<jupyter_start><jupyter_text>Mount Microsft One Drive in Google Colab 1. Download & Install Latest Setuphttps://rclone.org/downloads/ in terminal:rclone authorize "onedrive"<jupyter_code>!wget https://downloads.rclone.org/v1.55.1/rclone-v1.55.1-linux-amd64.deb
!apt install ./rclone-v1.55.1-linux-amd64.deb<jupyter_output>--2021-05-24 20:31:41--  https://downloads.rclone.org/v1.55.1/rclone-v1.55.1-linux-amd64.deb
Resolving downloads.rclone.org (downloads.rclone.org)... 172.16.31.10, fdf8:f53e:61e4::18
Connecting to downloads.rclone.org (downloads.rclone.org)|172.16.31.10|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 14707686 (14M) [application/x-debian-package]
Saving to: ‘rclone-v1.55.1-linux-amd64.deb’

rclone-v1.55.1-linu 100%[===================>]  14.03M  15.8MB/s    in 0.9s    

2021-05-24 20:31:42 (15.8 MB/s) - ‘rclone-v1.55.1-linux-amd64.deb’ saved [14707686/14707686]

Reading package lists... Done
Building dependency tree       
Reading state information... Done
Note, selecting 'rclone' instead of './rclone-v1.55.1-linux-amd64.deb'
The following package was automatically installed and is no longer required:
  libnvidia-common-460
Use 'apt autoremove' to remove it.
The following NEW packages will be installed:
  rclone
0 upgraded, 1 newly installed, 0 to remove and 34 [...]<jupyter_text>2. Authenticate One Drive<jupyter_code>!rclone config<jupyter_output>2021/05/24 20:31:49 NOTICE: Config file "/root/.config/rclone/rclone.conf" not found - using defaults
No remotes found - make a new one
n) New remote
s) Set configuration password
q) Quit config
n/s/q> n
name> onedrive
Type of storage to configure.
Enter a string value. Press Enter for the default ("").
Choose a number from below, or type in your own value
[91m 1 / 1Fichier
   \ "fichier"
[0m[92m 2 / Alias for an existing remote
   \ "alias"
[0m[91m 3 / Amazon Drive
   \ "amazon cloud drive"
[0m[92m 4 / Amazon S3 Compliant Storage Providers including AWS, Alibaba, Ceph, Digital Ocean, Dreamhost, IBM COS, Minio, and Tencent COS
   \ "s3"
[0m[91m 5 / Backblaze B2
   \ "b2"
[0m[92m 6 / Box
   \ "box"
[0m[91m 7 / Cache a remote
   \ "cache"
[0m[92m 8 / Citrix Sharefile
   \ "sharefile"
[0m[91m 9 / Compress a remote
   \ "compress"
[0m[92m10 / Dropbox
   \ "dropbox"
[0m[91m11 / Encrypt/Decrypt a remote
   \ "crypt"
[0m[92m12 / Enterprise File Fabric
   \ "filefabric"
[...]<jupyter_text>3. Mount One DriveTo stream files we need to mount One Drive.<jupyter_code>#!sudo mkdir /content/onedrive
#!nohup rclone --vfs-cache-mode writes mount onedrive: /content/nnguyen19 &

!nohup rclone --vfs-cache-mode writes mount onedrive: /content/onedrive &
%cd /content/onedrive/

%ls<jupyter_output>[0m[01;34mAttachments[0m/
'<NAME>, <NAME>, <NAME> - Metabolic Engineering_ Principles and Methodologies-Academic Press (1998).pdf'
 [01;34mimanis-hr-paperwork[0m/
 [01;34mproteinE[0m/
 [01;34mprotocols[0m/
[01;34m'R&D Nhan Nguyen'[0m/
 [01;34mRis-math[0m/
 [01;34msafety-procedures[0m/
 [01;34mseminar-recording[0m/
 test.txt
 [01;34mtryme_nn[0m/
'UNITEDHEALTHCARE CHOICE PLUS HDHP 1500.docx'
 [01;34mWyzant[0m/<jupyter_text>4. Protein database downloadupload 'download_proteins.sh' script<jupyter_code>!bash /content/download_proteins.sh
%cd /content/
!sudo mkdir /content/data_file
!wget http://s3.amazonaws.com/proteindata/data_pytorch/secondary_structure.tar.gz
!tar -xzf secondary_structure.tar.gz -C ./data_file
!wget http://s3.amazonaws.com/proteindata/data_pytorch/proteinnet.tar.gz
!wget http://s3.amazonaws.com/proteindata/data_pytorch/remote_homology.tar.gz
!wget http://s3.amazonaws.com/proteindata/data_pytorch/fluorescence.tar.gz
!wget http://s3.amazonaws.com/proteindata/data_pytorch/stability.tar.gz


!tar -xzf proteinnet.tar.gz -C ./data_file
!tar -xzf remote_homology.tar.gz -C ./data_file
!tar -xzf fluorescence.tar.gz -C ./data_file
!tar -xzf stability.tar.gz -C ./data_file
!mv data_file/secondary_structure/ onedrive/proteinE/
!mv data_file/ onedrive/proteinE/<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1466535433070866, 'fraction_numerical': 0.07283464566929133, 'mean_word_length': 4.341655716162943, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 20, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 2, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 11, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '383776', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1806, 'n_tokens_neox': 1574, 'n_words': 352, 'path': 'Protein_engineering_process.ipynb', 'repo_name': 'nnguyen19/ECNet', 'size': '4102'}