A.G. antiface

## peakdet.m
function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
%        [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
%        maxima and minima ("peaks") in the vector V.
%        MAXTAB and MINTAB consists of two columns. Column 1
%        contains indices in V, and column 2 the found values.
%
%        With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
%        in MAXTAB and MINTAB are replaced with the corresponding
%        X-values.

## pixel-image-generator.py
'''
Created by Monica Pabin. Used in the Twitter bot @pixelbotart, also created by her.
This program generates random 'pixel art' using the below parameters.
It will save the image to the same directory the program resides as 'pixel_img.png'
'''

# an example of what to expect from the parameters:
# given: max_num_colors = 10; min_size = 200; max_size = 250
# output: an image anywhere between 200 and 250 pixels large, using up to 10 random colors

## matplotlib_randnumber plot.py
import matplotlib.pyplot as plt
import numpy as np
x=np.random.randn(10000)
plt.hist(x,100)
plt.title(r'Normal Distribution with $\mu = 0 and \sigma = 1$ ')
plt.savefig('matplotlib_histogram.png')
plt.show()

## normal_distribution.py
import math
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats


mean = 0
variance = 1
sigma = math.sqrt(variance)

## helloevolve.py
"""
helloevolve.py implements a genetic algorithm that starts with a base
population of randomly generated strings, iterates over a certain number of
generations while implementing 'natural selection', and prints out the most fit
string.

The parameters of the simulation can be changed by modifying one of the many
global variables. To change the "most fit" string, modify OPTIMAL. POP_SIZE
controls the size of each generation, and GENERATIONS is the amount of
generations that the simulation will loop through before returning the fittest

## TwitterGraphs.ipynb

      
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                antiface
                / TwitterGraphs.ipynb
            
            
              Created
              May 31, 2013 20:34
                — forked from fperez/TwitterGraphs.ipynb
            
          
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## snakecoin-server-full-code.py
from flask import Flask
from flask import request
import json
import requests
import hashlib as hasher
import datetime as date
node = Flask(__name__)

# Define what a Snakecoin block is
class Block:

## snakecoin.py
import hashlib as hasher
import datetime as date

# Define what a Snakecoin block is
class Block:
  def __init__(self, index, timestamp, data, previous_hash):
    self.index = index
    self.timestamp = timestamp
    self.data = data
    self.previous_hash = previous_hash

## EnterSourceCode.py
# [ENTER_SOURCE_CODE]
# A.G. (c) 2017. All Rights Reserved.
# January 4th, 2017

import time

class EnterSourceCode:
	def __init__(self, time, filename, sourcecode, category):
		self.time = time
		self.filename = filename

## lucas_lehmer.py

# very simple Mersenne prime search.
# AW2016-01-23

import time
import math

# http://stackoverflow.com/questions/16004407/a-fast-prime-number-sieve-in-python
def prime_sieve(n):
    size = n//2
	function [maxtab, mintab]=peakdet(v, delta, x)
	%PEAKDET Detect peaks in a vector
	% [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
	% maxima and minima ("peaks") in the vector V.
	% MAXTAB and MINTAB consists of two columns. Column 1
	% contains indices in V, and column 2 the found values.
	%
	% With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices
	% in MAXTAB and MINTAB are replaced with the corresponding
	% X-values.
	'''
	Created by Monica Pabin. Used in the Twitter bot @pixelbotart, also created by her.
	This program generates random 'pixel art' using the below parameters.
	It will save the image to the same directory the program resides as 'pixel_img.png'
	'''

	# an example of what to expect from the parameters:
	# given: max_num_colors = 10; min_size = 200; max_size = 250
	# output: an image anywhere between 200 and 250 pixels large, using up to 10 random colors
	import matplotlib.pyplot as plt
	import numpy as np
	x=np.random.randn(10000)
	plt.hist(x,100)
	plt.title(r'Normal Distribution with $\mu = 0 and \sigma = 1$ ')
	plt.savefig('matplotlib_histogram.png')
	plt.show()
	import math
	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.stats


	mean = 0
	variance = 1
	sigma = math.sqrt(variance)
	"""
	helloevolve.py implements a genetic algorithm that starts with a base
	population of randomly generated strings, iterates over a certain number of
	generations while implementing 'natural selection', and prints out the most fit
	string.

	The parameters of the simulation can be changed by modifying one of the many
	global variables. To change the "most fit" string, modify OPTIMAL. POP_SIZE
	controls the size of each generation, and GENERATIONS is the amount of
	generations that the simulation will loop through before returning the fittest
	from flask import Flask
	from flask import request
	import json
	import requests
	import hashlib as hasher
	import datetime as date
	node = Flask(__name__)

	# Define what a Snakecoin block is
	class Block:
	# [ENTER_SOURCE_CODE]
	# A.G. (c) 2017. All Rights Reserved.
	# January 4th, 2017

	import time

	class EnterSourceCode:
	def __init__(self, time, filename, sourcecode, category):
	self.time = time
	self.filename = filename

	# very simple Mersenne prime search.
	# AW2016-01-23

	import time
	import math

	# http://stackoverflow.com/questions/16004407/a-fast-prime-number-sieve-in-python
	def prime_sieve(n):
	size = n//2