# Numpy and pandas by default assume a narrow screen - this fixes that
from fastai.vision.all import *
from nbdev.showdoc import *
from ipywidgets import widgets
from pandas.api.types import CategoricalDtype

import matplotlib as mpl
# mpl.rcParams['figure.dpi']= 200
mpl.rcParams['savefig.dpi']= 200
mpl.rcParams['font.size']=12

set_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
pd.set_option('display.max_columns',999)
np.set_printoptions(linewidth=200)
torch.set_printoptions(linewidth=200)

import graphviz
def gv(s): return graphviz.Source('digraph G{ rankdir="LR"' + s + '; }')

def get_image_files_sorted(path, recurse=True, folders=None): return get_image_files(path, recurse, folders).sorted()


# +
# pip install azure-cognitiveservices-search-imagesearch

# from azure.cognitiveservices.search.imagesearch import ImageSearchClient as api
# from msrest.authentication import CognitiveServicesCredentials as auth

# def search_images_bing(key, term, min_sz=128, max_images=150):    
#      params = {'q':term, 'count':max_images, 'min_height':min_sz, 'min_width':min_sz}
#      headers = {"Ocp-Apim-Subscription-Key":key}
#      search_url = "https://api.bing.microsoft.com/v7.0/images/search"
#      response = requests.get(search_url, headers=headers, params=params)
#      response.raise_for_status()
#      search_results = response.json()    
#      return L(search_results['value'])


# # -

# def search_images_ddg(key,max_n=200):
#      """Search for 'key' with DuckDuckGo and return a unique urls of 'max_n' images
#         (Adopted from https://github.com/deepanprabhu/duckduckgo-images-api)
#      """
#      url        = 'https://duckduckgo.com/'
#      params     = {'q':key}
#      res        = requests.post(url,data=params)
#      searchObj  = re.search(r'vqd=([\d-]+)\&',res.text)
#      if not searchObj: print('Token Parsing Failed !'); return
#      requestUrl = url + 'i.js'
#      headers    = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:71.0) Gecko/20100101 Firefox/71.0'}
#      params     = (('l','us-en'),('o','json'),('q',key),('vqd',searchObj.group(1)),('f',',,,'),('p','1'),('v7exp','a'))
#      urls       = []
#      while True:
#          try:
#              res  = requests.get(requestUrl,headers=headers,params=params)
#              data = json.loads(res.text)
#              for obj in data['results']:
#                  urls.append(obj['image'])
#                  max_n = max_n - 1
#                  if max_n < 1: return L(set(urls))     # dedupe
#              if 'next' not in data: return L(set(urls))
#              requestUrl = url + data['next']
#          except:
#              pass


def plot_function(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)):
    x = torch.linspace(min,max)
    fig,ax = plt.subplots(figsize=figsize)
    ax.plot(x,f(x))
    if tx is not None: ax.set_xlabel(tx)
    if ty is not None: ax.set_ylabel(ty)
    if title is not None: ax.set_title(title)

# +
from sklearn.tree import export_graphviz

def draw_tree(t, df, size=10, ratio=0.6, precision=0, **kwargs):
    s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True,
                      special_characters=True, rotate=False, precision=precision, **kwargs)
    return graphviz.Source(re.sub('Tree {', f'Tree {{ size={size}; ratio={ratio}', s))


# +
from scipy.cluster import hierarchy as hc

def cluster_columns(df, figsize=(10,6), font_size=12):
    corr = np.round(scipy.stats.spearmanr(df).correlation, 4)
    corr_condensed = hc.distance.squareform(1-corr)
    z = hc.linkage(corr_condensed, method='average')
    fig = plt.figure(figsize=figsize)
    hc.dendrogram(z, labels=df.columns, orientation='left', leaf_font_size=font_size)
    plt.show()
