 'example.wav', '11k16bitpcm.wav', 'newsegment.wav', 'claxon_freesound.wav', 'robot0.wav', 'my_test.wav', 'speech_whistling2.wav', 'robot1.wav', 'mywav_reduced_noise.wav', 'robot3.wav', 'test.wav', 'noise_add.wav', 'robot2.wav', 'Sample_audio.wav', 'tone_440.wav', 'claxon_1m.wav', 'output.wav']\n"
     ]
    }
   ],
   "source": [
    "# searching all files in a directory and subdirectories\n",
    "import glob\n",
    "my_wavs = glob.glob(r'*.wav', recursive=True)\n",
    "print(my_wavs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newsegment.wav\n"
     ]
    }
   ],
   "source": [
    "#selecting the latest downloaded file\n",
    "import glob\n",
    "import os\n",
    "\n",
    "list_of_files = glob.glob('*.wav') # with * select all .wav files\n",
    "latest_file = max(list_of_files, key=os.path.getctime)\n",
    "print(latest_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/michielbontenbal/Desktop/Notebooks'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from glob import glob\n",
    "\n",
    "my_ipynbs = glob('*.ipynb')\n",
    "len(my_ipynbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1.0 - Notebook on basics of Python and Jupyter Notebooks.ipynb',\n",
       " '1.01 Python fundamentals Notebook.ipynb',\n",
       " '1.02 Basics of Data Analysis.ipynb',\n",
       " '1.03 Working with files and data serialization.ipynb',\n",
       " '1.04 Functions.ipynb',\n",
       " '1.05 Getting data from the web - requests, wget, curl, urllib.ipynb',\n",
       " '1.06 Graphs with Matplotlib.ipynb',\n",
       " '1.07 Working with images.ipynb',\n",
       " '1.08 Pandas basics (tutorialise).ipynb',\n",
       " '1.09 Jupyter notebook cell magic & other stuff.ipynb',\n",
       " '1.10 Converting basic data types.ipynb',\n",
       " '2.00 Python Intermediate (exeptions, assert, decorators etc).ipynb',\n",
       " '2.01 Time and datetime.ipynb',\n",
       " '2.02 Introduction to Object Oriented Programming (OOP) (clean up).ipynb',\n",
       " '2.03 Python and SQL databases.ipynb',\n",
       " '2.04 Webscraping, BeautifulSoup & converting webpages to pdf (some work to do).ipynb',\n",
       " '2.05 Python and excel (tutorialise).ipynb',\n",
       " '2.06 Working with the OS.ipynb',\n",
       " '2.07 Python and Linux.ipynb',\n",
       " '2.08 Time Series with Pandas and SciPy (data source missing).ipynb',\n",
       " '2.09 Collections = special data types.ipynb',\n",
       " '2.10 Intro to Numpy.ipynb',\n",
       " '2.11 Image_data_augmentation.ipynb',\n",
       " '2.12 Python for geodata and maps .ipynb',\n",
       " '2.13 Graphs advanced - Seaborn, Plotly, Plotnine, IPYwidgets (to do).ipynb',\n",
       " '2.14 Regular expressions and pattern matching (work in progress).ipynb',\n",
       " '2.15 Testing with pytest, mock, nose and selenium.ipynb',\n",
       " '2.16 Intro to Flask (work in progress).ipynb',\n",
       " '2.17 Pynput (in progress).ipynb',\n",
       " '2022_09_08_OpenCV_opdracht.ipynb',\n",
       " '3.01 a very short introduction to Deep Learning (work in progress).ipynb',\n",
       " '3.02 Neural Networks with Numpy.ipynb',\n",
       " '3.03 What is gradient descent ? .ipynb',\n",
       " '3.04 Computer Vision basics on Images with OpenCV.ipynb',\n",
       " '3.05 Computer Vision basics on Video with OpenCV.ipynb',\n",
       " '3.06 Teachable Machine (tutorialise, to clean up).ipynb',\n",
       " '3.07 Image Classification with Keras from scratch.ipynb',\n",
       " '3.08 Image Classification - MNIST with Keras.ipynb',\n",
       " '3.09 Image Classification with_ResNet50.ipynb',\n",
       " '3.09 Image Segmentation with Pixellib (PointRed only on linux not windows).ipynb',\n",
       " '3.10 Face detection with Haar,  MTCNN and Keras-Facenet (HvA).ipynb',\n",
       " '3.12 Object Detection with YOLO v3, v5 and v8 (to clean up...).ipynb',\n",
       " '3.13 Object Detection with ImageAI (to be updated to Pytorch).ipynb',\n",
       " '3.14 Object_detection with TF2.ipynb',\n",
       " '3.15  Multi Object Tracking with Motpy (work in progress).ipynb',\n",
       " '3.16 NLP with Laurence Moroney.ipynb',\n",
       " '3.17 NLP - Sentiment in text (NLP zero to hero 3).ipynb',\n",
       " '3.18 NLP classifying movie reviews with Keras.ipynb',\n",
       " '3.19 Transformers with Huggingface (Work in Progress).ipynb',\n",
       " \"3.20 Translation API's.ipynb\",\n",
       " '3.21 NLP - Train_your_tokenizer Huggingface.ipynb',\n",
       " '3.22 Introduction to Pytorch - Work in progress.ipynb',\n",
       " '3.23 NLP encoding, decoding and some tokenizers.ipynb',\n",
       " \"3.24 Andrej Karpathy's GPT tutorial.ipynb\",\n",
       " '4.00 Introduction to Big Data and Data Engineering.ipynb',\n",
       " '4.01 Sending and receiving messages with AMQP.ipynb',\n",
       " '4.02 Asynchronous communication using websockets (work in progress).ipynb',\n",
       " '4.03 Publish & subscribe with MQTT.ipynb',\n",
       " '4.05 Time Series database with InfluxDB.ipynb',\n",
       " '4.06 Apache Avro Data Serialization.ipynb',\n",
       " '4.07 XML and XSD.ipynb',\n",
       " '4.07b XML Element Tree.ipynb',\n",
       " '4.08 Validating JSON files with JSON Schema.ipynb',\n",
       " '4.10 Generate APIspec.ipynb',\n",
       " '4.11 parsing openAPI specs.ipynb',\n",
       " '4.12 Reverse Engineering the Petstore API (Medium article).ipynb',\n",
       " '4.12b Reverse Engineering Petstore OpenAPI spec.ipynb',\n",
       " '4.13 Zeep a python SOAP client.ipynb',\n",
       " '4.14 Creating UML diagrams with PlantUML (to clean up).ipynb',\n",
       " '5.01 Audio with Python- the basics.ipynb',\n",
       " '5.02 Audio with Python -  advanced.ipynb',\n",
       " '5.03 Visualising audio - creating waveform, spectogram, mel specto with librosa.ipynb',\n",
       " '5.04 Another way to create a mel spectrogram.ipynb',\n",
       " '5.05 Fourier Transform & more audio visualisation (in progress).ipynb',\n",
       " '5.06 Real time audio visualizing.ipynb',\n",
       " '5.07 Energy and Sound Pressure Level.ipynb',\n",
       " '5.08 Audio Yamnet_visualisation.ipynb',\n",
       " '5.09 Creating a Voice Assistant.ipynb',\n",
       " '5.10 Speech2text & Text2speech.ipynb',\n",
       " \"5.11 Speech to text with Youtube and Twitter video's.ipynb\",\n",
       " '5.12 Audio Data Augmentation 2.ipynb',\n",
       " '5.12 Audio Data Augmentation.ipynb',\n",
       " '5.13 Sound classification with YAMNet.ipynb',\n",
       " '5.14 Audio Classification with OpenL3.ipynb',\n",
       " '5.16 Transfer_learning_audio (works in Colab not local).ipynb',\n",
       " '5.17 recognize keywords with tensorflow.ipynb',\n",
       " '5.18 Torchopenl3 (not working locally).ipynb',\n",
       " '5.19 Filter & denoise.ipynb',\n",
       " '5.20 A_quick_start_guide_for_soundscape_IR.ipynb',\n",
       " '5.21 Analyzing the Amsterdam Urban Sounds dataset.ipynb',\n",
       " '5.22 scripts to rename the files in Amsterdam Sound Dataset.ipynb',\n",
       " '5.23 Gradio Audio Demo.ipynb',\n",
       " '5.24 Noisereduce full tutorial.ipynb',\n",
       " '6.01 Python Forest map.ipynb',\n",
       " '6.02 Barchart race original.ipynb',\n",
       " '6.03 Maps of Amsterdam.ipynb',\n",
       " '6.04 Pre-assignment AI café Michiel.ipynb',\n",
       " '6.05 Snoring (some work to do).ipynb',\n",
       " '6.05 Snoring.ipynb',\n",
       " '6.06 HRV logger app (for personal use only).ipynb',\n",
       " '6.07 Oura Sleep Data (to do).ipynb',\n",
       " '6.07b Oura as a function.ipynb',\n",
       " '6.08 Pytesseract OCR Ukrainian (work in progress).ipynb',\n",
       " '6.09 Pytesseract 2 Sententie Bontenbal.ipynb',\n",
       " '6.10 WebRTC (basic).ipynb',\n",
       " '6.11 Mastodon (work in progress).ipynb',\n",
       " '6.12 Programming some algorithms.ipynb',\n",
       " '6.13 Boids algorithm and simulating bird flock behaviour (work in progress).ipynb',\n",
       " '6.x Boids algorithm and simulating bird flock behaviour (work in progress).ipynb',\n",
       " '7.0 HvA oefeningen.ipynb',\n",
       " 'Face_in_keras.ipynb',\n",
       " 'gesproken_woorden_hva.ipynb',\n",
       " 'tutorial.ipynb']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_list = sorted(my_ipynbs)\n",
    "sorted_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Pathlib\n",
    "\n",
    "with pathlib you can add a path to the glob command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pathlib\n",
      "  Downloading pathlib-1.0.1-py3-none-any.whl (14 kB)\n",
      "Installing collected packages: pathlib\n",
      "Successfully installed pathlib-1.0.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/michielbontenbal/Documents/GitHub/Notebooks'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/michielbontenbal')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "Path.home()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object Path.glob at 0x7f92859f62d0>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "generator"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this will create a python 'generator'\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "\n",
    "notebook_files = Path('/Users/michielbontenbal/Documents/GitHub/Notebooks/').glob('*.ipynb')\n",
    "\n",
    "print(notebook_files)\n",
    "type(notebook_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.15  Multi Object Tracking with Motpy (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.11 parsing openAPI specs.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.12 Audio Data Augmentation 2.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.06 Working with the OS.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/gesproken_woorden_hva.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.13 Zeep a python SOAP client.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.08 Time Series with Pandas and SciPy (data source missing).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.01 Audio with Python- the basics.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.09 Image Classification with_ResNet50.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.15 Testing with pytest, mock, nose and selenium.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.04 Computer Vision basics on Images with OpenCV.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.08 Image Classification - MNIST with Keras.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.07 Working with images.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.x Boids algorithm and simulating bird flock behaviour (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.06 Teachable Machine (tutorialise, to clean up).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.08 Pandas basics (tutorialise).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.13 Sound classification with YAMNet.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.11 Image_data_augmentation.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.07 Python and Linux.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.17 recognize keywords with tensorflow.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.12 Python for geodata and maps .ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.09 Image Segmentation with Pixellib (PointRed only on linux not windows).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.12 Audio Data Augmentation.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.02 Neural Networks with Numpy.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.09 Collections = special data types.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.01 Sending and receiving messages with AMQP.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.05 Time Series database with InfluxDB.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.19 Transformers with Huggingface (Work in Progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.05 Snoring (some work to do).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.04 Functions.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.10 Intro to Numpy.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.14 Object_detection with TF2.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.04 Webscraping, BeautifulSoup & converting webpages to pdf (some work to do).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.17 NLP - Sentiment in text (NLP zero to hero 3).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.05 Getting data from the web - requests, wget, curl, urllib.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.03 Maps of Amsterdam.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.18 NLP classifying movie reviews with Keras.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.08 Validating JSON files with JSON Schema.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.13 Graphs advanced - Seaborn, Plotly, Plotnine, IPYwidgets.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.05 Snoring.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.09 Jupyter notebook cell magic & other stuff.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.09 Creating a Voice Assistant.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.00 Introduction to Big Data and Data Engineering.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.03 Python and SQL databases.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/Face_in_keras.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.13 Boids algorithm and simulating bird flock behaviour (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.16 NLP with Laurence Moroney.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.12 Object Detection with YOLO v3, v5 and v8 (to clean up...).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.17 Pynput (in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.07b Oura as a function.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.11 Mastodon (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2022_09_08_OpenCV_opdracht.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.02 Asynchronous communication using websockets (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.04 Another way to create a mel spectrogram.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.03 Visualising audio - creating waveform, spectogram, mel specto with librosa.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.00 Python Intermediate (exeptions, assert, decorators etc).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.09 Pytesseract 2 Sententie Bontenbal.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.14 Creating UML diagrams with PlantUML (to clean up).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.02 Basics of Data Analysis.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.19 Filter & denoise.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.03 Working with files and data serialization.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.23 NLP encoding, decoding and some tokenizers.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.10 Face detection with Haar,  MTCNN and Keras-Facenet (HvA).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.12 Reverse Engineering the Petstore API (Medium article).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.04 Pre-assignment AI café Michiel.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.07 Energy and Sound Pressure Level.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.24 Noisereduce full tutorial.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.02 Barchart race original.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.14 Regular expressions and pattern matching (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.21 NLP - Train_your_tokenizer Huggingface.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.06 Real time audio visualizing.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.01 Python fundamentals Notebook.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.13 Object Detection with ImageAI (to be updated to Pytorch).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.06 HRV logger app (for personal use only).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.07 Oura Sleep Data (to do).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.03 Publish & subscribe with MQTT.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.16 Intro to Flask (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.12b Reverse Engineering Petstore OpenAPI spec.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.07b XML Element Tree.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.06 Graphs with Matplotlib.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.03 What is gradient descent ? .ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.12 Programming some algorithms.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.22 Introduction to Pytorch - Work in progress.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.05 Fourier Transform & more audio visualisation (in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/7.0 HvA oefeningen.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.10 Generate APIspec.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.10 WebRTC (basic).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.01 Time and datetime.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.01 a very short introduction to Deep Learning (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.01 Python Forest map.ipynb'),\n",
       " PosixPath(\"/Users/michielbontenbal/Documents/GitHub/Notebooks/3.24 Andrej Karpathy's GPT tutorial.ipynb\"),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.10 Converting basic data types.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/1.0 - Notebook on basics of Python and Jupyter Notebooks.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.10 Speech2text & Text2speech.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/5.08 Audio Yamnet_visualisation.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/2.05 Python and excel (tutorialise).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.05 Computer Vision basics on Video with OpenCV.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.07 XML and XSD.ipynb'),\n",
       " PosixPath(\"/Users/michielbontenbal/Documents/GitHub/Notebooks/3.20 Translation API's.ipynb\"),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/tutorial.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/6.08 Pytesseract OCR Ukrainian (work in progress).ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/4.06 Apache Avro Data Serialization.ipynb'),\n",
       " PosixPath('/Users/michielbontenbal/Documents/GitHub/Notebooks/3.07 Image Classification with Keras from scratch.ipynb')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(notebook_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. OS library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\31653\\\\Documents\\\\GitHub\\\\Notebooks'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/michielbontenbal/Documents/GitHub/Notebooks\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use linux command pwd\n",
    "os.system('pwd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'dummy'.svg\n",
      "'dummy'.uml\n",
      "1.0 - Michiel's Notebook on basics of Python and Jupyter Notebooks.ipynb\n",
      "1.01 Michiels' Python fundamentals Notebook.ipynb\n",
      "1.02 Basics of Data Analysis.ipynb\n",
      "1.03 Working with files and data serialization.ipynb\n",
      "1.04 Michiels notebook on images.ipynb\n",
      "1.05 Getting data from the web - requests, wget and curl.ipynb\n",
      "1.06 Functions.ipynb\n",
      "1.07 Pandas basics.ipynb\n",
      "1.08 Simple graphs Matplotlib.ipynb\n",
      "1.09 IPython and Markdown.ipynb\n",
      "1.10 Converting data types.ipynb\n",
      "1.11 Some special data types .ipynb\n",
      "11k16bitpcm.wav\n",
      "2.0 Michiel's Intermediate Notebook.ipynb\n",
      "2.01 Time and datetime.ipynb\n",
      "2.02 Arrays with Numpy.ipynb\n",
      "2.03 Michiel's notebook on python for maps - GeoPandas issue.ipynb\n",
      "2.04 Webscraping, BeautifulSoup & converting webpages to pdf.ipynb\n",
      "2.05 Python and excel.ipynb\n",
      "2.06 Working with the OS - in progress.ipynb\n",
      "2.07 Graphs advanced - Seaborn, Plotly, Plotnine, IPYwidgets.ipynb\n",
      "2.08 Time Series with Pandas and SciPy.ipynb\n",
      "2.09 SciPy various scripts.ipynb\n",
      "2.10 Python and Linux.ipynb\n",
      "2.11 Image_data_augmentation.ipynb\n",
      "2.12 Python and SQL and other databases - to do.ipynb\n",
      "2.13 Pynput.ipynb\n",
      "2021-12-5_Features_.csv\n",
      "3.01 Michiels notebook Deep learning with python - 1.ipynb\n",
      "3.02 Michiel's Neural Network Notebook.ipynb\n",
      "3.03 Michiel's notebook on Pytorch - Work in progress.ipynb\n",
      "3.04 Michiel's notebook on OpenCV.ipynb\n",
      "3.05-classifying-movie-reviews.ipynb\n",
      "3.06 Deep learning for python -2e versie.ipynb\n",
      "3.07 Keras_Image_classification_from_scratch.ipynb\n",
      "3.08 MNIST in Keras.ipynb\n",
      "3.09 Teachable Machine.ipynb\n",
      "3.11 Object Detection Work in Progress.ipynb\n",
      "3.13 ImageAI.ipynb\n",
      "3.14 NLP Sentiment in text (NLP zero to hero 3).ipynb\n",
      "3.15 Motpy.ipynb\n",
      "3.18 Huggingface.ipynb\n",
      "3.19 Face detection (not working).ipynb\n",
      "3.20 Translation API's.ipynb\n",
      "4.01 AMQP with RabbitMQ.ipynb\n",
      "4.02 Kafka.ipynb\n",
      "4.03 MQTT.ipynb\n",
      "4.05 OData.ipynb\n",
      "4.06 websockets.ipynb\n",
      "4.07 Creating UML diagrams with PlantUML.ipynb\n",
      "4.08 AsyncAPI.ipynb\n",
      "4.09 Apache Avro Data Serialization.ipynb\n",
      "4.10 gRPC.ipynb\n",
      "4.11 Zeep - wsdl.ipynb\n",
      "4.12 APIspec.ipynb\n",
      "4.12 XML schema (xsd).ipynb\n",
      "4.13 pytest & mock.ipynb\n",
      "4.14 JSON Schema.ipynb\n",
      "4.15 parsing openAPI specs.ipynb\n",
      "4.16 XML Element Tree.ipynb\n",
      "4.17 Pydantic data validation.ipynb\n",
      "4.18 Reverse Engineering the Petstore API (Medium article).ipynb\n",
      "4.19 Reverse Engineering Petstore OpenAPI spec.17 Reverse Engineering the Petstore OAS.ipynb\n",
      "5.03 Visualising audio - creating waveform, spectogram, mel specto with librosa.ipynb\n",
      "5.04 Another way to create a mel spectrogram.ipynb\n",
      "5.05 Fourier Transform & more audio visualisation (in progress).ipynb\n",
      "5.06 Real time audio visualizing.ipynb\n",
      "5.07 Energy and Sound Pressure Level.ipynb\n",
      "5.08 Audio Yamnet_visualisation (h5py dependency).ipynb\n",
      "5.09 Creating a Voice Assistant.ipynb\n",
      "5.10 Speech2text & Text2speech.ipynb\n",
      "5.11 Speech to text with Youtube and Twitter video's.ipynb\n",
      "5.13 Sound classification with YAMNet (works in colab not locally).ipynb\n",
      "5.14 Audio Classification with OpenL3 (needs h5py).ipynb\n",
      "5.15 TO DO Building a RNN for speech recognition.ipynb\n",
      "5.16 Transfer_learning_audio (works in Colab not local).ipynb\n",
      "5.17 recognize keywords with tensorflow.ipynb\n",
      "5.18 Torchopenl3.ipynb\n",
      "5.20 A_quick_start_guide_for_soundscape_IR.ipynb\n",
      "6.01 Python Forest map.ipynb\n",
      "6.02 Maps of Amsterdam.ipynb\n",
      "6.04 Pre-assignment AI café Michiel.ipynb\n",
      "6.05 Snoring.ipynb\n",
      "6.06 HRV logger app.ipynb\n",
      "6.07 Oura Sleep Data.ipynb\n",
      "6.07b Oura as a function.ipynb\n",
      "6.08 Pytesseract OCR Ukrainian (work in progress).ipynb\n",
      "6.09 Pytesseract 2 Sententie Bontenbal.ipynb\n",
      "6.10 WebRTC (basic).ipynb\n",
      "7.0 HvA oefeningen.ipynb\n",
      "Do. Or do not. There is no try.-BQ4yd2W50No.mkv\n",
      "Do. Or do not. There is no try.-BQ4yd2W50No.mp4\n",
      "Jay in Kyiv - Russians having a rough time holding Kherson.-1564190523849691139.mp4\n",
      "MQTT_steve.py\n",
      "OEtest_210505_youtube_traffic.csv\n",
      "OEtest_210506_youtubetraffic.csv\n",
      "OpenCV_rgb.png\n",
      "Pytesseract\n",
      "README.md\n",
      "Sample_audio.wav\n",
      "Snoring-data-2021-12-03 09_15_24.csv\n",
      "Snoring-data-2021-12-04 11_41_48.csv\n",
      "Snoring-data-2021-12-04 11_51_51.csv\n",
      "Snoring-data-2021-12-06 19_03_36.csv\n",
      "Snoring-data-2021-12-10 13_27_41.csv\n",
      "Snoring-data-2021-12-12 09_38_24.csv\n",
      "TTF.pdf\n",
      "UMTMVS.csv\n",
      "Untitled.ipynb\n",
      "Visegrád 24 - Ukrainian man finds a drunk Russian soldier.  Via @WarNewsPL1-1541178599918673927.mp4\n",
      "XML_file.txt\n",
      "__pycache__\n",
      "a_file.txt\n",
      "accounts.txt\n",
      "accounts2.txt\n",
      "amqp_receive.py\n",
      "amqp_send.py\n",
      "anatomy of an array.png\n",
      "avro_exmple.py\n",
      "buik3.jpg\n",
      "buik3.thumbnail\n",
      "claxon_1m.jpg\n",
      "claxon_1m.thumbnail\n",
      "claxon_1m.wav\n",
      "claxon_1m_log.jpg\n",
      "claxon_1m_log.thumbnail\n",
      "claxon_close.jpg\n",
      "claxon_close.thumbnail\n",
      "claxon_close.wav\n",
      "claxon_close_log.jpg\n",
      "claxon_close_log.thumbnail\n",
      "claxon_colorbar_log.jpg\n",
      "claxon_colorbar_log.thumbnail\n",
      "claxon_freesound.wav\n",
      "claxon_iphone.wav\n",
      "claxon_iphone_log.jpg\n",
      "claxon_iphone_log.thumbnail\n",
      "claxon_michiel.wav\n",
      "claxon_michiel_log.jpg\n",
      "claxon_michiel_log.thumbnail\n",
      "columns.xlsx\n",
      "curl\n",
      "data.yaml\n",
      "de_A\n",
      "demo.txt\n",
      "download (1).wget\n",
      "download.wget\n",
      "drop.avi\n",
      "edgeimpulse.json\n",
      "example.wav\n",
      "file.wav\n",
      "foo.csv\n",
      "haar_cascade_stop.xml\n",
      "haarcascade_frontalface_default.xml\n",
      "hello_world.xlsx\n",
      "hello_world_append.xlsx\n",
      "items.yaml\n",
      "keyword_model.zip\n",
      "leegmelden_plantuml.txt\n",
      "loudness.wav\n",
      "miaow_16k.wav\n",
      "movies.xml\n",
      "mqtt_open_ears_test.py\n",
      "mqtt_sub.py\n",
      "my-audio.wav\n",
      "my_test.wav\n",
      "my_wav.wav\n",
      "myenv\n",
      "mywav_reduced_noise.wav\n",
      "mywav_reduced_noise2.wav\n",
      "new_file.ogg\n",
      "new_file.wav\n",
      "newimage.jpg\n",
      "newimage.thumbnail\n",
      "newsegment.wav\n",
      "noise_add.wav\n",
      "note.xsd\n",
      "oe_wavs\n",
      "opencv.png\n",
      "out.pdf\n",
      "out.wav\n",
      "output.avi\n",
      "output.jpg\n",
      "output.mp4\n",
      "output.thumbnail\n",
      "output.wav\n",
      "person.json\n",
      "petstore_openapi3.json\n",
      "petstore_openapi3.yaml\n",
      "piano_c.wav\n",
      "pixellib\n",
      "pixellib_example.jpg\n",
      "pixellib_example.thumbnail\n",
      "plane.json\n",
      "plantuml.png\n",
      "plantuml.txt\n",
      "plantuml_complex.txt\n",
      "plantuml_complex2.txt\n",
      "plot.png\n",
      "recordedFile.wav\n",
      "requirements.txt\n",
      "road.jpg\n",
      "road.thumbnail\n",
      "road2.thumbnail\n",
      "robot0.wav\n",
      "robot1.wav\n",
      "robot2.wav\n",
      "robot3.wav\n",
      "rps\n",
      "rps-test-set\n",
      "save.pkl\n",
      "saved_keyword_model\n",
      "scatter.jpg\n",
      "scatter.thumbnail\n",
      "servers.txt\n",
      "silence.wav\n",
      "simple.xml\n",
      "simple.xsd\n",
      "sleep_df_oura_202112011-12.csv\n",
      "snoring_2021_12_3-4b.csv\n",
      "speech_whistling2.wav\n",
      "streamlit.py\n",
      "streamlit_example.py\n",
      "temp.mp3\n",
      "temp1.png\n",
      "tesseract_sample.jpeg\n",
      "test.wav\n",
      "test2.wav\n",
      "test3.wav\n",
      "thuis.jpg\n",
      "tone_220.wav\n",
      "tone_440.wav\n",
      "tranquilizer.py\n",
      "tutorial.ipynb\n",
      "untitled.txt\n",
      "user.avsc\n",
      "users.avro\n",
      "websocketbrowser.html\n",
      "websocketbrowser.py\n",
      "welcome.mp3\n",
      "welcome.wav\n",
      "world-happiness\n",
      "world-happiness11nnjglf.tmp\n",
      "world-happinesstjpjti8t.tmp\n",
      "world-happinesswfvezx54.tmp\n",
      "youtube-dl test video ''_ä↭𝕐-BaW_jenozKc.m4a\n",
      "youtube-dl test video ''_ä↭𝕐-BaW_jenozKc.mp4\n"
     ]
    }
   ],
   "source": [
    "# use linux command ls\n",
    "import os\n",
    "os.system('ls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RGracht.234.567.890\n",
      ".wav\n"
     ]
    }
   ],
   "source": [
    "# a script to split name and extension of a file\n",
    "import glob, os\n",
    "\n",
    "pngs=glob.glob('*.png')\n",
    "infile = 'RGracht.234.567.890.wav'#pngs[0] #select first item in list\n",
    "file, ext = os.path.splitext(infile)\n",
    "#print(pngs[0]) #print first item in the list of png's\n",
    "print(file)\n",
    "print(ext)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b. Ping status with OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PING 192.168.178.36 (192.168.178.36): 56 data bytes\n",
      "Request timeout for icmp_seq 0\n",
      "Request timeout for icmp_seq 1\n",
      "Request timeout for icmp_seq 2\n",
      "Request timeout for icmp_seq 3\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Network Error'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 192.168.178.36 ping statistics ---\n",
      "5 packets transmitted, 0 packets received, 100.0% packet loss\n"
     ]
    }
   ],
   "source": [
    "#For my raspberry\n",
    "import os\n",
    "response = os.system(\"ping 192.168.178.36\")\n",
    "if response == 0:\n",
    "    pingstatus = \"Network Active\"\n",
    "else:\n",
    "    pingstatus = \"Network Error\"\n",
    "pingstatus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3c. OS Listdir\n",
    "Convert a OS listdir to a python list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['my-audio.wav', 'mqtt_open_ears_test.py', '3.15  Multi Object Tracking with Motpy (work in progress).ipynb', 'movies.xml', 'new_file.wav']\n"
     ]
    }
   ],
   "source": [
    "#use os.listdir to get a list of items in a folder.\n",
    "import os\n",
    "my_list= os.listdir('/Users/michielbontenbal/Documents/GitHub/Notebooks')\n",
    "print(my_list[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3d. os.rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename one file\n",
    "import os\n",
    "\n",
    "# Absolute path of a file\n",
    "old_name = r\"accounts.txt\"\n",
    "new_name = r\"accounts2.txt\"\n",
    "\n",
    "# Renaming the file\n",
    "os.rename(old_name, new_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename multiple flies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: dummy: File exists\n",
      "/Users/michielbontenbal/Desktop/Notebooks/dummy\n"
     ]
    }
   ],
   "source": [
    "#create a new folder to store the new dummy text files\n",
    "%mkdir dummy\n",
    "%cd /Users/michielbontenbal/Desktop/Notebooks/dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dummy[2].txt', 'dummy_2', 'dummy[0].txt', 'dummy[1].txt', 'dummy_1', 'dummy_0']\n"
     ]
    }
   ],
   "source": [
    "#create multiple empty files\n",
    "for i in range(0,3):\n",
    "    with open('dummy'+str([i])+'.txt', 'w') as f:\n",
    "        f.write('new textfile')\n",
    "print(os.listdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dummy_2', 'dummy_1', 'dummy_0']\n"
     ]
    }
   ],
   "source": [
    "#rename for a list of files\n",
    "from glob import glob\n",
    "my_txts = glob(\"*.txt\")\n",
    "my_txts\n",
    "for i in range(len(my_txts)):\n",
    "    os.rename(my_txts[i],'dummy_'+str(i))\n",
    "print(os.listdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dummy', 'dummy_2', 'dummy_1', 'dummy_0']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "filesRename = ['dummy_1.txt', 'dummy_2.txt', 'dummy_3.txt',]\n",
    "folder = r\"/Users/michielbontenbal/Desktop/Notebooks/dummy\"\n",
    "\n",
    "# Iterate\n",
    "for file in os.listdir(folder):\n",
    "    # Checking if the file is present in the list\n",
    "    if file in filesRename:\n",
    "        oldName = os.path.join(folder, file)\n",
    "        n = os.path.splitext(file)[0]\n",
    "\n",
    "        b = n + '_new' + '.txt'\n",
    "        newName = os.path.join(folder, b)\n",
    "\n",
    "        # Rename the file\n",
    "        os.rename(oldName, newName)\n",
    "\n",
    "res = os.listdir(folder)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mdummy\u001b[m\u001b[m   dummy_0 dummy_1 dummy_2\r\n"
     ]
    }
   ],
   "source": [
    "!cd dummy && ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Setting environment variable to store passwords etc\n",
    "\n",
    "source: https://medium.com/dataseries/hiding-secret-info-in-python-using-environment-variables-a2bab182eea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User's Environment variable:\n"
     ]
    }
   ],
   "source": [
    "# source: https://www.geeksforgeeks.org/python-os-environ-object/\n",
    "# Python program to explain os.environ object \n",
    "  \n",
    "# importing os module \n",
    "import os\n",
    "import pprint\n",
    "  \n",
    "# Get the list of user's\n",
    "# environment variables\n",
    "env_var = os.environ\n",
    "  \n",
    "# Print the list of user's\n",
    "# environment variables\n",
    "print(\"User's Environment variable:\")\n",
    "#pprint.pprint(dict(env_var), width = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None None\n"
     ]
    }
   ],
   "source": [
    "#https://dev.to/biplov/handling-passwords-and-secret-keys-using-environment-variables-2ei0\n",
    "\n",
    "#first, go to system properties, then set an 'environmental variable'. Note: NOT WORKING in my Jupyter NB.\n",
    "\n",
    "import os \n",
    "user_name = os.environ.get('sensemakers')\n",
    "password = os.environ.get('password')\n",
    "\n",
    "print(user_name, password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#source: https://able.bio/rhett/how-to-set-and-get-environment-variables-in-python--274rgt5\n",
    "#Set the environmental Password\n",
    "import os\n",
    "os.environ['API_USER'] = 'Michiel' #but don't keep it in your source code :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Michiel\n"
     ]
    }
   ],
   "source": [
    "#getting that environmental variable:\n",
    "import os\n",
    "#api_user = os.environ.get('API_USER')\n",
    "print(os.environ.get('API_USER'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Dropbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting dropbox\n",
      "  Downloading dropbox-11.2.0-py3-none-any.whl (555 kB)\n",
      "Collecting stone>=2.*\n",
      "  Downloading stone-3.2.1-py3-none-any.whl (160 kB)\n",
      "Requirement already satisfied: requests>=2.16.2 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from dropbox) (2.24.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from dropbox) (1.12.0)\n",
      "Requirement already satisfied: ply>=3.4 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from stone>=2.*->dropbox) (3.11)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from requests>=2.16.2->dropbox) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from requests>=2.16.2->dropbox) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from requests>=2.16.2->dropbox) (2020.6.20)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\31653\\anaconda3\\lib\\site-packages (from requests>=2.16.2->dropbox) (1.25.11)\n",
      "Installing collected packages: stone, dropbox\n",
      "Successfully installed dropbox-11.2.0 stone-3.2.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install dropbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "\n",
    "import dropbox\n",
    "from dropbox import DropboxOAuth2FlowNoRedirect\n",
    "\n",
    "'''\n",
    "Populate your app key in order to run this locally\n",
    "'''\n",
    "APP_KEY = \n",
    "\n",
    "auth_flow = DropboxOAuth2FlowNoRedirect(APP_KEY, use_pkce=True, token_access_type='offline')\n",
    "\n",
    "authorize_url = auth_flow.start()\n",
    "print(\"1. Go to: \" + authorize_url)\n",
    "print(\"2. Click \\\"Allow\\\" (you might have to log in first).\")\n",
    "print(\"3. Copy the authorization code.\")\n",
    "auth_code = input(\"Enter the authorization code here: \").strip()\n",
    "\n",
    "try:\n",
    "    oauth_result = auth_flow.finish(auth_code)\n",
    "except Exception as e:\n",
    "    print('Error: %s' % (e,))\n",
    "    exit(1)\n",
    "\n",
    "with dropbox.Dropbox(oauth2_refresh_token=oauth_result.refresh_token, app_key=APP_KEY) as dbx:\n",
    "    dbx.users_get_current_account()\n",
    "    print(\"Successfully set up client!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Copying files with shutil\n",
    "source: https://towardsdatascience.com/copy-files-python-ad17998264db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['demo.txt', 'XML_file.txt', 'requirements.txt', 'plantuml_complex2.txt', 'servers.txt', 'leegmelden_plantuml.txt', 'NOAA_data.txt', 'accounts2.txt', 'untitled.txt', 'accounts.txt', 'plantuml.txt', 'a_file.txt', 'plantuml_complex.txt']\n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "my_txts = glob('*.txt')\n",
    "print(my_txts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new dir\n",
    "import os\n",
    "os.mkdir('test')\n",
    "#os. rmdir('test') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'test/accounts.txt'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shutil\n",
    "# Copy file example.txt into a new file called example_copy.txt\n",
    "#shutil.copy('accounts.txt', 'accounts_copy.txt')\n",
    "# Copy file example.txt into directory test/\n",
    "shutil.copy('accounts.txt', 'test/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/test/accounts.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#os. remove('accounts_copy.txt')\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/test/accounts.txt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/test/accounts.txt'"
     ]
    }
   ],
   "source": [
    "#and use this to remove the item\n",
    "import os\n",
    "#os. remove('accounts_copy.txt')\n",
    "os.remove('/test/accounts.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\31653\\\\Desktop\\\\DeAlliantie\\\\wsdl\\\\ax-woningnet.wsdl'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just put r before your normal string it converts normal string to raw string:\n",
    "import shutil\n",
    "shutil.copy(r'C:\\Users\\31653\\Desktop\\DeAlliantie\\ax-woningnet.wsdl', r'C:\\Users\\31653\\Desktop\\DeAlliantie\\wsdl\\ax-woningnet.wsdl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. PS Util to get things like battery status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.2\n",
      "sbattery(percent=56, secsleft=6840, power_plugged=False)\n"
     ]
    }
   ],
   "source": [
    "# a script to get data on CPU usage and battery status\n",
    "import psutil\n",
    "# gives a single float value\n",
    "print(psutil.cpu_percent())\n",
    "print(psutil.sensors_battery())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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