Explicit 1D Benchmarks

In [3]:

    

from pypge.benchmarks import explicit

import numpy as np
import pandas as pd

# visualization libraries
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# plot the visuals in ipython
%matplotlib inline


    

In [11]:

    

# Set your output directories
img_dir = "../img/benchmarks/nist-linear/"
data_dir = "../data/benchmarks/nist-linear/"

prob_name = "filip"

df = pd.read_csv(data_dir + prob_name + ".tsv", delim_whitespace=True, skipinitialspace=True)
df.plot()


    

    
Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x10793fb90>






    

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print prob['name'], prob['eqn']
print prob['xpts'].shape

fig = plt.figure()
fig.set_size_inches(16, 12)

plt.plot(prob['xpts'][0], prob['ypure'], 'r.')
plt.legend(loc='center left', bbox_to_anchor=(0.67, 0.12))

plt.title(prob['name'] + " Clean", fontsize=36)
plt.savefig(img_dir + prob['name'].lower() + "_clean.png", dpi=200)
# plt.show()

### You can only do one of 'savefig()' or 'show()'

fig = plt.figure()
fig.set_size_inches(16, 12)

plt.plot(prob['xpts'][0], prob['ypts'], 'b.')
plt.legend(loc='center left', bbox_to_anchor=(0.67, 0.12))

plt.title(prob['name'] + " Noisy", fontsize=36)
plt.savefig(img_dir + prob['name'].lower() + "_noisy.png", dpi=200)
# plt.show()


    

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data = np.array([prob['xpts'][0], prob['ypts']]).T
print data.shape

cols = [['x', 'out']]
out_data = cols + data.tolist()
import json
json_out = json.dumps( out_data, indent=4)
# print json_out

f_json = open(data_dir + prob['name'].lower() + ".json", 'w')
f_json.write(json_out)
f_json.close()

f_csv = open(data_dir + prob['name'].lower() + ".csv", 'w')
for row in out_data:
    line = ", ".join([str(col) for col in row]) + "\n"
    f_csv.write(line)
f_csv.close()


    

In [ ]:

    

data = np.array([prob['xpts'][0], prob['ypure']]).T
print data.shape

cols = [['x', 'out']]
out_data = cols + data.tolist()
import json
json_out = json.dumps( out_data, indent=4)
# print json_out

f_json = open(data_dir + prob['name'].lower() + "_clean.json", 'w')
f_json.write(json_out)
f_json.close()

f_csv = open(data_dir + prob['name'].lower() + "_clean.csv", 'w')
for row in out_data:
    line = ", ".join([str(col) for col in row]) + "\n"
    f_csv.write(line)
f_csv.close()