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update nbconvert to nbformat 4
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@@ -1,174 +1,174 b''
1 1 """Module containing single call export functions."""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 from functools import wraps
7 7
8 from IPython.nbformat.v3.nbbase import NotebookNode
8 from IPython.nbformat.current import NotebookNode
9 9 from IPython.utils.decorators import undoc
10 10 from IPython.utils.py3compat import string_types
11 11
12 12 from .exporter import Exporter
13 13 from .templateexporter import TemplateExporter
14 14 from .html import HTMLExporter
15 15 from .slides import SlidesExporter
16 16 from .latex import LatexExporter
17 17 from .pdf import PDFExporter
18 18 from .markdown import MarkdownExporter
19 19 from .python import PythonExporter
20 20 from .rst import RSTExporter
21 21 from .notebook import NotebookExporter
22 22
23 23 #-----------------------------------------------------------------------------
24 24 # Classes
25 25 #-----------------------------------------------------------------------------
26 26
27 27 @undoc
28 28 def DocDecorator(f):
29 29
30 30 #Set docstring of function
31 31 f.__doc__ = f.__doc__ + """
32 32 nb : :class:`~{nbnode_mod}.NotebookNode`
33 33 The notebook to export.
34 34 config : config (optional, keyword arg)
35 35 User configuration instance.
36 36 resources : dict (optional, keyword arg)
37 37 Resources used in the conversion process.
38 38
39 39 Returns
40 40 -------
41 41 tuple- output, resources, exporter_instance
42 42 output : str
43 43 Jinja 2 output. This is the resulting converted notebook.
44 44 resources : dictionary
45 45 Dictionary of resources used prior to and during the conversion
46 46 process.
47 47 exporter_instance : Exporter
48 48 Instance of the Exporter class used to export the document. Useful
49 49 to caller because it provides a 'file_extension' property which
50 50 specifies what extension the output should be saved as.
51 51
52 52 Notes
53 53 -----
54 54 WARNING: API WILL CHANGE IN FUTURE RELEASES OF NBCONVERT
55 55 """.format(nbnode_mod=NotebookNode.__module__)
56 56
57 57 @wraps(f)
58 58 def decorator(*args, **kwargs):
59 59 return f(*args, **kwargs)
60 60
61 61 return decorator
62 62
63 63
64 64 #-----------------------------------------------------------------------------
65 65 # Functions
66 66 #-----------------------------------------------------------------------------
67 67
68 68 __all__ = [
69 69 'export',
70 70 'export_html',
71 71 'export_custom',
72 72 'export_slides',
73 73 'export_latex',
74 74 'export_pdf',
75 75 'export_markdown',
76 76 'export_python',
77 77 'export_rst',
78 78 'export_by_name',
79 79 'get_export_names',
80 80 'ExporterNameError'
81 81 ]
82 82
83 83
84 84 class ExporterNameError(NameError):
85 85 pass
86 86
87 87 @DocDecorator
88 88 def export(exporter, nb, **kw):
89 89 """
90 90 Export a notebook object using specific exporter class.
91 91
92 92 Parameters
93 93 ----------
94 94 exporter : class:`~IPython.nbconvert.exporters.exporter.Exporter` class or instance
95 95 Class type or instance of the exporter that should be used. If the
96 96 method initializes it's own instance of the class, it is ASSUMED that
97 97 the class type provided exposes a constructor (``__init__``) with the same
98 98 signature as the base Exporter class.
99 99 """
100 100
101 101 #Check arguments
102 102 if exporter is None:
103 103 raise TypeError("Exporter is None")
104 104 elif not isinstance(exporter, Exporter) and not issubclass(exporter, Exporter):
105 105 raise TypeError("exporter does not inherit from Exporter (base)")
106 106 if nb is None:
107 107 raise TypeError("nb is None")
108 108
109 109 #Create the exporter
110 110 resources = kw.pop('resources', None)
111 111 if isinstance(exporter, Exporter):
112 112 exporter_instance = exporter
113 113 else:
114 114 exporter_instance = exporter(**kw)
115 115
116 116 #Try to convert the notebook using the appropriate conversion function.
117 117 if isinstance(nb, NotebookNode):
118 118 output, resources = exporter_instance.from_notebook_node(nb, resources)
119 119 elif isinstance(nb, string_types):
120 120 output, resources = exporter_instance.from_filename(nb, resources)
121 121 else:
122 122 output, resources = exporter_instance.from_file(nb, resources)
123 123 return output, resources
124 124
125 125 exporter_map = dict(
126 126 custom=TemplateExporter,
127 127 html=HTMLExporter,
128 128 slides=SlidesExporter,
129 129 latex=LatexExporter,
130 130 pdf=PDFExporter,
131 131 markdown=MarkdownExporter,
132 132 python=PythonExporter,
133 133 rst=RSTExporter,
134 134 notebook=NotebookExporter,
135 135 )
136 136
137 137 def _make_exporter(name, E):
138 138 """make an export_foo function from a short key and Exporter class E"""
139 139 def _export(nb, **kw):
140 140 return export(E, nb, **kw)
141 141 _export.__doc__ = """Export a notebook object to {0} format""".format(name)
142 142 return _export
143 143
144 144 g = globals()
145 145
146 146 for name, E in exporter_map.items():
147 147 g['export_%s' % name] = DocDecorator(_make_exporter(name, E))
148 148
149 149 @DocDecorator
150 150 def export_by_name(format_name, nb, **kw):
151 151 """
152 152 Export a notebook object to a template type by its name. Reflection
153 153 (Inspect) is used to find the template's corresponding explicit export
154 154 method defined in this module. That method is then called directly.
155 155
156 156 Parameters
157 157 ----------
158 158 format_name : str
159 159 Name of the template style to export to.
160 160 """
161 161
162 162 function_name = "export_" + format_name.lower()
163 163
164 164 if function_name in globals():
165 165 return globals()[function_name](nb, **kw)
166 166 else:
167 167 raise ExporterNameError("template for `%s` not found" % function_name)
168 168
169 169
170 170 def get_export_names():
171 171 """Return a list of the currently supported export targets
172 172
173 173 WARNING: API WILL CHANGE IN FUTURE RELEASES OF NBCONVERT"""
174 174 return sorted(exporter_map.keys())
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@@ -1,177 +1,168 b''
1 1 {
2 "metadata": {
3 "name": "notebook2"
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
4 "cell_type": "heading",
5 "level": 1,
6 "metadata": {},
7 "source": [
8 "NumPy and Matplotlib examples"
9 ]
10 },
11 {
12 "cell_type": "markdown",
13 "metadata": {},
14 "source": [
15 "First import NumPy and Matplotlib:"
16 ]
17 },
18 {
19 "cell_type": "code",
20 "metadata": {
21 "collapsed": false
22 },
23 "outputs": [
10 24 {
11 "cell_type": "heading",
12 "level": 1,
13 25 "metadata": {},
14 "source": [
15 "NumPy and Matplotlib examples"
16 ]
17 },
26 "name": "stdout",
27 "output_type": "stream",
28 "text": "\nWelcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\nFor more information, type 'help(pylab)'.\n"
29 }
30 ],
31 "prompt_number": 1,
32 "source": [
33 "%pylab inline"
34 ]
35 },
36 {
37 "cell_type": "code",
38 "metadata": {
39 "collapsed": false
40 },
41 "outputs": [],
42 "prompt_number": 2,
43 "source": [
44 "import numpy as np"
45 ]
46 },
47 {
48 "cell_type": "markdown",
49 "metadata": {},
50 "source": [
51 "Now we show some very basic examples of how they can be used."
52 ]
53 },
54 {
55 "cell_type": "code",
56 "metadata": {
57 "collapsed": false
58 },
59 "outputs": [],
60 "prompt_number": 6,
61 "source": [
62 "a = np.random.uniform(size=(100,100))"
63 ]
64 },
65 {
66 "cell_type": "code",
67 "metadata": {
68 "collapsed": false
69 },
70 "outputs": [
18 71 {
19 "cell_type": "markdown",
20 72 "metadata": {},
21 "source": [
22 "First import NumPy and Matplotlib:"
73 "output_type": "execute_result",
74 "prompt_number": 7,
75 "text/plain": [
76 "(100, 100)"
23 77 ]
24 },
25 {
26 "cell_type": "code",
27 "collapsed": false,
28 "input": [
29 "%pylab inline"
30 ],
31 "language": "python",
32 "metadata": {},
33 "outputs": [
34 {
35 "output_type": "stream",
36 "stream": "stdout",
37 "text": [
38 "\n",
39 "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
40 "For more information, type 'help(pylab)'.\n"
41 ]
42 }
43 ],
44 "prompt_number": 1
45 },
46 {
47 "cell_type": "code",
48 "collapsed": false,
49 "input": [
50 "import numpy as np"
51 ],
52 "language": "python",
53 "metadata": {},
54 "outputs": [],
55 "prompt_number": 2
56 },
78 }
79 ],
80 "prompt_number": 7,
81 "source": [
82 "a.shape"
83 ]
84 },
85 {
86 "cell_type": "code",
87 "metadata": {
88 "collapsed": false
89 },
90 "outputs": [],
91 "prompt_number": 8,
92 "source": [
93 "evs = np.linalg.eigvals(a)"
94 ]
95 },
96 {
97 "cell_type": "code",
98 "metadata": {
99 "collapsed": false
100 },
101 "outputs": [
57 102 {
58 "cell_type": "markdown",
59 103 "metadata": {},
60 "source": [
61 "Now we show some very basic examples of how they can be used."
104 "output_type": "execute_result",
105 "prompt_number": 10,
106 "text/plain": [
107 "(100,)"
62 108 ]
63 },
64 {
65 "cell_type": "code",
66 "collapsed": false,
67 "input": [
68 "a = np.random.uniform(size=(100,100))"
69 ],
70 "language": "python",
71 "metadata": {},
72 "outputs": [],
73 "prompt_number": 6
74 },
75 {
76 "cell_type": "code",
77 "collapsed": false,
78 "input": [
79 "a.shape"
80 ],
81 "language": "python",
82 "metadata": {},
83 "outputs": [
84 {
85 "metadata": {},
86 "output_type": "pyout",
87 "prompt_number": 7,
88 "text": [
89 "(100, 100)"
90 ]
91 }
92 ],
93 "prompt_number": 7
94 },
95 {
96 "cell_type": "code",
97 "collapsed": false,
98 "input": [
99 "evs = np.linalg.eigvals(a)"
100 ],
101 "language": "python",
102 "metadata": {},
103 "outputs": [],
104 "prompt_number": 8
105 },
106 {
107 "cell_type": "code",
108 "collapsed": false,
109 "input": [
110 "evs.shape"
111 ],
112 "language": "python",
113 "metadata": {},
114 "outputs": [
115 {
116 "metadata": {},
117 "output_type": "pyout",
118 "prompt_number": 10,
119 "text": [
120 "(100,)"
121 ]
122 }
123 ],
124 "prompt_number": 10
125 },
109 }
110 ],
111 "prompt_number": 10,
112 "source": [
113 "evs.shape"
114 ]
115 },
116 {
117 "cell_type": "markdown",
118 "metadata": {},
119 "source": [
120 "Here is a cell that has both text and PNG output:"
121 ]
122 },
123 {
124 "cell_type": "code",
125 "metadata": {
126 "collapsed": false
127 },
128 "outputs": [
126 129 {
127 "cell_type": "markdown",
128 130 "metadata": {},
129 "source": [
130 "Here is a cell that has both text and PNG output:"
131 "output_type": "execute_result",
132 "prompt_number": 14,
133 "text/plain": [
134 "(array([95, 4, 0, 0, 0, 0, 0, 0, 0, 1]),\n",
135 " array([ -2.93566063, 2.35937011, 7.65440086, 12.9494316 ,\n",
136 " 18.24446235, 23.53949309, 28.83452384, 34.12955458,\n",
137 " 39.42458533, 44.71961607, 50.01464682]),\n",
138 " <a list of 10 Patch objects>)"
131 139 ]
132 140 },
133 141 {
134 "cell_type": "code",
135 "collapsed": false,
136 "input": [
137 "hist(evs.real)"
138 ],
139 "language": "python",
142 "image/png": 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1zjvvaMuWLerr6/PM/JK0efNmlZWVpS+M8NLslmUpGo3q0KFDisfjkrw1/7p162Tbtrq6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\n",
140 143 "metadata": {},
141 "outputs": [
142 {
143 "metadata": {},
144 "output_type": "pyout",
145 "prompt_number": 14,
146 "text": [
147 "(array([95, 4, 0, 0, 0, 0, 0, 0, 0, 1]),\n",
148 " array([ -2.93566063, 2.35937011, 7.65440086, 12.9494316 ,\n",
149 " 18.24446235, 23.53949309, 28.83452384, 34.12955458,\n",
150 " 39.42458533, 44.71961607, 50.01464682]),\n",
151 " <a list of 10 Patch objects>)"
152 ]
153 },
154 {
155 "metadata": {},
156 "output_type": "display_data",
157 "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD9CAYAAAC2l2x5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
158 "text": [
159 "<matplotlib.figure.Figure at 0x108c8f1d0>"
160 ]
161 }
162 ],
163 "prompt_number": 14
164 },
165 {
166 "cell_type": "code",
167 "collapsed": false,
168 "input": [],
169 "language": "python",
170 "metadata": {},
171 "outputs": []
144 "output_type": "display_data",
145 "text/plain": [
146 "<matplotlib.figure.Figure at 0x108c8f1d0>"
147 ]
172 148 }
173 149 ],
174 "metadata": {}
150 "prompt_number": 14,
151 "source": [
152 "hist(evs.real)"
153 ]
154 },
155 {
156 "cell_type": "code",
157 "metadata": {
158 "collapsed": false
159 },
160 "outputs": [],
161 "prompt_number": null,
162 "source": []
175 163 }
176 ]
164 ],
165 "metadata": {},
166 "nbformat": 4,
167 "nbformat_minor": 0
177 168 } No newline at end of file
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@@ -1,84 +1,77 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
10 {
11 "cell_type": "raw",
12 "metadata": {
13 "raw_mimetype": "text/html"
14 },
15 "source": [
16 "<b>raw html</b>"
17 ]
18 },
19 {
20 "cell_type": "raw",
21 "metadata": {
22 "raw_mimetype": "text/markdown"
23 },
24 "source": [
25 "* raw markdown\n",
26 "* bullet\n",
27 "* list"
28 ]
29 },
30 {
31 "cell_type": "raw",
32 "metadata": {
33 "raw_mimetype": "text/restructuredtext"
34 },
35 "source": [
36 "``raw rst``\n",
37 "\n",
38 ".. sourcecode:: python\n",
39 "\n",
40 " def foo(): pass\n"
41 ]
42 },
43 {
44 "cell_type": "raw",
45 "metadata": {
46 "raw_mimetype": "text/x-python"
47 },
48 "source": [
49 "def bar():\n",
50 " \"\"\"raw python\"\"\"\n",
51 " pass"
52 ]
53 },
54 {
55 "cell_type": "raw",
56 "metadata": {
57 "raw_mimetype": "text/latex"
58 },
59 "source": [
60 "\\LaTeX\n",
61 "% raw latex"
62 ]
63 },
64 {
65 "cell_type": "raw",
66 "metadata": {},
67 "source": [
68 "# no raw_mimetype metadata, should be included by default"
69 ]
70 },
71 {
72 "cell_type": "raw",
73 "metadata": {
74 "raw_mimetype": "doesnotexist"
75 },
76 "source": [
77 "garbage format defined, should never be included"
78 ]
79 }
80 ],
81 "metadata": {}
4 "cell_type": "raw",
5 "metadata": {
6 "raw_mimetype": "text/html"
7 },
8 "source": [
9 "<b>raw html</b>"
10 ]
11 },
12 {
13 "cell_type": "raw",
14 "metadata": {
15 "raw_mimetype": "text/markdown"
16 },
17 "source": [
18 "* raw markdown\n",
19 "* bullet\n",
20 "* list"
21 ]
22 },
23 {
24 "cell_type": "raw",
25 "metadata": {
26 "raw_mimetype": "text/restructuredtext"
27 },
28 "source": [
29 "``raw rst``\n",
30 "\n",
31 ".. sourcecode:: python\n",
32 "\n",
33 " def foo(): pass\n"
34 ]
35 },
36 {
37 "cell_type": "raw",
38 "metadata": {
39 "raw_mimetype": "text/x-python"
40 },
41 "source": [
42 "def bar():\n",
43 " \"\"\"raw python\"\"\"\n",
44 " pass"
45 ]
46 },
47 {
48 "cell_type": "raw",
49 "metadata": {
50 "raw_mimetype": "text/latex"
51 },
52 "source": [
53 "\\LaTeX\n",
54 "% raw latex"
55 ]
56 },
57 {
58 "cell_type": "raw",
59 "metadata": {},
60 "source": [
61 "# no raw_mimetype metadata, should be included by default"
62 ]
63 },
64 {
65 "cell_type": "raw",
66 "metadata": {
67 "raw_mimetype": "doesnotexist"
68 },
69 "source": [
70 "garbage format defined, should never be included"
71 ]
82 72 }
83 ]
84 }
73 ],
74 "metadata": {},
75 "nbformat": 4,
76 "nbformat_minor": 0
77 } No newline at end of file
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@@ -1,118 +1,116 b''
1 1 """Tests for Latex exporter"""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 import os.path
7 7 import textwrap
8 8 import re
9 9
10 10 from .base import ExportersTestsBase
11 11 from ..latex import LatexExporter
12 12 from IPython.nbformat import current
13 13 from IPython.testing.decorators import onlyif_cmds_exist
14 14 from IPython.utils.tempdir import TemporaryDirectory
15 15
16 16
17 17 class TestLatexExporter(ExportersTestsBase):
18 18 """Contains test functions for latex.py"""
19 19
20 20 exporter_class = LatexExporter
21 21 should_include_raw = ['latex']
22 22
23 23 def test_constructor(self):
24 24 """
25 25 Can a LatexExporter be constructed?
26 26 """
27 27 LatexExporter()
28 28
29 29
30 30 @onlyif_cmds_exist('pandoc')
31 31 def test_export(self):
32 32 """
33 33 Can a LatexExporter export something?
34 34 """
35 35 (output, resources) = LatexExporter().from_filename(self._get_notebook())
36 36 assert len(output) > 0
37 37
38 38
39 39 @onlyif_cmds_exist('pandoc')
40 40 def test_export_book(self):
41 41 """
42 42 Can a LatexExporter export using 'report' template?
43 43 """
44 44 (output, resources) = LatexExporter(template_file='report').from_filename(self._get_notebook())
45 45 assert len(output) > 0
46 46
47 47
48 48 @onlyif_cmds_exist('pandoc')
49 49 def test_export_basic(self):
50 50 """
51 51 Can a LatexExporter export using 'article' template?
52 52 """
53 53 (output, resources) = LatexExporter(template_file='article').from_filename(self._get_notebook())
54 54 assert len(output) > 0
55 55
56 56
57 57 @onlyif_cmds_exist('pandoc')
58 58 def test_export_article(self):
59 59 """
60 60 Can a LatexExporter export using 'article' template?
61 61 """
62 62 (output, resources) = LatexExporter(template_file='article').from_filename(self._get_notebook())
63 63 assert len(output) > 0
64 64
65 65 @onlyif_cmds_exist('pandoc')
66 66 def test_very_long_cells(self):
67 67 """
68 68 Torture test that long cells do not cause issues
69 69 """
70 70 lorem_ipsum_text = textwrap.dedent("""\
71 71 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec
72 72 dignissim, ipsum non facilisis tempus, dui felis tincidunt metus,
73 73 nec pulvinar neque odio eget risus. Nulla nisi lectus, cursus
74 74 suscipit interdum at, ultrices sit amet orci. Mauris facilisis
75 75 imperdiet elit, vitae scelerisque ipsum dignissim non. Integer
76 76 consequat malesuada neque sit amet pulvinar. Curabitur pretium
77 77 ut turpis eget aliquet. Maecenas sagittis lacus sed lectus
78 78 volutpat, eu adipiscing purus pulvinar. Maecenas consequat
79 79 luctus urna, eget cursus quam mollis a. Aliquam vitae ornare
80 80 erat, non hendrerit urna. Sed eu diam nec massa egestas pharetra
81 81 at nec tellus. Fusce feugiat lacus quis urna sollicitudin volutpat.
82 82 Quisque at sapien non nibh feugiat tempus ac ultricies purus.
83 83 """)
84 84 lorem_ipsum_text = lorem_ipsum_text.replace("\n"," ") + "\n\n"
85 85 large_lorem_ipsum_text = "".join([lorem_ipsum_text]*3000)
86 86
87 87 notebook_name = "lorem_ipsum_long.ipynb"
88 88 nb = current.new_notebook(
89 worksheets=[
90 current.new_worksheet(cells=[
91 current.new_text_cell('markdown',source=large_lorem_ipsum_text)
92 ])
89 cells=[
90 current.new_markdown_cell(source=large_lorem_ipsum_text)
93 91 ]
94 92 )
95 93
96 94 with TemporaryDirectory() as td:
97 95 nbfile = os.path.join(td, notebook_name)
98 96 with open(nbfile, 'w') as f:
99 97 current.write(nb, f, 'ipynb')
100 98
101 99 (output, resources) = LatexExporter(template_file='article').from_filename(nbfile)
102 100 assert len(output) > 0
103 101
104 102 @onlyif_cmds_exist('pandoc')
105 103 def test_prompt_number_color(self):
106 104 """
107 105 Does LatexExporter properly format input and output prompts in color?
108 106 """
109 107 (output, resources) = LatexExporter().from_filename(
110 108 self._get_notebook(nb_name="prompt_numbers.ipynb"))
111 109 in_regex = r"In \[\{\\color\{incolor\}(.*)\}\]:"
112 110 out_regex = r"Out\[\{\\color\{outcolor\}(.*)\}\]:"
113 111
114 112 ins = ["2", "10", " ", " ", "*", "0"]
115 113 outs = ["10"]
116 114
117 115 assert re.findall(in_regex, output) == ins
118 116 assert re.findall(out_regex, output) == outs
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@@ -1,65 +1,62 b''
1 1 """Tests for RSTExporter"""
2 2
3 3 #-----------------------------------------------------------------------------
4 4 # Copyright (c) 2013, the IPython Development Team.
5 5 #
6 6 # Distributed under the terms of the Modified BSD License.
7 7 #
8 8 # The full license is in the file COPYING.txt, distributed with this software.
9 9 #-----------------------------------------------------------------------------
10 10
11 11 #-----------------------------------------------------------------------------
12 12 # Imports
13 13 #-----------------------------------------------------------------------------
14 14
15 15 import io
16 16
17 17 from IPython.nbformat import current
18 18
19 19 from .base import ExportersTestsBase
20 20 from ..rst import RSTExporter
21 21 from IPython.testing.decorators import onlyif_cmds_exist
22 22
23 #-----------------------------------------------------------------------------
24 # Class
25 #-----------------------------------------------------------------------------
26 23
27 24 class TestRSTExporter(ExportersTestsBase):
28 25 """Tests for RSTExporter"""
29 26
30 27 exporter_class = RSTExporter
31 28 should_include_raw = ['rst']
32 29
33 30 def test_constructor(self):
34 31 """
35 32 Can a RSTExporter be constructed?
36 33 """
37 34 RSTExporter()
38 35
39 36
40 37 @onlyif_cmds_exist('pandoc')
41 38 def test_export(self):
42 39 """
43 40 Can a RSTExporter export something?
44 41 """
45 42 (output, resources) = RSTExporter().from_filename(self._get_notebook())
46 43 assert len(output) > 0
47 44
48 45 @onlyif_cmds_exist('pandoc')
49 46 def test_empty_code_cell(self):
50 47 """No empty code cells in rst"""
51 48 nbname = self._get_notebook()
52 49 with io.open(nbname, encoding='utf8') as f:
53 50 nb = current.read(f, 'json')
54 51
55 52 exporter = self.exporter_class()
56 53
57 54 (output, resources) = exporter.from_notebook_node(nb)
58 55 # add an empty code cell
59 nb.worksheets[0].cells.append(
60 current.new_code_cell(input="")
56 nb.cells.append(
57 current.new_code_cell(source="")
61 58 )
62 59 (output2, resources) = exporter.from_notebook_node(nb)
63 60 # adding an empty code cell shouldn't change output
64 61 self.assertEqual(output.strip(), output2.strip())
65 62
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@@ -1,93 +1,92 b''
1 1 """Base class for preprocessors"""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 from ..utils.base import NbConvertBase
7 7 from IPython.utils.traitlets import Bool
8 8
9 9
10 10 class Preprocessor(NbConvertBase):
11 11 """ A configurable preprocessor
12 12
13 13 Inherit from this class if you wish to have configurability for your
14 14 preprocessor.
15 15
16 16 Any configurable traitlets this class exposed will be configurable in
17 17 profiles using c.SubClassName.attribute = value
18 18
19 19 you can overwrite :meth:`preprocess_cell` to apply a transformation
20 20 independently on each cell or :meth:`preprocess` if you prefer your own
21 21 logic. See corresponding docstring for informations.
22 22
23 23 Disabled by default and can be enabled via the config by
24 24 'c.YourPreprocessorName.enabled = True'
25 25 """
26 26
27 27 enabled = Bool(False, config=True)
28 28
29 29 def __init__(self, **kw):
30 30 """
31 31 Public constructor
32 32
33 33 Parameters
34 34 ----------
35 35 config : Config
36 36 Configuration file structure
37 37 **kw : misc
38 38 Additional arguments
39 39 """
40 40
41 41 super(Preprocessor, self).__init__(**kw)
42 42
43 43
44 44 def __call__(self, nb, resources):
45 45 if self.enabled:
46 46 self.log.debug("Applying preprocessor: %s", self.__class__.__name__)
47 47 return self.preprocess(nb,resources)
48 48 else:
49 49 return nb, resources
50 50
51 51
52 52 def preprocess(self, nb, resources):
53 53 """
54 54 Preprocessing to apply on each notebook.
55 55
56 56 Must return modified nb, resources.
57 57
58 58 If you wish to apply your preprocessing to each cell, you might want
59 59 to override preprocess_cell method instead.
60 60
61 61 Parameters
62 62 ----------
63 63 nb : NotebookNode
64 64 Notebook being converted
65 65 resources : dictionary
66 66 Additional resources used in the conversion process. Allows
67 67 preprocessors to pass variables into the Jinja engine.
68 68 """
69 for worksheet in nb.worksheets:
70 for index, cell in enumerate(worksheet.cells):
71 worksheet.cells[index], resources = self.preprocess_cell(cell, resources, index)
69 for index, cell in enumerate(nb.cells):
70 nb.cells[index], resources = self.preprocess_cell(cell, resources, index)
72 71 return nb, resources
73 72
74 73
75 74 def preprocess_cell(self, cell, resources, index):
76 75 """
77 76 Override if you want to apply some preprocessing to each cell.
78 77 Must return modified cell and resource dictionary.
79 78
80 79 Parameters
81 80 ----------
82 81 cell : NotebookNode cell
83 82 Notebook cell being processed
84 83 resources : dictionary
85 84 Additional resources used in the conversion process. Allows
86 85 preprocessors to pass variables into the Jinja engine.
87 86 index : int
88 87 Index of the cell being processed
89 88 """
90 89
91 90 raise NotImplementedError('should be implemented by subclass')
92 91 return cell, resources
93 92
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@@ -1,77 +1,75 b''
1 1 """Preprocessor for merging consecutive stream outputs for easier handling."""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 import re
7 from IPython.utils.log import get_logger
7 8
8 9 def cell_preprocessor(function):
9 10 """
10 11 Wrap a function to be executed on all cells of a notebook
11 12
12 13 The wrapped function should have these parameters:
13 14
14 15 cell : NotebookNode cell
15 16 Notebook cell being processed
16 17 resources : dictionary
17 18 Additional resources used in the conversion process. Allows
18 19 preprocessors to pass variables into the Jinja engine.
19 20 index : int
20 21 Index of the cell being processed
21 22 """
22 23
23 24 def wrappedfunc(nb, resources):
24 from IPython.config import Application
25 if Application.initialized():
26 Application.instance().log.debug(
25 get_logger().debug(
27 26 "Applying preprocessor: %s", function.__name__
28 27 )
29 for worksheet in nb.worksheets:
30 for index, cell in enumerate(worksheet.cells):
31 worksheet.cells[index], resources = function(cell, resources, index)
28 for index, cell in enumerate(nb.cells):
29 nb.cells[index], resources = function(cell, resources, index)
32 30 return nb, resources
33 31 return wrappedfunc
34 32
35 33 cr_pat = re.compile(r'.*\r(?=[^\n])')
36 34
37 35 @cell_preprocessor
38 36 def coalesce_streams(cell, resources, index):
39 37 """
40 38 Merge consecutive sequences of stream output into single stream
41 39 to prevent extra newlines inserted at flush calls
42 40
43 41 Parameters
44 42 ----------
45 43 cell : NotebookNode cell
46 44 Notebook cell being processed
47 45 resources : dictionary
48 46 Additional resources used in the conversion process. Allows
49 47 transformers to pass variables into the Jinja engine.
50 48 index : int
51 49 Index of the cell being processed
52 50 """
53 51
54 52 outputs = cell.get('outputs', [])
55 53 if not outputs:
56 54 return cell, resources
57 55
58 56 last = outputs[0]
59 57 new_outputs = [last]
60 58 for output in outputs[1:]:
61 59 if (output.output_type == 'stream' and
62 60 last.output_type == 'stream' and
63 last.stream == output.stream
61 last.name == output.name
64 62 ):
65 63 last.text += output.text
66 64
67 65 else:
68 66 new_outputs.append(output)
69 67 last = output
70 68
71 69 # process \r characters
72 70 for output in new_outputs:
73 71 if output.output_type == 'stream' and '\r' in output.text:
74 72 output.text = cr_pat.sub('', output.text)
75 73
76 74 cell.outputs = new_outputs
77 75 return cell, resources
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@@ -1,143 +1,134 b''
1 1 """Module containing a preprocessor that removes the outputs from code cells"""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 import os
7 7 import sys
8 8
9 9 try:
10 10 from queue import Empty # Py 3
11 11 except ImportError:
12 12 from Queue import Empty # Py 2
13 13
14 14 from IPython.utils.traitlets import List, Unicode
15 15
16 from IPython.nbformat.current import reads, NotebookNode, writes
16 from IPython.nbformat.current import reads, writes, new_output
17 17 from .base import Preprocessor
18 18 from IPython.utils.traitlets import Integer
19 19
20 20 class ExecutePreprocessor(Preprocessor):
21 21 """
22 22 Executes all the cells in a notebook
23 23 """
24 24
25 25 timeout = Integer(30, config=True,
26 26 help="The time to wait (in seconds) for output from executions."
27 27 )
28 # FIXME: to be removed with nbformat v4
29 # map msg_type to v3 output_type
30 msg_type_map = {
31 "error" : "pyerr",
32 "execute_result" : "pyout",
33 }
34
35 # FIXME: to be removed with nbformat v4
36 # map mime-type to v3 mime-type keys
37 mime_map = {
38 "text/plain" : "text",
39 "text/html" : "html",
40 "image/svg+xml" : "svg",
41 "image/png" : "png",
42 "image/jpeg" : "jpeg",
43 "text/latex" : "latex",
44 "application/json" : "json",
45 "application/javascript" : "javascript",
46 }
47 28
48 29 extra_arguments = List(Unicode)
49 30
50 31 def preprocess(self, nb, resources):
51 32 from IPython.kernel import run_kernel
52 33 kernel_name = nb.metadata.get('kernelspec', {}).get('name', 'python')
53 34 self.log.info("Executing notebook with kernel: %s" % kernel_name)
54 35 with run_kernel(kernel_name=kernel_name,
55 36 extra_arguments=self.extra_arguments,
56 37 stderr=open(os.devnull, 'w')) as kc:
57 38 self.kc = kc
58 39 nb, resources = super(ExecutePreprocessor, self).preprocess(nb, resources)
59 40 return nb, resources
60 41
61 42 def preprocess_cell(self, cell, resources, cell_index):
62 43 """
63 44 Apply a transformation on each code cell. See base.py for details.
64 45 """
65 46 if cell.cell_type != 'code':
66 47 return cell, resources
67 48 try:
68 49 outputs = self.run_cell(self.kc.shell_channel, self.kc.iopub_channel, cell)
69 50 except Exception as e:
70 51 self.log.error("failed to run cell: " + repr(e))
71 self.log.error(str(cell.input))
52 self.log.error(str(cell.source))
72 53 raise
73 54 cell.outputs = outputs
74 55 return cell, resources
75 56
76 57 def run_cell(self, shell, iopub, cell):
77 msg_id = shell.execute(cell.input)
78 self.log.debug("Executing cell:\n%s", cell.input)
58 msg_id = shell.execute(cell.source)
59 self.log.debug("Executing cell:\n%s", cell.source)
79 60 # wait for finish, with timeout
80 61 while True:
81 62 try:
82 63 msg = shell.get_msg(timeout=self.timeout)
83 64 except Empty:
84 65 self.log.error("Timeout waiting for execute reply")
85 66 raise
86 67 if msg['parent_header'].get('msg_id') == msg_id:
87 68 break
88 69 else:
89 70 # not our reply
90 71 continue
91 72
92 73 outs = []
93 74
94 75 while True:
95 76 try:
96 77 msg = iopub.get_msg(timeout=self.timeout)
97 78 except Empty:
98 79 self.log.warn("Timeout waiting for IOPub output")
99 80 break
100 81 if msg['parent_header'].get('msg_id') != msg_id:
101 82 # not an output from our execution
102 83 continue
103 84
104 85 msg_type = msg['msg_type']
105 86 self.log.debug("output: %s", msg_type)
106 87 content = msg['content']
107 out = NotebookNode(output_type=self.msg_type_map.get(msg_type, msg_type))
108 88
109 89 # set the prompt number for the input and the output
110 90 if 'execution_count' in content:
111 91 cell['prompt_number'] = content['execution_count']
112 92 out.prompt_number = content['execution_count']
113 93
114 94 if msg_type == 'status':
115 95 if content['execution_state'] == 'idle':
116 96 break
117 97 else:
118 98 continue
119 elif msg_type in {'execute_input', 'pyin'}:
99 elif msg_type in {'execute_input'}:
120 100 continue
121 101 elif msg_type == 'clear_output':
122 102 outs = []
123 103 continue
124 104
125 if msg_type == 'stream':
126 out.stream = content['name']
127 out.text = content['text']
128 elif msg_type in ('display_data', 'execute_result'):
129 out['metadata'] = content['metadata']
130 for mime, data in content['data'].items():
131 # map mime-type keys to nbformat v3 keys
132 # this will be unnecessary in nbformat v4
133 key = self.mime_map.get(mime, mime)
134 out[key] = data
105 # set the prompt number for the input and the output
106 if msg_type == 'execute_result':
107 cell['prompt_number'] = content['execution_count']
108 out = new_output(output_type=msg_type,
109 metadata=content['metadata'],
110 mime_bundle=content['data'],
111 prompt_number=content['execution_count'],
112 )
113
114 elif msg_type == 'stream':
115 out = new_output(output_type=msg_type,
116 name=content['name'],
117 data=content['data'],
118 )
119 elif msg_type == 'display_data':
120 out = new_output(output_type=msg_type,
121 metadata=content['metadata'],
122 mime_bundle=content['data'],
123 )
135 124 elif msg_type == 'error':
136 out.ename = content['ename']
137 out.evalue = content['evalue']
138 out.traceback = content['traceback']
125 out = new_output(output_type=msg_type,
126 ename=content['ename'],
127 evalue=content['evalue'],
128 traceback=content['traceback'],
129 )
139 130 else:
140 131 self.log.error("unhandled iopub msg: " + msg_type)
141 132
142 133 outs.append(out)
143 134 return outs
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@@ -1,112 +1,101 b''
1 """Module containing a preprocessor that extracts all of the outputs from the
1 """A preprocessor that extracts all of the outputs from the
2 2 notebook file. The extracted outputs are returned in the 'resources' dictionary.
3 3 """
4 #-----------------------------------------------------------------------------
5 # Copyright (c) 2013, the IPython Development Team.
6 #
7 # Distributed under the terms of the Modified BSD License.
8 #
9 # The full license is in the file COPYING.txt, distributed with this software.
10 #-----------------------------------------------------------------------------
11 4
12 #-----------------------------------------------------------------------------
13 # Imports
14 #-----------------------------------------------------------------------------
5 # Copyright (c) IPython Development Team.
6 # Distributed under the terms of the Modified BSD License.
15 7
16 8 import base64
17 9 import sys
18 10 import os
19 11 from mimetypes import guess_extension
20 12
21 13 from IPython.utils.traitlets import Unicode, Set
22 14 from .base import Preprocessor
23 15 from IPython.utils import py3compat
24 16
25 #-----------------------------------------------------------------------------
26 # Classes
27 #-----------------------------------------------------------------------------
28 17
29 18 class ExtractOutputPreprocessor(Preprocessor):
30 19 """
31 20 Extracts all of the outputs from the notebook file. The extracted
32 21 outputs are returned in the 'resources' dictionary.
33 22 """
34 23
35 24 output_filename_template = Unicode(
36 25 "{unique_key}_{cell_index}_{index}{extension}", config=True)
37 26
38 extract_output_types = Set({'png', 'jpeg', 'svg', 'application/pdf'}, config=True)
27 extract_output_types = Set({'image/png', 'image/jpeg', 'image/svg+xml', 'application/pdf'}, config=True)
39 28
40 29 def preprocess_cell(self, cell, resources, cell_index):
41 30 """
42 31 Apply a transformation on each cell,
43 32
44 33 Parameters
45 34 ----------
46 35 cell : NotebookNode cell
47 36 Notebook cell being processed
48 37 resources : dictionary
49 38 Additional resources used in the conversion process. Allows
50 39 preprocessors to pass variables into the Jinja engine.
51 40 cell_index : int
52 41 Index of the cell being processed (see base.py)
53 42 """
54 43
55 44 #Get the unique key from the resource dict if it exists. If it does not
56 45 #exist, use 'output' as the default. Also, get files directory if it
57 46 #has been specified
58 47 unique_key = resources.get('unique_key', 'output')
59 48 output_files_dir = resources.get('output_files_dir', None)
60 49
61 50 #Make sure outputs key exists
62 51 if not isinstance(resources['outputs'], dict):
63 52 resources['outputs'] = {}
64 53
65 54 #Loop through all of the outputs in the cell
66 55 for index, out in enumerate(cell.get('outputs', [])):
67 56
68 57 #Get the output in data formats that the template needs extracted
69 58 for out_type in self.extract_output_types:
70 59 if out_type in out:
71 60 data = out[out_type]
72 61
73 62 #Binary files are base64-encoded, SVG is already XML
74 if out_type in {'png', 'jpeg', 'application/pdf'}:
63 if out_type in {'image/png', 'image/jpeg', 'application/pdf'}:
75 64
76 65 # data is b64-encoded as text (str, unicode)
77 66 # decodestring only accepts bytes
78 67 data = py3compat.cast_bytes(data)
79 68 data = base64.decodestring(data)
80 69 elif sys.platform == 'win32':
81 70 data = data.replace('\n', '\r\n').encode("UTF-8")
82 71 else:
83 72 data = data.encode("UTF-8")
84 73
85 74 # Build an output name
86 75 # filthy hack while we have some mimetype output, and some not
87 76 if '/' in out_type:
88 77 ext = guess_extension(out_type)
89 78 if ext is None:
90 79 ext = '.' + out_type.rsplit('/')[-1]
91 80 else:
92 81 ext = '.' + out_type
93 82
94 83 filename = self.output_filename_template.format(
95 84 unique_key=unique_key,
96 85 cell_index=cell_index,
97 86 index=index,
98 87 extension=ext)
99 88
100 89 #On the cell, make the figure available via
101 90 # cell.outputs[i].svg_filename ... etc (svg in example)
102 91 # Where
103 92 # cell.outputs[i].svg contains the data
104 93 if output_files_dir is not None:
105 94 filename = os.path.join(output_files_dir, filename)
106 95 out[out_type + '_filename'] = filename
107 96
108 97 #In the resources, make the figure available via
109 98 # resources['outputs']['filename'] = data
110 99 resources['outputs'][filename] = data
111 100
112 101 return cell, resources
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@@ -1,113 +1,100 b''
1 1 """This preprocessor detect cells using a different language through
2 2 magic extensions such as `%%R` or `%%octave`. Cell's metadata is marked
3 3 so that the appropriate highlighter can be used in the `highlight`
4 4 filter.
5 5 """
6 6
7 #-----------------------------------------------------------------------------
8 # Copyright (c) 2013, the IPython Development Team.
9 #
7 # Copyright (c) IPython Development Team.
10 8 # Distributed under the terms of the Modified BSD License.
11 #
12 # The full license is in the file COPYING.txt, distributed with this software.
13 #-----------------------------------------------------------------------------
14
15 #-----------------------------------------------------------------------------
16 # Imports
17 #-----------------------------------------------------------------------------
18 9
19 10 from __future__ import print_function, absolute_import
20 11
21 12 import re
22 13
23 14 # Our own imports
24 15 from .base import Preprocessor
25 16 from IPython.utils.traitlets import Dict
26 17
27 #-----------------------------------------------------------------------------
28 # Classes
29 #-----------------------------------------------------------------------------
30
31 18
32 19 class HighlightMagicsPreprocessor(Preprocessor):
33 20 """
34 21 Detects and tags code cells that use a different languages than Python.
35 22 """
36 23
37 24 # list of magic language extensions and their associated pygment lexers
38 25 default_languages = Dict(
39 26 default_value={
40 27 '%%R': 'r',
41 28 '%%bash': 'bash',
42 29 '%%cython': 'cython',
43 30 '%%javascript': 'javascript',
44 31 '%%julia': 'julia',
45 32 '%%latex': 'latex',
46 33 '%%octave': 'octave',
47 34 '%%perl': 'perl',
48 35 '%%ruby': 'ruby',
49 36 '%%sh': 'sh'})
50 37
51 38 # user defined language extensions
52 39 languages = Dict(
53 40 config=True,
54 41 help=("Syntax highlighting for magic's extension languages. "
55 42 "Each item associates a language magic extension such as %%R, "
56 43 "with a pygments lexer such as r."))
57 44
58 45 def __init__(self, config=None, **kw):
59 46 """Public constructor"""
60 47
61 48 super(HighlightMagicsPreprocessor, self).__init__(config=config, **kw)
62 49
63 50 # Update the default languages dict with the user configured ones
64 51 self.default_languages.update(self.languages)
65 52
66 53 # build a regular expression to catch language extensions and choose
67 54 # an adequate pygments lexer
68 55 any_language = "|".join(self.default_languages.keys())
69 56 self.re_magic_language = re.compile(
70 57 r'^\s*({0})\s+'.format(any_language))
71 58
72 59 def which_magic_language(self, source):
73 60 """
74 61 When a cell uses another language through a magic extension,
75 62 the other language is returned.
76 63 If no language magic is detected, this function returns None.
77 64
78 65 Parameters
79 66 ----------
80 67 source: str
81 68 Source code of the cell to highlight
82 69 """
83 70
84 71 m = self.re_magic_language.match(source)
85 72
86 73 if m:
87 74 # By construction of the re, the matched language must be in the
88 75 # languages dictionary
89 76 return self.default_languages[m.group(1)]
90 77 else:
91 78 return None
92 79
93 80 def preprocess_cell(self, cell, resources, cell_index):
94 81 """
95 82 Tags cells using a magic extension language
96 83
97 84 Parameters
98 85 ----------
99 86 cell : NotebookNode cell
100 87 Notebook cell being processed
101 88 resources : dictionary
102 89 Additional resources used in the conversion process. Allows
103 90 preprocessors to pass variables into the Jinja engine.
104 91 cell_index : int
105 92 Index of the cell being processed (see base.py)
106 93 """
107 94
108 95 # Only tag code cells
109 if hasattr(cell, "input") and cell.cell_type == "code":
110 magic_language = self.which_magic_language(cell.input)
96 if cell.cell_type == "code":
97 magic_language = self.which_magic_language(cell.source)
111 98 if magic_language:
112 99 cell['metadata']['magics_language'] = magic_language
113 100 return cell, resources
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@@ -1,75 +1,62 b''
1 """Module that pre-processes the notebook for export via Reveal.
2 """
3 #-----------------------------------------------------------------------------
4 # Copyright (c) 2013, the IPython Development Team.
5 #
6 # Distributed under the terms of the Modified BSD License.
7 #
8 # The full license is in the file COPYING.txt, distributed with this software.
9 #-----------------------------------------------------------------------------
1 """Module that pre-processes the notebook for export via Reveal."""
10 2
11 #-----------------------------------------------------------------------------
12 # Imports
13 #-----------------------------------------------------------------------------
3 # Copyright (c) IPython Development Team.
4 # Distributed under the terms of the Modified BSD License.
14 5
15 6 from .base import Preprocessor
16 7 from IPython.utils.traitlets import Unicode
17 8
18 #-----------------------------------------------------------------------------
19 # Classes and functions
20 #-----------------------------------------------------------------------------
21 9
22 10 class RevealHelpPreprocessor(Preprocessor):
23 11
24 12 url_prefix = Unicode('reveal.js', config=True,
25 13 help="""The URL prefix for reveal.js.
26 14 This can be a a relative URL for a local copy of reveal.js,
27 15 or point to a CDN.
28 16
29 17 For speaker notes to work, a local reveal.js prefix must be used.
30 18 """
31 19 )
32 20
33 21 def preprocess(self, nb, resources):
34 22 """
35 23 Called once to 'preprocess' contents of the notebook.
36 24
37 25 Parameters
38 26 ----------
39 27 nb : NotebookNode
40 28 Notebook being converted
41 29 resources : dictionary
42 30 Additional resources used in the conversion process. Allows
43 31 preprocessors to pass variables into the Jinja engine.
44 32 """
45 33
46 for worksheet in nb.worksheets:
47 for index, cell in enumerate(worksheet.cells):
48
49 #Make sure the cell has slideshow metadata.
50 cell.metadata.slide_type = cell.get('metadata', {}).get('slideshow', {}).get('slide_type', '-')
51
52 #Get the slide type. If type is start of subslide or slide,
53 #end the last subslide/slide.
54 if cell.metadata.slide_type in ['slide']:
55 worksheet.cells[index - 1].metadata.slide_helper = 'slide_end'
56 if cell.metadata.slide_type in ['subslide']:
57 worksheet.cells[index - 1].metadata.slide_helper = 'subslide_end'
58 #Prevent the rendering of "do nothing" cells before fragments
59 #Group fragments passing frag_number to the data-fragment-index
60 if cell.metadata.slide_type in ['fragment']:
61 worksheet.cells[index].metadata.frag_number = index
62 i = 1
63 while i < len(worksheet.cells) - index:
64 worksheet.cells[index + i].metadata.frag_helper = 'fragment_end'
65 worksheet.cells[index + i].metadata.frag_number = index
66 i += 1
67 #Restart the slide_helper when the cell status is changed
68 #to other types.
69 if cell.metadata.slide_type in ['-', 'skip', 'notes', 'fragment']:
70 worksheet.cells[index - 1].metadata.slide_helper = '-'
34 for index, cell in enumerate(nb.cells):
35
36 #Make sure the cell has slideshow metadata.
37 cell.metadata.slide_type = cell.get('metadata', {}).get('slideshow', {}).get('slide_type', '-')
38
39 # Get the slide type. If type is start, subslide, or slide,
40 # end the last subslide/slide.
41 if cell.metadata.slide_type in ['slide']:
42 nb.cells[index - 1].metadata.slide_helper = 'slide_end'
43 if cell.metadata.slide_type in ['subslide']:
44 nb.cells[index - 1].metadata.slide_helper = 'subslide_end'
45 # Prevent the rendering of "do nothing" cells before fragments
46 # Group fragments passing frag_number to the data-fragment-index
47 if cell.metadata.slide_type in ['fragment']:
48 nb.cells[index].metadata.frag_number = index
49 i = 1
50 while i < len(nb.cells) - index:
51 nb.cells[index + i].metadata.frag_helper = 'fragment_end'
52 nb.cells[index + i].metadata.frag_number = index
53 i += 1
54 # Restart the slide_helper when the cell status is changed
55 # to other types.
56 if cell.metadata.slide_type in ['-', 'skip', 'notes', 'fragment']:
57 nb.cells[index - 1].metadata.slide_helper = '-'
71 58
72 59 if not isinstance(resources['reveal'], dict):
73 60 resources['reveal'] = {}
74 61 resources['reveal']['url_prefix'] = self.url_prefix
75 62 return nb, resources
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@@ -1,56 +1,41 b''
1 """
2 Module with utility functions for preprocessor tests
3 """
1 """utility functions for preprocessor tests"""
4 2
5 #-----------------------------------------------------------------------------
6 # Copyright (c) 2013, the IPython Development Team.
7 #
3 # Copyright (c) IPython Development Team.
8 4 # Distributed under the terms of the Modified BSD License.
9 #
10 # The full license is in the file COPYING.txt, distributed with this software.
11 #-----------------------------------------------------------------------------
12
13 #-----------------------------------------------------------------------------
14 # Imports
15 #-----------------------------------------------------------------------------
16 5
17 6 from IPython.nbformat import current as nbformat
18 7
19 8 from ...tests.base import TestsBase
20 9 from ...exporters.exporter import ResourcesDict
21 10
22 #-----------------------------------------------------------------------------
23 # Class
24 #-----------------------------------------------------------------------------
25 11
26 12 class PreprocessorTestsBase(TestsBase):
27 13 """Contains test functions preprocessor tests"""
28 14
29 15
30 16 def build_notebook(self):
31 17 """Build a notebook in memory for use with preprocessor tests"""
32 18
33 outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="a"),
34 nbformat.new_output(output_type="text", output_text="b"),
35 nbformat.new_output(output_type="stream", stream="stdout", output_text="c"),
36 nbformat.new_output(output_type="stream", stream="stdout", output_text="d"),
37 nbformat.new_output(output_type="stream", stream="stderr", output_text="e"),
38 nbformat.new_output(output_type="stream", stream="stderr", output_text="f"),
39 nbformat.new_output(output_type="png", output_png='Zw==')] # g
40 out = nbformat.new_output(output_type="application/pdf")
41 out['application/pdf'] = 'aA==' # h
19 outputs = [nbformat.new_output(output_type="stream", name="stdout", text="a"),
20 nbformat.new_output(output_type="display_data", mime_bundle={'text/plain': 'b'}),
21 nbformat.new_output(output_type="stream", name="stdout", text="c"),
22 nbformat.new_output(output_type="stream", name="stdout", text="d"),
23 nbformat.new_output(output_type="stream", name="stderr", text="e"),
24 nbformat.new_output(output_type="stream", name="stderr", text="f"),
25 nbformat.new_output(output_type="display_data", mime_bundle={'image/png': 'Zw=='})] # g
26 out = nbformat.new_output(output_type="display_data")
27 out['application/pdf'] = 'aA=='
42 28 outputs.append(out)
43 29
44 cells=[nbformat.new_code_cell(input="$ e $", prompt_number=1,outputs=outputs),
45 nbformat.new_text_cell('markdown', source="$ e $")]
46 worksheets = [nbformat.new_worksheet(cells=cells)]
30 cells=[nbformat.new_code_cell(source="$ e $", prompt_number=1, outputs=outputs),
31 nbformat.new_markdown_cell(source="$ e $")]
47 32
48 return nbformat.new_notebook(name="notebook1", worksheets=worksheets)
33 return nbformat.new_notebook(cells=cells)
49 34
50 35
51 36 def build_resources(self):
52 37 """Build an empty resources dictionary."""
53 38
54 39 res = ResourcesDict()
55 40 res['metadata'] = ResourcesDict()
56 41 return res
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@@ -1,46 +1,38 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
10 {
11 "cell_type": "code",
12 "collapsed": false,
13 "input": [
14 "from IPython.display import clear_output"
15 ],
16 "language": "python",
17 "metadata": {},
18 "outputs": [],
19 "prompt_number": 1
20 },
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [],
9 "prompt_number": 1,
10 "source": [
11 "from IPython.display import clear_output"
12 ]
13 },
14 {
15 "cell_type": "code",
16 "metadata": {
17 "collapsed": false
18 },
19 "outputs": [
21 20 {
22 "cell_type": "code",
23 "collapsed": false,
24 "input": [
25 "for i in range(10):\n",
26 " clear_output()\n",
27 " print(i)"
28 ],
29 "language": "python",
30 21 "metadata": {},
31 "outputs": [
32 {
33 "output_type": "stream",
34 "stream": "stdout",
35 "text": [
36 "9\n"
37 ]
38 }
39 ],
40 "prompt_number": 2
22 "name": "stdout",
23 "output_type": "stream",
24 "text": "9\n"
41 25 }
42 26 ],
43 "metadata": {}
27 "prompt_number": 2,
28 "source": [
29 "for i in range(10):\n",
30 " clear_output()\n",
31 " print(i)"
32 ]
44 33 }
45 ]
34 ],
35 "metadata": {},
36 "nbformat": 4,
37 "nbformat_minor": 0
46 38 } No newline at end of file
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@@ -1,55 +1,38 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
10 {
11 "cell_type": "code",
12 "collapsed": false,
13 "input": [
14 "i, j = 1, 1"
15 ],
16 "language": "python",
17 "metadata": {},
18 "outputs": [],
19 "prompt_number": 1
20 },
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [],
9 "prompt_number": 1,
10 "source": [
11 "i, j = 1, 1"
12 ]
13 },
14 {
15 "cell_type": "code",
16 "metadata": {
17 "collapsed": false
18 },
19 "outputs": [
21 20 {
22 "cell_type": "code",
23 "collapsed": false,
24 "input": [
25 "for m in range(10):\n",
26 " i, j = j, i + j\n",
27 " print(j)"
28 ],
29 "language": "python",
30 21 "metadata": {},
31 "outputs": [
32 {
33 "output_type": "stream",
34 "stream": "stdout",
35 "text": [
36 "2\n",
37 "3\n",
38 "5\n",
39 "8\n",
40 "13\n",
41 "21\n",
42 "34\n",
43 "55\n",
44 "89\n",
45 "144\n"
46 ]
47 }
48 ],
49 "prompt_number": 2
22 "name": "stdout",
23 "output_type": "stream",
24 "text": "2\n3\n5\n8\n13\n21\n34\n55\n89\n144\n"
50 25 }
51 26 ],
52 "metadata": {}
27 "prompt_number": 2,
28 "source": [
29 "for m in range(10):\n",
30 " i, j = j, i + j\n",
31 " print(j)"
32 ]
53 33 }
54 ]
34 ],
35 "metadata": {},
36 "nbformat": 4,
37 "nbformat_minor": 0
55 38 } No newline at end of file
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@@ -1,33 +1,25 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [
10 9 {
11 "cell_type": "code",
12 "collapsed": false,
13 "input": [
14 "print(\"Hello World\")"
15 ],
16 "language": "python",
17 10 "metadata": {},
18 "outputs": [
19 {
20 "output_type": "stream",
21 "stream": "stdout",
22 "text": [
23 "Hello World\n"
24 ]
25 }
26 ],
27 "prompt_number": 1
11 "name": "stdout",
12 "output_type": "stream",
13 "text": "Hello World\n"
28 14 }
29 15 ],
30 "metadata": {}
16 "prompt_number": 1,
17 "source": [
18 "print(\"Hello World\")"
19 ]
31 20 }
32 ]
21 ],
22 "metadata": {},
23 "nbformat": 4,
24 "nbformat_minor": 0
33 25 } No newline at end of file
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@@ -1,36 +1,29 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
10 {
11 "cell_type": "code",
12 "collapsed": false,
13 "input": [
14 "from IPython.display import Image"
15 ],
16 "language": "python",
17 "metadata": {},
18 "outputs": [],
19 "prompt_number": 1
20 },
21 {
22 "cell_type": "code",
23 "collapsed": false,
24 "input": [
25 "Image('../input/python.png');"
26 ],
27 "language": "python",
28 "metadata": {},
29 "outputs": [],
30 "prompt_number": 2
31 }
32 ],
33 "metadata": {}
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [],
9 "prompt_number": 1,
10 "source": [
11 "from IPython.display import Image"
12 ]
13 },
14 {
15 "cell_type": "code",
16 "metadata": {
17 "collapsed": false
18 },
19 "outputs": [],
20 "prompt_number": 2,
21 "source": [
22 "Image('../input/python.png');"
23 ]
34 24 }
35 ]
25 ],
26 "metadata": {},
27 "nbformat": 4,
28 "nbformat_minor": 0
36 29 } No newline at end of file
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@@ -1,53 +1,46 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
10 {
11 "cell_type": "code",
12 "collapsed": false,
13 "input": [
14 "from IPython.display import SVG"
15 ],
16 "language": "python",
17 "metadata": {},
18 "outputs": [],
19 "prompt_number": 1
20 },
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [],
9 "prompt_number": 1,
10 "source": [
11 "from IPython.display import SVG"
12 ]
13 },
14 {
15 "cell_type": "code",
16 "metadata": {
17 "collapsed": false
18 },
19 "outputs": [
21 20 {
22 "cell_type": "code",
23 "collapsed": false,
24 "input": [
25 "SVG(data='''\n",
21 "image/svg+xml": [
26 22 "<svg height=\"100\" width=\"100\">\n",
27 " <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"2\" fill=\"red\" />\n",
28 "</svg>''')"
23 " <circle cx=\"50\" cy=\"50\" fill=\"red\" r=\"40\" stroke=\"black\" stroke-width=\"2\"/>\n",
24 "</svg>"
29 25 ],
30 "language": "python",
31 26 "metadata": {},
32 "outputs": [
33 {
34 "metadata": {},
35 "output_type": "pyout",
36 "prompt_number": 2,
37 "svg": [
38 "<svg height=\"100\" width=\"100\">\n",
39 " <circle cx=\"50\" cy=\"50\" fill=\"red\" r=\"40\" stroke=\"black\" stroke-width=\"2\"/>\n",
40 "</svg>"
41 ],
42 "text": [
43 "<IPython.core.display.SVG at 0x10428e150>"
44 ]
45 }
46 ],
47 "prompt_number": 2
27 "output_type": "execute_result",
28 "prompt_number": 2,
29 "text/plain": [
30 "<IPython.core.display.SVG object>"
31 ]
48 32 }
49 33 ],
50 "metadata": {}
34 "prompt_number": 2,
35 "source": [
36 "SVG(data='''\n",
37 "<svg height=\"100\" width=\"100\">\n",
38 " <circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"2\" fill=\"red\" />\n",
39 "</svg>''')"
40 ]
51 41 }
52 ]
42 ],
43 "metadata": {},
44 "nbformat": 4,
45 "nbformat_minor": 0
53 46 } No newline at end of file
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@@ -1,57 +1,51 b''
1 1 {
2 "metadata": {
3 "name": "",
4 "signature": "sha256:9d47889f0678e9685429071216d0f3354db59bb66489f3225bcadfb6a1a9bbba"
5 },
6 "nbformat": 3,
7 "nbformat_minor": 0,
8 "worksheets": [
2 "cells": [
9 3 {
10 "cells": [
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [
11 9 {
12 "cell_type": "code",
13 "collapsed": false,
14 "input": [
15 "raise Exception(\"message\")"
16 ],
17 "language": "python",
10 "ename": "Exception",
11 "evalue": "message",
18 12 "metadata": {},
19 "outputs": [
20 {
21 "ename": "Exception",
22 "evalue": "message",
23 "output_type": "pyerr",
24 "traceback": [
25 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
26 "\u001b[1;31mException\u001b[0m Traceback (most recent call last)",
27 "\u001b[1;32m<ipython-input-1-335814d14fc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"message\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
28 "\u001b[1;31mException\u001b[0m: message"
29 ]
30 }
31 ],
32 "prompt_number": 1
33 },
13 "output_type": "error",
14 "traceback": [
15 "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
16 "\u001b[1;31mException\u001b[0m Traceback (most recent call last)",
17 "\u001b[1;32m<ipython-input-1-335814d14fc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"message\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
18 "\u001b[1;31mException\u001b[0m: message"
19 ]
20 }
21 ],
22 "prompt_number": 1,
23 "source": [
24 "raise Exception(\"message\")"
25 ]
26 },
27 {
28 "cell_type": "code",
29 "metadata": {
30 "collapsed": false
31 },
32 "outputs": [
34 33 {
35 "cell_type": "code",
36 "collapsed": false,
37 "input": [
38 "print('ok')"
39 ],
40 "language": "python",
41 34 "metadata": {},
42 "outputs": [
43 {
44 "output_type": "stream",
45 "stream": "stdout",
46 "text": [
47 "ok\n"
48 ]
49 }
50 ],
51 "prompt_number": 2
35 "name": "stdout",
36 "output_type": "stream",
37 "text": "ok\n"
52 38 }
53 39 ],
54 "metadata": {}
40 "prompt_number": 2,
41 "source": [
42 "print('ok')"
43 ]
55 44 }
56 ]
45 ],
46 "metadata": {
47 "signature": "sha256:9d47889f0678e9685429071216d0f3354db59bb66489f3225bcadfb6a1a9bbba"
48 },
49 "nbformat": 4,
50 "nbformat_minor": 0
57 51 } No newline at end of file
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@@ -1,33 +1,25 b''
1 1 {
2 "metadata": {
3 "name": ""
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
4 "cell_type": "code",
5 "metadata": {
6 "collapsed": false
7 },
8 "outputs": [
10 9 {
11 "cell_type": "code",
12 "collapsed": false,
13 "input": [
14 "print('\u2603')"
15 ],
16 "language": "python",
17 10 "metadata": {},
18 "outputs": [
19 {
20 "output_type": "stream",
21 "stream": "stdout",
22 "text": [
23 "\u2603\n"
24 ]
25 }
26 ],
27 "prompt_number": 1
11 "name": "stdout",
12 "output_type": "stream",
13 "text": "\u2603\n"
28 14 }
29 15 ],
30 "metadata": {}
16 "prompt_number": 1,
17 "source": [
18 "print('\u2603')"
19 ]
31 20 }
32 ]
21 ],
22 "metadata": {},
23 "nbformat": 4,
24 "nbformat_minor": 0
33 25 } No newline at end of file
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@@ -1,42 +1,35 b''
1 1 """
2 2 Module with tests for the clearoutput preprocessor.
3 3 """
4 4
5 5 # Copyright (c) IPython Development Team.
6 6 # Distributed under the terms of the Modified BSD License.
7 7
8 #-----------------------------------------------------------------------------
9 # Imports
10 #-----------------------------------------------------------------------------
11 8 from IPython.nbformat import current as nbformat
12 9
13 10 from .base import PreprocessorTestsBase
14 11 from ..clearoutput import ClearOutputPreprocessor
15 12
16 13
17 #-----------------------------------------------------------------------------
18 # Class
19 #-----------------------------------------------------------------------------
20
21 14 class TestClearOutput(PreprocessorTestsBase):
22 15 """Contains test functions for clearoutput.py"""
23 16
24 17
25 18 def build_preprocessor(self):
26 19 """Make an instance of a preprocessor"""
27 20 preprocessor = ClearOutputPreprocessor()
28 21 preprocessor.enabled = True
29 22 return preprocessor
30 23
31 24 def test_constructor(self):
32 25 """Can a ClearOutputPreprocessor be constructed?"""
33 26 self.build_preprocessor()
34 27
35 28 def test_output(self):
36 29 """Test the output of the ClearOutputPreprocessor"""
37 30 nb = self.build_notebook()
38 31 res = self.build_resources()
39 32 preprocessor = self.build_preprocessor()
40 33 nb, res = preprocessor(nb, res)
41 assert nb.worksheets[0].cells[0].outputs == []
42 assert nb.worksheets[0].cells[0].prompt_number is None
34 assert nb.cells[0].outputs == []
35 assert nb.cells[0].prompt_number is None
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@@ -1,60 +1,58 b''
1 1 """Tests for the coalescestreams preprocessor"""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 from IPython.nbformat import current as nbformat
7 7
8 8 from .base import PreprocessorTestsBase
9 9 from ..coalescestreams import coalesce_streams
10 10
11 11
12 12 class TestCoalesceStreams(PreprocessorTestsBase):
13 13 """Contains test functions for coalescestreams.py"""
14 14
15 15 def test_coalesce_streams(self):
16 16 """coalesce_streams preprocessor output test"""
17 17 nb = self.build_notebook()
18 18 res = self.build_resources()
19 19 nb, res = coalesce_streams(nb, res)
20 outputs = nb.worksheets[0].cells[0].outputs
20 outputs = nb.cells[0].outputs
21 21 self.assertEqual(outputs[0].text, "a")
22 self.assertEqual(outputs[1].output_type, "text")
22 self.assertEqual(outputs[1].output_type, "display_data")
23 23 self.assertEqual(outputs[2].text, "cd")
24 24 self.assertEqual(outputs[3].text, "ef")
25 25
26 26 def test_coalesce_sequenced_streams(self):
27 27 """Can the coalesce streams preprocessor merge a sequence of streams?"""
28 outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="0"),
29 nbformat.new_output(output_type="stream", stream="stdout", output_text="1"),
30 nbformat.new_output(output_type="stream", stream="stdout", output_text="2"),
31 nbformat.new_output(output_type="stream", stream="stdout", output_text="3"),
32 nbformat.new_output(output_type="stream", stream="stdout", output_text="4"),
33 nbformat.new_output(output_type="stream", stream="stdout", output_text="5"),
34 nbformat.new_output(output_type="stream", stream="stdout", output_text="6"),
35 nbformat.new_output(output_type="stream", stream="stdout", output_text="7")]
36 cells=[nbformat.new_code_cell(input="# None", prompt_number=1,outputs=outputs)]
37 worksheets = [nbformat.new_worksheet(cells=cells)]
38
39 nb = nbformat.new_notebook(name="notebook1", worksheets=worksheets)
28 outputs = [nbformat.new_output(output_type="stream", name="stdout", text="0"),
29 nbformat.new_output(output_type="stream", name="stdout", text="1"),
30 nbformat.new_output(output_type="stream", name="stdout", text="2"),
31 nbformat.new_output(output_type="stream", name="stdout", text="3"),
32 nbformat.new_output(output_type="stream", name="stdout", text="4"),
33 nbformat.new_output(output_type="stream", name="stdout", text="5"),
34 nbformat.new_output(output_type="stream", name="stdout", text="6"),
35 nbformat.new_output(output_type="stream", name="stdout", text="7")]
36 cells=[nbformat.new_code_cell(source="# None", prompt_number=1,outputs=outputs)]
37
38 nb = nbformat.new_notebook(cells=cells)
40 39 res = self.build_resources()
41 40 nb, res = coalesce_streams(nb, res)
42 outputs = nb.worksheets[0].cells[0].outputs
41 outputs = nb.cells[0].outputs
43 42 self.assertEqual(outputs[0].text, u'01234567')
44 43
45 44 def test_coalesce_replace_streams(self):
46 45 """Are \\r characters handled?"""
47 outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="z"),
48 nbformat.new_output(output_type="stream", stream="stdout", output_text="\ra"),
49 nbformat.new_output(output_type="stream", stream="stdout", output_text="\nz\rb"),
50 nbformat.new_output(output_type="stream", stream="stdout", output_text="\nz"),
51 nbformat.new_output(output_type="stream", stream="stdout", output_text="\rc\n"),
52 nbformat.new_output(output_type="stream", stream="stdout", output_text="z\rz\rd")]
53 cells=[nbformat.new_code_cell(input="# None", prompt_number=1,outputs=outputs)]
54 worksheets = [nbformat.new_worksheet(cells=cells)]
55
56 nb = nbformat.new_notebook(name="notebook1", worksheets=worksheets)
46 outputs = [nbformat.new_output(output_type="stream", name="stdout", text="z"),
47 nbformat.new_output(output_type="stream", name="stdout", text="\ra"),
48 nbformat.new_output(output_type="stream", name="stdout", text="\nz\rb"),
49 nbformat.new_output(output_type="stream", name="stdout", text="\nz"),
50 nbformat.new_output(output_type="stream", name="stdout", text="\rc\n"),
51 nbformat.new_output(output_type="stream", name="stdout", text="z\rz\rd")]
52 cells=[nbformat.new_code_cell(source="# None", prompt_number=1,outputs=outputs)]
53
54 nb = nbformat.new_notebook(cells=cells)
57 55 res = self.build_resources()
58 56 nb, res = coalesce_streams(nb, res)
59 outputs = nb.worksheets[0].cells[0].outputs
57 outputs = nb.cells[0].outputs
60 58 self.assertEqual(outputs[0].text, u'a\nb\nc\nd')
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@@ -1,90 +1,88 b''
1 1 """
2 2 Module with tests for the execute preprocessor.
3 3 """
4 4
5 5 # Copyright (c) IPython Development Team.
6 6 # Distributed under the terms of the Modified BSD License.
7 7
8 8 import copy
9 9 import glob
10 10 import os
11 11 import re
12 12
13 13 from IPython.nbformat import current as nbformat
14 14
15 15 from .base import PreprocessorTestsBase
16 16 from ..execute import ExecutePreprocessor
17 17
18 18 from IPython.nbconvert.filters import strip_ansi
19 19
20 20 addr_pat = re.compile(r'0x[0-9a-f]{7,9}')
21 21
22 22 class TestExecute(PreprocessorTestsBase):
23 23 """Contains test functions for execute.py"""
24 24
25 25 @staticmethod
26 26 def normalize_output(output):
27 27 """
28 28 Normalizes outputs for comparison.
29 29 """
30 30 output = dict(output)
31 31 if 'metadata' in output:
32 32 del output['metadata']
33 if 'text' in output:
34 output['text'] = re.sub(addr_pat, '<HEXADDR>', output['text'])
35 if 'svg' in output:
36 del output['text']
33 if 'text/plain' in output:
34 output['text/plain'] = re.sub(addr_pat, '<HEXADDR>', output['text/plain'])
37 35 if 'traceback' in output:
38 36 tb = []
39 37 for line in output['traceback']:
40 38 tb.append(strip_ansi(line))
41 39 output['traceback'] = tb
42 40
43 41 return output
44 42
45 43
46 44 def assert_notebooks_equal(self, expected, actual):
47 expected_cells = expected['worksheets'][0]['cells']
48 actual_cells = actual['worksheets'][0]['cells']
45 expected_cells = expected['cells']
46 actual_cells = actual['cells']
49 47 assert len(expected_cells) == len(actual_cells)
50 48
51 49 for expected_cell, actual_cell in zip(expected_cells, actual_cells):
52 50 expected_outputs = expected_cell.get('outputs', [])
53 51 actual_outputs = actual_cell.get('outputs', [])
54 52 normalized_expected_outputs = list(map(self.normalize_output, expected_outputs))
55 53 normalized_actual_outputs = list(map(self.normalize_output, actual_outputs))
56 54 assert normalized_expected_outputs == normalized_actual_outputs
57 55
58 56 expected_prompt_number = expected_cell.get('prompt_number', None)
59 57 actual_prompt_number = actual_cell.get('prompt_number', None)
60 58 assert expected_prompt_number == actual_prompt_number
61 59
62 60
63 61 def build_preprocessor(self):
64 62 """Make an instance of a preprocessor"""
65 63 preprocessor = ExecutePreprocessor()
66 64 preprocessor.enabled = True
67 65 return preprocessor
68 66
69 67
70 68 def test_constructor(self):
71 69 """Can a ExecutePreprocessor be constructed?"""
72 70 self.build_preprocessor()
73 71
74 72
75 73 def test_run_notebooks(self):
76 74 """Runs a series of test notebooks and compares them to their actual output"""
77 75 current_dir = os.path.dirname(__file__)
78 76 input_files = glob.glob(os.path.join(current_dir, 'files', '*.ipynb'))
79 77 for filename in input_files:
80 78 with open(os.path.join(current_dir, 'files', filename)) as f:
81 79 input_nb = nbformat.read(f, 'ipynb')
82 80 res = self.build_resources()
83 81 preprocessor = self.build_preprocessor()
84 82 cleaned_input_nb = copy.deepcopy(input_nb)
85 for cell in cleaned_input_nb.worksheets[0].cells:
83 for cell in cleaned_input_nb.cells:
86 84 if 'prompt_number' in cell:
87 85 del cell['prompt_number']
88 86 cell['outputs'] = []
89 87 output_nb, _ = preprocessor(cleaned_input_nb, res)
90 88 self.assert_notebooks_equal(output_nb, input_nb)
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@@ -1,74 +1,59 b''
1 """
2 Module with tests for the extractoutput preprocessor
3 """
1 """Tests for the extractoutput preprocessor"""
4 2
5 #-----------------------------------------------------------------------------
6 # Copyright (c) 2013, the IPython Development Team.
7 #
3 # Copyright (c) IPython Development Team.
8 4 # Distributed under the terms of the Modified BSD License.
9 #
10 # The full license is in the file COPYING.txt, distributed with this software.
11 #-----------------------------------------------------------------------------
12
13 #-----------------------------------------------------------------------------
14 # Imports
15 #-----------------------------------------------------------------------------
16 5
17 6 from .base import PreprocessorTestsBase
18 7 from ..extractoutput import ExtractOutputPreprocessor
19 8
20 9
21 #-----------------------------------------------------------------------------
22 # Class
23 #-----------------------------------------------------------------------------
24
25 10 class TestExtractOutput(PreprocessorTestsBase):
26 11 """Contains test functions for extractoutput.py"""
27 12
28 13
29 14 def build_preprocessor(self):
30 15 """Make an instance of a preprocessor"""
31 16 preprocessor = ExtractOutputPreprocessor()
32 preprocessor.extract_output_types = {'text', 'png', 'application/pdf'}
17 preprocessor.extract_output_types = {'text/plain', 'image/png', 'application/pdf'}
33 18 preprocessor.enabled = True
34 19 return preprocessor
35 20
36 21
37 22 def test_constructor(self):
38 23 """Can a ExtractOutputPreprocessor be constructed?"""
39 24 self.build_preprocessor()
40 25
41 26
42 27 def test_output(self):
43 28 """Test the output of the ExtractOutputPreprocessor"""
44 29 nb = self.build_notebook()
45 30 res = self.build_resources()
46 31 preprocessor = self.build_preprocessor()
47 32 nb, res = preprocessor(nb, res)
48 33
49 34 # Check if text was extracted.
50 output = nb.worksheets[0].cells[0].outputs[1]
51 assert 'text_filename' in output
52 text_filename = output['text_filename']
35 output = nb.cells[0].outputs[1]
36 assert 'text/plain_filename' in output
37 text_filename = output['text/plain_filename']
53 38
54 39 # Check if png was extracted.
55 output = nb.worksheets[0].cells[0].outputs[6]
56 assert 'png_filename' in output
57 png_filename = output['png_filename']
40 output = nb.cells[0].outputs[6]
41 assert 'image/png_filename' in output
42 png_filename = output['image/png_filename']
58 43
59 44 # Check that pdf was extracted
60 output = nb.worksheets[0].cells[0].outputs[7]
45 output = nb.cells[0].outputs[7]
61 46 assert 'application/pdf_filename' in output
62 47 pdf_filename = output['application/pdf_filename']
63 48
64 49 # Verify text output
65 50 assert text_filename in res['outputs']
66 51 self.assertEqual(res['outputs'][text_filename], b'b')
67 52
68 53 # Verify png output
69 54 assert png_filename in res['outputs']
70 55 self.assertEqual(res['outputs'][png_filename], b'g')
71 56
72 57 # Verify pdf output
73 58 assert pdf_filename in res['outputs']
74 59 self.assertEqual(res['outputs'][pdf_filename], b'h')
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@@ -1,68 +1,50 b''
1 """
2 Module with tests for the HighlightMagics preprocessor
3 """
4
5 #-----------------------------------------------------------------------------
6 # Copyright (c) 2013, the IPython Development Team.
7 #
8 # Distributed under the terms of the Modified BSD License.
9 #
10 # The full license is in the file COPYING.txt, distributed with this software.
11 #-----------------------------------------------------------------------------
12
13 #-----------------------------------------------------------------------------
14 # Imports
15 #-----------------------------------------------------------------------------
1 """Tests for the HighlightMagics preprocessor"""
16 2
17 3 from .base import PreprocessorTestsBase
18 4 from ..highlightmagics import HighlightMagicsPreprocessor
19 5
20 6
21 #-----------------------------------------------------------------------------
22 # Class
23 #-----------------------------------------------------------------------------
24
25 7 class TestHighlightMagics(PreprocessorTestsBase):
26 8 """Contains test functions for highlightmagics.py"""
27 9
28 10
29 11 def build_preprocessor(self):
30 12 """Make an instance of a preprocessor"""
31 13 preprocessor = HighlightMagicsPreprocessor()
32 14 preprocessor.enabled = True
33 15 return preprocessor
34 16
35 17 def test_constructor(self):
36 18 """Can a HighlightMagicsPreprocessor be constructed?"""
37 19 self.build_preprocessor()
38 20
39 21 def test_tagging(self):
40 22 """Test the HighlightMagicsPreprocessor tagging"""
41 23 nb = self.build_notebook()
42 24 res = self.build_resources()
43 25 preprocessor = self.build_preprocessor()
44 nb.worksheets[0].cells[0].input = """%%R -i x,y -o XYcoef
26 nb.cells[0].source = """%%R -i x,y -o XYcoef
45 27 lm.fit <- lm(y~x)
46 28 par(mfrow=c(2,2))
47 29 print(summary(lm.fit))
48 30 plot(lm.fit)
49 31 XYcoef <- coef(lm.fit)"""
50 32
51 33 nb, res = preprocessor(nb, res)
52 34
53 assert('magics_language' in nb.worksheets[0].cells[0]['metadata'])
35 assert('magics_language' in nb.cells[0]['metadata'])
54 36
55 self.assertEqual(nb.worksheets[0].cells[0]['metadata']['magics_language'], 'r')
37 self.assertEqual(nb.cells[0]['metadata']['magics_language'], 'r')
56 38
57 39 def test_no_false_positive(self):
58 40 """Test that HighlightMagicsPreprocessor does not tag false positives"""
59 41 nb = self.build_notebook()
60 42 res = self.build_resources()
61 43 preprocessor = self.build_preprocessor()
62 nb.worksheets[0].cells[0].input = """# this should not be detected
44 nb.cells[0].source = """# this should not be detected
63 45 print(\"""
64 46 %%R -i x, y
65 47 \""")"""
66 48 nb, res = preprocessor(nb, res)
67 49
68 assert('magics_language' not in nb.worksheets[0].cells[0]['metadata']) No newline at end of file
50 assert('magics_language' not in nb.cells[0]['metadata']) No newline at end of file
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@@ -1,51 +1,36 b''
1 """
2 Module with tests for the latex preprocessor
3 """
1 """Tests for the latex preprocessor"""
4 2
5 #-----------------------------------------------------------------------------
6 # Copyright (c) 2013, the IPython Development Team.
7 #
3 # Copyright (c) IPython Development Team.
8 4 # Distributed under the terms of the Modified BSD License.
9 #
10 # The full license is in the file COPYING.txt, distributed with this software.
11 #-----------------------------------------------------------------------------
12
13 #-----------------------------------------------------------------------------
14 # Imports
15 #-----------------------------------------------------------------------------
16 5
17 6 from .base import PreprocessorTestsBase
18 7 from ..latex import LatexPreprocessor
19 8
20 9
21 #-----------------------------------------------------------------------------
22 # Class
23 #-----------------------------------------------------------------------------
24
25 10 class TestLatex(PreprocessorTestsBase):
26 11 """Contains test functions for latex.py"""
27 12
28 13
29 14 def build_preprocessor(self):
30 15 """Make an instance of a preprocessor"""
31 16 preprocessor = LatexPreprocessor()
32 17 preprocessor.enabled = True
33 18 return preprocessor
34 19
35 20 def test_constructor(self):
36 21 """Can a LatexPreprocessor be constructed?"""
37 22 self.build_preprocessor()
38 23
39 24
40 25 def test_output(self):
41 26 """Test the output of the LatexPreprocessor"""
42 27 nb = self.build_notebook()
43 28 res = self.build_resources()
44 29 preprocessor = self.build_preprocessor()
45 30 nb, res = preprocessor(nb, res)
46 31
47 32 # Make sure the code cell wasn't modified.
48 self.assertEqual(nb.worksheets[0].cells[0].input, '$ e $')
33 self.assertEqual(nb.cells[0].source, '$ e $')
49 34
50 35 # Verify that the markdown cell wasn't processed.
51 self.assertEqual(nb.worksheets[0].cells[1].source, '$ e $')
36 self.assertEqual(nb.cells[1].source, '$ e $')
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@@ -1,79 +1,78 b''
1 1 """Tests for the revealhelp preprocessor"""
2 2
3 3 # Copyright (c) IPython Development Team.
4 4 # Distributed under the terms of the Modified BSD License.
5 5
6 6 from IPython.nbformat import current as nbformat
7 7
8 8 from .base import PreprocessorTestsBase
9 9 from ..revealhelp import RevealHelpPreprocessor
10 10
11 11
12 12 class Testrevealhelp(PreprocessorTestsBase):
13 13 """Contains test functions for revealhelp.py"""
14 14
15 15 def build_notebook(self):
16 16 """Build a reveal slides notebook in memory for use with tests.
17 17 Overrides base in PreprocessorTestsBase"""
18 18
19 outputs = [nbformat.new_output(output_type="stream", stream="stdout", output_text="a")]
19 outputs = [nbformat.new_output(output_type="stream", name="stdout", text="a")]
20 20
21 21 slide_metadata = {'slideshow' : {'slide_type': 'slide'}}
22 22 subslide_metadata = {'slideshow' : {'slide_type': 'subslide'}}
23 23
24 cells=[nbformat.new_code_cell(input="", prompt_number=1, outputs=outputs),
25 nbformat.new_text_cell('markdown', source="", metadata=slide_metadata),
26 nbformat.new_code_cell(input="", prompt_number=2, outputs=outputs),
27 nbformat.new_text_cell('markdown', source="", metadata=slide_metadata),
28 nbformat.new_text_cell('markdown', source="", metadata=subslide_metadata)]
29 worksheets = [nbformat.new_worksheet(cells=cells)]
24 cells=[nbformat.new_code_cell(source="", prompt_number=1, outputs=outputs),
25 nbformat.new_markdown_cell(source="", metadata=slide_metadata),
26 nbformat.new_code_cell(source="", prompt_number=2, outputs=outputs),
27 nbformat.new_markdown_cell(source="", metadata=slide_metadata),
28 nbformat.new_markdown_cell(source="", metadata=subslide_metadata)]
30 29
31 return nbformat.new_notebook(name="notebook1", worksheets=worksheets)
30 return nbformat.new_notebook(cells=cells)
32 31
33 32
34 33 def build_preprocessor(self):
35 34 """Make an instance of a preprocessor"""
36 35 preprocessor = RevealHelpPreprocessor()
37 36 preprocessor.enabled = True
38 37 return preprocessor
39 38
40 39
41 40 def test_constructor(self):
42 41 """Can a RevealHelpPreprocessor be constructed?"""
43 42 self.build_preprocessor()
44 43
45 44
46 45 def test_reveal_attribute(self):
47 46 """Make sure the reveal url_prefix resources is set"""
48 47 nb = self.build_notebook()
49 48 res = self.build_resources()
50 49 preprocessor = self.build_preprocessor()
51 50 nb, res = preprocessor(nb, res)
52 51 assert 'reveal' in res
53 52 assert 'url_prefix' in res['reveal']
54 53
55 54
56 55 def test_reveal_output(self):
57 56 """Make sure that the reveal preprocessor """
58 57 nb = self.build_notebook()
59 58 res = self.build_resources()
60 59 preprocessor = self.build_preprocessor()
61 60 nb, res = preprocessor(nb, res)
62 cells = nb.worksheets[0].cells
61 cells = nb.cells
63 62
64 63 # Make sure correct metadata tags are available on every cell.
65 64 for cell in cells:
66 65 assert 'slide_type' in cell.metadata
67 66
68 67 # Make sure slide end is only applied to the cells preceeding slide
69 68 # cells.
70 69 assert 'slide_helper' in cells[1].metadata
71 70 self.assertEqual(cells[1].metadata['slide_helper'], '-')
72 71
73 72 # Verify 'slide-end'
74 73 assert 'slide_helper' in cells[0].metadata
75 74 self.assertEqual(cells[0].metadata['slide_helper'], 'slide_end')
76 75 assert 'slide_helper' in cells[2].metadata
77 76 self.assertEqual(cells[2].metadata['slide_helper'], 'slide_end')
78 77 assert 'slide_helper' in cells[3].metadata
79 78 self.assertEqual(cells[3].metadata['slide_helper'], 'subslide_end')
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@@ -1,90 +1,74 b''
1 """
2 Module with tests for the svg2pdf preprocessor
3 """
1 """Tests for the svg2pdf preprocessor"""
4 2
5 #-----------------------------------------------------------------------------
6 # Copyright (c) 2013, the IPython Development Team.
7 #
3 # Copyright (c) IPython Development Team.
8 4 # Distributed under the terms of the Modified BSD License.
9 #
10 # The full license is in the file COPYING.txt, distributed with this software.
11 #-----------------------------------------------------------------------------
12
13 #-----------------------------------------------------------------------------
14 # Imports
15 #-----------------------------------------------------------------------------
16 5
17 6 from IPython.testing import decorators as dec
18 7 from IPython.nbformat import current as nbformat
19 8
20 9 from .base import PreprocessorTestsBase
21 10 from ..svg2pdf import SVG2PDFPreprocessor
22 11
23 12
24 #-----------------------------------------------------------------------------
25 # Class
26 #-----------------------------------------------------------------------------
27
28 13 class Testsvg2pdf(PreprocessorTestsBase):
29 14 """Contains test functions for svg2pdf.py"""
30 15
31 16 simple_svg = """<?xml version="1.0" encoding="UTF-8" standalone="no"?>
32 17 <!-- Created with Inkscape (http://www.inkscape.org/) -->
33 18 <svg
34 19 xmlns:svg="http://www.w3.org/2000/svg"
35 20 xmlns="http://www.w3.org/2000/svg"
36 21 version="1.0"
37 22 x="0.00000000"
38 23 y="0.00000000"
39 24 width="500.00000"
40 25 height="500.00000"
41 26 id="svg2">
42 27 <defs
43 28 id="defs4" />
44 29 <g
45 30 id="layer1">
46 31 <rect
47 32 width="300.00000"
48 33 height="300.00000"
49 34 x="100.00000"
50 35 y="100.00000"
51 36 style="opacity:1.0000000;fill:none;fill-opacity:1.0000000;fill-rule:evenodd;stroke:#000000;stroke-width:8.0000000;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:4.0000000;stroke-dasharray:none;stroke-dashoffset:0.00000000;stroke-opacity:1.0000000"
52 37 id="rect5719" />
53 38 </g>
54 39 </svg>"""
55 40
56 41 def build_notebook(self):
57 42 """Build a reveal slides notebook in memory for use with tests.
58 43 Overrides base in PreprocessorTestsBase"""
59 44
60 45 outputs = [nbformat.new_output(output_type="svg", output_svg=self.simple_svg)]
61 46
62 47 slide_metadata = {'slideshow' : {'slide_type': 'slide'}}
63 48 subslide_metadata = {'slideshow' : {'slide_type': 'subslide'}}
64 49
65 cells=[nbformat.new_code_cell(input="", prompt_number=1, outputs=outputs)]
66 worksheets = [nbformat.new_worksheet(name="worksheet1", cells=cells)]
50 cells=[nbformat.new_code_cell(source="", prompt_number=1, outputs=outputs)]
67 51
68 return nbformat.new_notebook(name="notebook1", worksheets=worksheets)
52 return nbformat.new_notebook(cells=cells)
69 53
70 54
71 55 def build_preprocessor(self):
72 56 """Make an instance of a preprocessor"""
73 57 preprocessor = SVG2PDFPreprocessor()
74 58 preprocessor.enabled = True
75 59 return preprocessor
76 60
77 61
78 62 def test_constructor(self):
79 63 """Can a SVG2PDFPreprocessor be constructed?"""
80 64 self.build_preprocessor()
81 65
82 66
83 67 @dec.onlyif_cmds_exist('inkscape')
84 68 def test_output(self):
85 69 """Test the output of the SVG2PDFPreprocessor"""
86 70 nb = self.build_notebook()
87 71 res = self.build_resources()
88 72 preprocessor = self.build_preprocessor()
89 73 nb, res = preprocessor(nb, res)
90 assert 'svg' in nb.worksheets[0].cells[0].outputs[0]
74 assert 'svg' in nb.cells[0].outputs[0]
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@@ -1,203 +1,203 b''
1 1 {%- extends 'display_priority.tpl' -%}
2 2
3 3
4 4 {% block codecell %}
5 5 <div class="cell border-box-sizing code_cell rendered">
6 6 {{ super() }}
7 7 </div>
8 8 {%- endblock codecell %}
9 9
10 10 {% block input_group -%}
11 11 <div class="input">
12 12 {{ super() }}
13 13 </div>
14 14 {% endblock input_group %}
15 15
16 16 {% block output_group %}
17 17 <div class="output_wrapper">
18 18 <div class="output">
19 19 {{ super() }}
20 20 </div>
21 21 </div>
22 22 {% endblock output_group %}
23 23
24 24 {% block in_prompt -%}
25 25 <div class="prompt input_prompt">
26 26 {%- if cell.prompt_number is defined -%}
27 27 In&nbsp;[{{ cell.prompt_number|replace(None, "&nbsp;") }}]:
28 28 {%- else -%}
29 29 In&nbsp;[&nbsp;]:
30 30 {%- endif -%}
31 31 </div>
32 32 {%- endblock in_prompt %}
33 33
34 34 {% block empty_in_prompt -%}
35 35 <div class="prompt input_prompt">
36 36 </div>
37 37 {%- endblock empty_in_prompt %}
38 38
39 39 {#
40 40 output_prompt doesn't do anything in HTML,
41 41 because there is a prompt div in each output area (see output block)
42 42 #}
43 43 {% block output_prompt %}
44 44 {% endblock output_prompt %}
45 45
46 46 {% block input %}
47 47 <div class="inner_cell">
48 48 <div class="input_area">
49 {{ cell.input | highlight_code(metadata=cell.metadata) }}
49 {{ cell.source | highlight_code(metadata=cell.metadata) }}
50 50 </div>
51 51 </div>
52 52 {%- endblock input %}
53 53
54 54 {% block output %}
55 55 <div class="output_area">
56 {%- if output.output_type == 'pyout' -%}
56 {%- if output.output_type == 'execute_result' -%}
57 57 <div class="prompt output_prompt">
58 58 {%- if cell.prompt_number is defined -%}
59 59 Out[{{ cell.prompt_number|replace(None, "&nbsp;") }}]:
60 60 {%- else -%}
61 61 Out[&nbsp;]:
62 62 {%- endif -%}
63 63 {%- else -%}
64 64 <div class="prompt">
65 65 {%- endif -%}
66 66 </div>
67 67 {{ super() }}
68 68 </div>
69 69 {% endblock output %}
70 70
71 71 {% block markdowncell scoped %}
72 72 <div class="cell border-box-sizing text_cell rendered">
73 73 {{ self.empty_in_prompt() }}
74 74 <div class="inner_cell">
75 75 <div class="text_cell_render border-box-sizing rendered_html">
76 76 {{ cell.source | markdown2html | strip_files_prefix }}
77 77 </div>
78 78 </div>
79 79 </div>
80 80 {%- endblock markdowncell %}
81 81
82 82 {% block headingcell scoped %}
83 83 <div class="cell border-box-sizing text_cell rendered">
84 84 {{ self.empty_in_prompt() }}
85 85 <div class="inner_cell">
86 86 <div class="text_cell_render border-box-sizing rendered_html">
87 87 {{ ("#" * cell.level + cell.source) | replace('\n', ' ') | markdown2html | strip_files_prefix | add_anchor }}
88 88 </div>
89 89 </div>
90 90 </div>
91 91 {% endblock headingcell %}
92 92
93 93 {% block unknowncell scoped %}
94 94 unknown type {{ cell.type }}
95 95 {% endblock unknowncell %}
96 96
97 {% block pyout -%}
98 {%- set extra_class="output_pyout" -%}
97 {% block execute_result -%}
98 {%- set extra_class="output_execute_result" -%}
99 99 {% block data_priority scoped %}
100 100 {{ super() }}
101 101 {% endblock %}
102 102 {%- set extra_class="" -%}
103 {%- endblock pyout %}
103 {%- endblock execute_result %}
104 104
105 105 {% block stream_stdout -%}
106 106 <div class="output_subarea output_stream output_stdout output_text">
107 107 <pre>
108 108 {{- output.text | ansi2html -}}
109 109 </pre>
110 110 </div>
111 111 {%- endblock stream_stdout %}
112 112
113 113 {% block stream_stderr -%}
114 114 <div class="output_subarea output_stream output_stderr output_text">
115 115 <pre>
116 116 {{- output.text | ansi2html -}}
117 117 </pre>
118 118 </div>
119 119 {%- endblock stream_stderr %}
120 120
121 121 {% block data_svg scoped -%}
122 122 <div class="output_svg output_subarea {{extra_class}}">
123 123 {%- if output.svg_filename %}
124 124 <img src="{{output.svg_filename | posix_path}}"
125 125 {%- else %}
126 126 {{ output.svg }}
127 127 {%- endif %}
128 128 </div>
129 129 {%- endblock data_svg %}
130 130
131 131 {% block data_html scoped -%}
132 132 <div class="output_html rendered_html output_subarea {{extra_class}}">
133 133 {{ output.html }}
134 134 </div>
135 135 {%- endblock data_html %}
136 136
137 137 {% block data_png scoped %}
138 138 <div class="output_png output_subarea {{extra_class}}">
139 139 {%- if output.png_filename %}
140 140 <img src="{{output.png_filename | posix_path}}"
141 141 {%- else %}
142 142 <img src="data:image/png;base64,{{ output.png }}"
143 143 {%- endif %}
144 144 {%- if 'metadata' in output and 'width' in output.metadata.get('png', {}) %}
145 145 width={{output.metadata['png']['width']}}
146 146 {%- endif %}
147 147 {%- if 'metadata' in output and 'height' in output.metadata.get('png', {}) %}
148 148 height={{output.metadata['png']['height']}}
149 149 {%- endif %}
150 150 >
151 151 </div>
152 152 {%- endblock data_png %}
153 153
154 154 {% block data_jpg scoped %}
155 155 <div class="output_jpeg output_subarea {{extra_class}}">
156 156 {%- if output.jpeg_filename %}
157 157 <img src="{{output.jpeg_filename | posix_path}}"
158 158 {%- else %}
159 159 <img src="data:image/jpeg;base64,{{ output.jpeg }}"
160 160 {%- endif %}
161 161 {%- if 'metadata' in output and 'width' in output.metadata.get('jpeg', {}) %}
162 162 width={{output.metadata['jpeg']['width']}}
163 163 {%- endif %}
164 164 {%- if 'metadata' in output and 'height' in output.metadata.get('jpeg', {}) %}
165 165 height={{output.metadata['jpeg']['height']}}
166 166 {%- endif %}
167 167 >
168 168 </div>
169 169 {%- endblock data_jpg %}
170 170
171 171 {% block data_latex scoped %}
172 172 <div class="output_latex output_subarea {{extra_class}}">
173 173 {{ output.latex }}
174 174 </div>
175 175 {%- endblock data_latex %}
176 176
177 {% block pyerr -%}
178 <div class="output_subarea output_text output_pyerr">
177 {% block error -%}
178 <div class="output_subarea output_text output_error">
179 179 <pre>
180 180 {{- super() -}}
181 181 </pre>
182 182 </div>
183 {%- endblock pyerr %}
183 {%- endblock error %}
184 184
185 185 {%- block traceback_line %}
186 186 {{ line | ansi2html }}
187 187 {%- endblock traceback_line %}
188 188
189 189 {%- block data_text scoped %}
190 190 <div class="output_text output_subarea {{extra_class}}">
191 191 <pre>
192 192 {{- output.text | ansi2html -}}
193 193 </pre>
194 194 </div>
195 195 {%- endblock -%}
196 196
197 197 {%- block data_javascript scoped %}
198 198 <div class="output_subarea output_javascript {{extra_class}}">
199 199 <script type="text/javascript">
200 200 {{ output.javascript }}
201 201 </script>
202 202 </div>
203 203 {%- endblock -%}
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@@ -1,223 +1,223 b''
1 1 ((= Latex base template (must inherit)
2 2 This template builds upon the abstract template, adding common latex output
3 3 functions. Figures, data_text,
4 4 This template does not define a docclass, the inheriting class must define this.=))
5 5
6 6 ((*- extends 'display_priority.tplx' -*))
7 7
8 8 %===============================================================================
9 9 % Abstract overrides
10 10 %===============================================================================
11 11
12 12 ((* block header *))
13 13 ((* block docclass *))((* endblock docclass *))
14 14
15 15 ((* block packages *))
16 16 \usepackage{graphicx} % Used to insert images
17 17 \usepackage{adjustbox} % Used to constrain images to a maximum size
18 18 \usepackage{color} % Allow colors to be defined
19 19 \usepackage{enumerate} % Needed for markdown enumerations to work
20 20 \usepackage{geometry} % Used to adjust the document margins
21 21 \usepackage{amsmath} % Equations
22 22 \usepackage{amssymb} % Equations
23 23 \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
24 24 \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document
25 25 \usepackage{fancyvrb} % verbatim replacement that allows latex
26 26 \usepackage{grffile} % extends the file name processing of package graphics
27 27 % to support a larger range
28 28 % The hyperref package gives us a pdf with properly built
29 29 % internal navigation ('pdf bookmarks' for the table of contents,
30 30 % internal cross-reference links, web links for URLs, etc.)
31 31 \usepackage{hyperref}
32 32 \usepackage{longtable} % longtable support required by pandoc >1.10
33 33 \usepackage{booktabs} % table support for pandoc > 1.12.2
34 34 ((* endblock packages *))
35 35
36 36 ((* block definitions *))
37 37 \definecolor{orange}{cmyk}{0,0.4,0.8,0.2}
38 38 \definecolor{darkorange}{rgb}{.71,0.21,0.01}
39 39 \definecolor{darkgreen}{rgb}{.12,.54,.11}
40 40 \definecolor{myteal}{rgb}{.26, .44, .56}
41 41 \definecolor{gray}{gray}{0.45}
42 42 \definecolor{lightgray}{gray}{.95}
43 43 \definecolor{mediumgray}{gray}{.8}
44 44 \definecolor{inputbackground}{rgb}{.95, .95, .85}
45 45 \definecolor{outputbackground}{rgb}{.95, .95, .95}
46 46 \definecolor{traceback}{rgb}{1, .95, .95}
47 47 % ansi colors
48 48 \definecolor{red}{rgb}{.6,0,0}
49 49 \definecolor{green}{rgb}{0,.65,0}
50 50 \definecolor{brown}{rgb}{0.6,0.6,0}
51 51 \definecolor{blue}{rgb}{0,.145,.698}
52 52 \definecolor{purple}{rgb}{.698,.145,.698}
53 53 \definecolor{cyan}{rgb}{0,.698,.698}
54 54 \definecolor{lightgray}{gray}{0.5}
55 55
56 56 % bright ansi colors
57 57 \definecolor{darkgray}{gray}{0.25}
58 58 \definecolor{lightred}{rgb}{1.0,0.39,0.28}
59 59 \definecolor{lightgreen}{rgb}{0.48,0.99,0.0}
60 60 \definecolor{lightblue}{rgb}{0.53,0.81,0.92}
61 61 \definecolor{lightpurple}{rgb}{0.87,0.63,0.87}
62 62 \definecolor{lightcyan}{rgb}{0.5,1.0,0.83}
63 63
64 64 % commands and environments needed by pandoc snippets
65 65 % extracted from the output of `pandoc -s`
66 66 \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
67 67 % Add ',fontsize=\small' for more characters per line
68 68 \newenvironment{Shaded}{}{}
69 69 \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
70 70 \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
71 71 \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
72 72 \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
73 73 \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
74 74 \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
75 75 \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
76 76 \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
77 77 \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
78 78 \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
79 79 \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
80 80 \newcommand{\RegionMarkerTok}[1]{{#1}}
81 81 \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
82 82 \newcommand{\NormalTok}[1]{{#1}}
83 83
84 84 % Define a nice break command that doesn't care if a line doesn't already
85 85 % exist.
86 86 \def\br{\hspace*{\fill} \\* }
87 87 % Math Jax compatability definitions
88 88 \def\gt{>}
89 89 \def\lt{<}
90 90 % Document parameters
91 91 ((* block title *))\title{((( resources.metadata.name | ascii_only | escape_latex )))}((* endblock title *))
92 92 ((* block date *))((* endblock date *))
93 93 ((* block author *))((* endblock author *))
94 94 ((* endblock definitions *))
95 95
96 96 ((* block commands *))
97 97 % Prevent overflowing lines due to hard-to-break entities
98 98 \sloppy
99 99 % Setup hyperref package
100 100 \hypersetup{
101 101 breaklinks=true, % so long urls are correctly broken across lines
102 102 colorlinks=true,
103 103 urlcolor=blue,
104 104 linkcolor=darkorange,
105 105 citecolor=darkgreen,
106 106 }
107 107 % Slightly bigger margins than the latex defaults
108 108 ((* block margins *))
109 109 \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in}
110 110 ((* endblock margins *))
111 111 ((* endblock commands *))
112 112 ((* endblock header *))
113 113
114 114 ((* block body *))
115 115 \begin{document}
116 116
117 117 ((* block predoc *))
118 118 ((* block maketitle *))\maketitle((* endblock maketitle *))
119 119 ((* block abstract *))((* endblock abstract *))
120 120 ((* endblock predoc *))
121 121
122 122 ((( super() )))
123 123
124 124 % Add a bibliography block to the postdoc
125 125 ((* block postdoc *))
126 126 ((* block bibliography *))((* endblock bibliography *))
127 127 ((* endblock postdoc *))
128 128 \end{document}
129 129 ((* endblock body *))
130 130
131 131 %===============================================================================
132 132 % Support blocks
133 133 %===============================================================================
134 134
135 135 % Displaying simple data text
136 136 ((* block data_text *))
137 137 \begin{verbatim}
138 138 ((( output.text )))
139 139 \end{verbatim}
140 140 ((* endblock data_text *))
141 141
142 142 % Display python error text as-is
143 ((* block pyerr *))
143 ((* block error *))
144 144 \begin{Verbatim}[commandchars=\\\{\}]
145 145 ((( super() )))
146 146 \end{Verbatim}
147 ((* endblock pyerr *))
147 ((* endblock error *))
148 148 ((* block traceback_line *))
149 149 ((( line | indent | strip_ansi | escape_latex )))
150 150 ((* endblock traceback_line *))
151 151
152 152 % Display stream ouput with coloring
153 153 ((* block stream *))
154 154 \begin{Verbatim}[commandchars=\\\{\}]
155 155 ((( output.text | escape_latex | ansi2latex )))
156 156 \end{Verbatim}
157 157 ((* endblock stream *))
158 158
159 159 % Display latex
160 160 ((* block data_latex -*))
161 161 ((*- if output.latex.startswith('$'): -*))
162 162 ((= Replace $ symbols with more explicit, equation block. =))
163 163 \begin{equation*}\adjustbox{max width=\hsize}{$
164 164 ((( output.latex | strip_dollars )))
165 165 $}\end{equation*}
166 166 ((*- else -*))
167 167 ((( output.latex )))
168 168 ((*- endif *))
169 169 ((* endblock data_latex *))
170 170
171 171 % Default mechanism for rendering figures
172 172 ((*- block data_png -*))((( draw_figure(output.png_filename) )))((*- endblock -*))
173 173 ((*- block data_jpg -*))((( draw_figure(output.jpeg_filename) )))((*- endblock -*))
174 174 ((*- block data_svg -*))((( draw_figure(output.svg_filename) )))((*- endblock -*))
175 175 ((*- block data_pdf -*))((( draw_figure(output['application/pdf_filename']) )))((*- endblock -*))
176 176
177 177 % Draw a figure using the graphicx package.
178 178 ((* macro draw_figure(filename) -*))
179 179 ((* set filename = filename | posix_path *))
180 180 ((*- block figure scoped -*))
181 181 \begin{center}
182 182 \adjustimage{max size={0.9\linewidth}{0.9\paperheight}}{((( filename )))}
183 183 \end{center}
184 184 { \hspace*{\fill} \\}
185 185 ((*- endblock figure -*))
186 186 ((*- endmacro *))
187 187
188 188 % Draw heading cell. Explicitly map different cell levels.
189 189 ((* block headingcell scoped *))
190 190
191 191 ((* if cell.level == 1 -*))
192 192 ((* block h1 -*))\section((* endblock h1 -*))
193 193 ((* elif cell.level == 2 -*))
194 194 ((* block h2 -*))\subsection((* endblock h2 -*))
195 195 ((* elif cell.level == 3 -*))
196 196 ((* block h3 -*))\subsubsection((* endblock h3 -*))
197 197 ((* elif cell.level == 4 -*))
198 198 ((* block h4 -*))\paragraph((* endblock h4 -*))
199 199 ((* elif cell.level == 5 -*))
200 200 ((* block h5 -*))\subparagraph((* endblock h5 -*))
201 201 ((* elif cell.level == 6 -*))
202 202 ((* block h6 -*))\\*\textit((* endblock h6 -*))
203 203 ((*- endif -*))
204 204 {((( cell.source | replace('\n', ' ') | citation2latex | strip_files_prefix | prevent_list_blocks | markdown2latex(markup='markdown_strict+tex_math_dollars') )))}
205 205
206 206
207 207 ((* endblock headingcell *))
208 208
209 % Redirect pyout to display data priority.
210 ((* block pyout scoped *))
209 % Redirect execute_result to display data priority.
210 ((* block execute_result scoped *))
211 211 ((* block data_priority scoped *))
212 212 ((( super() )))
213 213 ((* endblock *))
214 ((* endblock pyout *))
214 ((* endblock execute_result *))
215 215
216 216 % Render markdown
217 217 ((* block markdowncell scoped *))
218 218 ((( cell.source | citation2latex | strip_files_prefix | markdown2latex )))
219 219 ((* endblock markdowncell *))
220 220
221 221 % Don't display unknown types
222 222 ((* block unknowncell scoped *))
223 223 ((* endblock unknowncell *))
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1 1 ((= Auto-generated template file, DO NOT edit directly!
2 2 To edit this file, please refer to ../../skeleton/README.md =))
3 3
4 4
5 5 ((*- extends 'null.tplx' -*))
6 6
7 7 ((=display data priority=))
8 8
9 9
10 10 ((*- block data_priority scoped -*))
11 11 ((*- for type in output | filter_data_type -*))
12 ((*- if type in ['application/pdf']*))
12 ((*- if type == 'application/pdf' -*))
13 13 ((*- block data_pdf -*))
14 14 ((*- endblock -*))
15 ((*- endif -*))
16 ((*- if type in ['svg']*))
15 ((*- elif type == 'image/svg+xml' -*))
17 16 ((*- block data_svg -*))
18 17 ((*- endblock -*))
19 ((*- endif -*))
20 ((*- if type in ['png']*))
18 ((*- elif type == 'image/png' -*))
21 19 ((*- block data_png -*))
22 20 ((*- endblock -*))
23 ((*- endif -*))
24 ((*- if type in ['html']*))
21 ((*- elif type == 'text/html' -*))
25 22 ((*- block data_html -*))
26 23 ((*- endblock -*))
27 ((*- endif -*))
28 ((*- if type in ['jpeg']*))
24 ((*- elif type == 'image/jpeg' -*))
29 25 ((*- block data_jpg -*))
30 26 ((*- endblock -*))
31 ((*- endif -*))
32 ((*- if type in ['text']*))
27 ((*- elif type == 'text/plain' -*))
33 28 ((*- block data_text -*))
34 29 ((*- endblock -*))
35 ((*- endif -*))
36 ((*- if type in ['latex']*))
30 ((*- elif type == 'text/latex' -*))
37 31 ((*- block data_latex -*))
38 32 ((*- endblock -*))
39 ((*- endif -*))
40 ((*- if type in ['javascript']*))
33 ((*- elif type == 'application/javascript' -*))
41 34 ((*- block data_javascript -*))
42 35 ((*- endblock -*))
36 ((*- else -*))
37 ((*- block data_other -*))
38 ((*- endblock -*))
43 39 ((*- endif -*))
44 40 ((*- endfor -*))
45 41 ((*- endblock data_priority -*))
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@@ -1,98 +1,96 b''
1 1 ((= Auto-generated template file, DO NOT edit directly!
2 2 To edit this file, please refer to ../../skeleton/README.md =))
3 3
4 4
5 5 ((=
6 6
7 7 DO NOT USE THIS AS A BASE,
8 8 IF YOU ARE COPY AND PASTING THIS FILE
9 9 YOU ARE PROBABLY DOING THINGS INCORRECTLY.
10 10
11 11 Null template, does nothing except defining a basic structure
12 12 To layout the different blocks of a notebook.
13 13
14 14 Subtemplates can override blocks to define their custom representation.
15 15
16 16 If one of the block you do overwrite is not a leave block, consider
17 17 calling super.
18 18
19 19 ((*- block nonLeaveBlock -*))
20 20 #add stuff at beginning
21 21 ((( super() )))
22 22 #add stuff at end
23 23 ((*- endblock nonLeaveBlock -*))
24 24
25 25 consider calling super even if it is a leave block, we might insert more blocks later.
26 26
27 27 =))
28 28 ((*- block header -*))
29 29 ((*- endblock header -*))
30 30 ((*- block body -*))
31 ((*- for worksheet in nb.worksheets -*))
32 ((*- for cell in worksheet.cells -*))
33 ((*- block any_cell scoped -*))
34 ((*- if cell.cell_type in ['code'] -*))
35 ((*- block codecell scoped -*))
36 ((*- block input_group -*))
37 ((*- block in_prompt -*))((*- endblock in_prompt -*))
38 ((*- block input -*))((*- endblock input -*))
39 ((*- endblock input_group -*))
40 ((*- if cell.outputs -*))
41 ((*- block output_group -*))
42 ((*- block output_prompt -*))((*- endblock output_prompt -*))
43 ((*- block outputs scoped -*))
44 ((*- for output in cell.outputs -*))
45 ((*- block output scoped -*))
46 ((*- if output.output_type in ['pyout'] -*))
47 ((*- block pyout scoped -*))((*- endblock pyout -*))
48 ((*- elif output.output_type in ['stream'] -*))
49 ((*- block stream scoped -*))
50 ((*- if output.stream in ['stdout'] -*))
51 ((*- block stream_stdout scoped -*))
52 ((*- endblock stream_stdout -*))
53 ((*- elif output.stream in ['stderr'] -*))
54 ((*- block stream_stderr scoped -*))
55 ((*- endblock stream_stderr -*))
56 ((*- endif -*))
57 ((*- endblock stream -*))
58 ((*- elif output.output_type in ['display_data'] -*))
59 ((*- block display_data scoped -*))
60 ((*- block data_priority scoped -*))
61 ((*- endblock data_priority -*))
62 ((*- endblock display_data -*))
63 ((*- elif output.output_type in ['pyerr'] -*))
64 ((*- block pyerr scoped -*))
65 ((*- for line in output.traceback -*))
66 ((*- block traceback_line scoped -*))((*- endblock traceback_line -*))
67 ((*- endfor -*))
68 ((*- endblock pyerr -*))
69 ((*- endif -*))
70 ((*- endblock output -*))
71 ((*- endfor -*))
72 ((*- endblock outputs -*))
73 ((*- endblock output_group -*))
74 ((*- endif -*))
75 ((*- endblock codecell -*))
76 ((*- elif cell.cell_type in ['markdown'] -*))
77 ((*- block markdowncell scoped-*))
78 ((*- endblock markdowncell -*))
79 ((*- elif cell.cell_type in ['heading'] -*))
80 ((*- block headingcell scoped-*))
81 ((*- endblock headingcell -*))
82 ((*- elif cell.cell_type in ['raw'] -*))
83 ((*- block rawcell scoped -*))
84 ((* if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) *))
85 ((( cell.source )))
86 ((* endif *))
87 ((*- endblock rawcell -*))
88 ((*- else -*))
89 ((*- block unknowncell scoped-*))
90 ((*- endblock unknowncell -*))
91 ((*- endif -*))
92 ((*- endblock any_cell -*))
93 ((*- endfor -*))
31 ((*- for cell in nb.cells -*))
32 ((*- block any_cell scoped -*))
33 ((*- if cell.cell_type == 'code' -*))
34 ((*- block codecell scoped -*))
35 ((*- block input_group -*))
36 ((*- block in_prompt -*))((*- endblock in_prompt -*))
37 ((*- block input -*))((*- endblock input -*))
38 ((*- endblock input_group -*))
39 ((*- if cell.outputs -*))
40 ((*- block output_group -*))
41 ((*- block output_prompt -*))((*- endblock output_prompt -*))
42 ((*- block outputs scoped -*))
43 ((*- for output in cell.outputs -*))
44 ((*- block output scoped -*))
45 ((*- if output.output_type == 'execute_result' -*))
46 ((*- block execute_result scoped -*))((*- endblock execute_result -*))
47 ((*- elif output.output_type == 'stream' -*))
48 ((*- block stream scoped -*))
49 ((*- if output.name == 'stdout' -*))
50 ((*- block stream_stdout scoped -*))
51 ((*- endblock stream_stdout -*))
52 ((*- elif output.name == 'stderr' -*))
53 ((*- block stream_stderr scoped -*))
54 ((*- endblock stream_stderr -*))
55 ((*- endif -*))
56 ((*- endblock stream -*))
57 ((*- elif output.output_type == 'display_data' -*))
58 ((*- block display_data scoped -*))
59 ((*- block data_priority scoped -*))
60 ((*- endblock data_priority -*))
61 ((*- endblock display_data -*))
62 ((*- elif output.output_type == 'error' -*))
63 ((*- block error scoped -*))
64 ((*- for line in output.traceback -*))
65 ((*- block traceback_line scoped -*))((*- endblock traceback_line -*))
66 ((*- endfor -*))
67 ((*- endblock error -*))
68 ((*- endif -*))
69 ((*- endblock output -*))
70 ((*- endfor -*))
71 ((*- endblock outputs -*))
72 ((*- endblock output_group -*))
73 ((*- endif -*))
74 ((*- endblock codecell -*))
75 ((*- elif cell.cell_type in ['markdown'] -*))
76 ((*- block markdowncell scoped-*))
77 ((*- endblock markdowncell -*))
78 ((*- elif cell.cell_type in ['heading'] -*))
79 ((*- block headingcell scoped-*))
80 ((*- endblock headingcell -*))
81 ((*- elif cell.cell_type in ['raw'] -*))
82 ((*- block rawcell scoped -*))
83 ((* if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) *))
84 ((( cell.source )))
85 ((* endif *))
86 ((*- endblock rawcell -*))
87 ((*- else -*))
88 ((*- block unknowncell scoped-*))
89 ((*- endblock unknowncell -*))
90 ((*- endif -*))
91 ((*- endblock any_cell -*))
94 92 ((*- endfor -*))
95 93 ((*- endblock body -*))
96 94
97 95 ((*- block footer -*))
98 96 ((*- endblock footer -*))
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1 1 ((= Black&white ipython input/output style =))
2 2
3 3 ((*- extends 'base.tplx' -*))
4 4
5 5 %===============================================================================
6 6 % Input
7 7 %===============================================================================
8 8
9 9 ((* block input scoped *))
10 ((( add_prompt(cell.input, cell, 'In ') )))
10 ((( add_prompt(cell.source, cell, 'In ') )))
11 11 ((* endblock input *))
12 12
13 13
14 14 %===============================================================================
15 15 % Output
16 16 %===============================================================================
17 17
18 ((* block pyout scoped *))
18 ((* block execute_result scoped *))
19 19 ((*- for type in output | filter_data_type -*))
20 20 ((*- if type in ['text']*))
21 21 ((( add_prompt(output.text, cell, 'Out') )))
22 22 ((*- else -*))
23 23 \verb+Out[((( cell.prompt_number )))]:+((( super() )))
24 24 ((*- endif -*))
25 25 ((*- endfor -*))
26 ((* endblock pyout *))
26 ((* endblock execute_result *))
27 27
28 28
29 29 %==============================================================================
30 30 % Support Macros
31 31 %==============================================================================
32 32
33 33 % Name: draw_prompt
34 34 % Purpose: Renders an output/input prompt
35 35 ((* macro add_prompt(text, cell, prompt) -*))
36 36 ((*- if cell.prompt_number is defined -*))
37 37 ((*- set prompt_number = "" ~ (cell.prompt_number | replace(None, " ")) -*))
38 38 ((*- else -*))
39 39 ((*- set prompt_number = " " -*))
40 40 ((*- endif -*))
41 41 ((*- set indentation = " " * (prompt_number | length + 7) -*))
42 42 \begin{verbatim}
43 43 (((- text | add_prompts(first=prompt ~ '[' ~ prompt_number ~ ']: ', cont=indentation) -)))
44 44 \end{verbatim}
45 45 ((*- endmacro *))
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@@ -1,13 +1,13 b''
1 1 ((= Black&white Python input/output style =))
2 2
3 3 ((*- extends 'base.tplx' -*))
4 4
5 5 %===============================================================================
6 6 % Input
7 7 %===============================================================================
8 8
9 9 ((* block input scoped *))
10 10 \begin{verbatim}
11 ((( cell.input | add_prompts )))
11 ((( cell.source | add_prompts )))
12 12 \end{verbatim}
13 13 ((* endblock input *))
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@@ -1,58 +1,58 b''
1 1 ((= IPython input/output style =))
2 2
3 3 ((*- extends 'base.tplx' -*))
4 4
5 5 % Custom definitions
6 6 ((* block definitions *))
7 7 ((( super() )))
8 8
9 9 % Pygments definitions
10 10 ((( resources.latex.pygments_definitions )))
11 11
12 12 % Exact colors from NB
13 13 \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
14 14 \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0}
15 15
16 16 ((* endblock definitions *))
17 17
18 18 %===============================================================================
19 19 % Input
20 20 %===============================================================================
21 21
22 22 ((* block input scoped *))
23 ((( add_prompt(cell.input | highlight_code(strip_verbatim=True), cell, 'In ', 'incolor') )))
23 ((( add_prompt(cell.source | highlight_code(strip_verbatim=True), cell, 'In ', 'incolor') )))
24 24 ((* endblock input *))
25 25
26 26
27 27 %===============================================================================
28 28 % Output
29 29 %===============================================================================
30 30
31 ((* block pyout scoped *))
31 ((* block execute_result scoped *))
32 32 ((*- for type in output | filter_data_type -*))
33 33 ((*- if type in ['text']*))
34 34 ((( add_prompt(output.text | escape_latex, cell, 'Out', 'outcolor') )))
35 35 ((* else -*))
36 36 \texttt{\color{outcolor}Out[{\color{outcolor}((( cell.prompt_number )))}]:}((( super() )))
37 37 ((*- endif -*))
38 38 ((*- endfor -*))
39 ((* endblock pyout *))
39 ((* endblock execute_result *))
40 40
41 41
42 42 %==============================================================================
43 43 % Support Macros
44 44 %==============================================================================
45 45
46 46 % Name: draw_prompt
47 47 % Purpose: Renders an output/input prompt
48 48 ((* macro add_prompt(text, cell, prompt, prompt_color) -*))
49 49 ((*- if cell.prompt_number is defined -*))
50 50 ((*- set prompt_number = "" ~ (cell.prompt_number | replace(None, " ")) -*))
51 51 ((*- else -*))
52 52 ((*- set prompt_number = " " -*))
53 53 ((*- endif -*))
54 54 ((*- set indention = " " * (prompt_number | length + 7) -*))
55 55 \begin{Verbatim}[commandchars=\\\{\}]
56 56 ((( text | add_prompts(first='{\color{' ~ prompt_color ~ '}' ~ prompt ~ '[{\\color{' ~ prompt_color ~ '}' ~ prompt_number ~ '}]:} ', cont=indention) )))
57 57 \end{Verbatim}
58 58 ((*- endmacro *))
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@@ -1,21 +1,21 b''
1 1 ((= Python input/output style =))
2 2
3 3 ((*- extends 'base.tplx' -*))
4 4
5 5 % Custom definitions
6 6 ((* block definitions *))
7 7 ((( super() )))
8 8
9 9 % Pygments definitions
10 10 ((( resources.latex.pygments_definitions )))
11 11 ((* endblock definitions *))
12 12
13 13 %===============================================================================
14 14 % Input
15 15 %===============================================================================
16 16
17 17 ((* block input scoped *))
18 18 \begin{Verbatim}[commandchars=\\\{\}]
19 ((( cell.input | highlight_code(strip_verbatim=True) | add_prompts )))
19 ((( cell.source | highlight_code(strip_verbatim=True) | add_prompts )))
20 20 \end{Verbatim}
21 21 ((* endblock input *))
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@@ -1,68 +1,68 b''
1 1 {% extends 'display_priority.tpl' %}
2 2
3 3
4 4 {% block in_prompt %}
5 5 {% endblock in_prompt %}
6 6
7 7 {% block output_prompt %}
8 8 {%- endblock output_prompt %}
9 9
10 10 {% block input %}
11 {{ cell.input | indent(4)}}
11 {{ cell.source | indent(4)}}
12 12 {% endblock input %}
13 13
14 {% block pyerr %}
14 {% block error %}
15 15 {{ super() }}
16 {% endblock pyerr %}
16 {% endblock error %}
17 17
18 18 {% block traceback_line %}
19 19 {{ line | indent | strip_ansi }}
20 20 {% endblock traceback_line %}
21 21
22 {% block pyout %}
22 {% block execute_result %}
23 23
24 24 {% block data_priority scoped %}
25 25 {{ super() }}
26 26 {% endblock %}
27 {% endblock pyout %}
27 {% endblock execute_result %}
28 28
29 29 {% block stream %}
30 30 {{ output.text | indent }}
31 31 {% endblock stream %}
32 32
33 33 {% block data_svg %}
34 34 ![svg]({{ output.svg_filename | path2url }})
35 35 {% endblock data_svg %}
36 36
37 37 {% block data_png %}
38 38 ![png]({{ output.png_filename | path2url }})
39 39 {% endblock data_png %}
40 40
41 41 {% block data_jpg %}
42 42 ![jpeg]({{ output.jpeg_filename | path2url }})
43 43 {% endblock data_jpg %}
44 44
45 45 {% block data_latex %}
46 46 {{ output.latex }}
47 47 {% endblock data_latex %}
48 48
49 49 {% block data_html scoped %}
50 50 {{ output.html }}
51 51 {% endblock data_html %}
52 52
53 53 {% block data_text scoped %}
54 54 {{ output.text | indent }}
55 55 {% endblock data_text %}
56 56
57 57 {% block markdowncell scoped %}
58 58 {{ cell.source }}
59 59 {% endblock markdowncell %}
60 60
61 61
62 62 {% block headingcell scoped %}
63 63 {{ '#' * cell.level }} {{ cell.source | replace('\n', ' ') }}
64 64 {% endblock headingcell %}
65 65
66 66 {% block unknowncell scoped %}
67 67 unknown type {{ cell.type }}
68 68 {% endblock unknowncell %} No newline at end of file
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@@ -1,21 +1,21 b''
1 1 {%- extends 'null.tpl' -%}
2 2
3 3 {% block header %}
4 4 # coding: utf-8
5 5 {% endblock header %}
6 6
7 7 {% block in_prompt %}
8 8 # In[{{ cell.prompt_number if cell.prompt_number else ' ' }}]:
9 9 {% endblock in_prompt %}
10 10
11 11 {% block input %}
12 {{ cell.input | ipython2python }}
12 {{ cell.source | ipython2python }}
13 13 {% endblock input %}
14 14
15 15 {% block markdowncell scoped %}
16 16 {{ cell.source | comment_lines }}
17 17 {% endblock markdowncell %}
18 18
19 19 {% block headingcell scoped %}
20 20 {{ '#' * cell.level }}{{ cell.source | replace('\n', ' ') | comment_lines }}
21 21 {% endblock headingcell %}
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@@ -1,80 +1,80 b''
1 1 {%- extends 'display_priority.tpl' -%}
2 2
3 3
4 4 {% block in_prompt %}
5 5 {% endblock in_prompt %}
6 6
7 7 {% block output_prompt %}
8 8 {% endblock output_prompt %}
9 9
10 10 {% block input %}
11 {%- if cell.input.strip() -%}
11 {%- if cell.source.strip() -%}
12 12 .. code:: python
13 13
14 {{ cell.input | indent}}
14 {{ cell.source | indent}}
15 15 {%- endif -%}
16 16 {% endblock input %}
17 17
18 {% block pyerr %}
18 {% block error %}
19 19 ::
20 20
21 21 {{ super() }}
22 {% endblock pyerr %}
22 {% endblock error %}
23 23
24 24 {% block traceback_line %}
25 25 {{ line | indent | strip_ansi }}
26 26 {% endblock traceback_line %}
27 27
28 {% block pyout %}
28 {% block execute_result %}
29 29 {% block data_priority scoped %}
30 30 {{ super() }}
31 31 {% endblock %}
32 {% endblock pyout %}
32 {% endblock execute_result %}
33 33
34 34 {% block stream %}
35 35 .. parsed-literal::
36 36
37 37 {{ output.text | indent }}
38 38 {% endblock stream %}
39 39
40 40 {% block data_svg %}
41 41 .. image:: {{ output.svg_filename|urlencode }}
42 42 {% endblock data_svg %}
43 43
44 44 {% block data_png %}
45 45 .. image:: {{ output.png_filename|urlencode }}
46 46 {% endblock data_png %}
47 47
48 48 {% block data_jpg %}
49 49 .. image:: {{ output.jpeg_filename|urlencode }}
50 50 {% endblock data_jpg %}
51 51
52 52 {% block data_latex %}
53 53 .. math::
54 54
55 55 {{ output.latex | strip_dollars | indent }}
56 56 {% endblock data_latex %}
57 57
58 58 {% block data_text scoped %}
59 59 .. parsed-literal::
60 60
61 61 {{ output.text | indent }}
62 62 {% endblock data_text %}
63 63
64 64 {% block data_html scoped %}
65 65 .. raw:: html
66 66
67 67 {{ output.html | indent }}
68 68 {% endblock data_html %}
69 69
70 70 {% block markdowncell scoped %}
71 71 {{ cell.source | markdown2rst }}
72 72 {% endblock markdowncell %}
73 73
74 74 {% block headingcell scoped %}
75 75 {{ ("#" * cell.level + cell.source) | replace('\n', ' ') | markdown2rst }}
76 76 {% endblock headingcell %}
77 77
78 78 {% block unknowncell scoped %}
79 79 unknown type {{cell.type}}
80 80 {% endblock unknowncell %}
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@@ -1,41 +1,37 b''
1 1 {%- extends 'null.tpl' -%}
2 2
3 3 {#display data priority#}
4 4
5 5
6 6 {%- block data_priority scoped -%}
7 7 {%- for type in output | filter_data_type -%}
8 {%- if type in ['application/pdf']%}
8 {%- if type == 'application/pdf' -%}
9 9 {%- block data_pdf -%}
10 10 {%- endblock -%}
11 {%- endif -%}
12 {%- if type in ['svg']%}
11 {%- elif type == 'image/svg+xml' -%}
13 12 {%- block data_svg -%}
14 13 {%- endblock -%}
15 {%- endif -%}
16 {%- if type in ['png']%}
14 {%- elif type == 'image/png' -%}
17 15 {%- block data_png -%}
18 16 {%- endblock -%}
19 {%- endif -%}
20 {%- if type in ['html']%}
17 {%- elif type == 'text/html' -%}
21 18 {%- block data_html -%}
22 19 {%- endblock -%}
23 {%- endif -%}
24 {%- if type in ['jpeg']%}
20 {%- elif type == 'image/jpeg' -%}
25 21 {%- block data_jpg -%}
26 22 {%- endblock -%}
27 {%- endif -%}
28 {%- if type in ['text']%}
23 {%- elif type == 'text/plain' -%}
29 24 {%- block data_text -%}
30 25 {%- endblock -%}
31 {%- endif -%}
32 {%- if type in ['latex']%}
26 {%- elif type == 'text/latex' -%}
33 27 {%- block data_latex -%}
34 28 {%- endblock -%}
35 {%- endif -%}
36 {%- if type in ['javascript']%}
29 {%- elif type == 'application/javascript' -%}
37 30 {%- block data_javascript -%}
38 31 {%- endblock -%}
32 {%- else -%}
33 {%- block data_other -%}
34 {%- endblock -%}
39 35 {%- endif -%}
40 36 {%- endfor -%}
41 37 {%- endblock data_priority -%}
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@@ -1,94 +1,92 b''
1 1 {#
2 2
3 3 DO NOT USE THIS AS A BASE,
4 4 IF YOU ARE COPY AND PASTING THIS FILE
5 5 YOU ARE PROBABLY DOING THINGS INCORRECTLY.
6 6
7 7 Null template, does nothing except defining a basic structure
8 8 To layout the different blocks of a notebook.
9 9
10 10 Subtemplates can override blocks to define their custom representation.
11 11
12 12 If one of the block you do overwrite is not a leave block, consider
13 13 calling super.
14 14
15 15 {%- block nonLeaveBlock -%}
16 16 #add stuff at beginning
17 17 {{ super() }}
18 18 #add stuff at end
19 19 {%- endblock nonLeaveBlock -%}
20 20
21 21 consider calling super even if it is a leave block, we might insert more blocks later.
22 22
23 23 #}
24 24 {%- block header -%}
25 25 {%- endblock header -%}
26 26 {%- block body -%}
27 {%- for worksheet in nb.worksheets -%}
28 {%- for cell in worksheet.cells -%}
29 {%- block any_cell scoped -%}
30 {%- if cell.cell_type in ['code'] -%}
31 {%- block codecell scoped -%}
32 {%- block input_group -%}
33 {%- block in_prompt -%}{%- endblock in_prompt -%}
34 {%- block input -%}{%- endblock input -%}
35 {%- endblock input_group -%}
36 {%- if cell.outputs -%}
37 {%- block output_group -%}
38 {%- block output_prompt -%}{%- endblock output_prompt -%}
39 {%- block outputs scoped -%}
40 {%- for output in cell.outputs -%}
41 {%- block output scoped -%}
42 {%- if output.output_type in ['pyout'] -%}
43 {%- block pyout scoped -%}{%- endblock pyout -%}
44 {%- elif output.output_type in ['stream'] -%}
45 {%- block stream scoped -%}
46 {%- if output.stream in ['stdout'] -%}
47 {%- block stream_stdout scoped -%}
48 {%- endblock stream_stdout -%}
49 {%- elif output.stream in ['stderr'] -%}
50 {%- block stream_stderr scoped -%}
51 {%- endblock stream_stderr -%}
52 {%- endif -%}
53 {%- endblock stream -%}
54 {%- elif output.output_type in ['display_data'] -%}
55 {%- block display_data scoped -%}
56 {%- block data_priority scoped -%}
57 {%- endblock data_priority -%}
58 {%- endblock display_data -%}
59 {%- elif output.output_type in ['pyerr'] -%}
60 {%- block pyerr scoped -%}
61 {%- for line in output.traceback -%}
62 {%- block traceback_line scoped -%}{%- endblock traceback_line -%}
63 {%- endfor -%}
64 {%- endblock pyerr -%}
65 {%- endif -%}
66 {%- endblock output -%}
67 {%- endfor -%}
68 {%- endblock outputs -%}
69 {%- endblock output_group -%}
70 {%- endif -%}
71 {%- endblock codecell -%}
72 {%- elif cell.cell_type in ['markdown'] -%}
73 {%- block markdowncell scoped-%}
74 {%- endblock markdowncell -%}
75 {%- elif cell.cell_type in ['heading'] -%}
76 {%- block headingcell scoped-%}
77 {%- endblock headingcell -%}
78 {%- elif cell.cell_type in ['raw'] -%}
79 {%- block rawcell scoped -%}
80 {% if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %}
81 {{ cell.source }}
82 {% endif %}
83 {%- endblock rawcell -%}
84 {%- else -%}
85 {%- block unknowncell scoped-%}
86 {%- endblock unknowncell -%}
87 {%- endif -%}
88 {%- endblock any_cell -%}
89 {%- endfor -%}
27 {%- for cell in nb.cells -%}
28 {%- block any_cell scoped -%}
29 {%- if cell.cell_type == 'code' -%}
30 {%- block codecell scoped -%}
31 {%- block input_group -%}
32 {%- block in_prompt -%}{%- endblock in_prompt -%}
33 {%- block input -%}{%- endblock input -%}
34 {%- endblock input_group -%}
35 {%- if cell.outputs -%}
36 {%- block output_group -%}
37 {%- block output_prompt -%}{%- endblock output_prompt -%}
38 {%- block outputs scoped -%}
39 {%- for output in cell.outputs -%}
40 {%- block output scoped -%}
41 {%- if output.output_type == 'execute_result' -%}
42 {%- block execute_result scoped -%}{%- endblock execute_result -%}
43 {%- elif output.output_type == 'stream' -%}
44 {%- block stream scoped -%}
45 {%- if output.name == 'stdout' -%}
46 {%- block stream_stdout scoped -%}
47 {%- endblock stream_stdout -%}
48 {%- elif output.name == 'stderr' -%}
49 {%- block stream_stderr scoped -%}
50 {%- endblock stream_stderr -%}
51 {%- endif -%}
52 {%- endblock stream -%}
53 {%- elif output.output_type == 'display_data' -%}
54 {%- block display_data scoped -%}
55 {%- block data_priority scoped -%}
56 {%- endblock data_priority -%}
57 {%- endblock display_data -%}
58 {%- elif output.output_type == 'error' -%}
59 {%- block error scoped -%}
60 {%- for line in output.traceback -%}
61 {%- block traceback_line scoped -%}{%- endblock traceback_line -%}
62 {%- endfor -%}
63 {%- endblock error -%}
64 {%- endif -%}
65 {%- endblock output -%}
66 {%- endfor -%}
67 {%- endblock outputs -%}
68 {%- endblock output_group -%}
69 {%- endif -%}
70 {%- endblock codecell -%}
71 {%- elif cell.cell_type in ['markdown'] -%}
72 {%- block markdowncell scoped-%}
73 {%- endblock markdowncell -%}
74 {%- elif cell.cell_type in ['heading'] -%}
75 {%- block headingcell scoped-%}
76 {%- endblock headingcell -%}
77 {%- elif cell.cell_type in ['raw'] -%}
78 {%- block rawcell scoped -%}
79 {% if cell.metadata.get('raw_mimetype', '').lower() in resources.get('raw_mimetypes', ['']) %}
80 {{ cell.source }}
81 {% endif %}
82 {%- endblock rawcell -%}
83 {%- else -%}
84 {%- block unknowncell scoped-%}
85 {%- endblock unknowncell -%}
86 {%- endif -%}
87 {%- endblock any_cell -%}
90 88 {%- endfor -%}
91 89 {%- endblock body -%}
92 90
93 91 {%- block footer -%}
94 92 {%- endblock footer -%}
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@@ -1,164 +1,149 b''
1 """
2 Contains base test class for nbconvert
3 """
4 #-----------------------------------------------------------------------------
5 #Copyright (c) 2013, the IPython Development Team.
6 #
7 #Distributed under the terms of the Modified BSD License.
8 #
9 #The full license is in the file COPYING.txt, distributed with this software.
10 #-----------------------------------------------------------------------------
11
12 #-----------------------------------------------------------------------------
13 # Imports
14 #-----------------------------------------------------------------------------
1 """Base test class for nbconvert"""
2
3 # Copyright (c) IPython Development Team.
4 # Distributed under the terms of the Modified BSD License.
15 5
16 6 import io
17 7 import os
18 8 import glob
19 9 import shutil
20 10 import unittest
21 11
22 12 import IPython
23 13 from IPython.nbformat import current
24 14 from IPython.utils.tempdir import TemporaryWorkingDirectory
25 15 from IPython.utils.path import get_ipython_package_dir
26 16 from IPython.utils.process import get_output_error_code
27 17 from IPython.testing.tools import get_ipython_cmd
28 18
29 19 # a trailing space allows for simpler concatenation with the other arguments
30 20 ipy_cmd = get_ipython_cmd(as_string=True) + " "
31 21
32 #-----------------------------------------------------------------------------
33 # Classes and functions
34 #-----------------------------------------------------------------------------
35
36 22
37 23 class TestsBase(unittest.TestCase):
38 24 """Base tests class. Contains useful fuzzy comparison and nbconvert
39 25 functions."""
40 26
41 27
42 28 def fuzzy_compare(self, a, b, newlines_are_spaces=True, tabs_are_spaces=True,
43 29 fuzzy_spacing=True, ignore_spaces=False,
44 30 ignore_newlines=False, case_sensitive=False, leave_padding=False):
45 31 """
46 32 Performs a fuzzy comparison of two strings. A fuzzy comparison is a
47 33 comparison that ignores insignificant differences in the two comparands.
48 34 The significance of certain differences can be specified via the keyword
49 35 parameters of this method.
50 36 """
51 37
52 38 if not leave_padding:
53 39 a = a.strip()
54 40 b = b.strip()
55 41
56 42 if ignore_newlines:
57 43 a = a.replace('\n', '')
58 44 b = b.replace('\n', '')
59 45
60 46 if newlines_are_spaces:
61 47 a = a.replace('\n', ' ')
62 48 b = b.replace('\n', ' ')
63 49
64 50 if tabs_are_spaces:
65 51 a = a.replace('\t', ' ')
66 52 b = b.replace('\t', ' ')
67 53
68 54 if ignore_spaces:
69 55 a = a.replace(' ', '')
70 56 b = b.replace(' ', '')
71 57
72 58 if fuzzy_spacing:
73 59 a = self.recursive_replace(a, ' ', ' ')
74 60 b = self.recursive_replace(b, ' ', ' ')
75 61
76 62 if not case_sensitive:
77 63 a = a.lower()
78 64 b = b.lower()
79 65
80 66 self.assertEqual(a, b)
81 67
82 68
83 69 def recursive_replace(self, text, search, replacement):
84 70 """
85 71 Performs a recursive replacement operation. Replaces all instances
86 72 of a search string in a text string with a replacement string until
87 73 the search string no longer exists. Recursion is needed because the
88 74 replacement string may generate additional search strings.
89 75
90 76 For example:
91 77 Replace "ii" with "i" in the string "Hiiii" yields "Hii"
92 78 Another replacement cds "Hi" (the desired output)
93 79
94 80 Parameters
95 81 ----------
96 82 text : string
97 83 Text to replace in.
98 84 search : string
99 85 String to search for within "text"
100 86 replacement : string
101 87 String to replace "search" with
102 88 """
103 89 while search in text:
104 90 text = text.replace(search, replacement)
105 91 return text
106 92
107 93 def create_temp_cwd(self, copy_filenames=None):
108 94 temp_dir = TemporaryWorkingDirectory()
109 95
110 96 #Copy the files if requested.
111 97 if copy_filenames is not None:
112 98 self.copy_files_to(copy_filenames, dest=temp_dir.name)
113 99
114 100 #Return directory handler
115 101 return temp_dir
116 102
117 103 def create_empty_notebook(self, path):
118 104 nb = current.new_notebook()
119 nb.worksheets.append(current.new_worksheet())
120 105 with io.open(path, 'w', encoding='utf-8') as f:
121 106 current.write(nb, f, 'json')
122 107
123 108
124 109 def copy_files_to(self, copy_filenames, dest='.'):
125 110 "Copy test files into the destination directory"
126 111 if not os.path.isdir(dest):
127 112 os.makedirs(dest)
128 113 files_path = self._get_files_path()
129 114 for pattern in copy_filenames:
130 115 for match in glob.glob(os.path.join(files_path, pattern)):
131 116 shutil.copyfile(match, os.path.join(dest, os.path.basename(match)))
132 117
133 118
134 119 def _get_files_path(self):
135 120
136 121 #Get the relative path to this module in the IPython directory.
137 122 names = self.__module__.split('.')[1:-1]
138 123 names.append('files')
139 124
140 125 #Build a path using the IPython directory and the relative path we just
141 126 #found.
142 127 path = get_ipython_package_dir()
143 128 for name in names:
144 129 path = os.path.join(path, name)
145 130 return path
146 131
147 132
148 133 def call(self, parameters, ignore_return_code=False):
149 134 """
150 135 Execute a, IPython shell command, listening for both Errors and non-zero
151 136 return codes.
152 137
153 138 Parameters
154 139 ----------
155 140 parameters : str
156 141 List of parameters to pass to IPython.
157 142 ignore_return_code : optional bool (default False)
158 143 Throw an OSError if the return code
159 144 """
160 145
161 146 stdout, stderr, retcode = get_output_error_code(ipy_cmd + parameters)
162 147 if not (retcode == 0 or ignore_return_code):
163 148 raise OSError(stderr)
164 149 return stdout, stderr
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@@ -1,149 +1,143 b''
1 1 {
2 "metadata": {
3 "name": "notebook1"
4 },
5 "nbformat": 3,
6 "nbformat_minor": 0,
7 "worksheets": [
2 "cells": [
8 3 {
9 "cells": [
10 {
11 "cell_type": "heading",
12 "level": 1,
13 "metadata": {},
14 "source": [
15 "A simple SymPy example"
16 ]
17 },
4 "cell_type": "heading",
5 "level": 1,
6 "metadata": {},
7 "source": [
8 "A simple SymPy example"
9 ]
10 },
11 {
12 "cell_type": "markdown",
13 "metadata": {},
14 "source": [
15 "First we import SymPy and initialize printing:"
16 ]
17 },
18 {
19 "cell_type": "code",
20 "metadata": {
21 "collapsed": false
22 },
23 "outputs": [],
24 "prompt_number": 2,
25 "source": [
26 "from sympy import init_printing\n",
27 "from sympy import *\n",
28 " init_printing()"
29 ]
30 },
31 {
32 "cell_type": "markdown",
33 "metadata": {},
34 "source": [
35 "Create a few symbols:"
36 ]
37 },
38 {
39 "cell_type": "code",
40 "metadata": {
41 "collapsed": false
42 },
43 "outputs": [],
44 "prompt_number": 4,
45 "source": [
46 "x,y,z = symbols('x y z')"
47 ]
48 },
49 {
50 "cell_type": "markdown",
51 "metadata": {},
52 "source": [
53 "Here is a basic expression:"
54 ]
55 },
56 {
57 "cell_type": "code",
58 "metadata": {
59 "collapsed": false
60 },
61 "outputs": [
18 62 {
19 "cell_type": "markdown",
63 "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKMAAAAZBAMAAACvE4OgAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n",
20 64 "metadata": {},
21 "source": [
22 "First we import SymPy and initialize printing:"
23 ]
24 },
25 {
26 "cell_type": "code",
27 "collapsed": false,
28 "input": [
29 "from sympy import init_printing\n",
30 "from sympy import *\n",
31 " init_printing()"
65 "output_type": "execute_result",
66 "prompt_number": 6,
67 "text/latex": [
68 "$$x^{2} + 2.0 y + \\sin{\\left (z \\right )}$$"
32 69 ],
33 "language": "python",
34 "metadata": {},
35 "outputs": [],
36 "prompt_number": 2
37 },
38 {
39 "cell_type": "markdown",
40 "metadata": {},
41 "source": [
42 "Create a few symbols:"
70 "text/plain": [
71 " 2 \n",
72 "x + 2.0\u22c5y + sin(z)"
43 73 ]
44 },
45 {
46 "cell_type": "code",
47 "collapsed": false,
48 "input": [
49 "x,y,z = symbols('x y z')"
50 ],
51 "language": "python",
52 "metadata": {},
53 "outputs": [],
54 "prompt_number": 4
55 },
56 {
57 "cell_type": "markdown",
58 "metadata": {},
59 "source": [
60 "Here is a basic expression:"
61 ]
62 },
63 {
64 "cell_type": "code",
65 "collapsed": false,
66 "input": [
67 "e = x**2 + 2.0*y + sin(z); e"
68 ],
69 "language": "python",
70 "metadata": {},
71 "outputs": [
72 {
73 "latex": [
74 "$$x^{2} + 2.0 y + \\sin{\\left (z \\right )}$$"
75 ],
76 "metadata": {},
77 "output_type": "pyout",
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79 "prompt_number": 6,
80 "text": [
81 " 2 \n",
82 "x + 2.0\u22c5y + sin(z)"
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84 }
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86 "prompt_number": 6
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75 ],
76 "prompt_number": 6,
77 "source": [
78 "e = x**2 + 2.0*y + sin(z); e"
79 ]
80 },
81 {
82 "cell_type": "code",
83 "metadata": {
84 "collapsed": false
85 },
86 "outputs": [
88 87 {
89 "cell_type": "code",
90 "collapsed": false,
91 "input": [
92 "diff(e, x)"
93 ],
94 "language": "python",
88 "image/png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAOBAMAAADd6iHDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAAgElEQVQIHWNgVDJ2YICAMAb2H1BmKgPDTChzFgNDvgOEvT8AzgQKrA9gPZPYUwNk\ncXxnCGd4dWA1kMllwFDKUB9wEchUZmAIYNgMZDDwJIDIPyDiEgOjAAPLFwZWBhYFBh6BqzwfGI4y\nSJUXZXH8Zf7A+IBh////v1hzjh5/xwAAW80hUDE8HYkAAAAASUVORK5CYII=\n",
95 89 "metadata": {},
96 "outputs": [
97 {
98 "latex": [
99 "$$2 x$$"
100 ],
101 "metadata": {},
102 "output_type": "pyout",
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104 "prompt_number": 7,
105 "text": [
106 "2\u22c5x"
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92 "text/latex": [
93 "$$2 x$$"
109 94 ],
110 "prompt_number": 7
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95 "text/plain": [
96 "2\u22c5x"
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100 "prompt_number": 7,
101 "source": [
102 "diff(e, x)"
103 ]
104 },
105 {
106 "cell_type": "code",
107 "metadata": {
108 "collapsed": false
109 },
110 "outputs": [
112 111 {
113 "cell_type": "code",
114 "collapsed": false,
115 "input": [
116 "integrate(e, z)"
117 ],
118 "language": "python",
112 "image/png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAZBAMAAACbakK8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
119 113 "metadata": {},
120 "outputs": [
121 {
122 "latex": [
123 "$$x^{2} z + 2.0 y z - \\cos{\\left (z \\right )}$$"
124 ],
125 "metadata": {},
126 "output_type": "pyout",
127 "png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAZBAMAAACbakK8AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
128 "prompt_number": 8,
129 "text": [
130 " 2 \n",
131 "x \u22c5z + 2.0\u22c5y\u22c5z - cos(z)"
132 ]
133 }
114 "output_type": "execute_result",
115 "prompt_number": 8,
116 "text/latex": [
117 "$$x^{2} z + 2.0 y z - \\cos{\\left (z \\right )}$$"
134 118 ],
135 "prompt_number": 8
136 },
137 {
138 "cell_type": "code",
139 "collapsed": false,
140 "input": [],
141 "language": "python",
142 "metadata": {},
143 "outputs": []
119 "text/plain": [
120 " 2 \n",
121 "x \u22c5z + 2.0\u22c5y\u22c5z - cos(z)"
122 ]
144 123 }
145 124 ],
146 "metadata": {}
125 "prompt_number": 8,
126 "source": [
127 "integrate(e, z)"
128 ]
129 },
130 {
131 "cell_type": "code",
132 "metadata": {
133 "collapsed": false
134 },
135 "outputs": [],
136 "prompt_number": null,
137 "source": []
147 138 }
148 ]
139 ],
140 "metadata": {},
141 "nbformat": 4,
142 "nbformat_minor": 0
149 143 } No newline at end of file
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@@ -1,224 +1,215 b''
1 1 {
2 "metadata": {
3 "name": "",
4 "signature": "sha256:9fffd84e69e3d9b8aee7b4cde2099ca5d4158a45391698b191f94fabaf394b41"
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8 "worksheets": [
2 "cells": [
9 3 {
10 "cells": [
4 "cell_type": "heading",
5 "level": 1,
6 "metadata": {},
7 "source": [
8 "NumPy and Matplotlib examples"
9 ]
10 },
11 {
12 "cell_type": "markdown",
13 "metadata": {},
14 "source": [
15 "First import NumPy and Matplotlib:"
16 ]
17 },
18 {
19 "cell_type": "code",
20 "metadata": {
21 "collapsed": false
22 },
23 "outputs": [
11 24 {
12 "cell_type": "heading",
13 "level": 1,
14 25 "metadata": {},
15 "source": [
16 "NumPy and Matplotlib examples"
17 ]
18 },
26 "name": "stdout",
27 "output_type": "stream",
28 "text": "module://IPython.kernel.zmq.pylab.backend_inline\n"
29 }
30 ],
31 "prompt_number": 1,
32 "source": [
33 "%matplotlib inline\n",
34 "import matplotlib\n",
35 "import matplotlib.pyplot as plt\n",
36 "print(matplotlib.backends.backend)"
37 ]
38 },
39 {
40 "cell_type": "code",
41 "metadata": {
42 "collapsed": false
43 },
44 "outputs": [],
45 "prompt_number": 2,
46 "source": [
47 "from IPython.display import set_matplotlib_formats\n",
48 "set_matplotlib_formats('png', 'pdf')\n",
49 "matplotlib.rcParams['figure.figsize'] = (2,1)"
50 ]
51 },
52 {
53 "cell_type": "code",
54 "metadata": {
55 "collapsed": false
56 },
57 "outputs": [
19 58 {
20 "cell_type": "markdown",
21 59 "metadata": {},
22 "source": [
23 "First import NumPy and Matplotlib:"
60 "output_type": "execute_result",
61 "prompt_number": 3,
62 "text/plain": [
63 "{matplotlib.figure.Figure: <function IPython.core.pylabtools.<lambda>>}"
24 64 ]
25 },
26 {
27 "cell_type": "code",
28 "collapsed": false,
29 "input": [
30 "%matplotlib inline\n",
31 "import matplotlib\n",
32 "import matplotlib.pyplot as plt\n",
33 "print(matplotlib.backends.backend)"
34 ],
35 "language": "python",
36 "metadata": {},
37 "outputs": [
38 {
39 "output_type": "stream",
40 "stream": "stdout",
41 "text": [
42 "module://IPython.kernel.zmq.pylab.backend_inline\n"
43 ]
44 }
45 ],
46 "prompt_number": 1
47 },
48 {
49 "cell_type": "code",
50 "collapsed": false,
51 "input": [
52 "from IPython.display import set_matplotlib_formats\n",
53 "set_matplotlib_formats('png', 'pdf')\n",
54 "matplotlib.rcParams['figure.figsize'] = (2,1)"
55 ],
56 "language": "python",
57 "metadata": {},
58 "outputs": [],
59 "prompt_number": 2
60 },
61 {
62 "cell_type": "code",
63 "collapsed": false,
64 "input": [
65 "ip.display_formatter.formatters['application/pdf'].type_printers"
66 ],
67 "language": "python",
68 "metadata": {},
69 "outputs": [
70 {
71 "metadata": {},
72 "output_type": "pyout",
73 "prompt_number": 3,
74 "text": [
75 "{matplotlib.figure.Figure: <function IPython.core.pylabtools.<lambda>>}"
76 ]
77 }
78 ],
79 "prompt_number": 3
80 },
81 {
82 "cell_type": "code",
83 "collapsed": false,
84 "input": [
85 "import numpy as np"
86 ],
87 "language": "python",
88 "metadata": {},
89 "outputs": [],
90 "prompt_number": 4
91 },
65 }
66 ],
67 "prompt_number": 3,
68 "source": [
69 "ip.display_formatter.formatters['application/pdf'].type_printers"
70 ]
71 },
72 {
73 "cell_type": "code",
74 "metadata": {
75 "collapsed": false
76 },
77 "outputs": [],
78 "prompt_number": 4,
79 "source": [
80 "import numpy as np"
81 ]
82 },
83 {
84 "cell_type": "markdown",
85 "metadata": {},
86 "source": [
87 "Now we show some very basic examples of how they can be used."
88 ]
89 },
90 {
91 "cell_type": "code",
92 "metadata": {
93 "collapsed": false
94 },
95 "outputs": [],
96 "prompt_number": 5,
97 "source": [
98 "a = np.random.uniform(size=(100,100))"
99 ]
100 },
101 {
102 "cell_type": "code",
103 "metadata": {
104 "collapsed": false
105 },
106 "outputs": [
92 107 {
93 "cell_type": "markdown",
94 108 "metadata": {},
95 "source": [
96 "Now we show some very basic examples of how they can be used."
109 "output_type": "execute_result",
110 "prompt_number": 6,
111 "text/plain": [
112 "(100, 100)"
97 113 ]
98 },
99 {
100 "cell_type": "code",
101 "collapsed": false,
102 "input": [
103 "a = np.random.uniform(size=(100,100))"
104 ],
105 "language": "python",
106 "metadata": {},
107 "outputs": [],
108 "prompt_number": 5
109 },
110 {
111 "cell_type": "code",
112 "collapsed": false,
113 "input": [
114 "a.shape"
115 ],
116 "language": "python",
117 "metadata": {},
118 "outputs": [
119 {
120 "metadata": {},
121 "output_type": "pyout",
122 "prompt_number": 6,
123 "text": [
124 "(100, 100)"
125 ]
126 }
127 ],
128 "prompt_number": 6
129 },
130 {
131 "cell_type": "code",
132 "collapsed": false,
133 "input": [
134 "evs = np.linalg.eigvals(a)"
135 ],
136 "language": "python",
137 "metadata": {},
138 "outputs": [],
139 "prompt_number": 7
140 },
141 {
142 "cell_type": "code",
143 "collapsed": false,
144 "input": [
145 "evs.shape"
146 ],
147 "language": "python",
148 "metadata": {},
149 "outputs": [
150 {
151 "metadata": {},
152 "output_type": "pyout",
153 "prompt_number": 8,
154 "text": [
155 "(100,)"
156 ]
157 }
158 ],
159 "prompt_number": 8
160 },
114 }
115 ],
116 "prompt_number": 6,
117 "source": [
118 "a.shape"
119 ]
120 },
121 {
122 "cell_type": "code",
123 "metadata": {
124 "collapsed": false
125 },
126 "outputs": [],
127 "prompt_number": 7,
128 "source": [
129 "evs = np.linalg.eigvals(a)"
130 ]
131 },
132 {
133 "cell_type": "code",
134 "metadata": {
135 "collapsed": false
136 },
137 "outputs": [
161 138 {
162 "cell_type": "heading",
163 "level": 2,
164 139 "metadata": {},
165 "source": [
166 "Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap"
140 "output_type": "execute_result",
141 "prompt_number": 8,
142 "text/plain": [
143 "(100,)"
167 144 ]
168 },
145 }
146 ],
147 "prompt_number": 8,
148 "source": [
149 "evs.shape"
150 ]
151 },
152 {
153 "cell_type": "heading",
154 "level": 2,
155 "metadata": {},
156 "source": [
157 "Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap"
158 ]
159 },
160 {
161 "cell_type": "markdown",
162 "metadata": {},
163 "source": [
164 "Here is a cell that has both text and PNG output:"
165 ]
166 },
167 {
168 "cell_type": "code",
169 "metadata": {
170 "collapsed": false
171 },
172 "outputs": [
169 173 {
170 "cell_type": "markdown",
171 174 "metadata": {},
172 "source": [
173 "Here is a cell that has both text and PNG output:"
175 "output_type": "execute_result",
176 "prompt_number": 9,
177 "text/plain": [
178 "(array([97, 2, 0, 0, 0, 0, 0, 0, 0, 1]),\n",
179 " array([ -2.59479443, 2.67371141, 7.94221725, 13.21072308,\n",
180 " 18.47922892, 23.74773476, 29.0162406 , 34.28474644,\n",
181 " 39.55325228, 44.82175812, 50.09026395]),\n",
182 " <a list of 10 Patch objects>)"
174 183 ]
175 184 },
176 185 {
177 "cell_type": "code",
178 "collapsed": false,
179 "input": [
180 "plt.hist(evs.real)"
181 ],
182 "language": "python",
183 "metadata": {},
184 "outputs": [
185 {
186 "metadata": {},
187 "output_type": "pyout",
188 "prompt_number": 9,
189 "text": [
190 "(array([97, 2, 0, 0, 0, 0, 0, 0, 0, 1]),\n",
191 " array([ -2.59479443, 2.67371141, 7.94221725, 13.21072308,\n",
192 " 18.47922892, 23.74773476, 29.0162406 , 34.28474644,\n",
193 " 39.55325228, 44.82175812, 50.09026395]),\n",
194 " <a list of 10 Patch objects>)"
195 ]
196 },
197 {
198 "application/pdf": 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24ggOTv0ZOChW5CQOhp6pCtFoxOMinJW2MJfLS4uXTxQUDrF0LF4pFEq1kizJ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200 "output_type": "display_data",
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202 "text": [
203 "<matplotlib.figure.Figure at 0x10b0ecf10>"
204 ]
205 }
206 ],
207 "prompt_number": 9
208 },
209 {
210 "cell_type": "markdown",
186 "application/pdf": 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24ggOTv0ZOChW5CQOhp6pCtFoxOMinJW2MJfLS4uXTxQUDrF0LF4pFEq1kizJ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\nIDEgMCBSIC9TaXplIDIyID4+CnN0YXJ0eHJlZgo3Mzg0OQolJUVPRgo=\n",
187 "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJgAAABWCAYAAAAzIF/lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxCsMGOIVBgzxCgOGeIUBQ7z6LzWkj3n7AHKHAAAAAElFTkSuQmCC\n",
211 188 "metadata": {},
212 "source": [
213 "```python\n",
214 "def foo(bar=1):\n",
215 " \"\"\"docstring\"\"\"\n",
216 " raise Exception(\"message\")\n",
217 "```"
189 "output_type": "display_data",
190 "text/plain": [
191 "<matplotlib.figure.Figure at 0x10b0ecf10>"
218 192 ]
219 193 }
220 194 ],
221 "metadata": {}
195 "prompt_number": 9,
196 "source": [
197 "plt.hist(evs.real)"
198 ]
199 },
200 {
201 "cell_type": "markdown",
202 "metadata": {},
203 "source": [
204 "```python\n",
205 "def foo(bar=1):\n",
206 " \"\"\"docstring\"\"\"\n",
207 " raise Exception(\"message\")\n",
208 "```"
209 ]
222 210 }
223 ]
211 ],
212 "metadata": {},
213 "nbformat": 4,
214 "nbformat_minor": 0
224 215 } No newline at end of file
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@@ -1,96 +1,101 b''
1 1 """Python API for composing notebook elements
2 2
3 3 The Python representation of a notebook is a nested structure of
4 4 dictionary subclasses that support attribute access
5 5 (IPython.utils.ipstruct.Struct). The functions in this module are merely
6 6 helpers to build the structs in the right form.
7 7 """
8 8
9 9 # Copyright (c) IPython Development Team.
10 10 # Distributed under the terms of the Modified BSD License.
11 11
12 12 from IPython.utils.ipstruct import Struct
13 13
14 14 # Change this when incrementing the nbformat version
15 15 nbformat = 4
16 16 nbformat_minor = 0
17 17 nbformat_schema = 'nbformat.v4.schema.json'
18 18
19 19 def validate(node, ref=None):
20 20 """validate a v4 node"""
21 21 from ..current import validate
22 22 return validate(node, ref=ref, version=nbformat)
23 23
24 24 class NotebookNode(Struct):
25 25 pass
26 26
27 27 def from_dict(d):
28 28 if isinstance(d, dict):
29 29 newd = NotebookNode()
30 30 for k,v in d.items():
31 31 newd[k] = from_dict(v)
32 32 return newd
33 33 elif isinstance(d, (tuple, list)):
34 34 return [from_dict(i) for i in d]
35 35 else:
36 36 return d
37 37
38 38
39 39 def new_output(output_type, mime_bundle=None, **kwargs):
40 40 """Create a new output, to go in the ``cell.outputs`` list of a code cell."""
41 output = NotebookNode(output_type=output_type, **kwargs)
41 output = NotebookNode(output_type=output_type)
42 output.update(from_dict(kwargs))
42 43 if mime_bundle:
43 44 output.update(mime_bundle)
44 45 # populate defaults:
45 46 output.setdefault('metadata', NotebookNode())
46 47 if output_type == 'stream':
47 48 output.setdefault('name', 'stdout')
48 49 output.setdefault('text', '')
49 50 validate(output, output_type)
50 51 return output
51 52
52 53 def new_code_cell(source='', **kwargs):
53 54 """Create a new code cell"""
54 cell = NotebookNode(cell_type='code', source=source, **kwargs)
55 cell = NotebookNode(cell_type='code', source=source)
56 cell.update(from_dict(kwargs))
55 57 cell.setdefault('metadata', NotebookNode())
56 58 cell.setdefault('source', '')
57 59 cell.setdefault('prompt_number', None)
58 60 cell.setdefault('outputs', [])
59 61
60 62 validate(cell, 'code_cell')
61 63 return cell
62 64
63 65 def new_markdown_cell(source='', **kwargs):
64 66 """Create a new markdown cell"""
65 cell = NotebookNode(cell_type='markdown', source=source, **kwargs)
67 cell = NotebookNode(cell_type='markdown', source=source)
68 cell.update(from_dict(kwargs))
66 69 cell.setdefault('metadata', NotebookNode())
67 70
68 71 validate(cell, 'markdown_cell')
69 72 return cell
70 73
71 74 def new_heading_cell(source='', **kwargs):
72 75 """Create a new heading cell"""
73 cell = NotebookNode(cell_type='heading', source=source, **kwargs)
76 cell = NotebookNode(cell_type='heading', source=source)
77 cell.update(from_dict(kwargs))
74 78 cell.setdefault('metadata', NotebookNode())
75 79 cell.setdefault('level', 1)
76 80
77 81 validate(cell, 'heading_cell')
78 82 return cell
79 83
80 84 def new_raw_cell(source='', **kwargs):
81 85 """Create a new raw cell"""
82 cell = NotebookNode(cell_type='raw', source=source, **kwargs)
86 cell = NotebookNode(cell_type='raw', source=source)
87 cell.update(from_dict(kwargs))
83 88 cell.setdefault('metadata', NotebookNode())
84 89
85 90 validate(cell, 'raw_cell')
86 91 return cell
87 92
88 93 def new_notebook(**kwargs):
89 94 """Create a new notebook"""
90 nb = NotebookNode(**kwargs)
95 nb = from_dict(kwargs)
91 96 nb.nbformat = nbformat
92 97 nb.nbformat_minor = nbformat_minor
93 98 nb.setdefault('cells', [])
94 99 nb.setdefault('metadata', NotebookNode())
95 100 validate(nb)
96 101 return nb
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