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

# In[244]:


get_ipython().run_line_magic('matplotlib', 'inline')

import pandas as pd
import numpy as np
import scipy
import sklearn
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D


# In[4]:


names = ['name','landmass','zone','area','population','language','religion','bars','stripes','colours','red','green','blue','gold','white','black','orange','mainhue','circles','crosses','saltires','quarters','sunstars','crescent','triangle','icon','animate','text','topleft','botright']
dataset = pd.read_csv('flag.data',names=names,usecols=[*range(len(names))])

print(dataset.head(5))


# In[22]:


#all possible religious tags
religions = ['catholic','other christian','muslim','buddhist',
             'hindu','ethnic','marxist','other']

#sum of flags tagged by respective religion
totalFlags = []

for religion in religions:
    index = religions.index(religion)
    religionCount = sum(dataset['religion'] == index)
    totalFlags.append(religionCount)
    
#reshape into dataframe
totalFlags = pd.DataFrame(np.array(totalFlags).reshape(1,len(religions)))
totalFlags.columns = religions


# In[81]:


sns.set(style="whitegrid")
ax = sns.barplot(data=totalFlags)
ax.set(xlabel='religions', ylabel='amount')
sns.set(rc={'figure.figsize':(15,10)})
sns.set_palette(sns.hls_palette(8, l=.3, s=.8))


# In[55]:


#we only care about these columns
columns = ['mainhue','bars','stripes','sunstars']
plotData = dataset[columns]
plotData.columns = columns

plotData


# In[62]:


#represent the flag colors on plot
flagColors = ['green','red','blue','gold','white','orange','black','brown']
sns.set_palette(flagColors)

sns.relplot(x="stripes", y="sunstars", hue="mainhue", size="bars",sizes=(100, 500), 
            alpha=0.9,height=6, data=plotData, palette=flagColors)


# In[77]:


landMasses = ['N. America','S. America','Europe','Africa','Asia','Oceania']
columns = ['landmass','bars','stripes','circles','crosses','saltires','quarters','sunstars',
          'crescent','triangle','icon','animate','text']
flagFeatsByLandmass = dataset[columns]

for i in range(1 , len(landMasses) + 1):
    landMassLabel = landMasses[i - 1]
    flagFeatsByLandmass.loc[flagFeatsByLandmass['landmass'] == i,'landmass'] = landMassLabel
    
flagFeatsByLandmass = pd.melt(flagFeatsByLandmass,id_vars="landmass",
                              var_name="feats",value_name="amount")
flagFeatsByLandmass


# In[78]:


sns.set_palette(sns.hls_palette(8, l=.3, s=.8))
g = sns.factorplot(x='amount', y='feats', hue='landmass', data=flagFeatsByLandmass, kind='bar',
                  size=10,aspect=2,ci=None)


# In[166]:


landFeatsColor = dataset.drop(columns=['name','zone','area','population','language','religion',
                                     'red','green','blue','gold','white','black','orange',
                                      'topleft','botright','colours','bars','stripes',
                                      'crosses','saltires','circles','quarters','sunstars',
                                      'crescent','triangle','icon','animate','text'])

for i in range(1,7):
    landFeatsColor.loc[landFeatsColor['landmass'] == i,'landmass'] = landMasses[i - 1]


colors = ['red','green','blue','gold','white','black','orange']

mainColorsByLandmass = []

for land in landMasses:
    landMainhue = []
    landmass = landFeatsColor[landFeatsColor['landmass'] == land]

    for color in colors:
        landMainhue.append(sum(landmass['mainhue'] == color))
    
    mainColorsByLandmass.append(landMainhue)


mainColorsByLandmass = pd.DataFrame(np.array(mainColorsByLandmass).reshape(6,len(colors)))
mainColorsByLandmass.columns = colors
mainColorsByLandmass['landmass'] = landMasses
mainColorsByLandmass = pd.melt(mainColorsByLandmass,id_vars="landmass",
                               var_name="mainhue",value_name="amount")

h = sns.factorplot(x='landmass', y='amount', hue='mainhue', data=mainColorsByLandmass, 
                   kind='bar',size=10,aspect=2,ci=None)


# In[137]:


land1 = dataset[dataset['landmass'] == 1]

#bars, stripes, colorDiff, crosses, saltires, quarters, sunstars, icon
columns = ['red','green','blue','gold','white','black','orange']
flagColors = []

for color in columns:
    flagColors.append(sum(land1[color]))

flagColors = pd.DataFrame(np.array(flagColors).reshape(1,len(columns)))
flagColors.columns = columns

flatui = ["#e50000", "#15b01a", "#0343df", "#dbb40c", "#ffffff", "#000000", "#f97306"]
sns.set_palette(flatui)

ax = sns.barplot(data=flagColors)
ax.set(xlabel='colors', ylabel='total')
sns.set(rc={'figure.figsize':(13,3)})


# In[202]:


landMasses = ['N. America','S. America','Europe','Africa','Asia','Oceania']
labels = ['circles','crosses','saltires','quarters','crescent',
          'triangle','icon','animate','text']

radarData = []

for i in range(1 , len(landMasses) + 1):
    land = dataset[dataset['landmass'] == i]

    flagFeatures = []

    for feat in labels:
        flagFeatures.append(np.mean(land[feat]))
    
    radarData.append(flagFeatures)

radarData = pd.DataFrame(np.array(radarData).reshape(len(landMasses), len(labels)))
radarData = radarData.T
radarData.columns = landMasses

radarData


# In[239]:


def radarPlot(row,color,land,labels):
    angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)
    angles = np.concatenate((angles,[angles[0]]))

    ax = plt.subplot(2,3,row+1, polar=True)
    
    ax.set_theta_offset(np.pi / 2)
    
    plt.xticks(angles[:-1], labels, color='grey', size=8)

    # Draw ylabels
    ax.set_rlabel_position(0)
    plt.yticks([0.10,0.20,0.30,0.40], 
               ["0.10","0.20","0.30","0.40"], color="grey", size=7)

    plt.ylim(0,0.50)

    stats = radarData[land].values
    stats = np.concatenate((stats,[stats[0]]))

    ax.plot(angles, stats, color=color,linewidth=2,linestyle='solid',label=land)
    ax.fill(angles, stats, color=color, alpha=0.4)

    title = land
    plt.title(title, size=11, color=color, y=1.1)


# In[240]:


plotDpi=96
plt.figure(figsize=(1600/plotDpi, 1000/plotDpi), dpi=plotDpi)

colors = ['royalblue','navy','goldenrod','olivedrab','darkred','limegreen']

i = 0
for land in landMasses:
    color = colors[i]
    radarPlot(i,color,land,labels)
    i+=1


# In[242]:


parallel_coordinates(radarData, 'landmass', colormap=plt.get_cmap("Set2"))
plt.show()


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