Reproduce country flags using python and pandas.
Here's a small selection of flags that can be created with cluster-flag (python code)
cluster-flag relies on pandas and numpy, so make sure they're installed and up to date.
The stable version is on PyPi and can be installed via pip:
$ pip install cluster-flagTo work off the development version, you can clone this repo and install from the root folder
$ python setup.py installIt's called cluster-flag, as I thought flags could offer novel datasets to explore clustering algorithms. A huge range of datasets can be constructed through this package. Let's look at a few examples (the complete python code is here):
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from clusterflag.country_flags import *
# load clustering algorithms
from sklearn.cluster import AgglomerativeClustering, DBSCAN, KMeans
# for reproducibility
np.random.seed(60)
# construct toy dataset based on the cross flag
dataset0 = cross_flag(npoints=[100,1000,500,250,0], colours=['']*5)
# convert pandas dataframe to numpy array (format for clustering algorithms)
np_dataset0 = dataset0[['x','y']].values
# build k-means model with 4 clusters on this dataset
d0_kmeans = KMeans(n_clusters=4).fit(np_dataset0)
# plot the output
fig, (ax1,ax2) = plt.subplots(1,2, figsize=(12, 4), dpi=80)
ax1.set_axis_bgcolor('#e6e6e6')
ax1.set_title('Original Data',size=14)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_ylim([0,1])
ax1.set_xlim([0,1.5])
ax1.scatter(dataset0['x'], dataset0['y'], c='black',marker='o',linewidths=0.0)
ax2.set_axis_bgcolor('#e6e6e6')
ax2.set_title('k-means (n=4)',size=14)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_ylim([0,1])
ax2.set_xlim([0,1.5])
ax2.scatter(np_dataset0[:,0], np_dataset0[:,1],
c=d0_kmeans.predict(np_dataset0), marker='o', linewidths=0.0)
ax2.scatter(d0_kmeans.cluster_centers_[:,0], d0_kmeans.cluster_centers_[:,1],
c='white' ,marker='o', s=200)
plt.show()
k-means does a pretty good job on our Scandinavian flag based dataset. Let's see how it handles a crescent themed dataset.
# construct toy dataset based on the crescent flag
dataset1 = crescent_flag(npoints=[1000,1000,100,500,500],rect=0.2,ratio=1.5,
colours=['']*5,
bcx=0.35, bcy=0.5,
scx=0.4, scy=0.5, bradius=0.25, sradius=0.2,
starcx=0.5, starcy=0.5, starrx=0.125,
starry=0.125)
np_dataset1 = dataset1[['x','y']].values
# kmeans model with 4 clusters
d1_kmeans = KMeans(n_clusters=4).fit(np_dataset1)
# plot the output
k-means is not looking so good now. How about hierarchical clustering and DBSCAN?
# hierarchical clustering (n=4) with ward linkage
d1_hc = AgglomerativeClustering(linkage='ward',
n_clusters=4).fit_predict(np_dataset1)
# DBSCAN
d1_db = DBSCAN(eps=0.04, min_samples=5).fit_predict(np_dataset1)
# plot the output
DBSCAN is the winner!!! But it wouldn't be hard to configure an even harder dataset. The possibilities are endless!
Well, I think flags are cool. It's interesting that different societies place so much importance on these geometrical abstractions. I suppose national anthems are similarly strange if you think about it, but at least they can use lyrics (unless you're Spain) to convey history and culture. Anyway, with cluster-flag, you can easily reproduce all the great flags of the world (excluding those with stars and other complex shapes- i.e most flags).
# simple_flag defaults to the Irish tricolour
irishFlag = simple_flag(npoints=[10000]*3)
# we need to shift the cross centre and add some colour arguments to recreate the Swedish flag
swedenFlag = cross_flag(npoints=[10000]*5, cenx=0.4, colours=['blue']*4 + ['yellow'])
# plot flags
fig, ax = plt.subplots(1,2,figsize=(9, 3))
for (pos, (flag, country)) in enumerate(zip([irishFlag,swedenFlag],['Ireland', 'Sweden'])):
ax[pos].set_xticks([])
ax[pos].set_yticks([])
ax[pos].set_xlim([0,1.5])
ax[pos].set_ylim([0,1])
ax[pos].set_title(country + ' Flag', size=12)
ax[pos].scatter(flag['x'], flag['y'],c=flag['flag_col'], lw=0)
plt.show()
You can also use cluster-flag to invent flags that really should exist. Get ready for the inevitable political union of Sweden and Japan by pledging your allegiance to the flag of Japden (aka Swepan).
# the second value in the array is 0, as we don't want any background points in the Japan flag
japdenFlag = pd.concat([swedenFlag, japan_flag(npoints=[100000,0], cenx=0.4)])
plt.subplots(1,figsize=(4.5, 3))
plt.xticks([])
plt.yticks([])
plt.xlim([0,1.5])
plt.ylim([0,1])
plt.title('Japden Flag', size=12)
plt.scatter(japdenFlag['x'], japdenFlag['y'],c=japdenFlag['flag_col'], lw=0)
plt.show()
Each flag is simply a graphical representation of a pandas dataframe. And each dataframe constitues a 2-D numpy array appended with a column that defines the colour of the points. So, for novice users, flag-cluster offers a intuitive context in which to learn and perfect your pandas and numpy skills.
irishFlag = simple_flag(npoints=[10000]*3)
irishFlag.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 3 columns):
x 30000 non-null float64
y 30000 non-null float64
flag_col 30000 non-null object
dtypes: float64(2), object(1)
memory usage: 703.2+ KB
# get midpoint of each flag segment
irishFlag.groupby('flag_col', axis=0).mean()| x | y | |
|---|---|---|
| flag_col | ||
| green | 0.248995 | 0.501111 |
| orange | 1.249278 | 0.496614 |
| white | 0.751620 | 0.498237 |
# change Ireland flag to Germany flag using pandas methods
irishFlag.set_value(irishFlag['flag_col']=='green','flag_col','yellow')
irishFlag.set_value(irishFlag['flag_col']=='white','flag_col','red')
irishFlag.set_value(irishFlag['flag_col']=='orange','flag_col','black')
irishFlag['x'] /= 1.5
irishFlag['y'] *= 1.5
plt.subplots(1,figsize=(4.5, 3))
plt.xticks([])
plt.yticks([])
plt.xlim([0,1.5])
plt.ylim([0,1])
plt.title('Germany Flag', size=12)
plt.scatter(irishFlag['y'], irishFlag['x'],c=irishFlag['flag_col'], lw=0)
plt.show()
Whether it's a bug fix, new flag or an angry comment telling me how insulting it is to have the star on your national flag replaced with a rectangle, contributions are appreciated. Just follow the guidelines here.