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

## csv2pdf.py
import csv
from reportlab.platypus import Paragraph,SimpleDocTemplate
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.graphics.charts.piecharts import Pie
from reportlab.lib.colors import brown,blue
from reportlab.graphics.charts.legends import Legend
from reportlab.graphics.shapes import Drawing
from reportlab.lib.colors import black

# THIS FUNCTION CALCULATES THE PERCENTILE

## 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
	'''
	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
	import csv
	from reportlab.platypus import Paragraph,SimpleDocTemplate
	from reportlab.lib.styles import getSampleStyleSheet
	from reportlab.graphics.charts.piecharts import Pie
	from reportlab.lib.colors import brown,blue
	from reportlab.graphics.charts.legends import Legend
	from reportlab.graphics.shapes import Drawing
	from reportlab.lib.colors import black

	# THIS FUNCTION CALCULATES THE PERCENTILE
	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