"""
This is a demo of creating a pdf file with several pages,
as well as adding metadata and annotations to pdf files.
"""

import datetime
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt

# Create the PdfPages object to which we will save the pages:
# The with statement makes sure that the PdfPages object is closed properly at
# the end of the block, even if an Exception occurs.
with PdfPages('multipage_pdf.pdf') as pdf:
    plt.figure(figsize=(3, 3))
    
    plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
    plt.title('Page One')
    pdf.savefig()  # saves the current figure into a pdf page
    plt.close()

    plt.rc('text', usetex=True)
    plt.figure(figsize=(8, 6))
    x = np.arange(0, 5, 0.1)
    plt.plot(x, np.sin(x), 'b-')
    plt.title('Page Two')
    #pdf.attach_note("plot of sin(x)")  # you can add a pdf note to
                                       # attach metadata to a page
    pdf.savefig()
    plt.close()

    plt.rc('text', usetex=False)
    fig = plt.figure(figsize=(4, 5))
    plt.plot(x, x*x, 'ko')
    plt.title('Page Three')
    pdf.savefig(fig)  # or you can pass a Figure object to pdf.savefig
    plt.close()

    # We can also set the file's metadata via the PdfPages object:
    d = pdf.infodict()
    d['Title'] = 'Multipage PDF Example'
    d['Author'] = u'Jouni K. Sepp\xe4nen'
    d['Subject'] = 'How to create a multipage pdf file and set its metadata'
    d['Keywords'] = 'PdfPages multipage keywords author title subject'
    d['CreationDate'] = datetime.datetime(2009, 11, 13)
    d['ModDate'] = datetime.datetime.today()

# cross-validation
print(__doc__)


import numpy as np
import sklearn
from sklearn import cross_validation, datasets, svm

digits = datasets.load_digits()
X = digits.data
y = digits.target

svc = svm.SVC(kernel='linear')
C_s = np.logspace(-10, 0, 10)

Automatically created module for IPython interactive environment

print X.shape, y.shape, digits.images.shape

(1797, 64) (1797,) (1797, 8, 8)

scores = list()
scores_std = list()
for C in C_s:
    svc.C = C
    this_scores = cross_validation.cross_val_score(svc, X, y, n_jobs=1)
    scores.append(np.mean(this_scores))
    scores_std.append(np.std(this_scores))

with PdfPages('multipage_pdf.pdf') as pdf:
    plt.figure(figsize=(3, 3))
    
    plt.plot(C_s, 'r-o')
    plt.title('Page Five')
    pdf.savefig()  # saves the current figure into a pdf page
    plt.close()
    
    plt.rc('text', usetex=True)

    plt.figure(figsize=(8, 6))
    
    plt.semilogx(C_s, scores)
    plt.semilogx(C_s, np.array(scores) + np.array(scores_std), 'b--')
    plt.semilogx(C_s, np.array(scores) - np.array(scores_std), 'b--')
    locs, labels = plt.yticks()
    plt.yticks(locs, list(map(lambda x: "%g" % x, locs)))
    plt.ylabel('CV score')
    plt.xlabel('Parameter C')
    plt.ylim(0, 1.1)
    
    plt.title('Page Sive')
    pdf.savefig()  # or you can pass a Figure object to pdf.savefig
    plt.close()