Moazam mzmmoazam

## gprmax_colab_template.ipynb

      
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                mzmmoazam
                / gprmax_colab_template.ipynb
            
            
              Created
              April 23, 2021 08:46
            
              
                gprMax_colab_template.ipynb
              
          
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## youtube_in_vlc.py
'''
Well, it is to play youtube song playlists in your laptop (VLC).
you will say why would I do, it is much better
Try it

prerequisites

add vlc to your PATH
pip install pytube3

## Nthmin.py
def Nthmin(list, n):
    piot = list[0]
    min_list, max_list = [], []
    for i in range(1, len(list)):
        if list[i] < piot:
            min_list.append(list[i])
        else:
            max_list.append(list[i])
    if len(min_list) >= n and len(min_list) != 0:
        return Nthmin(min_list, n)

## file_traversal.py
from os import listdir,sep
from os.path import isfile,isdir,split,join

class Traversal(object):
    def __init__(self,starting_node,dfs=True,bfs=False):
        '''

        :param starting_node: give absolute path of the directory to be travesed
        :param dfs: set True for Debth first traversal ; default : True
        :param bfs: set true for breadth first traversal ; default : False

## formated_data.csv

          
            0
            7
            5
            mar
            fri
            86.2
            26.2
            94.3
            5.1
            8.2
            51
            6.7
            0.0
            0.0

            
              1
              7
              4
              oct
              tue
              90.6
              35.4
              669.1
              6.7
              18.0
              33
              0.9
              0.0
              0.0

            
              2
              7
              4
              oct
              sat
              90.6
              43.7
              686.9
              6.7
              14.6
              33
              1.3
              0.0
              0.0

            
              3
              8
              6
              mar
              fri
              91.7
              33.3
              77.5
              9.0
              8.3
              97
              4.0
              0.2
              0.0

            
              4
              8
              6
              mar
              sun
              89.3
              51.3
              102.2
              9.6
              11.4
              99
              1.8
              0.0
              0.0

            
              5
              8
              6
              aug
              sun
              92.3
              85.3
              488.0
              14.7
              22.2
              29
              5.4
              0.0
              0.0

            
              6
              8
              6
              aug
              mon
              92.3
              88.9
              495.6
              8.5
              24.1
              27
              3.1
              0.0
              0.0

            
              7
              8
              6
              aug
              mon
              91.5
              145.4
              608.2
              10.7
              8.0
              86
              2.2
              0.0
              0.0

            
              8
              8
              6
              sep
              tue
              91.0
              129.5
              692.6
              7.0
              13.1
              63
              5.4
              0.0
              0.0

            
              9
              7
              5
              sep
              sat
              92.5
              88.0
              698.6
              7.1
              22.8
              40
              4.0
              0.0
              0.0

## naive_bayes.py
import csv, random


class NaiveBayes(object):
    def __init__(self, filename, split_ratio):
        '''

        :param filename: a csv filename with absolute or full path
        :param split_ratio: test to train ratio
        '''

## Turkish_2_Eng.py
import Algorithmia, speech_recognition as sr, pyttsx

# Algorthmia Api key and end point
client = Algorithmia.client('simCM+BqJzKSw/iMrQsR/OTRwVE1')
algo = client.algo('translation/GoogleTranslate/0.1.1')

# pyttsx engine config
speech_engine = pyttsx.init('sapi5')  # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
speech_engine.setProperty('rate', 150)

## Eng_to_turkish.py
import speech_recognition as sr, pyttsx
from googletrans import Translator

# pyttsx engine config
speech_engine = pyttsx.init('sapi5')  # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
speech_engine.setProperty('rate', 150)


# translator object
translator = Translator()

## hack_openai.py
import gym
import random
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.data_utils import to_categorical
from tflearn.layers.estimator import regression
from statistics import median, mean
from collections import Counter
import glob

## url_tree.py
import requests
from urllib.parse import urlparse,urljoin
from bs4 import BeautifulSoup

all_urls = set()

class url_tree():
    def __init__(self, base_url,page_no=1 ,children=None):
        self.url = base_url
        self.page_no =page_no
	'''
	Well, it is to play youtube song playlists in your laptop (VLC).
	you will say why would I do, it is much better
	Try it

	prerequisites

	add vlc to your PATH
	pip install pytube3
	def Nthmin(list, n):
	piot = list[0]
	min_list, max_list = [], []
	for i in range(1, len(list)):
	if list[i] < piot:
	min_list.append(list[i])
	else:
	max_list.append(list[i])
	if len(min_list) >= n and len(min_list) != 0:
	return Nthmin(min_list, n)
	from os import listdir,sep
	from os.path import isfile,isdir,split,join

	class Traversal(object):
	def __init__(self,starting_node,dfs=True,bfs=False):
	'''

	:param starting_node: give absolute path of the directory to be travesed
	:param dfs: set True for Debth first traversal ; default : True
	:param bfs: set true for breadth first traversal ; default : False
0	7	5	mar	fri	86.2	26.2	94.3	5.1	8.2	51	6.7	0.0
1	7	4	oct	tue	90.6	35.4	669.1	6.7	18.0	33	0.9	0.0
2	7	4	oct	sat	90.6	43.7	686.9	6.7	14.6	33	1.3	0.0
3	8	6	mar	fri	91.7	33.3	77.5	9.0	8.3	97	4.0	0.2
4	8	6	mar	sun	89.3	51.3	102.2	9.6	11.4	99	1.8	0.0
5	8	6	aug	sun	92.3	85.3	488.0	14.7	22.2	29	5.4	0.0
6	8	6	aug	mon	92.3	88.9	495.6	8.5	24.1	27	3.1	0.0
7	8	6	aug	mon	91.5	145.4	608.2	10.7	8.0	86	2.2	0.0
8	8	6	sep	tue	91.0	129.5	692.6	7.0	13.1	63	5.4	0.0
9	7	5	sep	sat	92.5	88.0	698.6	7.1	22.8	40	4.0	0.0
	import csv, random


	class NaiveBayes(object):
	def __init__(self, filename, split_ratio):
	'''

	:param filename: a csv filename with absolute or full path
	:param split_ratio: test to train ratio
	'''
	import Algorithmia, speech_recognition as sr, pyttsx

	# Algorthmia Api key and end point
	client = Algorithmia.client('simCM+BqJzKSw/iMrQsR/OTRwVE1')
	algo = client.algo('translation/GoogleTranslate/0.1.1')

	# pyttsx engine config
	speech_engine = pyttsx.init('sapi5') # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
	speech_engine.setProperty('rate', 150)
	import speech_recognition as sr, pyttsx
	from googletrans import Translator

	# pyttsx engine config
	speech_engine = pyttsx.init('sapi5') # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
	speech_engine.setProperty('rate', 150)


	# translator object
	translator = Translator()
	import gym
	import random
	import numpy as np
	import tflearn
	from tflearn.layers.core import input_data, dropout, fully_connected
	from tflearn.data_utils import to_categorical
	from tflearn.layers.estimator import regression
	from statistics import median, mean
	from collections import Counter
	import glob
	import requests
	from urllib.parse import urlparse,urljoin
	from bs4 import BeautifulSoup

	all_urls = set()

	class url_tree():
	def __init__(self, base_url,page_no=1 ,children=None):
	self.url = base_url
	self.page_no =page_no