Adam Golinski talesa

## fill_spreadsheet.html
<!DOCTYPE html>
<html>
  <head>
    <title>Google Sheets API Quickstart</title>
    <meta charset='utf-8' />

    <!-- <link rel="stylesheet" type="text/css" href="./mystyle.css"> -->
  </head>
  <body>
    <p>Google Sheets API Quickstart</p>

## maze.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>

body {
  background: #000;
}

</style>
<body>

## foppl_temp.clj
(let [[G E]
      (foppl-query
        (defn loopi [i mu] (assoc mu (+ i 1) (sample (normal (get mu i) 1))))
        (loop 4 (vector 1 1 1 1 1) loopi))]
  (print-graph G)
  (:body E))

;;; Vertices V: #{x38089 x38096 x38075 x38082}
;;;
;;; Arcs A: #{}

## profiles.clj
{:user
 {:plugins [[lein-exec "0.3.6"]
            [lein-gorilla "0.4.0"]]
  :dependencies [[proto-repl "0.3.1"]]}}

## adjacency-list-to-adjacency-matrix.py

# coding: utf-8

# Importing libraries

# In[1]:


import pandas as pd
import numpy as np

## notebook.ipynb

      
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                talesa
                / notebook.ipynb
            
            
              Created
              January 1, 2018 23:44
            
          
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## pump_dump.py
# you have to execute this code line by line so use jupyter notebook or hydrogen inside atom editor

# import libraries
import ccxt
from datetime import datetime

# create exchange API handle
exchange = getattr(ccxt, 'binance')()
# paste in your API key and secret here (if you're afraid they're gonna get stolen, inspect the ccxt library open source code on github)
exchange.apiKey = ''

## check_balance.py
import asyncio
import ccxt.async as ccxt
from collections import defaultdict

exchanges = list()
exchanges.append(('binance', apiKey, secret))
exchanges.append(('livecoin', apiKey, secret))

tasks = list()
tasks += [ccxt.coinmarketcap({'timeout':100000}).fetch_tickers(params={'limit':0})]

## b14_change_of_variables.py
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

import scipy.special as special

import scipy

plt.rcParams["figure.figsize"] = (16,10)

## minimize_kl_between_distributions.py
import torch
from torch import tensor
import torch.distributions as td
import torch.optim as optim
import matplotlib.pyplot as plt
from torch import isinf, isnan
from tqdm import tqdm as tqdm
import numpy as np
import scipy.stats
	<!DOCTYPE html>
	<html>
	<head>
	<title>Google Sheets API Quickstart</title>
	<meta charset='utf-8' />

	<!-- <link rel="stylesheet" type="text/css" href="./mystyle.css"> -->
	</head>
	<body>
	<p>Google Sheets API Quickstart</p>
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>

	body {
	background: #000;
	}

	</style>
	<body>
	(let [[G E]
	(foppl-query
	(defn loopi [i mu] (assoc mu (+ i 1) (sample (normal (get mu i) 1))))
	(loop 4 (vector 1 1 1 1 1) loopi))]
	(print-graph G)
	(:body E))

	;;; Vertices V: #{x38089 x38096 x38075 x38082}
	;;;
	;;; Arcs A: #{}
	{:user
	{:plugins [[lein-exec "0.3.6"]
	[lein-gorilla "0.4.0"]]
	:dependencies [[proto-repl "0.3.1"]]}}

	# coding: utf-8

	# Importing libraries

	# In[1]:


	import pandas as pd
	import numpy as np
	# you have to execute this code line by line so use jupyter notebook or hydrogen inside atom editor

	# import libraries
	import ccxt
	from datetime import datetime

	# create exchange API handle
	exchange = getattr(ccxt, 'binance')()
	# paste in your API key and secret here (if you're afraid they're gonna get stolen, inspect the ccxt library open source code on github)
	exchange.apiKey = ''
	import asyncio
	import ccxt.async as ccxt
	from collections import defaultdict

	exchanges = list()
	exchanges.append(('binance', apiKey, secret))
	exchanges.append(('livecoin', apiKey, secret))

	tasks = list()
	tasks += [ccxt.coinmarketcap({'timeout':100000}).fetch_tickers(params={'limit':0})]
	import matplotlib
	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.stats as stats

	import scipy.special as special

	import scipy

	plt.rcParams["figure.figsize"] = (16,10)
	import torch
	from torch import tensor
	import torch.distributions as td
	import torch.optim as optim
	import matplotlib.pyplot as plt
	from torch import isinf, isnan
	from tqdm import tqdm as tqdm
	import numpy as np
	import scipy.stats