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main.py
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import uuid
import numpy
import matplotlib
import pylab
import csv
import random
import json
from bottle import route, run, template, static_file
from bottle import request, response, template, HTTPResponse
from numpy import vstack,array
from numpy.random import rand
from scipy.cluster.vq import kmeans,vq
from scipy.cluster.vq import *
matplotlib.use('Agg')
pylab.close()
field_names = ["GEO_ID","AREA_NAME","TABLE_ID","LINE_NUMBER","LINE_DESCRIPTION","ESTIMATE","MARGIN_OF_ERROR"]
def distance(p0,p1):
return math.sqrt((p0[0] - p1[0])**2 + (p0[1] - p1[1])**2)
#print len(field_names)
# getting all the unique fields::;
def get_unique_lists(x_param, y_param):
x_list = []
y_list = []
vector = []
csvfile = open("stats.csv","r")
reader = csv.DictReader(csvfile, fieldnames=field_names)
for row in reader:
vector_element = []
#print(row['Report No.'], row['Report Date'])
x_list.append(row[x_param])
y_list.append(row[y_param])
vector_element.append(row[x_param])
vector_element.append(row[y_param])
vector.append(vector_element)
x_list = list(set(x_list))
y_list = list(set(y_list))
csvfile.close()
return x_list,y_list,vector
# convert vector to decimal points
def get_decimal_vector(x_list,y_list,vector):
d_vector = []
for element in vector:
x_index= x_list.index(element[0])
#print x_index
y_index = y_list.index(element[1])
#print y_index
d_vector_ele = []
jitter1 = [random.random() for _ in range(0, 1)][0]
jitter2 = [random.random() for _ in range(0, 1)][0]
d_vector_ele.append(x_index+jitter1)
d_vector_ele.append(y_index+jitter2)
d_vector.append(d_vector_ele)
return d_vector
def get_decimal_vector2(x_param,y_param):
d_vector = []
csvfile = open("stats.csv","r")
reader = csv.DictReader(csvfile, fieldnames=field_names)
reader.next()
for row in reader:
vector_element = []
row[x_param] = clean(row[x_param])
row[y_param] = clean(row[y_param])
print row[x_param]
print row[y_param]
x_cor = int(float(clean(row[x_param]))) if (row[x_param]!='') else 0
y_cor = int(float(clean(row[y_param]))) if (row[y_param]!='') else 0
vector_element.append(x_cor)
vector_element.append(y_cor)
d_vector.append(vector_element)
return d_vector
def clean(e):
e = e.replace(",","").strip("+/-").strip()
e = e.replace("N","").replace("X","")
return e
def vector_to_image(d_vector):
#data = array(d_vector)
name = str(uuid.uuid4())
data = vstack(d_vector)
# computing K-Means with K = 2 (2 clusters)
centroids,_ = kmeans(data,2)
# assign each sample to a cluster
idx,_ = vq(data,centroids)
# some plotting using numpy's logical indexing
#pylab.plot(data[idx==0,0],data[idx==0,1],'ob',
# data[idx==1,0],data[idx==1,1],'or')
pylab.plot(data[idx==0,0],data[idx==0,1],'ob',
data[idx==1,0],data[idx==1,1],'or',markersize=2,marker='o')
pylab.plot(centroids[:,0],centroids[:,1],'sg',markersize=12,marker='o')
#pylab.plot(centroids[:,0],centroids[:,1],centroids[:,2],'sg',markersize=1)
filename = name+".png"
pylab.savefig("./static/"+filename)
return filename
def vector_to_image2(d_vector,num_of_clusters):
name = str(uuid.uuid4())
xy = []
xy = array(d_vector)
res, idx = kmeans2(xy,num_of_clusters)
colors = ([([0.4,1,0.4],[1,0.4,0.4],[0.1,0.8,1],[1,1,1],[0.3,0.3,0.3],[0.1,0.1,0.1])[i] for i in idx])
cluster_dict = {}
for x in colors:
if str(x) in cluster_dict:
cluster_dict[str(x)] += 1
else:
cluster_dict[str(x)] = 0
pylab.scatter(xy[:,0],xy[:,1], c=colors)
pylab.scatter(res[:,0],res[:,1], marker='o', s = 500, linewidths=2, c='none')
pylab.scatter(res[:,0],res[:,1], marker='x', s = 500, linewidths=2)
filename = name+".png"
pylab.savefig("./static/"+filename)
return filename,cluster_dict
@route('/static/<filename>')
def server_static(filename):
return static_file(filename, root="static")
@route('/')
def index():
#x_list,y_list,vector = get_unique_lists('Product Type','City')
#d_vector = get_decimal_vector(x_list,y_list,vector)
#name = vector_to_image(d_vector)
#return template('ui', name=name)
return template('ui')
@route('/clusterimage', method='POST')
def clusterimage():
#pdb.set_trace()
# function creates output of the queries based on the posted parameters
if request.headers.get('X-Requested-With') == 'XMLHttpRequest':
posted_dict = request.forms.dict
x_param = posted_dict["x_param"][0]
y_param = posted_dict["y_param"][0]
nc = int(posted_dict["noofclusters"][0])
print(x_param)
print(y_param)
#x_list,y_list,vector = get_unique_lists(x_param,y_param)
#d_vector = get_decimal_vector(x_list,y_list,vector)
d_vector = get_decimal_vector2(x_param,y_param)
d_vector = array(d_vector)
name,cluster_dict = vector_to_image2(d_vector,nc)
#data = json.dumps(posted_dict)
counters = json.dumps(cluster_dict)
resp = HTTPResponse(body=name+"#"+counters,status=200)
return resp
else:
return 'This is a normal request'
def get_decimal_vector3(x_param,y_param):
d_vector = []
csvfile = open("stats.csv","r")
reader = csv.DictReader(csvfile, fieldnames=field_names)
reader.next()
for row in reader:
vector_element = {}
row[x_param] = clean(row[x_param])
row[y_param] = clean(row[y_param])
print row[x_param]
print row[y_param]
x_cor = int(float(clean(row[x_param]))) if (row[x_param]!='') else 0
y_cor = int(float(clean(row[y_param]))) if (row[y_param]!='') else 0
vector_element['key'] = x_cor
vector_element['value']= y_cor
d_vector.append(vector_element)
return d_vector
@route('/scatterplot', method='POST')
def scatterplot():
# function creates output of the queries based on the posted parameters
if request.headers.get('X-Requested-With') == 'XMLHttpRequest':
posted_dict = request.forms.dict
x_param = posted_dict["x_param"][0]
y_param = posted_dict["y_param"][0]
d_vector = get_decimal_vector2(x_param,y_param)
data = json.dumps(d_vector)
resp = HTTPResponse(body=data,status=200)
return resp
else:
return 'This is a normal request'
@route('/bargraph', method='POST')
def bargraph():
if request.headers.get('X-Requested-With') == 'XMLHttpRequest':
posted_dict = request.forms.dict
x_param = posted_dict["x_param"][0]
y_param = posted_dict["y_param"][0]
print x_param
print y_param
d_vector = get_decimal_vector3(x_param,y_param)
data = json.dumps(d_vector)
resp = HTTPResponse(body=data,status=200)
return resp
else:
return 'This is a normal request'
run(host='0.0.0.0', port=8000)