In [1]:

    

%pylab inline

import csv, os
from glob import glob

import pandas as pd

import shapely.affinity as af
import shapely.geometry as sh

from equivalent_ellipse import *
from scaled_figures import *

pylab.rcParams['savefig.dpi'] = 254/4


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

x_list = list()
y_list = list()

with open('../data/cutout_x.csv', 'r') as x_csvfile:
    with open('../data/cutout_y.csv', 'r') as y_csvfile:
        
        x_reader = csv.reader(x_csvfile, delimiter=',', lineterminator='\n')
        y_reader = csv.reader(y_csvfile, delimiter=',', lineterminator='\n')
        
        for row in x_reader:
            x_list += [row]
  
        for row in y_reader:
            y_list += [row]

            
num_cutouts = len(x_list)


x_array = [0,]*num_cutouts
y_array = [0,]*num_cutouts


for i in range(num_cutouts):

    x_array[i] = array(x_list[i], dtype='float')
    y_array[i] = array(y_list[i], dtype='float')

    
cutout = [0,]*num_cutouts

for i in range(num_cutouts):
    cutout[i] = shapely_cutout(x_array[i],y_array[i])


    

In [3]:

    

cutout_dimensions = pd.DataFrame.from_csv('../data/cutout_dimensions.csv')

ellipse_def = transpose(array([cutout_dimensions['a'].values,
                               cutout_dimensions['b'].values,
                               cutout_dimensions['width'].values,
                               cutout_dimensions['length'].values,
                               cutout_dimensions['theta'].values]))


    

In [4]:

    

width_init = cutout_dimensions['width'].values

cutoutRef_init = arange(len(width_init))

sorted_ref = cutoutRef_init[argsort(width_init)]

similar_list = diff(width_init[sorted_ref]) < 0.0001
duplicates_removed_ref = sorted_ref[~similar_list]

to_measure = duplicates_removed_ref


    

In [5]:

    

# cutouts to measure sorted by width
width_sorted = to_measure[argsort(ellipse_def[to_measure,2])]


    

In [6]:

    

toRemove = glob('../figures/saved/*')
for path in toRemove:
    os.remove(path)


    

In [7]:

    

j = 0

circle_width = arange(3,11)

scaled_fig_start(12,12)

for i in circle_width:
    
    ellipse = create_ellipse([0,0,i,i,0])
    
    plot(ellipse.exterior.xy[0],ellipse.exterior.xy[1])
    
    text(-5.5,5,'Circles',fontsize=11)
   

scaled_fig_end(12,12)
stringj = "%03d" %(j)

savefig('../figures/saved/circles.png')


    

In [8]:

    

for i in width_sorted:

    ellipse = create_ellipse(ellipse_def[i,:])


    scaled_fig_start(12,12)

    plot(cutout[i].exterior.xy[0],cutout[i].exterior.xy[1])
    plot(ellipse.exterior.xy[0],ellipse.exterior.xy[1],'r--')

    plot(ellipse.centroid.xy[0],ellipse.centroid.xy[1],'go')
    
    
    text(-5.5,5,('Cutout: %d' % (i)),fontsize=11)
    text(-5.5,-5,('Width: %.2f cm' % (ellipse_def[i,2])),fontsize=11)
    text(-5.5,-5.5,('Length: %.2f cm' % (ellipse_def[i,3])),fontsize=11)
    text(3,5,('Ratio: %.2f' % ((ellipse_def[i,2]/ellipse_def[i,3]) )),fontsize=11)
    
    scaled_fig_end(12,12)
    
    stringi = "%03d" %(i)
    
    savefig('../figures/saved/'+stringi+'.png')


    

    




C:\Users\sbiggs\AppData\Local\Continuum\Anaconda3\lib\site-packages\matplotlib\pyplot.py:412: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_num_figures`).
  max_open_warning, RuntimeWarning)