Article Outline
Python matplotlib example 'classR'
Modules used in program:
import pickle
import urllib.request
import pickle
import rpy2.robjects.numpy2ri as npr
import rpy2.robjects.packages as rpackages
import rpy2.robjects as robjects
import rpy2
import numpy as np
python classR
Python matplotlib example: classR
# helper class to interface between R and python using rpy2
# © Monte J. Shaffer
# [email protected]
# MIT license
import numpy as np
import rpy2
import rpy2.robjects as robjects
import rpy2.robjects.packages as rpackages
from rpy2.robjects.packages import importr
import rpy2.robjects.numpy2ri as npr
npr.activate()
base = importr('base')
utils = importr('utils')
stats = importr('stats')
# helper class to interface between R and python using rpy2
class classR:
def __init__(self, plotme=True):
self.version = {}
self.version["rpy2"] = rpy2.__version__
from rpy2.rinterface import R_VERSION_BUILD
self.version["R"] = R_VERSION_BUILD
system = base.Sys_info() # base.set_seed(123)
#utils.str(system)
self.version["bit"] = system.rx2("machine")
print(system)
# rather than doing a bunch of 'from ... import'
# common functions here
# http://rpy.sourceforge.net/rpy2/doc-2.2/html/vector.html#creating-vectors
self.Vector = robjects.Vector
self.IntVector = robjects.IntVector
self.FloatVector = robjects.FloatVector
self.FactorVector = robjects.FactorVector
self.BoolVector = robjects.BoolVector
self.StrVector = robjects.StrVector
self.ListVector = robjects.ListVector
self.plotme = plotme
if(plotme == True):
# http://www.lfd.uci.edu/~gohlke/pythonlibs/#pyqt4
# pip install .\PyQt...
# https://github.com/matplotlib/matplotlib/issues/2134/
import matplotlib
matplotlib.use("qt4agg")
from matplotlib import pyplot as plt
self.plt = plt
print("pyplot loaded")
def mirror(self,idx=1):
utils.chooseCRANmirror(False,ind=idx) # F and FALSE don't work, False and 0 works
def library(self,package):
return importr(package)
def install(self,package):
utils.install_packages(package)
def print(self,what):
base.print(what)
def summary(self,what):
base.summary(what)
def str(self,what):
utils.str(what)
def castValue(self,val):
vlen = len(val)
if vlen == 1:
return val[0]
else:
return np.asarray(val)
return val
def getValue(self,what): # may not work on dataframe element rx not rx2
a = what.split("$") # partition
e = str(a[0])
for i in range(1,len(a)):
e = e + ".rx2(" + '"' + str(a[i]) + '"' + ")"
val = eval(e)
return self.castValue(val)
# http://ipython.readthedocs.io/en/stable/interactive/tutorial.html
# POWERSHELL ... cd C:\Python3.6.3\_monte_\
# ipython
# %run -d classR.py
#####################################################################
# USAGE
#####################################################################
"""
# web delivery of data 'points'
import pickle
import urllib.request
raw = urllib.request.urlopen('http://www.mshaffer.com/arizona/dissertation/points').read()
points = pickle.loads(raw) # notice plural
"""
"""
# local delivery of data 'points'
import pickle
with open ('points', 'rb') as fp:
points = pickle.load(fp)
# initiate class
R = classR()
# R.mirror(1) # choose mirror [1 - Cloud] before installing package
# R.install("fpc")
fpc = R.library("fpc")
cluster = R.library('cluster')
k = R.IntVector(range(3, 8)) # r-syntax 3:7 # I expect 5
pamk_clusters = fpc.pamk(points,k) # %time # very slow [Wall time: 1min 11s]
# k = 5 ... as expected
pam_clusters = cluster.pam(points,5) # %time # much slower [Wall time: 10.6 s]
kmeans_clusters = stats.kmeans(points,5) # %time # much faster [Wall time: 5.5 ms]
print(kmeans_clusters)
R.print(kmeans_clusters)
R.str(kmeans_clusters)
centers = R.getValue("kmeans_clusters$centers")
wss = R.getValue("kmeans_clusters$withinss")
tot = R.getValue("kmeans_clusters$tot.withinss")
wss/tot
size = R.getValue("kmeans_clusters$size")
member = R.getValue("kmeans_clusters$cluster") # cluster may be package
nmember = member.reshape(len(member),1)
npoints = np.hstack((points,nmember))
plt.figure("scatter")
plt.scatter(npoints[:,0],npoints[:,1],c=npoints[:,2])
plt.show(block=False)
sidx = np.argmax(size)
smax = size[sidx]
smember = 1 + sidx
scenter = centers[sidx]
spoints = npoints[npoints[:,2]==smember]
minx = min(spoints[:,0])
maxx = max(spoints[:,0])
cx = (minx + maxx)/2
miny = min(spoints[:,1])
maxy = max(spoints[:,1])
cy = (miny + maxy)/2
deltax = cx - scenter[0]
deltay = cy - scenter[1]
w = maxx - minx
h = maxy - miny
from matplotlib.patches import Ellipse
plt.figure("scatter")
plt.scatter(spoints[:,0],spoints[:,1],c=spoints[:,2])
plt.scatter(scenter[0],scenter[1])
ax = plt.gca()
ellipse = Ellipse(xy=(scenter[0] + deltax,scenter[1] + deltay), width=w, height=h,
edgecolor='r', fc='None', lw=2)
ax.add_patch(ellipse)
plt.show(block=False)
"""
Python links
- Learn Python: https://pythonbasics.org/
- Python Tutorial: https://pythonprogramminglanguage.com