misc python code
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ys
331
code/misc/python/scripts/DigitalPaper Management.ipynb
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331
code/misc/python/scripts/DigitalPaper Management.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import os\n",
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"\n",
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"from dptrp1.dptrp1 import DigitalPaper\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"password = getpass.getpass()\n",
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"command = \"sudo -S sudo chmod 666 /dev/ttyACM0\" #can be any command but don't forget -S as it enables input from stdin\n",
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"os.system('echo %s | %s' % (password, command))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/dev/ttyACM0\n"
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]
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}
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],
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"source": [
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"import serial\n",
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"ser = serial.Serial('/dev/ttyACM0') # open serial port\n",
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"print(ser.name) # check which port was really used\n",
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"ser.write(b'\\x01\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x04') # write a string b\"\\x01\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x04\"\n",
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"ser.close() # close port\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"client_id=''\n",
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"key=''\n",
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"SYNC_DIR = '/home/dl92/Downloads/DigitalPaper/'\n",
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"def connect(address=''):\n",
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" \"\"\"\n",
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" Loads the key and client ID to authenticate with the DPT-RP1\n",
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" \"\"\"\n",
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" with open('/home/dl92/.dpt-client.txt', 'r') as f:\n",
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" client_id = f.readline().strip()\n",
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"\n",
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" with open('/home/dl92/.dpt-key.txt', 'r') as f:\n",
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" key = f.read()\n",
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"\n",
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" dpt = DigitalPaper(address)\n",
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" dpt.authenticate(client_id, key)\n",
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" return dpt\n",
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"\n",
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"def sync(dpt):\n",
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" \"\"\"\n",
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" Given an authenticated DigitalPaper instance, download all note files to a\n",
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" specified directory.\n",
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" \"\"\"\n",
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" for doc in [f for f in dpt.list_documents() if (is_modified_note(f) and is_ReadingFolder(f)) ]:\n",
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" #docpath=os.path.dirname(doc['entry_path'])\n",
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" #if docpath=='Document/Reading' :\n",
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" data = dpt.download(doc['entry_path'])\n",
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" local_path = SYNC_DIR + os.path.basename(doc['entry_path'])\n",
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" with open(local_path, 'wb') as f:\n",
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" f.write(data)\n",
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" print('Saved {} to {}'.format(doc['entry_path'], local_path))\n",
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" \n",
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"def is_modified_note(doc):\n",
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" import dateparser\n",
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" if doc['document_type'] == 'note' or doc['document_type'] == 'normal':\n",
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" local_path = SYNC_DIR + os.path.basename(doc['entry_path'])\n",
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" \n",
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" if not os.path.exists(local_path):\n",
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" return True\n",
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" else:\n",
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" #print (local_path,doc['modified_date'], os.path.getmtime(local_path), dateparser.parse(doc['modified_date']).timestamp())\n",
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" return os.path.getmtime(local_path) < dateparser.parse(doc['modified_date']).timestamp()\n",
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" \n",
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"def is_ReadingFolder(doc):\n",
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" docpath=os.path.dirname(doc['entry_path'])\n",
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" if docpath=='Document/Reading':\n",
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" return True\n",
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" else:\n",
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" return False\n",
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" \n",
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"\n",
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"\n",
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"def upload_overwrite(dpt,localpath, remotepath='Document/Reading'):\n",
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" import glob, os\n",
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" \n",
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" files= glob.glob(localpath+'/*.pdf')\n",
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" for f in files:\n",
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" print (f)\n",
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" dpt.upload_file(f,remotepath)\n",
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" \n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"dpt=connect('192.168.0.131')\n",
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"\n",
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"\n",
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"#dpt=connect('https://192.168.0.13')\n",
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"#dpt=connect('22:6f:5d:46:3f:16')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'device_color': '#ffffff',\n",
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" 'model_name': 'DPT-RP1',\n",
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" 'serial_number': '5021254',\n",
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" 'sku_code': 'U'}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dpt.get_info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"sync(dpt)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/home/dl92/Downloads/DigitalPaper/Upload/Kevin P. Murphy_Machine Learning_ A Probabilistic Perspective.pdf\n",
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"/home/dl92/Downloads/DigitalPaper/Upload/John D. Kelleher_Deep Learning.pdf\n",
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"/home/dl92/Downloads/DigitalPaper/Upload/Andriy Burkov_The Hundred-Page Machine Learning Book.pdf\n",
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"/home/dl92/Downloads/DigitalPaper/Upload/John D. Kelleher_Fundamentals of Machine Learning for Predictive Data Analytics_ Algorithms, Worked Examples, and Case Studies.pdf\n"
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]
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}
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],
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"source": [
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"UPLOAD_DIR='/home/dl92/Downloads/DigitalPaper/Upload/'\n",
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"upload_overwrite(dpt,UPLOAD_DIR)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'health': 'good',\n",
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" 'icon_type': 'level_bar_4',\n",
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" 'level': '95',\n",
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" 'pen': '100',\n",
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" 'plugged': 'not_plugged',\n",
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" 'status': 'discharging'}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dpt.get_battery()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"dpt.new_folder('Document/Work')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'pypdf'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-29-913447a672fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpypdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pypdf'"
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]
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}
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],
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"source": [
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"import pypdf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"rate=0.33\n",
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"price=394\n",
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"motherboardasset=price-2*price*rate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"rate=0.33\n",
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"price=216\n",
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"powersupplyasset=price-2*price*rate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"73.44"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"powersupplyasset\n",
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"#motherboardasset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"celltoolbar": "Attachments",
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"kernelspec": {
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"display_name": "Python 3.8.10 ('General')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"vscode": {
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"interpreter": {
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"hash": "97f3fdd28b09b8a55d08fd0c4c2cf066e1a4715931f0fa71c8f74dcb74701738"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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