Merge pull request #7244 from minrk/rm-signature

don't store signatures in notebooks

IPython/html/services/contents/tests/test_manager.py

@@ -1,434 +1,436 b''
 # coding: utf-8
 """Tests for the notebook manager."""
 from __future__ import print_function
 
 import os
+import time
 
 from tornado.web import HTTPError
 from unittest import TestCase
 from tempfile import NamedTemporaryFile
 
 from IPython.nbformat import v4 as nbformat
 
 from IPython.utils.tempdir import TemporaryDirectory
 from IPython.utils.traitlets import TraitError
 from IPython.html.utils import url_path_join
 from IPython.testing import decorators as dec
 
 from ..filemanager import FileContentsManager
 
 
 def _make_dir(contents_manager, api_path):
     """
     Make a directory.
     """
     os_path = contents_manager._get_os_path(api_path)
     try:
         os.makedirs(os_path)
     except OSError:
         print("Directory already exists: %r" % os_path)
 
 
 class TestFileContentsManager(TestCase):
 
     def symlink(self, contents_manager, src, dst):
         """Make a symlink to src from dst
         
         src and dst are api_paths
         """
         src_os_path = contents_manager._get_os_path(src)
         dst_os_path = contents_manager._get_os_path(dst)
         print(src_os_path, dst_os_path, os.path.isfile(src_os_path))
         os.symlink(src_os_path, dst_os_path)
 
     def test_root_dir(self):
         with TemporaryDirectory() as td:
             fm = FileContentsManager(root_dir=td)
             self.assertEqual(fm.root_dir, td)
 
     def test_missing_root_dir(self):
         with TemporaryDirectory() as td:
             root = os.path.join(td, 'notebook', 'dir', 'is', 'missing')
             self.assertRaises(TraitError, FileContentsManager, root_dir=root)
 
     def test_invalid_root_dir(self):
         with NamedTemporaryFile() as tf:
             self.assertRaises(TraitError, FileContentsManager, root_dir=tf.name)
 
     def test_get_os_path(self):
         # full filesystem path should be returned with correct operating system
         # separators.
         with TemporaryDirectory() as td:
             root = td
             fm = FileContentsManager(root_dir=root)
             path = fm._get_os_path('/path/to/notebook/test.ipynb')
             rel_path_list =  '/path/to/notebook/test.ipynb'.split('/')
             fs_path = os.path.join(fm.root_dir, *rel_path_list)
             self.assertEqual(path, fs_path)
 
             fm = FileContentsManager(root_dir=root)
             path = fm._get_os_path('test.ipynb')
             fs_path = os.path.join(fm.root_dir, 'test.ipynb')
             self.assertEqual(path, fs_path)
 
             fm = FileContentsManager(root_dir=root)
             path = fm._get_os_path('////test.ipynb')
             fs_path = os.path.join(fm.root_dir, 'test.ipynb')
             self.assertEqual(path, fs_path)
 
     def test_checkpoint_subdir(self):
         subd = u'sub ∂ir'
         cp_name = 'test-cp.ipynb'
         with TemporaryDirectory() as td:
             root = td
             os.mkdir(os.path.join(td, subd))
             fm = FileContentsManager(root_dir=root)
             cp_dir = fm.get_checkpoint_path('cp', 'test.ipynb')
             cp_subdir = fm.get_checkpoint_path('cp', '/%s/test.ipynb' % subd)
         self.assertNotEqual(cp_dir, cp_subdir)
         self.assertEqual(cp_dir, os.path.join(root, fm.checkpoint_dir, cp_name))
         self.assertEqual(cp_subdir, os.path.join(root, subd, fm.checkpoint_dir, cp_name))
     
     @dec.skip_win32
     def test_bad_symlink(self):
         with TemporaryDirectory() as td:
             cm = FileContentsManager(root_dir=td)
             path = 'test bad symlink'
             _make_dir(cm, path)
 
             file_model = cm.new_untitled(path=path, ext='.txt')
 
             # create a broken symlink
             self.symlink(cm, "target", '%s/%s' % (path, 'bad symlink'))
             model = cm.get(path)
             self.assertEqual(model['content'], [file_model])
     
     @dec.skip_win32
     def test_good_symlink(self):
         with TemporaryDirectory() as td:
             cm = FileContentsManager(root_dir=td)
             parent = 'test good symlink'
             name = 'good symlink'
             path = '{0}/{1}'.format(parent, name)
             _make_dir(cm, parent)
 
             file_model = cm.new(path=parent + '/zfoo.txt')
 
             # create a good symlink
             self.symlink(cm, file_model['path'], path)
             symlink_model = cm.get(path, content=False)
             dir_model = cm.get(parent)
             self.assertEqual(
                 sorted(dir_model['content'], key=lambda x: x['name']),
                 [symlink_model, file_model],
             )
 
 
 class TestContentsManager(TestCase):
     
     def setUp(self):
         self._temp_dir = TemporaryDirectory()
         self.td = self._temp_dir.name
         self.contents_manager = FileContentsManager(
             root_dir=self.td,
         )
 
     def tearDown(self):
         self._temp_dir.cleanup()
 
     def make_dir(self, api_path):
         """make a subdirectory at api_path
         
         override in subclasses if contents are not on the filesystem.
         """
         _make_dir(self.contents_manager, api_path)
 
     def add_code_cell(self, nb):
         output = nbformat.new_output("display_data", {'application/javascript': "alert('hi');"})
         cell = nbformat.new_code_cell("print('hi')", outputs=[output])
         nb.cells.append(cell)
 
     def new_notebook(self):
         cm = self.contents_manager
         model = cm.new_untitled(type='notebook')
         name = model['name']
         path = model['path']
 
         full_model = cm.get(path)
         nb = full_model['content']
+        nb['metadata']['counter'] = int(1e6 * time.time())
         self.add_code_cell(nb)
 
         cm.save(full_model, path)
         return nb, name, path
 
     def test_new_untitled(self):
         cm = self.contents_manager
         # Test in root directory
         model = cm.new_untitled(type='notebook')
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertIn('type', model)
         self.assertEqual(model['type'], 'notebook')
         self.assertEqual(model['name'], 'Untitled.ipynb')
         self.assertEqual(model['path'], 'Untitled.ipynb')
 
         # Test in sub-directory
         model = cm.new_untitled(type='directory')
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertIn('type', model)
         self.assertEqual(model['type'], 'directory')
         self.assertEqual(model['name'], 'Untitled Folder')
         self.assertEqual(model['path'], 'Untitled Folder')
         sub_dir = model['path']
         
         model = cm.new_untitled(path=sub_dir)
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertIn('type', model)
         self.assertEqual(model['type'], 'file')
         self.assertEqual(model['name'], 'untitled')
         self.assertEqual(model['path'], '%s/untitled' % sub_dir)
 
     def test_get(self):
         cm = self.contents_manager
         # Create a notebook
         model = cm.new_untitled(type='notebook')
         name = model['name']
         path = model['path']
 
         # Check that we 'get' on the notebook we just created
         model2 = cm.get(path)
         assert isinstance(model2, dict)
         self.assertIn('name', model2)
         self.assertIn('path', model2)
         self.assertEqual(model['name'], name)
         self.assertEqual(model['path'], path)
 
         nb_as_file = cm.get(path, content=True, type='file')
         self.assertEqual(nb_as_file['path'], path)
         self.assertEqual(nb_as_file['type'], 'file')
         self.assertEqual(nb_as_file['format'], 'text')
         self.assertNotIsInstance(nb_as_file['content'], dict)
 
         nb_as_bin_file = cm.get(path, content=True, type='file', format='base64')
         self.assertEqual(nb_as_bin_file['format'], 'base64')
 
         # Test in sub-directory
         sub_dir = '/foo/'
         self.make_dir('foo')
         model = cm.new_untitled(path=sub_dir, ext='.ipynb')
         model2 = cm.get(sub_dir + name)
         assert isinstance(model2, dict)
         self.assertIn('name', model2)
         self.assertIn('path', model2)
         self.assertIn('content', model2)
         self.assertEqual(model2['name'], 'Untitled.ipynb')
         self.assertEqual(model2['path'], '{0}/{1}'.format(sub_dir.strip('/'), name))
 
         # Test with a regular file.
         file_model_path = cm.new_untitled(path=sub_dir, ext='.txt')['path']
         file_model = cm.get(file_model_path)
         self.assertDictContainsSubset(
             {
                 'content': u'',
                 'format': u'text',
                 'mimetype': u'text/plain',
                 'name': u'untitled.txt',
                 'path': u'foo/untitled.txt',
                 'type': u'file',
                 'writable': True,
             },
             file_model,
         )
         self.assertIn('created', file_model)
         self.assertIn('last_modified', file_model)
 
         # Test getting directory model
 
         # Create a sub-sub directory to test getting directory contents with a
         # subdir.
         self.make_dir('foo/bar')
         dirmodel = cm.get('foo')
         self.assertEqual(dirmodel['type'], 'directory')
         self.assertIsInstance(dirmodel['content'], list)
         self.assertEqual(len(dirmodel['content']), 3)
         self.assertEqual(dirmodel['path'], 'foo')
         self.assertEqual(dirmodel['name'], 'foo')
 
         # Directory contents should match the contents of each individual entry
         # when requested with content=False.
         model2_no_content = cm.get(sub_dir + name, content=False)
         file_model_no_content = cm.get(u'foo/untitled.txt', content=False)
         sub_sub_dir_no_content = cm.get('foo/bar', content=False)
         self.assertEqual(sub_sub_dir_no_content['path'], 'foo/bar')
         self.assertEqual(sub_sub_dir_no_content['name'], 'bar')
 
         for entry in dirmodel['content']:
             # Order isn't guaranteed by the spec, so this is a hacky way of
             # verifying that all entries are matched.
             if entry['path'] == sub_sub_dir_no_content['path']:
                 self.assertEqual(entry, sub_sub_dir_no_content)
             elif entry['path'] == model2_no_content['path']:
                 self.assertEqual(entry, model2_no_content)
             elif entry['path'] == file_model_no_content['path']:
                 self.assertEqual(entry, file_model_no_content)
             else:
                 self.fail("Unexpected directory entry: %s" % entry())
 
         with self.assertRaises(HTTPError):
             cm.get('foo', type='file')
 
     def test_update(self):
         cm = self.contents_manager
         # Create a notebook
         model = cm.new_untitled(type='notebook')
         name = model['name']
         path = model['path']
 
         # Change the name in the model for rename
         model['path'] = 'test.ipynb'
         model = cm.update(model, path)
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertEqual(model['name'], 'test.ipynb')
 
         # Make sure the old name is gone
         self.assertRaises(HTTPError, cm.get, path)
 
         # Test in sub-directory
         # Create a directory and notebook in that directory
         sub_dir = '/foo/'
         self.make_dir('foo')
         model = cm.new_untitled(path=sub_dir, type='notebook')
         path = model['path']
 
         # Change the name in the model for rename
         d = path.rsplit('/', 1)[0]
         new_path = model['path'] = d + '/test_in_sub.ipynb'
         model = cm.update(model, path)
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertEqual(model['name'], 'test_in_sub.ipynb')
         self.assertEqual(model['path'], new_path)
 
         # Make sure the old name is gone
         self.assertRaises(HTTPError, cm.get, path)
 
     def test_save(self):
         cm = self.contents_manager
         # Create a notebook
         model = cm.new_untitled(type='notebook')
         name = model['name']
         path = model['path']
 
         # Get the model with 'content'
         full_model = cm.get(path)
 
         # Save the notebook
         model = cm.save(full_model, path)
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertEqual(model['name'], name)
         self.assertEqual(model['path'], path)
 
         # Test in sub-directory
         # Create a directory and notebook in that directory
         sub_dir = '/foo/'
         self.make_dir('foo')
         model = cm.new_untitled(path=sub_dir, type='notebook')
         name = model['name']
         path = model['path']
         model = cm.get(path)
 
         # Change the name in the model for rename
         model = cm.save(model, path)
         assert isinstance(model, dict)
         self.assertIn('name', model)
         self.assertIn('path', model)
         self.assertEqual(model['name'], 'Untitled.ipynb')
         self.assertEqual(model['path'], 'foo/Untitled.ipynb')
 
     def test_delete(self):
         cm = self.contents_manager
         # Create a notebook
         nb, name, path = self.new_notebook()
 
         # Delete the notebook
         cm.delete(path)
 
         # Check that deleting a non-existent path raises an error.
         self.assertRaises(HTTPError, cm.delete, path)
 
         # Check that a 'get' on the deleted notebook raises and error
         self.assertRaises(HTTPError, cm.get, path)
 
     def test_copy(self):
         cm = self.contents_manager
         parent = u'å b'
         name = u'nb √.ipynb'
         path = u'{0}/{1}'.format(parent, name)
         self.make_dir(parent)
 
         orig = cm.new(path=path)
         # copy with unspecified name
         copy = cm.copy(path)
         self.assertEqual(copy['name'], orig['name'].replace('.ipynb', '-Copy1.ipynb'))
 
         # copy with specified name
         copy2 = cm.copy(path, u'å b/copy 2.ipynb')
         self.assertEqual(copy2['name'], u'copy 2.ipynb')
         self.assertEqual(copy2['path'], u'å b/copy 2.ipynb')
         # copy with specified path
         copy2 = cm.copy(path, u'/')
         self.assertEqual(copy2['name'], name)
         self.assertEqual(copy2['path'], name)
 
     def test_trust_notebook(self):
         cm = self.contents_manager
         nb, name, path = self.new_notebook()
 
         untrusted = cm.get(path)['content']
         assert not cm.notary.check_cells(untrusted)
 
         # print(untrusted)
         cm.trust_notebook(path)
         trusted = cm.get(path)['content']
         # print(trusted)
         assert cm.notary.check_cells(trusted)
 
     def test_mark_trusted_cells(self):
         cm = self.contents_manager
         nb, name, path = self.new_notebook()
 
         cm.mark_trusted_cells(nb, path)
         for cell in nb.cells:
             if cell.cell_type == 'code':
                 assert not cell.metadata.trusted
 
         cm.trust_notebook(path)
         nb = cm.get(path)['content']
         for cell in nb.cells:
             if cell.cell_type == 'code':
                 assert cell.metadata.trusted
 
     def test_check_and_sign(self):
         cm = self.contents_manager
         nb, name, path = self.new_notebook()
 
         cm.mark_trusted_cells(nb, path)
         cm.check_and_sign(nb, path)
         assert not cm.notary.check_signature(nb)
 
         cm.trust_notebook(path)
         nb = cm.get(path)['content']
         cm.mark_trusted_cells(nb, path)
         cm.check_and_sign(nb, path)
         assert cm.notary.check_signature(nb)
 

IPython/nbformat/sign.py

@@ -1,331 +1,427 b''
-"""Functions for signing notebooks"""
+"""Utilities for signing notebooks"""
 
 # Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
 
 import base64
 from contextlib import contextmanager
+from datetime import datetime
 import hashlib
 from hmac import HMAC
 import io
 import os
 
+try:
+    import sqlite3
+except ImportError:
+    try:
+        from pysqlite2 import dbapi2 as sqlite3
+    except ImportError:
+        sqlite3 = None
+
 from IPython.utils.io import atomic_writing
-from IPython.utils.py3compat import string_types, unicode_type, cast_bytes
-from IPython.utils.traitlets import Instance, Bytes, Enum, Any, Unicode, Bool
+from IPython.utils.py3compat import unicode_type, cast_bytes
+from IPython.utils.traitlets import Instance, Bytes, Enum, Any, Unicode, Bool, Integer
 from IPython.config import LoggingConfigurable, MultipleInstanceError
 from IPython.core.application import BaseIPythonApplication, base_flags
 
 from . import read, write, NO_CONVERT
 
 try:
     # Python 3
     algorithms = hashlib.algorithms_guaranteed
 except AttributeError:
     algorithms = hashlib.algorithms
 
 
 def yield_everything(obj):
     """Yield every item in a container as bytes
     
     Allows any JSONable object to be passed to an HMAC digester
     without having to serialize the whole thing.
     """
     if isinstance(obj, dict):
         for key in sorted(obj):
             value = obj[key]
             yield cast_bytes(key)
             for b in yield_everything(value):
                 yield b
     elif isinstance(obj, (list, tuple)):
         for element in obj:
             for b in yield_everything(element):
                 yield b
     elif isinstance(obj, unicode_type):
         yield obj.encode('utf8')
     else:
         yield unicode_type(obj).encode('utf8')
 
 def yield_code_cells(nb):
     """Iterator that yields all cells in a notebook
     
     nbformat version independent
     """
     if nb.nbformat >= 4:
         for cell in nb['cells']:
             if cell['cell_type'] == 'code':
                 yield cell
     elif nb.nbformat == 3:
         for ws in nb['worksheets']:
             for cell in ws['cells']:
                 if cell['cell_type'] == 'code':
                     yield cell
 
 @contextmanager
 def signature_removed(nb):
     """Context manager for operating on a notebook with its signature removed
     
     Used for excluding the previous signature when computing a notebook's signature.
     """
     save_signature = nb['metadata'].pop('signature', None)
     try:
         yield
     finally:
         if save_signature is not None:
             nb['metadata']['signature'] = save_signature
 
 
 class NotebookNotary(LoggingConfigurable):
     """A class for computing and verifying notebook signatures."""
     
     profile_dir = Instance("IPython.core.profiledir.ProfileDir")
     def _profile_dir_default(self):
         from IPython.core.application import BaseIPythonApplication
         app = None
         try:
             if BaseIPythonApplication.initialized():
                 app = BaseIPythonApplication.instance()
         except MultipleInstanceError:
             pass
         if app is None:
             # create an app, without the global instance
             app = BaseIPythonApplication()
             app.initialize(argv=[])
         return app.profile_dir
     
+    db_file = Unicode(config=True,
+        help="""The sqlite file in which to store notebook signatures.
+        By default, this will be in your IPython profile.
+        You can set it to ':memory:' to disable sqlite writing to the filesystem.
+        """)
+    def _db_file_default(self):
+        if self.profile_dir is None:
+            return ':memory:'
+        return os.path.join(self.profile_dir.security_dir, u'nbsignatures.db')
+    
+    # 64k entries ~ 12MB
+    cache_size = Integer(65535, config=True,
+        help="""The number of notebook signatures to cache.
+        When the number of signatures exceeds this value,
+        the oldest 25% of signatures will be culled.
+        """
+    )
+    db = Any()
+    def _db_default(self):
+        if sqlite3 is None:
+            self.log.warn("Missing SQLite3, all notebooks will be untrusted!")
+            return
+        kwargs = dict(detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
+        db = sqlite3.connect(self.db_file, **kwargs)
+        self.init_db(db)
+        return db
+    
+    def init_db(self, db):
+        db.execute("""
+        CREATE TABLE IF NOT EXISTS nbsignatures
+        (
+            id integer PRIMARY KEY AUTOINCREMENT,
+            algorithm text,
+            signature text,
+            path text,
+            last_seen timestamp
+        )""")
+        db.execute("""
+        CREATE INDEX IF NOT EXISTS algosig ON nbsignatures(algorithm, signature)
+        """)
+        db.commit()
+    
     algorithm = Enum(algorithms, default_value='sha256', config=True,
         help="""The hashing algorithm used to sign notebooks."""
     )
     def _algorithm_changed(self, name, old, new):
         self.digestmod = getattr(hashlib, self.algorithm)
     
     digestmod = Any()
     def _digestmod_default(self):
         return getattr(hashlib, self.algorithm)
     
     secret_file = Unicode(config=True,
         help="""The file where the secret key is stored."""
     )
     def _secret_file_default(self):
         if self.profile_dir is None:
             return ''
         return os.path.join(self.profile_dir.security_dir, 'notebook_secret')
     
     secret = Bytes(config=True,
         help="""The secret key with which notebooks are signed."""
     )
     def _secret_default(self):
         # note : this assumes an Application is running
         if os.path.exists(self.secret_file):
             with io.open(self.secret_file, 'rb') as f:
                 return f.read()
         else:
             secret = base64.encodestring(os.urandom(1024))
             self._write_secret_file(secret)
             return secret
     
     def _write_secret_file(self, secret):
         """write my secret to my secret_file"""
         self.log.info("Writing notebook-signing key to %s", self.secret_file)
         with io.open(self.secret_file, 'wb') as f:
             f.write(secret)
         try:
             os.chmod(self.secret_file, 0o600)
         except OSError:
             self.log.warn(
                 "Could not set permissions on %s",
                 self.secret_file
             )
         return secret
     
     def compute_signature(self, nb):
         """Compute a notebook's signature
         
         by hashing the entire contents of the notebook via HMAC digest.
         """
         hmac = HMAC(self.secret, digestmod=self.digestmod)
         # don't include the previous hash in the content to hash
         with signature_removed(nb):
             # sign the whole thing
             for b in yield_everything(nb):
                 hmac.update(b)
         
         return hmac.hexdigest()
     
     def check_signature(self, nb):
         """Check a notebook's stored signature
         
         If a signature is stored in the notebook's metadata,
         a new signature is computed and compared with the stored value.
         
         Returns True if the signature is found and matches, False otherwise.
         
         The following conditions must all be met for a notebook to be trusted:
         - a signature is stored in the form 'scheme:hexdigest'
         - the stored scheme matches the requested scheme
         - the requested scheme is available from hashlib
         - the computed hash from notebook_signature matches the stored hash
         """
         if nb.nbformat < 3:
             return False
-        stored_signature = nb['metadata'].get('signature', None)
-        if not stored_signature \
-            or not isinstance(stored_signature, string_types) \
-            or ':' not in stored_signature:
+        if self.db is None:
             return False
-        stored_algo, sig = stored_signature.split(':', 1)
-        if self.algorithm != stored_algo:
+        signature = self.compute_signature(nb)
+        r = self.db.execute("""SELECT id FROM nbsignatures WHERE
+            algorithm = ? AND
+            signature = ?;
+            """, (self.algorithm, signature)).fetchone()
+        if r is None:
             return False
-        my_signature = self.compute_signature(nb)
-        return my_signature == sig
+        self.db.execute("""UPDATE nbsignatures SET last_seen = ? WHERE
+            algorithm = ? AND
+            signature = ?;
+            """,
+            (datetime.utcnow(), self.algorithm, signature),
+        )
+        self.db.commit()
+        return True
     
     def sign(self, nb):
-        """Sign a notebook, indicating that its output is trusted
-        
-        stores 'algo:hmac-hexdigest' in notebook.metadata.signature
+        """Sign a notebook, indicating that its output is trusted on this machine
         
-        e.g. 'sha256:deadbeef123...'
+        Stores hash algorithm and hmac digest in a local database of trusted notebooks.
         """
         if nb.nbformat < 3:
             return
         signature = self.compute_signature(nb)
-        nb['metadata']['signature'] = "%s:%s" % (self.algorithm, signature)
+        self.store_signature(signature, nb)
+
+    def store_signature(self, signature, nb):
+        if self.db is None:
+            return
+        self.db.execute("""INSERT OR IGNORE INTO nbsignatures
+            (algorithm, signature, last_seen) VALUES (?, ?, ?)""",
+            (self.algorithm, signature, datetime.utcnow())
+        )
+        self.db.execute("""UPDATE nbsignatures SET last_seen = ? WHERE
+            algorithm = ? AND
+            signature = ?;
+            """,
+            (datetime.utcnow(), self.algorithm, signature),
+        )
+        self.db.commit()
+        n, = self.db.execute("SELECT Count(*) FROM nbsignatures").fetchone()
+        if n > self.cache_size:
+            self.cull_db()
+    
+    def unsign(self, nb):
+        """Ensure that a notebook is untrusted
+        
+        by removing its signature from the trusted database, if present.
+        """
+        signature = self.compute_signature(nb)
+        self.db.execute("""DELETE FROM nbsignatures WHERE
+                algorithm = ? AND
+                signature = ?;
+            """,
+            (self.algorithm, signature)
+        )
+        self.db.commit()
+    
+    def cull_db(self):
+        """Cull oldest 25% of the trusted signatures when the size limit is reached"""
+        self.db.execute("""DELETE FROM nbsignatures WHERE id IN (
+            SELECT id FROM nbsignatures ORDER BY last_seen DESC LIMIT -1 OFFSET ?
+        );
+        """, (max(int(0.75 * self.cache_size), 1),))
     
     def mark_cells(self, nb, trusted):
         """Mark cells as trusted if the notebook's signature can be verified
         
         Sets ``cell.metadata.trusted = True | False`` on all code cells,
         depending on whether the stored signature can be verified.
         
         This function is the inverse of check_cells
         """
         if nb.nbformat < 3:
             return
         
         for cell in yield_code_cells(nb):
             cell['metadata']['trusted'] = trusted
     
     def _check_cell(self, cell, nbformat_version):
         """Do we trust an individual cell?
         
         Return True if:
         
         - cell is explicitly trusted
         - cell has no potentially unsafe rich output
         
         If a cell has no output, or only simple print statements,
         it will always be trusted.
         """
         # explicitly trusted
         if cell['metadata'].pop("trusted", False):
             return True
         
         # explicitly safe output
         if nbformat_version >= 4:
-            safe = {'text/plain', 'image/png', 'image/jpeg'}
             unsafe_output_types = ['execute_result', 'display_data']
             safe_keys = {"output_type", "execution_count", "metadata"}
         else: # v3
-            safe = {'text', 'png', 'jpeg'}
             unsafe_output_types = ['pyout', 'display_data']
             safe_keys = {"output_type", "prompt_number", "metadata"}
         
         for output in cell['outputs']:
             output_type = output['output_type']
             if output_type in unsafe_output_types:
                 # if there are any data keys not in the safe whitelist
                 output_keys = set(output)
                 if output_keys.difference(safe_keys):
                     return False
         
         return True
     
     def check_cells(self, nb):
         """Return whether all code cells are trusted
         
         If there are no code cells, return True.
         
         This function is the inverse of mark_cells.
         """
         if nb.nbformat < 3:
             return False
         trusted = True
         for cell in yield_code_cells(nb):
             # only distrust a cell if it actually has some output to distrust
             if not self._check_cell(cell, nb.nbformat):
                 trusted = False
 
         return trusted
 
 
 trust_flags = {
     'reset' : (
         {'TrustNotebookApp' : { 'reset' : True}},
-        """Generate a new key for notebook signature.
+        """Delete the trusted notebook cache.
         All previously signed notebooks will become untrusted.
         """
     ),
 }
 trust_flags.update(base_flags)
 trust_flags.pop('init')
 
 
 class TrustNotebookApp(BaseIPythonApplication):
     
     description="""Sign one or more IPython notebooks with your key,
     to trust their dynamic (HTML, Javascript) output.
     
     Trusting a notebook only applies to the current IPython profile.
     To trust a notebook for use with a profile other than default,
     add `--profile [profile name]`.
     
     Otherwise, you will have to re-execute the notebook to see output.
     """
     
     examples = """
     ipython trust mynotebook.ipynb and_this_one.ipynb
     ipython trust --profile myprofile mynotebook.ipynb
     """
     
     flags = trust_flags
     
     reset = Bool(False, config=True,
-        help="""If True, generate a new key for notebook signature.
+        help="""If True, delete the trusted signature cache.
         After reset, all previously signed notebooks will become untrusted.
         """
     )
     
     notary = Instance(NotebookNotary)
     def _notary_default(self):
         return NotebookNotary(parent=self, profile_dir=self.profile_dir)
     
     def sign_notebook(self, notebook_path):
         if not os.path.exists(notebook_path):
             self.log.error("Notebook missing: %s" % notebook_path)
             self.exit(1)
         with io.open(notebook_path, encoding='utf8') as f:
             nb = read(f, NO_CONVERT)
         if self.notary.check_signature(nb):
             print("Notebook already signed: %s" % notebook_path)
         else:
             print("Signing notebook: %s" % notebook_path)
             self.notary.sign(nb)
             with atomic_writing(notebook_path) as f:
                 write(nb, f, NO_CONVERT)
     
     def generate_new_key(self):
         """Generate a new notebook signature key"""
         print("Generating new notebook key: %s" % self.notary.secret_file)
         self.notary._write_secret_file(os.urandom(1024))
     
     def start(self):
         if self.reset:
+            if os.path.exists(self.notary.db_file):
+                print("Removing trusted signature cache: %s" % self.notary.db_file)
+                os.remove(self.notary.db_file)
             self.generate_new_key()
             return
         if not self.extra_args:
             self.log.critical("Specify at least one notebook to sign.")
             self.exit(1)
         
         for notebook_path in self.extra_args:
             self.sign_notebook(notebook_path)
 

IPython/nbformat/tests/test_sign.py

@@ -1,150 +1,191 b''
 """Test Notebook signing"""
 
 # Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
 
+import copy
+import time
+
 from .base import TestsBase
 
 from IPython.nbformat import read, sign
 from IPython.core.getipython import get_ipython
 
 
 class TestNotary(TestsBase):
     
     def setUp(self):
         self.notary = sign.NotebookNotary(
+            db_file=':memory:',
             secret=b'secret',
-            profile_dir=get_ipython().profile_dir
+            profile_dir=get_ipython().profile_dir,
         )
         with self.fopen(u'test3.ipynb', u'r') as f:
             self.nb = read(f, as_version=4)
         with self.fopen(u'test3.ipynb', u'r') as f:
             self.nb3 = read(f, as_version=3)
     
     def test_algorithms(self):
         last_sig = ''
         for algo in sign.algorithms:
             self.notary.algorithm = algo
-            self.notary.sign(self.nb)
-            sig = self.nb.metadata.signature
-            print(sig)
-            self.assertEqual(sig[:len(self.notary.algorithm)+1], '%s:' % self.notary.algorithm)
+            sig = self.notary.compute_signature(self.nb)
             self.assertNotEqual(last_sig, sig)
             last_sig = sig
     
     def test_sign_same(self):
         """Multiple signatures of the same notebook are the same"""
         sig1 = self.notary.compute_signature(self.nb)
         sig2 = self.notary.compute_signature(self.nb)
         self.assertEqual(sig1, sig2)
     
     def test_change_secret(self):
         """Changing the secret changes the signature"""
         sig1 = self.notary.compute_signature(self.nb)
         self.notary.secret = b'different'
         sig2 = self.notary.compute_signature(self.nb)
         self.assertNotEqual(sig1, sig2)
     
     def test_sign(self):
+        self.assertFalse(self.notary.check_signature(self.nb))
+        self.notary.sign(self.nb)
+        self.assertTrue(self.notary.check_signature(self.nb))
+    
+    def test_unsign(self):
         self.notary.sign(self.nb)
-        sig = self.nb.metadata.signature
-        self.assertEqual(sig[:len(self.notary.algorithm)+1], '%s:' % self.notary.algorithm)
+        self.assertTrue(self.notary.check_signature(self.nb))
+        self.notary.unsign(self.nb)
+        self.assertFalse(self.notary.check_signature(self.nb))
+        self.notary.unsign(self.nb)
+        self.assertFalse(self.notary.check_signature(self.nb))
+    
+    def test_cull_db(self):
+        # this test has various sleeps of 2ms
+        # to ensure low resolution timestamps compare as expected
+        dt = 2e-3
+        nbs = [
+            copy.deepcopy(self.nb) for i in range(10)
+        ]
+        for row in self.notary.db.execute("SELECT * FROM nbsignatures"):
+            print(row)
+        self.notary.cache_size = 8
+        for i, nb in enumerate(nbs[:8]):
+            nb.metadata.dirty = i
+            self.notary.sign(nb)
+        
+        for i, nb in enumerate(nbs[:8]):
+            time.sleep(dt)
+            self.assertTrue(self.notary.check_signature(nb), 'nb %i is trusted' % i)
+        
+        # signing the 9th triggers culling of first 3
+        # (75% of 8 = 6, 9 - 6 = 3 culled)
+        self.notary.sign(nbs[8])
+        self.assertFalse(self.notary.check_signature(nbs[0]))
+        self.assertFalse(self.notary.check_signature(nbs[1]))
+        self.assertFalse(self.notary.check_signature(nbs[2]))
+        self.assertTrue(self.notary.check_signature(nbs[3]))
+        # checking nb3 should keep it from being culled:
+        self.notary.sign(nbs[0])
+        self.notary.sign(nbs[1])
+        self.notary.sign(nbs[2])
+        self.assertTrue(self.notary.check_signature(nbs[3]))
+        self.assertFalse(self.notary.check_signature(nbs[4]))
     
     def test_check_signature(self):
         nb = self.nb
         md = nb.metadata
         notary = self.notary
         check_signature = notary.check_signature
         # no signature:
         md.pop('signature', None)
         self.assertFalse(check_signature(nb))
         # hash only, no algo
         md.signature = notary.compute_signature(nb)
         self.assertFalse(check_signature(nb))
         # proper signature, algo mismatch
         notary.algorithm = 'sha224'
         notary.sign(nb)
         notary.algorithm = 'sha256'
         self.assertFalse(check_signature(nb))
         # check correctly signed notebook
         notary.sign(nb)
         self.assertTrue(check_signature(nb))
     
     def test_mark_cells_untrusted(self):
         cells = self.nb.cells
         self.notary.mark_cells(self.nb, False)
         for cell in cells:
             self.assertNotIn('trusted', cell)
             if cell.cell_type == 'code':
                 self.assertIn('trusted', cell.metadata)
                 self.assertFalse(cell.metadata.trusted)
             else:
                 self.assertNotIn('trusted', cell.metadata)
     
     def test_mark_cells_trusted(self):
         cells = self.nb.cells
         self.notary.mark_cells(self.nb, True)
         for cell in cells:
             self.assertNotIn('trusted', cell)
             if cell.cell_type == 'code':
                 self.assertIn('trusted', cell.metadata)
                 self.assertTrue(cell.metadata.trusted)
             else:
                 self.assertNotIn('trusted', cell.metadata)
     
     def test_check_cells(self):
         nb = self.nb
         self.notary.mark_cells(nb, True)
         self.assertTrue(self.notary.check_cells(nb))
         for cell in nb.cells:
             self.assertNotIn('trusted', cell)
         self.notary.mark_cells(nb, False)
         self.assertFalse(self.notary.check_cells(nb))
         for cell in nb.cells:
             self.assertNotIn('trusted', cell)
     
     def test_trust_no_output(self):
         nb = self.nb
         self.notary.mark_cells(nb, False)
         for cell in nb.cells:
             if cell.cell_type == 'code':
                 cell.outputs = []
         self.assertTrue(self.notary.check_cells(nb))
     
     def test_mark_cells_untrusted_v3(self):
         nb = self.nb3
         cells = nb.worksheets[0].cells
         self.notary.mark_cells(nb, False)
         for cell in cells:
             self.assertNotIn('trusted', cell)
             if cell.cell_type == 'code':
                 self.assertIn('trusted', cell.metadata)
                 self.assertFalse(cell.metadata.trusted)
             else:
                 self.assertNotIn('trusted', cell.metadata)
     
     def test_mark_cells_trusted_v3(self):
         nb = self.nb3
         cells = nb.worksheets[0].cells
         self.notary.mark_cells(nb, True)
         for cell in cells:
             self.assertNotIn('trusted', cell)
             if cell.cell_type == 'code':
                 self.assertIn('trusted', cell.metadata)
                 self.assertTrue(cell.metadata.trusted)
             else:
                 self.assertNotIn('trusted', cell.metadata)
     
     def test_check_cells_v3(self):
         nb = self.nb3
         cells = nb.worksheets[0].cells
         self.notary.mark_cells(nb, True)
         self.assertTrue(self.notary.check_cells(nb))
         for cell in cells:
             self.assertNotIn('trusted', cell)
         self.notary.mark_cells(nb, False)
         self.assertFalse(self.notary.check_cells(nb))
         for cell in cells:
             self.assertNotIn('trusted', cell)
         
 

IPython/nbformat/v4/convert.py

@@ -1,252 +1,253 b''
 """Code for converting notebooks to and from v3."""
 
 # Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
 
 import json
 import re
 
 from .nbbase import (
     nbformat, nbformat_minor,
     NotebookNode,
 )
 
 from IPython.nbformat import v3
 from IPython.utils.log import get_logger
 
 def _warn_if_invalid(nb, version):
     """Log validation errors, if there are any."""
     from IPython.nbformat import validate, ValidationError
     try:
         validate(nb, version=version)
     except ValidationError as e:
         get_logger().error("Notebook JSON is not valid v%i: %s", version, e)
 
 def upgrade(nb, from_version=3, from_minor=0):
     """Convert a notebook to v4.
 
     Parameters
     ----------
     nb : NotebookNode
         The Python representation of the notebook to convert.
     from_version : int
         The original version of the notebook to convert.
     from_minor : int
         The original minor version of the notebook to convert (only relevant for v >= 3).
     """
     if from_version == 3:
         # Validate the notebook before conversion
         _warn_if_invalid(nb, from_version)
 
         # Mark the original nbformat so consumers know it has been converted
         orig_nbformat = nb.pop('orig_nbformat', None)
         nb.metadata.orig_nbformat = orig_nbformat or 3
 
         # Mark the new format
         nb.nbformat = nbformat
         nb.nbformat_minor = nbformat_minor
 
         # remove worksheet(s)
         nb['cells'] = cells = []
         # In the unlikely event of multiple worksheets,
         # they will be flattened
         for ws in nb.pop('worksheets', []):
             # upgrade each cell
             for cell in ws['cells']:
                 cells.append(upgrade_cell(cell))
         # upgrade metadata
         nb.metadata.pop('name', '')
+        nb.metadata.pop('signature', '')
         # Validate the converted notebook before returning it
         _warn_if_invalid(nb, nbformat)
         return nb
     elif from_version == 4:
         # nothing to do
         if from_minor != nbformat_minor:
             nb.metadata.orig_nbformat_minor = from_minor
         nb.nbformat_minor = nbformat_minor
 
         return nb
     else:
         raise ValueError('Cannot convert a notebook directly from v%s to v4.  ' \
                 'Try using the IPython.nbformat.convert module.' % from_version)
 
 def upgrade_cell(cell):
     """upgrade a cell from v3 to v4
 
     heading cell:
         - -> markdown heading
     code cell:
         - remove language metadata
         - cell.input -> cell.source
         - cell.prompt_number -> cell.execution_count
         - update outputs
     """
     cell.setdefault('metadata', NotebookNode())
     if cell.cell_type == 'code':
         cell.pop('language', '')
         if 'collapsed' in cell:
             cell.metadata['collapsed'] = cell.pop('collapsed')
         cell.source = cell.pop('input', '')
         cell.execution_count = cell.pop('prompt_number', None)
         cell.outputs = upgrade_outputs(cell.outputs)
     elif cell.cell_type == 'heading':
         cell.cell_type = 'markdown'
         level = cell.pop('level', 1)
         cell.source = u'{hashes} {single_line}'.format(
             hashes='#' * level,
             single_line = ' '.join(cell.get('source', '').splitlines()),
         )
     elif cell.cell_type == 'html':
         # Technically, this exists. It will never happen in practice.
         cell.cell_type = 'markdown'
     return cell
 
 def downgrade_cell(cell):
     """downgrade a cell from v4 to v3
 
     code cell:
         - set cell.language
         - cell.input <- cell.source
         - cell.prompt_number <- cell.execution_count
         - update outputs
     markdown cell:
         - single-line heading -> heading cell
     """
     if cell.cell_type == 'code':
         cell.language = 'python'
         cell.input = cell.pop('source', '')
         cell.prompt_number = cell.pop('execution_count', None)
         cell.collapsed = cell.metadata.pop('collapsed', False)
         cell.outputs = downgrade_outputs(cell.outputs)
     elif cell.cell_type == 'markdown':
         source = cell.get('source', '')
         if '\n' not in source and source.startswith('#'):
             prefix, text = re.match(r'(#+)\s*(.*)', source).groups()
             cell.cell_type = 'heading'
             cell.source = text
             cell.level = len(prefix)
     return cell
 
 _mime_map = {
     "text" : "text/plain",
     "html" : "text/html",
     "svg" : "image/svg+xml",
     "png" : "image/png",
     "jpeg" : "image/jpeg",
     "latex" : "text/latex",
     "json" : "application/json",
     "javascript" : "application/javascript",
 };
 
 def to_mime_key(d):
     """convert dict with v3 aliases to plain mime-type keys"""
     for alias, mime in _mime_map.items():
         if alias in d:
             d[mime] = d.pop(alias)
     return d
 
 def from_mime_key(d):
     """convert dict with mime-type keys to v3 aliases"""
     for alias, mime in _mime_map.items():
         if mime in d:
             d[alias] = d.pop(mime)
     return d
 
 def upgrade_output(output):
     """upgrade a single code cell output from v3 to v4
 
     - pyout -> execute_result
     - pyerr -> error
     - output.type -> output.data.mime/type
     - mime-type keys
     - stream.stream -> stream.name
     """
     if output['output_type'] in {'pyout', 'display_data'}:
         output.setdefault('metadata', NotebookNode())
         if output['output_type'] == 'pyout':
             output['output_type'] = 'execute_result'
             output['execution_count'] = output.pop('prompt_number', None)
 
         # move output data into data sub-dict
         data = {}
         for key in list(output):
             if key in {'output_type', 'execution_count', 'metadata'}:
                 continue
             data[key] = output.pop(key)
         to_mime_key(data)
         output['data'] = data
         to_mime_key(output.metadata)
         if 'application/json' in data:
             data['application/json'] = json.loads(data['application/json'])
         # promote ascii bytes (from v2) to unicode
         for key in ('image/png', 'image/jpeg'):
             if key in data and isinstance(data[key], bytes):
                 data[key] = data[key].decode('ascii')
     elif output['output_type'] == 'pyerr':
         output['output_type'] = 'error'
     elif output['output_type'] == 'stream':
         output['name'] = output.pop('stream')
     return output
 
 def downgrade_output(output):
     """downgrade a single code cell output to v3 from v4
 
     - pyout <- execute_result
     - pyerr <- error
     - output.data.mime/type -> output.type
     - un-mime-type keys
     - stream.stream <- stream.name
     """
     if output['output_type'] in {'execute_result', 'display_data'}:
         if output['output_type'] == 'execute_result':
             output['output_type'] = 'pyout'
             output['prompt_number'] = output.pop('execution_count', None)
 
         # promote data dict to top-level output namespace
         data = output.pop('data', {})
         if 'application/json' in data:
             data['application/json'] = json.dumps(data['application/json'])
         from_mime_key(data)
         output.update(data)
         from_mime_key(output.get('metadata', {}))
     elif output['output_type'] == 'error':
         output['output_type'] = 'pyerr'
     elif output['output_type'] == 'stream':
         output['stream'] = output.pop('name')
     return output
 
 def upgrade_outputs(outputs):
     """upgrade outputs of a code cell from v3 to v4"""
     return [upgrade_output(op) for op in outputs]
 
 def downgrade_outputs(outputs):
     """downgrade outputs of a code cell to v3 from v4"""
     return [downgrade_output(op) for op in outputs]
 
 def downgrade(nb):
     """Convert a v4 notebook to v3.
 
     Parameters
     ----------
     nb : NotebookNode
         The Python representation of the notebook to convert.
     """
     if nb.nbformat != nbformat:
         return nb
 
     # Validate the notebook before conversion
     _warn_if_invalid(nb, nbformat)
 
     nb.nbformat = v3.nbformat
     nb.nbformat_minor = v3.nbformat_minor
     cells = [ downgrade_cell(cell) for cell in nb.pop('cells') ]
     nb.worksheets = [v3.new_worksheet(cells=cells)]
     nb.metadata.setdefault('name', '')
     
     # Validate the converted notebook before returning it
     _warn_if_invalid(nb, v3.nbformat)
     
     nb.orig_nbformat = nb.metadata.pop('orig_nbformat', nbformat)
     nb.orig_nbformat_minor = nb.metadata.pop('orig_nbformat_minor', nbformat_minor)
     
     return nb

IPython/nbformat/v4/nbformat.v4.schema.json

@@ -1,375 +1,371 b''
 {
     "$schema": "http://json-schema.org/draft-04/schema#",
     "description": "IPython Notebook v4.0 JSON schema.",
     "type": "object",
     "additionalProperties": false,
     "required": ["metadata", "nbformat_minor", "nbformat", "cells"],
     "properties": {
         "metadata": {
             "description": "Notebook root-level metadata.",
             "type": "object",
             "additionalProperties": true,
             "properties": {
                 "kernelspec": {
                     "description": "Kernel information.",
                     "type": "object",
                     "required": ["name", "display_name"],
                     "properties": {
                         "name": {
                             "description": "Name of the kernel specification.",
                             "type": "string"
                         },
                         "display_name": {
                             "description": "Name to display in UI.",
                             "type": "string"
                         }
                     }
                 },
                 "language_info": {
                   "description": "Kernel information.",
                   "type": "object",
                   "required": ["name"],
                   "properties": {
                     "name": {
                         "description": "The programming language which this kernel runs.",
                         "type": "string"
                     },
                     "codemirror_mode": {
                         "description": "The codemirror mode to use for code in this language.",
                         "oneOf": [
                           {"type": "string"},
                           {"type": "object"}
                         ]
                     },
                     "file_extension": {
                         "description": "The file extension for files in this language.",
                         "type": "string"
                     },
                     "mimetype": {
                         "description": "The mimetype corresponding to files in this language.",
                         "type": "string"
                     },
                     "pygments_lexer": {
                         "description": "The pygments lexer to use for code in this language.",
                         "type": "string"
                     }
                   }
                 },
-                "signature": {
-                    "description": "Hash of the notebook.",
-                    "type": "string"
-                },
                 "orig_nbformat": {
                     "description": "Original notebook format (major number) before converting the notebook between versions. This should never be written to a file.",
                     "type": "integer",
                     "minimum": 1
                 }
             }
         },
         "nbformat_minor": {
             "description": "Notebook format (minor number). Incremented for backward compatible changes to the notebook format.",
             "type": "integer",
             "minimum": 0
         },
         "nbformat": {
             "description": "Notebook format (major number). Incremented between backwards incompatible changes to the notebook format.",
             "type": "integer",
             "minimum": 4,
             "maximum": 4
         },
         "cells": {
             "description": "Array of cells of the current notebook.",
             "type": "array",
             "items": {"$ref": "#/definitions/cell"}
         }
     },
 
     "definitions": {
         "cell": {
             "type": "object",
             "oneOf": [
                 {"$ref": "#/definitions/raw_cell"},
                 {"$ref": "#/definitions/markdown_cell"},
                 {"$ref": "#/definitions/code_cell"}
             ]
         },
 
         "raw_cell": {
             "description": "Notebook raw nbconvert cell.",
             "type": "object",
             "additionalProperties": false,
             "required": ["cell_type", "metadata", "source"],
             "properties": {
                 "cell_type": {
                     "description": "String identifying the type of cell.",
                     "enum": ["raw"]
                 },
                 "metadata": {
                     "description": "Cell-level metadata.",
                     "type": "object",
                     "additionalProperties": true,
                     "properties": {
                         "format": {
                             "description": "Raw cell metadata format for nbconvert.",
                             "type": "string"
                         },
                         "name": {"$ref": "#/definitions/misc/metadata_name"},
                         "tags": {"$ref": "#/definitions/misc/metadata_tags"}
                     }
                 },
                 "source": {"$ref": "#/definitions/misc/source"}
             }
         },
 
         "markdown_cell": {
             "description": "Notebook markdown cell.",
             "type": "object",
             "additionalProperties": false,
             "required": ["cell_type", "metadata", "source"],
             "properties": {
                 "cell_type": {
                     "description": "String identifying the type of cell.",
                     "enum": ["markdown"]
                 },
                 "metadata": {
                     "description": "Cell-level metadata.",
                     "type": "object",
                     "properties": {
                         "name": {"$ref": "#/definitions/misc/metadata_name"},
                         "tags": {"$ref": "#/definitions/misc/metadata_tags"}
                     },
                     "additionalProperties": true
                 },
                 "source": {"$ref": "#/definitions/misc/source"}
             }
         },
 
         "code_cell": {
             "description": "Notebook code cell.",
             "type": "object",
             "additionalProperties": false,
             "required": ["cell_type", "metadata", "source", "outputs", "execution_count"],
             "properties": {
                 "cell_type": {
                     "description": "String identifying the type of cell.",
                     "enum": ["code"]
                 },
                 "metadata": {
                     "description": "Cell-level metadata.",
                     "type": "object",
                     "additionalProperties": true,
                     "properties": {
                         "collapsed": {
                             "description": "Whether the cell is collapsed/expanded.",
                             "type": "boolean"
                         },
                         "autoscroll": {
                             "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.",
                             "enum": [true, false, "auto"]
                         },
                         "name": {"$ref": "#/definitions/misc/metadata_name"},
                         "tags": {"$ref": "#/definitions/misc/metadata_tags"}
                     }
                 },
                 "source": {"$ref": "#/definitions/misc/source"},
                 "outputs": {
                     "description": "Execution, display, or stream outputs.",
                     "type": "array",
                     "items": {"$ref": "#/definitions/output"}
                 },
                 "execution_count": {
                     "description": "The code cell's prompt number. Will be null if the cell has not been run.",
                     "type": ["integer", "null"],
                     "minimum": 0
                 }
             }
         },
         
         "unrecognized_cell": {
             "description": "Unrecognized cell from a future minor-revision to the notebook format.",
             "type": "object",
             "additionalProperties": true,
             "required": ["cell_type", "metadata"],
             "properties": {
                 "cell_type": {
                     "description": "String identifying the type of cell.",
                     "not" : {
                       "enum": ["markdown", "code", "raw"]
                     }
                 },
                 "metadata": {
                     "description": "Cell-level metadata.",
                     "type": "object",
                     "properties": {
                         "name": {"$ref": "#/definitions/misc/metadata_name"},
                         "tags": {"$ref": "#/definitions/misc/metadata_tags"}
                     },
                     "additionalProperties": true
                 }
             }
         },
         
         "output": {
             "type": "object",
             "oneOf": [
                 {"$ref": "#/definitions/execute_result"},
                 {"$ref": "#/definitions/display_data"},
                 {"$ref": "#/definitions/stream"},
                 {"$ref": "#/definitions/error"}
             ]
         },
 
         "execute_result": {
             "description": "Result of executing a code cell.",
             "type": "object",
             "additionalProperties": false,
             "required": ["output_type", "data", "metadata", "execution_count"],
             "properties": {
                 "output_type": {
                     "description": "Type of cell output.",
                     "enum": ["execute_result"]
                 },
                 "execution_count": {
                     "description": "A result's prompt number.",
                     "type": ["integer", "null"],
                     "minimum": 0
                 },
                 "data": {"$ref": "#/definitions/misc/mimebundle"},
                 "metadata": {"$ref": "#/definitions/misc/output_metadata"}
             }
         },
 
         "display_data": {
             "description": "Data displayed as a result of code cell execution.",
             "type": "object",
             "additionalProperties": false,
             "required": ["output_type", "data", "metadata"],
             "properties": {
                 "output_type": {
                     "description": "Type of cell output.",
                     "enum": ["display_data"]
                 },
                 "data": {"$ref": "#/definitions/misc/mimebundle"},
                 "metadata": {"$ref": "#/definitions/misc/output_metadata"}
             }
         },
 
         "stream": {
             "description": "Stream output from a code cell.",
             "type": "object",
             "additionalProperties": false,
             "required": ["output_type", "name", "text"],
             "properties": {
                 "output_type": {
                     "description": "Type of cell output.",
                     "enum": ["stream"]
                 },
                 "name": {
                     "description": "The name of the stream (stdout, stderr).",
                     "type": "string"
                 },
                 "text": {
                     "description": "The stream's text output, represented as an array of strings.",
                     "$ref": "#/definitions/misc/multiline_string"
                 }
             }
         },
 
         "error": {
             "description": "Output of an error that occurred during code cell execution.",
             "type": "object",
             "additionalProperties": false,
             "required": ["output_type", "ename", "evalue", "traceback"],
             "properties": {
                 "output_type": {
                     "description": "Type of cell output.",
                     "enum": ["error"]
                 },
                 "ename": {
                     "description": "The name of the error.",
                     "type": "string"
                 },
                 "evalue": {
                     "description": "The value, or message, of the error.",
                     "type": "string"
                 },
                 "traceback": {
                     "description": "The error's traceback, represented as an array of strings.",
                     "type": "array",
                     "items": {"type": "string"}
                 }
             }
         },
 
         "unrecognized_output": {
             "description": "Unrecognized output from a future minor-revision to the notebook format.",
             "type": "object",
             "additionalProperties": true,
             "required": ["output_type"],
             "properties": {
                 "output_type": {
                     "description": "Type of cell output.",
                     "not": {
                         "enum": ["execute_result", "display_data", "stream", "error"]
                     }
                 }
             }
         },
 
         "misc": {
             "metadata_name": {
                 "description": "The cell's name. If present, must be a non-empty string.",
                 "type": "string",
                 "pattern": "^.+$"
             },
             "metadata_tags": {
                 "description": "The cell's tags. Tags must be unique, and must not contain commas.",
                 "type": "array",
                 "uniqueItems": true,
                 "items": {
                     "type": "string",
                     "pattern": "^[^,]+$"
                 }
             },
             "source": {
                 "description": "Contents of the cell, represented as an array of lines.",
                 "$ref": "#/definitions/misc/multiline_string"
             },
             "execution_count": {
                 "description": "The code cell's prompt number. Will be null if the cell has not been run.",
                 "type": ["integer", "null"],
                 "minimum": 0
             },
             "mimebundle": {
                 "description": "A mime-type keyed dictionary of data",
                 "type": "object",
                 "additionalProperties": false,
                 "properties": {
                     "application/json": {
                         "type": "object"
                     }
                 },
                 "patternProperties": {
                     "^(?!application/json$)[a-zA-Z0-9]+/[a-zA-Z0-9\\-\\+\\.]+$": {
                         "description": "mimetype output (e.g. text/plain), represented as either an array of strings or a string.",
                         "$ref": "#/definitions/misc/multiline_string"
                     }
                 }
             },
             "output_metadata": {
                 "description": "Cell output metadata.",
                 "type": "object",
                 "additionalProperties": true
             },
             "multiline_string": {
                 "oneOf" : [
                     {"type": "string"},
                     {
                         "type": "array",
                         "items": {"type": "string"}
                     }
                 ]
             }
         }
     }
 }

IPython/nbformat/v4/rwbase.py

@@ -1,95 +1,96 b''
 """Base classes and utilities for readers and writers."""
 
 # Copyright (c) IPython Development Team.
 # Distributed under the terms of the Modified BSD License.
 
 from IPython.utils.py3compat import string_types, cast_unicode_py2
 
 
 def rejoin_lines(nb):
     """rejoin multiline text into strings
 
     For reversing effects of ``split_lines(nb)``.
 
     This only rejoins lines that have been split, so if text objects were not split
     they will pass through unchanged.
 
     Used when reading JSON files that may have been passed through split_lines.
     """
     for cell in nb.cells:
         if 'source' in cell and isinstance(cell.source, list):
             cell.source = ''.join(cell.source)
         if cell.get('cell_type', None) == 'code':
             for output in cell.get('outputs', []):
                 output_type = output.get('output_type', '')
                 if output_type in {'execute_result', 'display_data'}:
                     for key, value in output.get('data', {}).items():
                         if key != 'application/json' and isinstance(value, list):
                             output.data[key] = ''.join(value)
                 elif output_type:
                     if isinstance(output.get('text', ''), list):
                         output.text = ''.join(output.text)
     return nb
 
 
 def split_lines(nb):
     """split likely multiline text into lists of strings
 
     For file output more friendly to line-based VCS. ``rejoin_lines(nb)`` will
     reverse the effects of ``split_lines(nb)``.
 
     Used when writing JSON files.
     """
     for cell in nb.cells:
         source = cell.get('source', None)
         if isinstance(source, string_types):
             cell['source'] = source.splitlines(True)
 
         if cell.cell_type == 'code':
             for output in cell.outputs:
                 if output.output_type in {'execute_result', 'display_data'}:
                     for key, value in output.data.items():
                         if key != 'application/json' and isinstance(value, string_types):
                             output.data[key] = value.splitlines(True)
                 elif output.output_type == 'stream':
                     if isinstance(output.text, string_types):
                         output.text = output.text.splitlines(True)
     return nb
 
 
 def strip_transient(nb):
     """Strip transient values that shouldn't be stored in files.
 
     This should be called in *both* read and write.
     """
     nb.metadata.pop('orig_nbformat', None)
     nb.metadata.pop('orig_nbformat_minor', None)
+    nb.metadata.pop('signature', None)
     for cell in nb.cells:
         cell.metadata.pop('trusted', None)
     return nb
 
 
 class NotebookReader(object):
     """A class for reading notebooks."""
 
     def reads(self, s, **kwargs):
         """Read a notebook from a string."""
         raise NotImplementedError("loads must be implemented in a subclass")
 
     def read(self, fp, **kwargs):
         """Read a notebook from a file like object"""
         nbs = cast_unicode_py2(fp.read())
         return self.reads(nbs, **kwargs)
 
 
 class NotebookWriter(object):
     """A class for writing notebooks."""
 
     def writes(self, nb, **kwargs):
         """Write a notebook to a string."""
         raise NotImplementedError("loads must be implemented in a subclass")
 
     def write(self, nb, fp, **kwargs):
         """Write a notebook to a file like object"""
         nbs = cast_unicode_py2(self.writes(nb, **kwargs))
         return fp.write(nbs)

examples/Customization/Index.ipynb

@@ -1,103 +1,101 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<img src=\"../images/ipython_logo.png\">"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Back to the main [Index](../Index.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Customization"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "IPython has rich APIs for customization. Many behaviors of the different IPython applications can be configured using command line arguments or configuration files. IPython's core syntax and command line features can also be customized through input filters, custom magic commands, etc."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Tutorials"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Coming soon."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Examples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Coming soon."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Non-notebook examples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This directory also contains examples that are regular Python (`.py`) files."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<a href='appconfig.py' target='_blank'>appconfig.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Customization/appconfig.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "%run ../utils/list_pyfiles.ipy"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:de8cb1aff3da9097ba3fc7afab4327fc589f2bb1c154feeeb5ed97c87e37486f"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/Embedding/Index.ipynb

@@ -1,194 +1,192 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<img src=\"../images/ipython_logo.png\">"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Back to the main [Index](../Index.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Embedding IPython Into Other Applications"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The architecture of IPython is built with reusable components. These components include:\n",
     "\n",
     "* The configuration system for processing command line arguments and configuration files\n",
     "* The IPython `InteractiveShell` object that provides the core interactive features across the entire code base\n",
     "* The IPython kernel, which provides the capabilities of the `InteractiveShell` object over a ZeroMQ/JSON based message protocol to various frontends\n",
     "* The IPython frontends (Notebook, Qt Console, Console, Terminal)\n",
     "\n",
     "These components can be embedded into other applications."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Tutorials"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Coming soon."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Examples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Coming soon."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Non-notebook examples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This directory also contains examples that are regular Python (`.py`) files."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<a href='embed_class_long.py' target='_blank'>embed_class_long.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_class_long.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='embed_class_short.py' target='_blank'>embed_class_short.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_class_short.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='embed_function.py' target='_blank'>embed_function.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_function.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='inprocess_qtconsole.py' target='_blank'>inprocess_qtconsole.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/inprocess_qtconsole.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='inprocess_terminal.py' target='_blank'>inprocess_terminal.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/inprocess_terminal.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='internal_ipkernel.py' target='_blank'>internal_ipkernel.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/internal_ipkernel.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='ipkernel_qtapp.py' target='_blank'>ipkernel_qtapp.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/ipkernel_qtapp.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='ipkernel_wxapp.py' target='_blank'>ipkernel_wxapp.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/ipkernel_wxapp.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "%run ../utils/list_pyfiles.ipy"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:627cdf03b8de558c9344f9d1e8f0beeb2448e37e492d676e6db7b07d33251a2b"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/IPython Kernel/Beyond Plain Python.ipynb

@@ -1,1807 +1,1805 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "# IPython: beyond plain Python"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "## First things first: running code, getting help"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "In the notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end.  Alternately, you can use:\n",
     "    \n",
     "- `Alt-Enter` to force the creation of a new cell unconditionally (useful when inserting new content in the middle of an existing notebook).\n",
     "- `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets that you don't need to keep permanently."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Hi\n"
      ]
     }
    ],
    "source": [
     "print \"Hi\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "Getting help:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "import collections\n",
     "collections.namedtuple?"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "collections.Counter??"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "*int*?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "An IPython quick reference card:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%quickref"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "## Tab completion"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Tab completion, especially for attributes, is a convenient way to explore the structure of any object you\u2019re dealing with. Simply type `object_name.<TAB>` to view the object\u2019s attributes. Besides Python objects and keywords, tab completion also works on file and directory names."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "collections."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "## The interactive workflow: input, output, history"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "12"
       ]
      },
      "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "2+10"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "22"
       ]
      },
      "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "_+10"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "You can suppress the storage and rendering of output if you append `;` to the last cell (this comes in handy when plotting with matplotlib, for example):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "10+20;"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "22"
       ]
      },
      "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "_"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "The output is stored in `_N` and `Out[N]` variables:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "True"
       ]
      },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "_10 == Out[10]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "And the last three have shorthands for convenience:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "last output: True\n",
       "next one   : 22\n",
       "and next   : 22\n"
      ]
     }
    ],
    "source": [
     "print 'last output:', _\n",
     "print 'next one   :', __\n",
     "print 'and next   :', ___"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "slideshow": {
      "slide_type": "-"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "u'_10 == Out[10]'"
       ]
      },
      "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "In[11]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "u'In[11]'"
       ]
      },
      "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "_i"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "u'In[11]'"
       ]
      },
      "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "_ii"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "slideshow": {
      "slide_type": "subslide"
     }
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "last input: _ii\n",
       "next one  : _i\n",
       "and next  : In[11]\n"
      ]
     }
    ],
    "source": [
     "print 'last input:', _i\n",
     "print 'next one  :', _ii\n",
     "print 'and next  :', _iii"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "   1: print \"Hi\"\n",
       "   2: ?\n",
       "   3:\n",
       "import collections\n",
       "collections.namedtuple?\n",
       "   4: collections.Counter??\n",
       "   5: *int*?\n"
      ]
     }
    ],
    "source": [
     "%history -n 1-5"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     }
    },
    "source": [
     "**Exercise**\n",
     "\n",
     "Write the last 10 lines of history to a file named `log.py`."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "source": [
     "## Accessing the underlying operating system"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "/home/fperez/ipython/tutorial/notebooks\r\n"
      ]
     }
    ],
    "source": [
     "!pwd"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "My current directory's files:\n",
       "['BackgroundJobs.ipynb', 'Custom Display Logic.ipynb', 'Customizing IPython - Condensed.ipynb', 'Customizing IPython - Config.ipynb', 'Customizing IPython - Extensions.ipynb', 'Customizing IPython - Magics.ipynb', 'data', 'figs', 'flare.json', 'Index.ipynb', 'Interactive Widgets.ipynb', 'IPython - beyond plain Python.ipynb', 'kernel-embedding', 'Markdown Cells.ipynb', 'myscript.py', 'nbconvert_arch.png', 'NbConvert from command line.ipynb', 'NbConvert Python library.ipynb', 'Notebook and javascript extension.ipynb', 'Notebook Basics.ipynb', 'Overview of IPython.parallel.ipynb', 'parallel', 'Rich Display System.ipynb', 'Running a Secure Public Notebook.ipynb', 'Running Code.ipynb', 'Sample.ipynb', 'soln', 'Terminal usage.ipynb', 'text_analysis.py', 'Typesetting Math Using MathJax.ipynb']\n"
      ]
     }
    ],
    "source": [
     "files = !ls\n",
     "print \"My current directory's files:\"\n",
     "print files"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[BackgroundJobs.ipynb, Custom Display Logic.ipynb, Customizing IPython - Condensed.ipynb, Customizing IPython - Config.ipynb, Customizing IPython - Extensions.ipynb, Customizing IPython - Magics.ipynb, data, figs, flare.json, Index.ipynb, Interactive Widgets.ipynb, IPython - beyond plain Python.ipynb, kernel-embedding, Markdown Cells.ipynb, myscript.py, nbconvert_arch.png, NbConvert from command line.ipynb, NbConvert Python library.ipynb, Notebook and javascript extension.ipynb, Notebook Basics.ipynb, Overview of IPython.parallel.ipynb, parallel, Rich Display System.ipynb, Running a Secure Public Notebook.ipynb, Running Code.ipynb, Sample.ipynb, soln, Terminal usage.ipynb, text_analysis.py, Typesetting Math Using MathJax.ipynb]\r\n"
      ]
     }
    ],
    "source": [
     "!echo $files"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "BACKGROUNDJOBS.IPYNB\r\n"
      ]
     }
    ],
    "source": [
     "!echo {files[0].upper()}"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Note that all this is available even in multiline blocks:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "00 - BackgroundJobs\r\n",
       "01 - Custom Display Logic\r\n",
       "02 - Customizing IPython - Condensed\r\n",
       "03 - Customizing IPython - Config\r\n",
       "04 - Customizing IPython - Extensions\r\n",
       "05 - Customizing IPython - Magics\r\n",
       "--\n",
       "--\n",
       "--\n",
       "09 - Index\r\n",
       "10 - Interactive Widgets\r\n",
       "11 - IPython - beyond plain Python\r\n",
       "--\n",
       "13 - Markdown Cells\r\n",
       "--\n",
       "--\n",
       "16 - NbConvert from command line\r\n",
       "17 - NbConvert Python library\r\n",
       "18 - Notebook and javascript extension\r\n",
       "19 - Notebook Basics\r\n",
       "20 - Overview of IPython.parallel\r\n",
       "--\n",
       "22 - Rich Display System\r\n",
       "23 - Running a Secure Public Notebook\r\n",
       "24 - Running Code\r\n",
       "25 - Sample\r\n",
       "--\n",
       "27 - Terminal usage\r\n",
       "--\n",
       "29 - Typesetting Math Using MathJax\r\n"
      ]
     }
    ],
    "source": [
     "import os\n",
     "for i,f in enumerate(files):\n",
     "    if f.endswith('ipynb'):\n",
     "        !echo {\"%02d\" % i} - \"{os.path.splitext(f)[0]}\"\n",
     "    else:\n",
     "        print '--'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Beyond Python: magic functions"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The IPyhton 'magic' functions are a set of commands, invoked by prepending one or two `%` signs to their name, that live in a namespace separate from your normal Python variables and provide a more command-like interface.  They take flags with `--` and arguments without quotes, parentheses or commas. The motivation behind this system is two-fold:\n",
     "    \n",
     "- To provide an orthogonal namespace for controlling IPython itself and exposing other system-oriented functionality.\n",
     "\n",
     "- To expose a calling mode that requires minimal verbosity and typing while working interactively.  Thus the inspiration taken from the classic Unix shell style for commands."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%magic"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Line vs cell magics:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "10000000 loops, best of 3: 190 ns per loop\n"
      ]
     }
    ],
    "source": [
     "%timeit range(10)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "1000000 loops, best of 3: 888 ns per loop\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "range(10)\n",
     "range(100)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Line magics can be used even inside code blocks:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "size: 010000000 loops, best of 3: 129 ns per loop\n",
       " size: 1001000000 loops, best of 3: 649 ns per loop\n",
       " size: 2001000000 loops, best of 3: 1.09 \u00b5s per loop\n",
       " size: 3001000000 loops, best of 3: 1.74 \u00b5s per loop\n",
       " size: 400100000 loops, best of 3: 2.72 \u00b5s per loop\n",
       "\n"
      ]
     }
    ],
    "source": [
     "for i in range(5):\n",
     "    size = i*100\n",
     "    print 'size:',size, \n",
     "    %timeit range(size)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Magics can do anything they want with their input, so it doesn't have to be valid Python:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "My shell is: /bin/bash\n",
       "My memory status is:\n",
       "             total       used       free     shared    buffers     cached\n",
       "Mem:       7870888    6389328    1481560          0     662860    2505172\n",
       "-/+ buffers/cache:    3221296    4649592\n",
       "Swap:      3905532       4852    3900680\n"
      ]
     }
    ],
    "source": [
     "%%bash\n",
     "echo \"My shell is:\" $SHELL\n",
     "echo \"My memory status is:\"\n",
     "free"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Another interesting cell magic: create any file you want locally from the notebook:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Writing test.txt\n"
      ]
     }
    ],
    "source": [
     "%%writefile test.txt\n",
     "This is a test file!\n",
     "It can contain anything I want...\n",
     "\n",
     "And more..."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "This is a test file!\r\n",
       "It can contain anything I want...\r\n",
       "\r\n",
       "And more..."
      ]
     }
    ],
    "source": [
     "!cat test.txt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Let's see what other magics are currently defined in the system:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "application/json": {
        "cell": {
         "!": "OSMagics",
         "HTML": "Other",
         "SVG": "Other",
         "bash": "Other",
         "capture": "ExecutionMagics",
         "debug": "ExecutionMagics",
         "file": "Other",
         "html": "DisplayMagics",
         "javascript": "DisplayMagics",
         "latex": "DisplayMagics",
         "perl": "Other",
         "prun": "ExecutionMagics",
         "pypy": "Other",
         "python": "Other",
         "python2": "Other",
         "python3": "Other",
         "ruby": "Other",
         "script": "ScriptMagics",
         "sh": "Other",
         "svg": "DisplayMagics",
         "sx": "OSMagics",
         "system": "OSMagics",
         "time": "ExecutionMagics",
         "timeit": "ExecutionMagics",
         "writefile": "OSMagics"
        },
        "line": {
         "alias": "OSMagics",
         "alias_magic": "BasicMagics",
         "autocall": "AutoMagics",
         "automagic": "AutoMagics",
         "autosave": "KernelMagics",
         "bookmark": "OSMagics",
         "cat": "Other",
         "cd": "OSMagics",
         "cl": "Other",
         "clear": "KernelMagics",
         "clk": "Other",
         "colors": "BasicMagics",
         "config": "ConfigMagics",
         "connect_info": "KernelMagics",
         "cp": "Other",
         "d": "Other",
         "dd": "Other",
         "debug": "ExecutionMagics",
         "dhist": "OSMagics",
         "dirs": "OSMagics",
         "dl": "Other",
         "doctest_mode": "KernelMagics",
         "dx": "Other",
         "ed": "Other",
         "edit": "KernelMagics",
         "env": "OSMagics",
         "gui": "BasicMagics",
         "hist": "Other",
         "history": "HistoryMagics",
         "install_default_config": "DeprecatedMagics",
         "install_ext": "ExtensionMagics",
         "install_profiles": "DeprecatedMagics",
         "killbgscripts": "ScriptMagics",
         "ldir": "Other",
         "less": "KernelMagics",
         "lf": "Other",
         "lk": "Other",
         "ll": "Other",
         "load": "CodeMagics",
         "load_ext": "ExtensionMagics",
         "loadpy": "CodeMagics",
         "logoff": "LoggingMagics",
         "logon": "LoggingMagics",
         "logstart": "LoggingMagics",
         "logstate": "LoggingMagics",
         "logstop": "LoggingMagics",
         "ls": "Other",
         "lsmagic": "BasicMagics",
         "lx": "Other",
         "macro": "ExecutionMagics",
         "magic": "BasicMagics",
         "man": "KernelMagics",
         "matplotlib": "PylabMagics",
         "mkdir": "Other",
         "more": "KernelMagics",
         "mv": "Other",
         "notebook": "BasicMagics",
         "page": "BasicMagics",
         "pastebin": "CodeMagics",
         "pdb": "ExecutionMagics",
         "pdef": "NamespaceMagics",
         "pdoc": "NamespaceMagics",
         "pfile": "NamespaceMagics",
         "pinfo": "NamespaceMagics",
         "pinfo2": "NamespaceMagics",
         "popd": "OSMagics",
         "pprint": "BasicMagics",
         "precision": "BasicMagics",
         "profile": "BasicMagics",
         "prun": "ExecutionMagics",
         "psearch": "NamespaceMagics",
         "psource": "NamespaceMagics",
         "pushd": "OSMagics",
         "pwd": "OSMagics",
         "pycat": "OSMagics",
         "pylab": "PylabMagics",
         "qtconsole": "KernelMagics",
         "quickref": "BasicMagics",
         "recall": "HistoryMagics",
         "rehashx": "OSMagics",
         "reload_ext": "ExtensionMagics",
         "rep": "Other",
         "rerun": "HistoryMagics",
         "reset": "NamespaceMagics",
         "reset_selective": "NamespaceMagics",
         "rm": "Other",
         "rmdir": "Other",
         "run": "ExecutionMagics",
         "save": "CodeMagics",
         "sc": "OSMagics",
         "store": "StoreMagics",
         "sx": "OSMagics",
         "system": "OSMagics",
         "tb": "ExecutionMagics",
         "time": "ExecutionMagics",
         "timeit": "ExecutionMagics",
         "unalias": "OSMagics",
         "unload_ext": "ExtensionMagics",
         "who": "NamespaceMagics",
         "who_ls": "NamespaceMagics",
         "whos": "NamespaceMagics",
         "xdel": "NamespaceMagics",
         "xmode": "BasicMagics"
        }
       },
       "text/plain": [
        "Available line magics:\n",
        "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %cl  %clear  %clk  %colors  %config  %connect_info  %cp  %d  %dd  %debug  %dhist  %dirs  %dl  %doctest_mode  %dx  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
        "\n",
        "Available cell magics:\n",
        "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
        "\n",
        "Automagic is ON, % prefix IS NOT needed for line magics."
       ]
      },
      "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "%lsmagic"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Running normal Python code: execution and errors"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Not only can you input normal Python code, you can even paste straight from a Python or IPython shell session:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "1\n",
       "1\n",
       "2\n",
       "3\n",
       "5\n",
       "8\n"
      ]
     }
    ],
    "source": [
     ">>> # Fibonacci series:\n",
     "... # the sum of two elements defines the next\n",
     "... a, b = 0, 1\n",
     ">>> while b < 10:\n",
     "...     print b\n",
     "...     a, b = b, a+b"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "0 1 2 3 4 5 6 7 8 9\n"
      ]
     }
    ],
    "source": [
     "In [1]: for i in range(10):\n",
     "   ...:     print i,\n",
     "   ...:     "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "And when your code produces errors, you can control how they are displayed with the `%xmode` magic:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Writing mod.py\n"
      ]
     }
    ],
    "source": [
     "%%writefile mod.py\n",
     "\n",
     "def f(x):\n",
     "    return 1.0/(x-1)\n",
     "\n",
     "def g(y):\n",
     "    return f(y+1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now let's call the function `g` with an argument that would produce an error:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "ename": "ZeroDivisionError",
      "evalue": "float division by zero",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
       "\u001b[1;32m<ipython-input-39-a54c5799f57e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
      ]
     }
    ],
    "source": [
     "import mod\n",
     "mod.g(0)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Exception reporting mode: Plain\n"
      ]
     },
     {
      "ename": "ZeroDivisionError",
      "evalue": "float division by zero",
      "output_type": "error",
      "traceback": [
       "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
       "  File \u001b[0;32m\"<ipython-input-40-5a5bcec1553f>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n    mod.g(0)\n",
       "  File \u001b[0;32m\"mod.py\"\u001b[0m, line \u001b[0;32m6\u001b[0m, in \u001b[0;35mg\u001b[0m\n    return f(y+1)\n",
       "\u001b[1;36m  File \u001b[1;32m\"mod.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[1;36m, in \u001b[1;35mf\u001b[1;36m\u001b[0m\n\u001b[1;33m    return 1.0/(x-1)\u001b[0m\n",
       "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m float division by zero\n"
      ]
     }
    ],
    "source": [
     "%xmode plain\n",
     "mod.g(0)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Exception reporting mode: Verbose\n"
      ]
     },
     {
      "ename": "ZeroDivisionError",
      "evalue": "float division by zero",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
       "\u001b[1;32m<ipython-input-41-81967cfaa0c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'xmode verbose'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[1;36mglobal\u001b[0m \u001b[0;36mmod.g\u001b[0m \u001b[1;34m= <function g at 0x237fc08>\u001b[0m\n",
       "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y=0)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[1;36mglobal\u001b[0m \u001b[0;36mf\u001b[0m \u001b[1;34m= <function f at 0x2367c08>\u001b[0m\u001b[1;34m\n        \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n",
       "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x=1)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mx\u001b[0m \u001b[1;34m= 1\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
      ]
     }
    ],
    "source": [
     "%xmode verbose\n",
     "mod.g(0)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The default `%xmode` is \"context\", which shows additional context but not all local variables.  Let's restore that one for the rest of our session."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Exception reporting mode: Context\n"
      ]
     }
    ],
    "source": [
     "%xmode context"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Running code in other languages with special `%%` magics"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "July"
      ]
     }
    ],
    "source": [
     "%%perl\n",
     "@months = (\"July\", \"August\", \"September\");\n",
     "print $months[0];"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Hello World!\n"
      ]
     }
    ],
    "source": [
     "%%ruby\n",
     "name = \"world\"\n",
     "puts \"Hello #{name.capitalize}!\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Exercise"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Write a cell that executes in Bash and prints your current working directory as well as the date.\n",
     "\n",
     "Apologies to Windows users who may not have Bash available, not sure how to obtain the equivalent result with `cmd.exe` or Powershell."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%load soln/bash-script"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Raw Input in the notebook"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Since 1.0 the IPython notebook web application support `raw_input` which for example allow us to invoke the `%debug` magic in the notebook:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 45,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "ename": "ZeroDivisionError",
      "evalue": "float division by zero",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
       "\u001b[1;32m<ipython-input-45-5e708f13c839>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
      ]
     }
    ],
    "source": [
     "mod.g(0)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[0;34m()\u001b[0m\n",
       "\u001b[0;32m      2 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[0;32m----> 3 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[0;32m      4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\n",
       "ipdb> x\n",
       "1\n",
       "ipdb> up\n",
       "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[0;34m()\u001b[0m\n",
       "\u001b[0;32m      4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[0;32m      5 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[0;32m----> 6 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\n",
       "ipdb> y\n",
       "0\n",
       "ipdb> up\n",
       "> \u001b[0;32m<ipython-input-37-5e708f13c839>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
       "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0m\n",
       "ipdb> exit\n"
      ]
     }
    ],
    "source": [
     "%debug"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Don't foget to exit your debugging session. Raw input can of course be use to ask for user input:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Are you enjoying this tutorial ?Yes !\n",
       "enjoy is : Yes !\n"
      ]
     }
    ],
    "source": [
     "enjoy = raw_input('Are you enjoying this tutorial ?')\n",
     "print 'enjoy is :', enjoy"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Plotting in the notebook"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This magic configures matplotlib to render its figures inline:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 46,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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       ],
       "text/plain": [
        "<matplotlib.figure.Figure at 0x3436950>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "x = np.linspace(0, 2*np.pi, 300)\n",
     "y = np.sin(x**2)\n",
     "plt.plot(x, y)\n",
     "plt.title(\"A little chirp\")\n",
     "fig = plt.gcf()  # let's keep the figure object around for later..."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## The IPython kernel/client model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "{\n",
       "  \"stdin_port\": 50023, \n",
       "  \"ip\": \"127.0.0.1\", \n",
       "  \"control_port\": 50024, \n",
       "  \"hb_port\": 50025, \n",
       "  \"signature_scheme\": \"hmac-sha256\", \n",
       "  \"key\": \"b54b8859-d64d-48bb-814a-909f9beb3316\", \n",
       "  \"shell_port\": 50021, \n",
       "  \"transport\": \"tcp\", \n",
       "  \"iopub_port\": 50022\n",
       "}\n",
       "\n",
       "Paste the above JSON into a file, and connect with:\n",
       "    $> ipython <app> --existing <file>\n",
       "or, if you are local, you can connect with just:\n",
       "    $> ipython <app> --existing kernel-30f00f4a-230c-4e64-bea5-0e5f6a52cb40.json \n",
       "or even just:\n",
       "    $> ipython <app> --existing \n",
       "if this is the most recent IPython session you have started.\n"
      ]
     }
    ],
    "source": [
     "%connect_info"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can connect automatically a Qt Console to the currently running kernel with the `%qtconsole` magic, or by typing `ipython console --existing <kernel-UUID>` in any terminal:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 83,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%qtconsole"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:31071a05d0ecd75ed72fe3f0de0ad447a6f85cffe382c26efa5e68db1fee54ee"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/IPython Kernel/Capturing Output.ipynb

@@ -1,489 +1,487 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Capturing Output With <tt>%%capture</tt>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "IPython has a [cell magic](Cell Magics.ipynb), `%%capture`, which captures the stdout/stderr of a cell. With this magic you can discard these streams or store them in a variable."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "from __future__ import print_function\n",
     "import sys"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "By default, `%%capture` discards these streams.  This is a simple way to suppress unwanted output."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%%capture\n",
     "print('hi, stdout')\n",
     "print('hi, stderr', file=sys.stderr)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "If you specify a name, then stdout/stderr will be stored in an object in your namespace."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%%capture captured\n",
     "print('hi, stdout')\n",
     "print('hi, stderr', file=sys.stderr)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<IPython.utils.capture.CapturedIO at 0x1076c9310>"
       ]
      },
      "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "captured"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Calling the object writes the output to stdout/stderr as appropriate."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "hi, stdout\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "hi, stderr\n"
      ]
     }
    ],
    "source": [
     "captured()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "'hi, stdout\\n'"
       ]
      },
      "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "captured.stdout"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "'hi, stderr\\n'"
       ]
      },
      "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "captured.stderr"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "`%%capture` grabs all output types, not just stdout/stderr, so you can do plots and use IPython's display system inside `%%capture`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%%capture wontshutup\n",
     "\n",
     "print(\"setting up X\")\n",
     "x = np.linspace(0,5,1000)\n",
     "print(\"step 2: constructing y-data\")\n",
     "y = np.sin(x)\n",
     "print(\"step 3: display info about y\")\n",
     "plt.plot(x,y)\n",
     "print(\"okay, I'm done now\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "setting up X\n",
       "step 2: constructing y-data\n",
       "step 3: display info about y\n",
       "okay, I'm done now\n"
      ]
     },
     {
      "data": {
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        "AAAASUVORK5CYII=\n"
       ],
       "text/plain": [
        "<matplotlib.figure.Figure at 0x10866ae90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "wontshutup()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "And you can selectively disable capturing stdout, stderr or rich display, by passing `--no-stdout`, `--no-stderr` and `--no-display`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "hello, stderr\n"
      ]
     }
    ],
    "source": [
     "%%capture cap --no-stderr\n",
     "print('hi, stdout')\n",
     "print(\"hello, stderr\", file=sys.stderr)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "'hi, stdout\\n'"
       ]
      },
      "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "cap.stdout"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "''"
       ]
      },
      "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "cap.stderr"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[]"
       ]
      },
      "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "cap.outputs"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:df6354daf203e842bc040989d149760382d8ceec769160e4efe8cde9dfcb9107"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/IPython Kernel/Custom Display Logic.ipynb

@@ -1,1325 +1,1323 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Custom Display Logic"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Overview"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "As described in the [Rich Output](Rich Output.ipynb) tutorial, the IPython display system can display rich representations of objects in the following formats:\n",
     "\n",
     "* JavaScript\n",
     "* HTML\n",
     "* PNG\n",
     "* JPEG\n",
     "* SVG\n",
     "* LaTeX\n",
     "* PDF\n",
     "\n",
     "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n",
     "\n",
     "1. Implementing special display methods such as `_repr_html_` when you define your class.\n",
     "2. Registering a display function for a particular existing class.\n",
     "\n",
     "This Notebook describes and illustrates both approaches."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Import the IPython display functions."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "from IPython.display import (\n",
     "    display, display_html, display_png, display_svg\n",
     ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Parts of this notebook need the matplotlib inline backend:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Special display methods"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The main idea of the first approach is that you have to implement special display methods when you define your class, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
     "\n",
     "* `_repr_html_`: return raw HTML as a string\n",
     "* `_repr_json_`: return raw JSON as a string\n",
     "* `_repr_jpeg_`: return raw JPEG data\n",
     "* `_repr_png_`: return raw PNG data\n",
     "* `_repr_svg_`: return raw SVG data as a string\n",
     "* `_repr_latex_`: return LaTeX commands in a string surrounded by \"$\"."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean and standard deviation. Here is the definition of the `Gaussian` class, which has a custom PNG and LaTeX representation."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "from IPython.core.pylabtools import print_figure\n",
     "from IPython.display import Image, SVG, Math\n",
     "\n",
     "class Gaussian(object):\n",
     "    \"\"\"A simple object holding data sampled from a Gaussian distribution.\n",
     "    \"\"\"\n",
     "    def __init__(self, mean=0.0, std=1, size=1000):\n",
     "        self.data = np.random.normal(mean, std, size)\n",
     "        self.mean = mean\n",
     "        self.std = std\n",
     "        self.size = size\n",
     "        # For caching plots that may be expensive to compute\n",
     "        self._png_data = None\n",
     "        \n",
     "    def _figure_data(self, format):\n",
     "        fig, ax = plt.subplots()\n",
     "        ax.hist(self.data, bins=50)\n",
     "        ax.set_title(self._repr_latex_())\n",
     "        ax.set_xlim(-10.0,10.0)\n",
     "        data = print_figure(fig, format)\n",
     "        # We MUST close the figure, otherwise IPython's display machinery\n",
     "        # will pick it up and send it as output, resulting in a double display\n",
     "        plt.close(fig)\n",
     "        return data\n",
     "        \n",
     "    def _repr_png_(self):\n",
     "        if self._png_data is None:\n",
     "            self._png_data = self._figure_data('png')\n",
     "        return self._png_data\n",
     "    \n",
     "    def _repr_latex_(self):\n",
     "        return r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n",
     "                                                                 self.std, self.size)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Create an instance of the Gaussian distribution and return it to display the default representation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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       ],
       "text/latex": [
        "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
       ],
       "text/plain": [
        "<__main__.Gaussian at 0x106e7ae10>"
       ]
      },
      "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "x = Gaussian(2.0, 1.0)\n",
     "x"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "You can also pass the object to the `display` function to display the default representation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
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        "fnMJ8p8CnqxbfyppkyR10FyC3KGOClBlcPBXePnlZ5g3z331EoQ5Ra06Bxgh1MgB1gH7gRvqttkO\n",
        "nDSH55CkfrQDeF0nnmggebLlwKHAw7izU5KicxHwPcLIe13BfZEkSZIE8B7gceBV4MyG29YRDh7a\n",
        "Cryjw/3qBSOE2UGbk8uFM26t6XggW752Ao8QXpPfLbYr0fk8MA48Wtc2BGwEtgEbgMUF9ItTgBXA\n",
        "/UwN8tMIdfRDCHX17XiKgKyuAz5SdCciN5/w2ltOeC26b2fufkAIH2X3VmAlU4P8RuBjyfI1wPWz\n",
        "PUg7gnQr4ZOk0cXAHYSjNHYS3kxnt+H5e52HKs6NB7K1h6/L1nwTaDxyahUwmiyPAqtne5BOjoh/\n",
        "klAWqPEAotZcDWwBbqWgr1yR80C2/FWB+4CHgCsK7ksvGCaUW0iuh2e7Q6unsd0ILGnSfi1wT4bH\n",
        "8aCig033t/0D4Cbgj5L1Pwb+DLi8Q/3qFb7m8vcWYBdwLOH1u5Uw0tTcVUnxmm01yC9o4T4/BOp/\n",
        "0uW4pE1Tpf3b3kK2D00Fja/DZUz9pqjsdiXXzwB3EcpXBnnrxgmDuTFgKfD0bHdod2mlvm62Hngf\n",
        "4eChE4GTcQ93Vkvrlt/N1B0kSuchwmtvOeG1+F7Ca1OtORJYmCwvIMxG83U5N+uBNcnyGuDuIjrx\n",
        "bkIN8iXCJ8rX6m67lrCjaSvwS53vWvS+QJjmtYXwnztr7UxNeSBbfk4kzPx5GHgM/55Z3QH8D7CX\n",
        "kJuXEWYA3UfB0w8lSZIkSZIkSZIkSZIkSZIkSZIktcn/A4eK9UXawRDUAAAAAElFTkSuQmCC\n"
       ],
       "text/latex": [
        "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
       ],
       "text/plain": [
        "<__main__.Gaussian at 0x106e7ae10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "display(x)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Use `display_png` to view the PNG representation:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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        "kJuXEWYA3UfB0w8lSZIkSZIkSZIkSZIkSZIkSZIktcn/A4eK9UXawRDUAAAAAElFTkSuQmCC\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "display_png(x)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "It is important to note a subtle different between <code>display</code> and <code>display_png</code>. The former computes <em>all</em> representations of the object, and lets the notebook UI decide which to display. The later only computes the PNG representation.\n",
     "</div>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Create a new Gaussian with different parameters:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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        "Zpl0W9y9wCWEh54WAm/Du+v1mpeavpDymzCa2mOE624B4Tq8mHBdKpuZwKxk+ihCDzivycbdC6xK\n",
        "plcBd3ewLGUuJLRrHiD85flm6mfXEm5obQN+of1F63m3E7qdbSH8D5+0LU5V+fBd8ywk9DjaDDyN\n",
        "5zOLdcD3gFcJuflBQu+jB+jCrpCSJEmSJEmSJEmSJEmSJEmSJEmS+tD/AzUxDUJku6WfAAAAAElF\n",
        "TkSuQmCC\n"
       ],
       "text/latex": [
        "$\\mathcal{N}(\\mu=0, \\sigma=2),\\ N=2000$"
       ],
       "text/plain": [
        "<__main__.Gaussian at 0x106e9ce90>"
       ]
      },
      "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "x2 = Gaussian(0, 2, 2000)\n",
     "x2"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "You can then compare the two Gaussians by displaying their histograms:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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      },
      "metadata": {},
      "output_type": "display_data"
     },
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        "/ftfQXGqt1nGl3WoycpfKhFYm2++8efZb01Kc/gBSYqQ4S5JETLcJSlChrskRchwl6QIGe6SFCHD\n",
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        "plcBd3ewLGUuJLRrHiD85flm6mfXEm5obQN+of1F63m3E7qdbSH8D5+0LU5V+fBd8ywk9DjaDDyN\n",
        "5zOLdcD3gFcJuflBQu+jB+jCrpCSJEmSJEmSJEmSJEmSJEmSJEmS+tD/AzUxDUJku6WfAAAAAElF\n",
        "TkSuQmCC\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "display_png(x)\n",
     "display_png(x2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Note that like `print`, you can call any of the `display` functions multiple times in a cell."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Adding IPython display support to existing objects"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "When you are directly writing your own classes, you can adapt them for display in IPython by following the above approach.  But in practice, you often need to work with existing classes that you can't easily modify. We now illustrate how to add rich output capabilities to existing objects. We will use the NumPy polynomials and change their default representation to be a formatted LaTeX expression."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "First, consider how a NumPy polynomial object renders by default:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1.,  1.])"
       ]
      },
      "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n",
     "p"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Next, define a function that pretty-prints a polynomial as a LaTeX string:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "def poly_to_latex(p):\n",
     "    terms = ['%.2g' % p.coef[0]]\n",
     "    if len(p) > 1:\n",
     "        term = 'x'\n",
     "        c = p.coef[1]\n",
     "        if c!=1:\n",
     "            term = ('%.2g ' % c) + term\n",
     "        terms.append(term)\n",
     "    if len(p) > 2:\n",
     "        for i in range(2, len(p)):\n",
     "            term = 'x^%d' % i\n",
     "            c = p.coef[i]\n",
     "            if c!=1:\n",
     "                term = ('%.2g ' % c) + term\n",
     "            terms.append(term)\n",
     "    px = '$P(x)=%s$' % '+'.join(terms)\n",
     "    dom = r', $x \\in [%.2g,\\ %.2g]$' % tuple(p.domain)\n",
     "    return px+dom"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This produces, on our polynomial ``p``, the following:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "'$P(x)=1+2 x+3 x^2$, $x \\\\in [-10,\\\\ 10]$'"
       ]
      },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "poly_to_latex(p)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "You can render this string using the `Latex` class:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/latex": [
        "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
       ],
       "text/plain": [
        "<IPython.core.display.Latex object>"
       ]
      },
      "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from IPython.display import Latex\n",
     "Latex(poly_to_latex(p))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "However, you can configure IPython to do this automatically by registering the `Polynomial` class and the `plot_to_latex` function with an IPython display formatter. Let's look at the default formatters provided by IPython:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "              text/plain : PlainTextFormatter\n",
       "              image/jpeg : JPEGFormatter\n",
       "               text/html : HTMLFormatter\n",
       "           image/svg+xml : SVGFormatter\n",
       "               image/png : PNGFormatter\n",
       "  application/javascript : JavascriptFormatter\n",
       "           text/markdown : MarkdownFormatter\n",
       "              text/latex : LatexFormatter\n",
       "        application/json : JSONFormatter\n",
       "         application/pdf : PDFFormatter\n"
      ]
     }
    ],
    "source": [
     "ip = get_ipython()\n",
     "for mime, formatter in ip.display_formatter.formatters.items():\n",
     "    print '%24s : %s' % (mime, formatter.__class__.__name__)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The `formatters` attribute is a dictionary keyed by MIME types. To define a custom LaTeX display function, you want a handle on the `text/latex` formatter:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "ip = get_ipython()\n",
     "latex_f = ip.display_formatter.formatters['text/latex']"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The formatter object has a couple of methods for registering custom display functions for existing types."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Help on method for_type in module IPython.core.formatters:\n",
       "\n",
       "for_type(self, typ, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
       "    Add a format function for a given type.\n",
       "    \n",
       "    Parameters\n",
       "    -----------\n",
       "    typ : type or '__module__.__name__' string for a type\n",
       "        The class of the object that will be formatted using `func`.\n",
       "    func : callable\n",
       "        A callable for computing the format data.\n",
       "        `func` will be called with the object to be formatted,\n",
       "        and will return the raw data in this formatter's format.\n",
       "        Subclasses may use a different call signature for the\n",
       "        `func` argument.\n",
       "        \n",
       "        If `func` is None or not specified, there will be no change,\n",
       "        only returning the current value.\n",
       "    \n",
       "    Returns\n",
       "    -------\n",
       "    oldfunc : callable\n",
       "        The currently registered callable.\n",
       "        If you are registering a new formatter,\n",
       "        this will be the previous value (to enable restoring later).\n",
       "\n"
      ]
     }
    ],
    "source": [
     "help(latex_f.for_type)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Help on method for_type_by_name in module IPython.core.formatters:\n",
       "\n",
       "for_type_by_name(self, type_module, type_name, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
       "    Add a format function for a type specified by the full dotted\n",
       "    module and name of the type, rather than the type of the object.\n",
       "    \n",
       "    Parameters\n",
       "    ----------\n",
       "    type_module : str\n",
       "        The full dotted name of the module the type is defined in, like\n",
       "        ``numpy``.\n",
       "    type_name : str\n",
       "        The name of the type (the class name), like ``dtype``\n",
       "    func : callable\n",
       "        A callable for computing the format data.\n",
       "        `func` will be called with the object to be formatted,\n",
       "        and will return the raw data in this formatter's format.\n",
       "        Subclasses may use a different call signature for the\n",
       "        `func` argument.\n",
       "        \n",
       "        If `func` is None or unspecified, there will be no change,\n",
       "        only returning the current value.\n",
       "    \n",
       "    Returns\n",
       "    -------\n",
       "    oldfunc : callable\n",
       "        The currently registered callable.\n",
       "        If you are registering a new formatter,\n",
       "        this will be the previous value (to enable restoring later).\n",
       "\n"
      ]
     }
    ],
    "source": [
     "help(latex_f.for_type_by_name)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "In this case, we will use `for_type_by_name` to register `poly_to_latex` as the display function for the `Polynomial` type:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "latex_f.for_type_by_name('numpy.polynomial.polynomial',\n",
     "                                 'Polynomial', poly_to_latex)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Once the custom display function has been registered, all NumPy `Polynomial` instances will be represented by their LaTeX form instead:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/latex": [
        "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
       ],
       "text/plain": [
        "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1.,  1.])"
       ]
      },
      "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "p"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/latex": [
        "$P(x)=-20+71 x+-15 x^2+x^3$, $x \\in [-1,\\ 1]$"
       ],
       "text/plain": [
        "Polynomial([-20.,  71., -15.,   1.], [-1.,  1.], [-1.,  1.])"
       ]
      },
      "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n",
     "p2"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## More complex display with `_ipython_display_`"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Rich output special methods and functions can only display one object or MIME type at a time. Sometimes this is not enough if you want to display multiple objects or MIME types at once. An example of this would be to use an HTML representation to put some HTML elements in the DOM and then use a JavaScript representation to add events to those elements.\n",
     "\n",
     "**IPython 2.0** recognizes another display method, `_ipython_display_`, which allows your objects to take complete control of displaying themselves. If this method is defined, IPython will call it, and make no effort to display the object using the above described `_repr_*_` methods for custom display functions. It's a way for you to say \"Back off, IPython, I can display this myself.\" Most importantly, your `_ipython_display_` method can make multiple calls to the top-level `display` functions to accomplish its goals.\n",
     "\n",
     "Here is an object that uses `display_html` and `display_javascript` to make a plot using the [Flot](http://www.flotcharts.org/) JavaScript plotting library:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "import json\n",
     "import uuid\n",
     "from IPython.display import display_javascript, display_html, display\n",
     "\n",
     "class FlotPlot(object):\n",
     "    def __init__(self, x, y):\n",
     "        self.x = x\n",
     "        self.y = y\n",
     "        self.uuid = str(uuid.uuid4())\n",
     "    \n",
     "    def _ipython_display_(self):\n",
     "        json_data = json.dumps(zip(self.x, self.y))\n",
     "        display_html('<div id=\"{}\" style=\"height: 300px; width:80%;\"></div>'.format(self.uuid),\n",
     "            raw=True\n",
     "        )\n",
     "        display_javascript(\"\"\"\n",
     "        require([\"//cdnjs.cloudflare.com/ajax/libs/flot/0.8.2/jquery.flot.min.js\"], function() {\n",
     "          var line = JSON.parse(\"%s\");\n",
     "          console.log(line);\n",
     "          $.plot(\"#%s\", [line]);\n",
     "        });\n",
     "        \"\"\" % (json_data, self.uuid), raw=True)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div id=\"e75b8189-92cb-4cbb-9996-bb8ad5ff1b4e\" style=\"height: 300px; width:80%;\"></div>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "application/javascript": [
        "\n",
        "        require([\"//cdnjs.cloudflare.com/ajax/libs/flot/0.8.2/jquery.flot.min.js\"], function() {\n",
        "          var line = JSON.parse(\"[[0.0, 0.0], [0.20408163265306123, 0.20266793654820095], [0.40816326530612246, 0.39692414892492234], [0.61224489795918369, 0.57470604121617908], [0.81632653061224492, 0.72863478346935029], [1.0204081632653061, 0.85232156971961837], [1.2244897959183674, 0.94063278511248671], [1.4285714285714286, 0.98990307637212394], [1.6326530612244898, 0.99808748213471832], [1.8367346938775511, 0.96484630898376322], [2.0408163265306123, 0.89155923041100371], [2.2448979591836737, 0.7812680235262639], [2.4489795918367347, 0.63855032022660208], [2.6530612244897958, 0.46932961277720098], [2.8571428571428572, 0.28062939951435684], [3.0612244897959187, 0.080281674842813497], [3.2653061224489797, -0.12339813736217871], [3.4693877551020407, -0.32195631507261868], [3.6734693877551021, -0.50715170948451438], [3.8775510204081636, -0.67129779355193209], [4.0816326530612246, -0.80758169096833643], [4.2857142857142856, -0.91034694431078278], [4.4897959183673475, -0.97532828606704558], [4.6938775510204085, -0.99982866838408957], [4.8979591836734695, -0.98283120392563061], [5.1020408163265305, -0.92504137173820289], [5.3061224489795915, -0.82885773637304272], [5.5102040816326534, -0.69827239556539955], [5.7142857142857144, -0.53870528838615628], [5.9183673469387754, -0.35677924089893803], [6.1224489795918373, -0.16004508604325057], [6.3265306122448983, 0.043331733368683463], [6.5306122448979593, 0.24491007101197931], [6.7346938775510203, 0.43632342647181932], [6.9387755102040813, 0.6096271964908323], [7.1428571428571432, 0.75762841539272019], [7.3469387755102042, 0.87418429881973347], [7.5510204081632653, 0.95445719973875187], [7.7551020408163271, 0.99511539477766364], [7.9591836734693882, 0.99447136726361685], [8.1632653061224492, 0.95255184753146038], [8.3673469387755102, 0.87109670348232071], [8.5714285714285712, 0.75348672743963763], [8.7755102040816322, 0.60460331650615429], [8.979591836734695, 0.43062587038273736], [9.183673469387756, 0.23877531564403087], [9.387755102040817, 0.037014401485062368], [9.591836734693878, -0.16628279384875641], [9.795918367346939, -0.36267842882654883], [10.0, -0.54402111088936989]]\");\n",
        "          console.log(line);\n",
        "          $.plot(\"#e75b8189-92cb-4cbb-9996-bb8ad5ff1b4e\", [line]);\n",
        "        });\n",
        "        "
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import numpy as np\n",
     "x = np.linspace(0,10)\n",
     "y = np.sin(x)\n",
     "FlotPlot(x, np.sin(x))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": []
   }
  ],
- "metadata": {
-  "signature": "sha256:86c779d5798c4a68bda7e71c8ef320cb7ba9d7e3d0f1bc4b828ee65f617a5ae3"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/IPython Kernel/Index.ipynb

@@ -1,168 +1,166 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<img src=\"../images/ipython_logo.png\">"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Back to the main [Index](../Index.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# IPython Kernel"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "IPython provides extensions to the Python programming language that make working interactively convenient and efficient. These extensions are implemented in the IPython Kernel and are available in all of the IPython Frontends (Notebook, Terminal, Console and Qt Console) when running this kernel."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Tutorials"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "* [Cell Magics](Cell Magics.ipynb)\n",
     "* [Script Magics](Script Magics.ipynb)\n",
     "* [Rich Output](Rich Output.ipynb)\n",
     "* [Custom Display Logic](Custom Display Logic.ipynb)\n",
     "* [Plotting in the Notebook](Plotting in the Notebook.ipynb)\n",
     "* [Capturing Output](Capturing Output.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Examples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "* [Background Jobs](Background Jobs.ipynb)\n",
     "* [Trapezoid Rule](Trapezoid Rule.ipynb)\n",
     "* [SymPy](SymPy.ipynb)\n",
     "* [Raw Input in the Notebook](Raw Input in the Notebook.ipynb)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Non-notebook examples"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "This directory also contains examples that are regular Python (`.py`) files."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<a href='example-demo.py' target='_blank'>example-demo.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/example-demo.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
       "text/html": [
        "<a href='ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>"
       ],
       "text/plain": [
        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/ipython-get-history.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "%run ../utils/list_pyfiles.ipy"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "There are also a set of examples that show how to integrate IPython with different GUI event loops:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "gui/<br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-glut.py' target='_blank'>gui-glut.py</a><br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-gtk.py' target='_blank'>gui-gtk.py</a><br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-gtk3.py' target='_blank'>gui-gtk3.py</a><br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-pyglet.py' target='_blank'>gui-pyglet.py</a><br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-qt.py' target='_blank'>gui-qt.py</a><br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-tk.py' target='_blank'>gui-tk.py</a><br>\n",
        "&nbsp;&nbsp;<a href='gui/gui-wx.py' target='_blank'>gui-wx.py</a><br>"
       ],
       "text/plain": [
        "gui/\n",
        "  gui-glut.py\n",
        "  gui-gtk.py\n",
        "  gui-gtk3.py\n",
        "  gui-pyglet.py\n",
        "  gui-qt.py\n",
        "  gui-tk.py\n",
        "  gui-wx.py"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "%run ../utils/list_subdirs.ipy"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:ee769d05a7e195e4b8546ef9a866ef03e59bff2f0fcba499d168c06b516aa79a"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/IPython Kernel/Plotting in the Notebook.ipynb

@@ -1,740 +1,738 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Plotting with Matplotlib"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## matplotlib mode"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "To make plots using Matplotlib, you must first enable IPython's matplotlib mode.\n",
     "\n",
     "To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.\n",
     "\n",
     "This magic takes an optional argument that specifies which Matplotlib backend should be used.  Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server.  You can always call the `display` function to paste figures into the Notebook document."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Making a simple plot"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "With matplotlib enabled, plotting should just work."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
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        "kL6BgYHBKIIhfQMDA4NRBEP6BgYGBqMI/x+3ghdT1LKwsgAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
        "<matplotlib.figure.Figure at 0x1084796d0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "x = np.linspace(0, 3*np.pi, 500)\n",
     "plt.plot(x, np.sin(x**2))\n",
     "plt.title('A simple chirp');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "These images can be resized by dragging the handle in the lower right corner.  Double clicking will return them to their original size."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Loading Matplotlib demos with %load"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "IPython's `%load` magic can be used to load any Matplotlib demo by its URL:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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        "iISh/weZPyRnS1m/IAAAAABJRU5ErkJggg==\n"
       ],
       "text/plain": [
        "<matplotlib.figure.Figure at 0x10848fb10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "\"\"\"\n",
     "Plot demonstrating the integral as the area under a curve.\n",
     "\n",
     "Although this is a simple example, it demonstrates some important tweaks:\n",
     "\n",
     "    * A simple line plot with custom color and line width.\n",
     "    * A shaded region created using a Polygon patch.\n",
     "    * A text label with mathtext rendering.\n",
     "    * figtext calls to label the x- and y-axes.\n",
     "    * Use of axis spines to hide the top and right spines.\n",
     "    * Custom tick placement and labels.\n",
     "\"\"\"\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.patches import Polygon\n",
     "\n",
     "\n",
     "def func(x):\n",
     "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
     "\n",
     "\n",
     "a, b = 2, 9 # integral limits\n",
     "x = np.linspace(0, 10)\n",
     "y = func(x)\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "plt.plot(x, y, 'r', linewidth=2)\n",
     "plt.ylim(ymin=0)\n",
     "\n",
     "# Make the shaded region\n",
     "ix = np.linspace(a, b)\n",
     "iy = func(ix)\n",
     "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
     "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
     "ax.add_patch(poly)\n",
     "\n",
     "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
     "         horizontalalignment='center', fontsize=20)\n",
     "\n",
     "plt.figtext(0.9, 0.05, '$x$')\n",
     "plt.figtext(0.1, 0.9, '$y$')\n",
     "\n",
     "ax.spines['right'].set_visible(False)\n",
     "ax.spines['top'].set_visible(False)\n",
     "ax.xaxis.set_ticks_position('bottom')\n",
     "\n",
     "ax.set_xticks((a, b))\n",
     "ax.set_xticklabels(('$a$', '$b$'))\n",
     "ax.set_yticks([])\n",
     "\n",
     "plt.show()\n"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:74dbf5caa25c937be70dfe2ab509783a01f4a2044850d7044e729300a8c3644d"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file

examples/IPython Kernel/Raw Input in the Notebook.ipynb

@@ -1,158 +1,156 b''
 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Using `raw_input` and `%debug` in the Notebook"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The Notebook has added support for `raw_input` and `%debug`, as of 1.0."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# Python 3 compat\n",
     "import sys\n",
     "if sys.version_info[0] >= 3:\n",
     "    raw_input = input"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "What is your name? Sir Robin\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "'Sir Robin'"
       ]
      },
      "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "name = raw_input(\"What is your name? \")\n",
     "name"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "**Python 2-only**: the eval input works as well (`input` is just `eval(raw_input(prompt))`)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "How many fingers? 4\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "(4, int)"
       ]
      },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "fingers = input(\"How many fingers? \")\n",
     "fingers, type(fingers)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "ename": "ZeroDivisionError",
      "evalue": "integer division or modulo by zero",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
       "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36mdiv\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
      ]
     }
    ],
    "source": [
     "def div(x, y):\n",
     "    return x/y\n",
     "\n",
     "div(1,0)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "> \u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m(2)\u001b[0;36mdiv\u001b[1;34m()\u001b[0m\n",
       "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[0m\u001b[1;32m      3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[0m\n",
       "ipdb> x\n",
       "1\n",
       "ipdb> y\n",
       "0\n",
       "ipdb> exit\n"
      ]
     }
    ],
    "source": [
     "%debug"
    ]
   }
  ],
- "metadata": {
-  "signature": "sha256:ac5c21534f3dd013c78d4d201527f3ed4dea5b6fad4116b8d23c67ba107e48c3"
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 0
 }
  No newline at end of file