"""Tools for working with Cryptopunk NFTs; this includes utilities for data analysis and image preparation for training machine learning models using Cryptopunks as training data. 

Functions:
    get_punk(id)
    pixel_to_img(pixel_str, dim)
    flatten(img)
    unflatten(img)
    sort_dict_by_function_of_value(d, f)
    add_index_to_colors(colors)

"""
import os
import time
import requests
from collections import OrderedDict
from bs4 import BeautifulSoup
from re import sub
import numpy as np
import pandas as pd

from matplotlib.colors import rgb2hex
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

__ROOT_DIR__ = os.path.dirname(os.path.abspath(__file__))
__PUNK_DIR__ = f"{__ROOT_DIR__}/images/training";

def camel_case(string):
  '''
     Convert string to camelCase
  '''
  string = string.strip("\n")
  string = sub(r"(_|-)+", " ", string).title().replace(" ", "")
  return string[0].lower() + string[1:]

def color_str_to_hex(s):
  '''
     Convert string representation of numpy pixel array 
     to a string hex value
  '''
  return rgb2hex([float(x) for x in s[1:-1].split(' ') if x != ''])

def get_punk(id):
    '''
       Returns a ndarray with loaded image
    ''' 
    return mpimg.imread(f'''{__PUNK_DIR__}/punk{"%04d" % id}.png''')

def pixel_to_img(pixel_str, dim = (24,24)):
    '''
       Take pixel of format "[r,g,b,b]"
       and return an image of size `dim` containing 
       only the pixel's color.
    '''
    (x,y) = dim 
    c = np.fromstring(pixel_str[1:-1], float, sep=' ')
    return np.full((x, y, 4), c)

def pixel_to_ximg(pixel_strs, dim = (24,24), n=3 ):
    '''
       Take pixel of format "[r,g,b,b]"
       and return an image of size `dim` containing
       a matrix of size n*n
    '''
    (x,y) = (dim[0]//n, dim[1]//n)
    m = []
    for i in range(0,n):
        l=[]
        for j in range(0,n):
            img = np.full((x, y, 4),
                           np.fromstring(pixel_strs[i*n + j][1:-1], float, sep=' '))
            l.append(img)
        m.append(np.concatenate(l, axis=1))
    return np.concatenate(m, axis=0)

def flatten(img): 
  '''
     Convert (x,y,z) array containing a pixel in z-dimension 
     to an (x,y) array with str values for each (i,j)
     the intention is to make this easier to work with in ML
     training.
  '''
  return np.array([[str(c) for c in row] 
                   for row in img])

def unflatten(img):
  '''
     Return a flattend image to valid .png format for display
  '''  
  return np.array([[np.fromstring(c[1:-1], float, sep=' ') 
                    for c in row] for row in img])

def sort_dict_by_function_of_value(d, f = len):
  sorted_tuples = sorted(d.items(),
          key=lambda item: len(item[1]))
  return {k: v for k, v in sorted_tuples}

def add_index_to_colors(colors):
  '''
     Add a unique, sequential index to the entry for 
     each color. returned dictionary will be of form
     {`color_string`: { `"id": `int`, "punkIds" : `list[int`}}
  '''
  i=0
  d={}
  for k in colors.keys():
    d[k] = {
      'id' : i,
      'punkIds' : colors[k]
    }
    i=i+1
  return d

def get_attr_dict():
    '''
       Read the attr csv and populate a default dict
    '''
    d=OrderedDict()
    with open(f"{__ROOT_DIR__}/data/list_attr_punx.csv") as f:
        for attr in f.read().strip('\n').split(','):
            d[attr]=-1
    return d

def get_punk_attrs(id):
    '''
       Retrieve `id` cryptopunk from larvalabs.com,
       parse HTML to extract type and attribute list
       to return list of attributes
    '''
    typeClass="col-md-10 col-md-offset-1 col-xs-12"
    punk_page=requests.get(f"https://www.larvalabs.com/cryptopunks/details/{id}")
    if(punk_page.status_code != 200):
        print(punk_page.status_code)
        return {}
    punk_html=punk_page.text

    soup = BeautifulSoup(punk_html, 'html.parser')
    details = soup.find(id="punkDetails")

    punkType = camel_case(details.find(class_=typeClass).find('a').contents[0])

    attrs=[punkType]
    attrTags = details.find(class_ = "row detail-row")
    for attrTag in attrTags.find_all('a'):
        attrs.append(camel_case(attrTag.contents[0]))
    return attrs


def get_punk_dict(id):
   '''
      Retrieve a punk page, pull type and attributes
      from HTML and return a dictionary of attribute to
      (-1,1) mapping where 1 is truthy for existence of 
      attribute
   '''
   od = {k:__ATTR_DICT__[k] for k in __ATTR_DICT__} 
   attrs = get_punk_attrs(id)
   for attr in attrs:
      od[attr]=1
   return od

def get_punks(start, end):
    '''
      Retrieve punks in range `start` to `end`
    '''
    punks={}
    for id in range(start, end):
        print(id)
        time.sleep(3.3)
        punks[id] = get_punk_dict(id)
    return punks

def plot_in_grid(n, images, predictions, labels):
  '''
    Plot `images` in an n*n grid with
    prediction and labels as header
  '''
  (x,y) = (n,n)
  fig = plt.figure(figsize=(9,14))
  i=0
  for i in range(1,(x*y)+1):
    fig.add_subplot(x, y, i)
    plt.imshow(images[i])
    plt.title(f"{predictions[i][0]},{labels[i][0]}")
    plt.axis('off')
    i=i+1
  return fig

"""Tools for working with Cryptopunk NFTs; this includes utilities for data analysis and image preparation for training machine learning models using Cryptopunks as training data. 

Functions:
    get_punk(id)
    pixel_to_img(pixel_str, dim)
    flatten(img)
    unflatten(img)
    sort_dict_by_function_of_value(d, f)
    add_index_to_colors(colors)

"""
import os
import time
import requests
from collections import OrderedDict
from bs4 import BeautifulSoup
from re import sub
import numpy as np
import pandas as pd

from matplotlib.colors import rgb2hex
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

__ROOT_DIR__ = os.path.dirname(os.path.abspath(__file__))
__PUNK_DIR__ = f"{__ROOT_DIR__}/images/training";

def camel_case(string):
  '''
     Convert string to camelCase
  '''
  string = string.strip("\n")
  string = sub(r"(_|-)+", " ", string).title().replace(" ", "")
  return string[0].lower() + string[1:]

def color_str_to_hex(s):
  '''
     Convert string representation of numpy pixel array 
     to a string hex value
  '''
  return rgb2hex([float(x) for x in s[1:-1].split(' ') if x != ''])

def get_punk(id):
    '''
       Returns a ndarray with loaded image
    ''' 
    return mpimg.imread(f'''{__PUNK_DIR__}/punk{"%04d" % id}.png''')

def pixel_to_img(pixel_str, dim = (24,24)):
    '''
       Take pixel of format "[r,g,b,b]"
       and return an image of size `dim` containing 
       only the pixel's color.
    '''
    (x,y) = dim 
    c = np.fromstring(pixel_str[1:-1], float, sep=' ')
    return np.full((x, y, 4), c)

def pixel_to_ximg(pixel_strs, dim = (24,24), n=3 ):
    '''
       Take pixel of format "[r,g,b,b]"
       and return an image of size `dim` containing
       a matrix of size n*n
    '''
    (x,y) = (dim[0]//n, dim[1]//n)
    m = []
    for i in range(0,n):
        l=[]
        for j in range(0,n):
            img = np.full((x, y, 4),
                           np.fromstring(pixel_strs[i*n + j][1:-1], float, sep=' '))
            l.append(img)
        m.append(np.concatenate(l, axis=1))
    return np.concatenate(m, axis=0)

def flatten(img): 
  '''
     Convert (x,y,z) array containing a pixel in z-dimension 
     to an (x,y) array with str values for each (i,j)
     the intention is to make this easier to work with in ML
     training.
  '''
  return np.array([[str(c) for c in row] 
                   for row in img])

def unflatten(img):
  '''
     Return a flattend image to valid .png format for display
  '''  
  return np.array([[np.fromstring(c[1:-1], float, sep=' ') 
                    for c in row] for row in img])

def sort_dict_by_function_of_value(d, f = len):
  sorted_tuples = sorted(d.items(),
          key=lambda item: len(item[1]))
  return {k: v for k, v in sorted_tuples}

def add_index_to_colors(colors):
  '''
     Add a unique, sequential index to the entry for 
     each color. returned dictionary will be of form
     {`color_string`: { `"id": `int`, "punkIds" : `list[int`}}
  '''
  i=0
  d={}
  for k in colors.keys():
    d[k] = {
      'id' : i,
      'punkIds' : colors[k]
    }
    i=i+1
  return d

def get_attr_dict():
    '''
       Read the attr csv and populate a default dict
    '''
    d=OrderedDict()
    with open(f"{__ROOT_DIR__}/data/list_attr_punx.csv") as f:
        for attr in f.read().strip('\n').split(','):
            d[attr]=-1
    return d

def get_punk_attrs(id):
    '''
       Retrieve `id` cryptopunk from larvalabs.com,
       parse HTML to extract type and attribute list
       to return list of attributes
    '''
    typeClass="col-md-10 col-md-offset-1 col-xs-12"
    punk_page=requests.get(f"https://www.larvalabs.com/cryptopunks/details/{id}")
    if(punk_page.status_code != 200):
        print(punk_page.status_code)
        return {}
    punk_html=punk_page.text

    soup = BeautifulSoup(punk_html, 'html.parser')
    details = soup.find(id="punkDetails")

    punkType = camel_case(details.find(class_=typeClass).find('a').contents[0])

    attrs=[punkType]
    attrTags = details.find(class_ = "row detail-row")
    for attrTag in attrTags.find_all('a'):
        attrs.append(camel_case(attrTag.contents[0]))
    return attrs


def get_punk_dict(id):
   '''
      Retrieve a punk page, pull type and attributes
      from HTML and return a dictionary of attribute to
      (-1,1) mapping where 1 is truthy for existence of 
      attribute
   '''
   od = {k:__ATTR_DICT__[k] for k in __ATTR_DICT__} 
   attrs = get_punk_attrs(id)
   for attr in attrs:
      od[attr]=1
   return od

def get_punks(start, end):
    '''
      Retrieve punks in range `start` to `end`
    '''
    punks={}
    for id in range(start, end):
        print(id)
        time.sleep(3.3)
        punks[id] = get_punk_dict(id)
    return punks

def plot_in_grid(n, images, predictions, labels):
  '''
    Plot `images` in an n*n grid with
    prediction and labels as header
  '''
  (x,y) = (n,n)
  fig = plt.figure(figsize=(9,14))
  i=0
  for i in range(1,(x*y)+1):
    fig.add_subplot(x, y, i)
    plt.imshow(images[i])
    plt.title(f"{predictions[i][0]},{labels[i][0]}")
    plt.axis('off')
    i=i+1
  return fig

Tools for working with Cryptopunk NFTs; this includes utilities for data analysis and image preparation for training machine learning models using Cryptopunks as training data.   Functions:     get_punk(id)     pixel_to_img(pixel_str, dim)     flatten(img)     unflatten(img)     sort_dict_by_function_of_value(d, f)     add_index_to_colors(colors) Convert string to camelCase Convert string representation of numpy pixel array       to a string hex value Returns a ndarray with loaded image {__PUNK_DIR__}/punk{"%04d" % id}.png Take pixel of format "[r,g,b,b]"        and return an image of size `dim` containing         only the pixel's color. Take pixel of format "[r,g,b,b]"        and return an image of size `dim` containing        a matrix of size n*n Convert (x,y,z) array containing a pixel in z-dimension       to an (x,y) array with str values for each (i,j)      the intention is to make this easier to work with in ML      training. Return a flattend image to valid .png format for display Add a unique, sequential index to the entry for       each color. returned dictionary will be of form      {`color_string`: { `"id": `int`, "punkIds" : `list[int`}} Read the attr csv and populate a default dict Retrieve `id` cryptopunk from larvalabs.com,        parse HTML to extract type and attribute list        to return list of attributes Retrieve a punk page, pull type and attributes       from HTML and return a dictionary of attribute to       (-1,1) mapping where 1 is truthy for existence of        attribute Retrieve punks in range `start` to `end` Plot `images` in an n*n grid with     prediction and labels as header