Yassine Alouini yassineAlouini

## denoise.py
import soundfile as sf
import numpy as np
import librosa
import noisereduce as nr

# If you don't have the libraries above:
# pip install noisereduce librosa numpy soundfile

# Run ffmpeg first to extract the sound from the video
# ffmpeg -i 'noisy_video.mp4' -vn -acodec copy 'noisy_signal.aac'

## day15.py
def main():
    import networkx as nx
    import numpy as np
    data = np.loadtxt("/home/yassinealouini/Documents/code/advent_of_code/aoc/year_2021/data/15.txt", "U")
    size = int(len(data[0]))
    data = data.view('U1').astype(int).reshape(data.shape[0], size)
    # Need digraph since moving from previous to next position is not the same as the reverse.
    G = nx.DiGraph()

    def new_grid(data):

## bfs_shortest_path.py
# Simple bfs implementation to explore a graph.
# An undirected graph as an adjancency representation.


from collections import deque

G = {0: (1, 2), 1: (0, 6), 6: (1, 3), 3: (2, 4, 6), 5: (2, ), 2: (5, 0, 3), 4: (3, )}
source = 6
end = 2

## aoc_24_2021.py
import math
import numpy as np
data = open("/home/yassinealouini/Documents/code/advent_of_code/aoc/year_2021/data/24.txt").read().rstrip()


OPERATIONS = {"add": lambda x: sum(x), "mul": lambda x: math.prod(x),
              "div": lambda x: x[0] // x[1], "mod": lambda x: x[0] % x[1],
              "inp": lambda x: x[1],
              "eql": lambda x: int(x[0] == x[1])}

## install_cuda.sh
# From https://heads0rtai1s.github.io/2021/02/25/gpu-setup-r-python-ubuntu/


# Fresh install
sudo apt-get --purge remove "*cublas*" "*cufft*" "*curand*" "*cusolver*" "*cusparse*" "*npp*" "*nvjpeg*" "cuda*" "nsight*"
sudo apt-get --purge remove "*nvidia*"
sudo apt-get autoremove

# Drivers
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin

## model.py
from sklearn import svm
from sklearn.datasets import load_iris
from joblib import dump
from joblib import load
import sys
import os
import pathlib
import pandas as pd

TRAIN = False

## total_variations.py
def get_input_df():

    df = pd.DataFrame({"date": pd.date_range("2017-1-1", "2020-1-1", freq="1D")})
    df["open"] = np.random.choice([1, 0], len(df))
    df["closed"] = np.random.choice([1, 0], len(df))
    df["location"] = np.random.choice(["a", "b", "c"], len(df))
    return df

def compute_total_variations_df(df):
    df = df.copy()

## remote_jupyter.sh
alias port_forward='nohup ssh -N -f -L localhost:8889:localhost:8889 deepmeaning'
alias remote_notebook_start='nohup ssh -f deepmeaning "jupyter notebook --no-browser --port=8889"; port_forward'
alias remote_notebook_stop='ssh deepmeaning "pkill -u yassinealouini jupyter"'

## df_csv_bz2_to_s3.py
import s3fs
import pandas as pd
import bz2file
s3 = s3fs.S3FileSystem()

df = pd.DataFrame() # Replace with your DataFrame
dest_s3_key = "" # Where to save in S3

with s3.open(dest_s3_key, 'wb') as dest_file:
  with bz2file.open(dest_file, 'wb') as compressed_file:

## upload_image.html
<!doctype html>
  <title>Upload</title>
  <h1>Upload an image to recognize</h1>
  <form method=post enctype=multipart/form-data action="{{url_for('upload_img')}}">
  <p><input type=file name=file> <input type=submit value=Upload> </p>
</form>
	import soundfile as sf
	import numpy as np
	import librosa
	import noisereduce as nr

	# If you don't have the libraries above:
	# pip install noisereduce librosa numpy soundfile

	# Run ffmpeg first to extract the sound from the video
	# ffmpeg -i 'noisy_video.mp4' -vn -acodec copy 'noisy_signal.aac'
	def main():
	import networkx as nx
	import numpy as np
	data = np.loadtxt("/home/yassinealouini/Documents/code/advent_of_code/aoc/year_2021/data/15.txt", "U")
	size = int(len(data[0]))
	data = data.view('U1').astype(int).reshape(data.shape[0], size)
	# Need digraph since moving from previous to next position is not the same as the reverse.
	G = nx.DiGraph()

	def new_grid(data):
	# Simple bfs implementation to explore a graph.
	# An undirected graph as an adjancency representation.


	from collections import deque

	G = {0: (1, 2), 1: (0, 6), 6: (1, 3), 3: (2, 4, 6), 5: (2, ), 2: (5, 0, 3), 4: (3, )}
	source = 6
	end = 2
	import math
	import numpy as np
	data = open("/home/yassinealouini/Documents/code/advent_of_code/aoc/year_2021/data/24.txt").read().rstrip()


	OPERATIONS = {"add": lambda x: sum(x), "mul": lambda x: math.prod(x),
	"div": lambda x: x[0] // x[1], "mod": lambda x: x[0] % x[1],
	"inp": lambda x: x[1],
	"eql": lambda x: int(x[0] == x[1])}
	# From https://heads0rtai1s.github.io/2021/02/25/gpu-setup-r-python-ubuntu/


	# Fresh install
	sudo apt-get --purge remove "cublas" "cufft" "curand" "cusolver" "cusparse" "npp" "nvjpeg" "cuda" "nsight"
	sudo apt-get --purge remove "nvidia"
	sudo apt-get autoremove

	# Drivers
	wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
	from sklearn import svm
	from sklearn.datasets import load_iris
	from joblib import dump
	from joblib import load
	import sys
	import os
	import pathlib
	import pandas as pd

	TRAIN = False
	def get_input_df():

	df = pd.DataFrame({"date": pd.date_range("2017-1-1", "2020-1-1", freq="1D")})
	df["open"] = np.random.choice([1, 0], len(df))
	df["closed"] = np.random.choice([1, 0], len(df))
	df["location"] = np.random.choice(["a", "b", "c"], len(df))
	return df

	def compute_total_variations_df(df):
	df = df.copy()
	alias port_forward='nohup ssh -N -f -L localhost:8889:localhost:8889 deepmeaning'
	alias remote_notebook_start='nohup ssh -f deepmeaning "jupyter notebook --no-browser --port=8889"; port_forward'
	alias remote_notebook_stop='ssh deepmeaning "pkill -u yassinealouini jupyter"'
	import s3fs
	import pandas as pd
	import bz2file
	s3 = s3fs.S3FileSystem()

	df = pd.DataFrame() # Replace with your DataFrame
	dest_s3_key = "" # Where to save in S3

	with s3.open(dest_s3_key, 'wb') as dest_file:
	with bz2file.open(dest_file, 'wb') as compressed_file:
	<!doctype html>
	<title>Upload</title>
	<h1>Upload an image to recognize</h1>
	<form method=post enctype=multipart/form-data action="{{url_for('upload_img')}}">
	<p><input type=file name=file> <input type=submit value=Upload> </p>
	</form>