#!/usr/bin/env python
# coding: utf-8

# ## 51% attack
# 
# 

# In[2]:


import requests
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
plt.style.use('seaborn-deep')
get_ipython().run_line_magic('matplotlib', 'inline')
get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")


# In[3]:


def scrape_table():
    """
    Scrapes crytpo51 table
    :return: pandas dataframe
    """
    r = requests.get("https://www.crypto51.app/")
    soup = BeautifulSoup(r.text, "lxml")
    data = []
    headers = []
    table = soup.find('table')
    column_names = table.find('thead').find_all('th')
    headers = [ele.text.strip() for ele in column_names]
    rows = table.find('tbody').find_all('tr')
    for row in rows:
        data.append([i.text.strip() for i in row.find_all('td') if i])
    return pd.DataFrame(data, columns=headers)


# In[4]:


raw_df = scrape_table()
# raw_df.to_pickle("data/raw_crypto51.p")
# raw_df = pd.read_pickle("data/raw_crypto51.p")
raw_df.head()


# In[5]:


raw_df.tail()


# In[6]:


def process_raw_market_cap(raw_mcap):
    """
    Converts market cap string into integer, removing symbols.
    """
    mcap = raw_mcap[1:].replace(",", "")
    if len(mcap.split(" ")) == 2:
        num, power = mcap.split(" ")
        if power == "M":
            return int(float(num) * 10**6)
        elif power == "B":
            return int(float(num) * 10**9)
    else:
        return int(mcap)
    
def process_raw_hashrate(raw_hashrate):
    """
    Converts all hash rate strings to ints in MH/s
    """
    num, power = raw_hashrate.replace(',', '').split(' ')
    power = power.split('H/s')[0]
    if power == "M":
        return int(num)
    elif power == "G":
        return int(num) * 10**3
    elif power == "T":
        return int(num) * 10**6
    elif power == "P":
        return int(num) * 10**9


# In[7]:


processed_df = raw_df.iloc[:-4,:].copy()
processed_df["Hash Rate"] = processed_df["Hash Rate"].apply(process_raw_hashrate)
processed_df["Market Cap"] = processed_df["Market Cap"].apply(process_raw_market_cap)
processed_df["1h Attack Cost"] = processed_df["1h Attack Cost"].str.replace(",", "").str.replace("$", "").astype(int)
processed_df["NiceHash-able"] = processed_df["NiceHash-able"].str.replace(",", "").str.replace("%", "").astype(float)/100


# In[8]:


processed_df.head()


# In[9]:


# Log transforms
processed_df["Log Market Cap"] = np.log(processed_df["Market Cap"])
processed_df["Log Attack Cost"] = np.log(1 + processed_df["1h Attack Cost"])
processed_df["Log NiceHash"] = np.log(1 + processed_df["NiceHash-able"])


# In[43]:


from scipy.stats import linregress

# Linear fit
print("Linear\n=========")
_, _, r_value_1, p_value_1, _ = linregress(processed_df["Market Cap"],
                                           processed_df["1h Attack Cost"])
print("R^2:", r_value_1**2)
print("p value:", p_value_1)

# Log-log fit
print("Power law\n=========")
slope, intercept, r_value, p_value, _ = linregress(processed_df["Log Market Cap"], 
                                                         processed_df["Log Attack Cost"])
print("R^2:", r_value**2)
print("p value:", p_value)

# Power law expression
multiplier = np.exp(intercept)
print(f"\ncost = {multiplier} * mcap ^ {slope:.2f}")


# In[50]:


from adjustText import adjust_text

fig, ax = plt.subplots(figsize=(10,6))
processed_df.plot.scatter("Log Market Cap", "Log Attack Cost", c="Log NiceHash", 
                          colormap="plasma", ax=ax)

# Labels
texts = []
def annotate_df(row):  
    texts.append(ax.annotate(row.Symbol, (row["Log Market Cap"], row["Log Attack Cost"]),
                xytext=(3,-5), 
                textcoords='offset points',
                size=5, 
                color='darkslategrey'))
ab = processed_df.apply(annotate_df, axis=1)    
# adjust_text(texts)

# Trendline
x = np.linspace(11, 26)
ax.plot(x, intercept + x * slope, "--k")

# plt.savefig("attackcost-mcap.png", dpi=500)
plt.show()