def test_config_from_metadata():
"""Test extraction of configuration data from notebook metadata."""
notebook = create_notebook()
notebook.metadata[META_KEY] = {
"diff_ignore": ["/", "/cells/*/outputs"],
"diff_replace": [
["/cells/0/outputs/0", r"\d{2,4}-\d{1,2}-\d{1,2}", "DATE-STAMP"]
],
}
notebook.cells.extend(
[
prepare_cell({"metadata": {}}),
prepare_cell({"metadata": {META_KEY: {"diff_ignore": ["/", "/outputs"]}}}),
prepare_cell(
{"metadata": {META_KEY: {"diff_replace": [["/source", "s", "p"]]}}}
),
]
)
config = config_from_metadata(notebook)
assert config == MetadataConfig(
diff_replace=(
("/cells/0/outputs/0", r"\d{2,4}-\d{1,2}-\d{1,2}", "DATE-STAMP"),
("/cells/2/source", "s", "p"),
),
diff_ignore={"/", "/cells/*/outputs", "/cells/1/", "/cells/1/outputs"},
)
def test_regex_replace_nb_source():
"""Test regex replacing notebook source."""
notebook = create_notebook()
notebook.cells.extend(
[
prepare_cell(
{"metadata": {}, "outputs": [], "source": ["print(1)\n", "print(2)"]}
),
prepare_cell(
{"metadata": {}, "outputs": [], "source": ["print(1)\n", "print(2)"]}
),
]
)
new_notebook = regex_replace_nb(
notebook, [("/cells/0/source", "p", "s"), ("/cells/1/source", "p", "t")]
)
new_notebook.nbformat_minor = None
assert mapping_to_dict(new_notebook) == {
"cells": [
{"metadata": {}, "outputs": [], "source": "srint(1)\nsrint(2)"},
{"metadata": {}, "outputs": [], "source": "trint(1)\ntrint(2)"},
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": None,
}
def test_beautifulsoup(data_regression):
"""Test applying beautifulsoup to html outputs."""
notebook = create_notebook()
notebook.cells.extend([
prepare_cell({
"cell_type":
"code",
"execution_count":
1,
"metadata": {},
"outputs": [{
"data": {
"text/html": [
"\n",
'<div class="section" id="submodules">\n',
' <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>\n', # noqa: E501
"</div>",
],
"text/plain": ["<IPython.core.display.HTML object>"],
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result",
}],
"source": [""],
}),
prepare_cell({
"cell_type":
"code",
"execution_count":
2,
"metadata": {},
"outputs": [{
"data": {
"image/svg+xml": [
'<svg height="100" width="100">\n',
' <circle cx="50" cy="50" fill="red" r="40" stroke="black" stroke-width="3"/></svg>\n', # noqa: E501
"",
],
"text/plain": ["<IPython.core.display.SVG object>"],
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result",
}],
"source": [""],
}),
])
new_notebook, resources = beautifulsoup(notebook, {})
assert resources["beautifulsoup"] == [
"/cells/0/outputs/0/text/html",
"/cells/1/outputs/0/image/svg+xml",
]
output = mapping_to_dict(new_notebook)
output["nbformat_minor"] = 2
data_regression.check(output)
def test_gather_paths_filter_str():
"""Test gathering paths in the notebook, filtering by only str objects."""
notebook = create_notebook()
notebook.cells.extend(
[
prepare_cell(
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{"name": "stdout", "output_type": "stream", "text": ["hallo\n"]}
],
"source": ["print('hallo')\n"],
}
)
]
)
paths = []
gather_json_paths(notebook, paths, types=(str,))
assert paths == [
("cells", 0, "cell_type"),
("cells", 0, "outputs", 0, "name"),
("cells", 0, "outputs", 0, "output_type"),
("cells", 0, "outputs", 0, "text"),
("cells", 0, "source"),
]
def test_gather_paths():
"""Test gathering paths in the notebook."""
notebook = create_notebook()
notebook.cells.extend(
[
prepare_cell(
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{"name": "stdout", "output_type": "stream", "text": ["hallo\n"]}
],
"source": ["print('hallo')\n"],
}
)
]
)
paths = []
gather_json_paths(notebook, paths)
assert paths == [
("nbformat",),
("nbformat_minor",),
("cells", 0, "cell_type"),
("cells", 0, "execution_count"),
("cells", 0, "outputs", 0, "name"),
("cells", 0, "outputs", 0, "output_type"),
("cells", 0, "outputs", 0, "text"),
("cells", 0, "source"),
]
def test_regex_replace_nb_output():
"""Test regex replacing notebook output."""
notebook = create_notebook()
notebook.cells.extend([
prepare_cell({
"metadata": {},
"outputs": [{
"name": "stdout",
"output_type": "stream",
"text": ["2019-08-11\n"],
}],
"source": ["from datetime import date\n", "print(date.today())"],
})
])
new_notebook = regex_replace_nb(
notebook,
[("/cells/0/outputs/0", r"\d{2,4}-\d{1,2}-\d{1,2}", "DATE-STAMP")])
output = mapping_to_dict(new_notebook)
output.pop("nbformat_minor")
assert output == {
"cells": [{
"metadata": {},
"outputs": [{
"name": "stdout",
"output_type": "stream",
"text": ["DATE-STAMP\n"],
}],
"source":
"from datetime import date\nprint(date.today())",
}],
"metadata": {},
"nbformat":
4,
}
def test_regex_replace_nb_no_change():
"""Test that, if no replacements are made, the notebook remains the same."""
notebook = create_notebook()
notebook.cells.extend(
[prepare_cell({"metadata": {}, "outputs": [], "source": ["print(1)\n"]})]
)
new_notebook = regex_replace_nb(notebook, [])
assert notebook == new_notebook
def test_coalesce_streams():
"""Test coalesce_streams if streams require merging."""
notebook = create_notebook()
notebook.cells.append(
prepare_cell({
"cell_type":
"code",
"execution_count":
3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": ["hallo1\n"]
},
{
"name": "stdout",
"output_type": "stream",
"text": ["hallo2\n"]
},
],
"source":
"".join(["print('hallo1')\n", "print('hallo2')"]),
}))
expected = create_notebook()
expected.cells.append(
prepare_cell({
"cell_type":
"code",
"execution_count":
3,
"metadata": {},
"outputs": [{
"name": "stdout",
"output_type": "stream",
"text": ["hallo1\nhallo2\n"],
}],
"source":
"".join(["print('hallo1')\n", "print('hallo2')"]),
}))
new_notebook, _ = coalesce_streams(notebook, {})
assert new_notebook == expected
def test_regex_replace_nb_with_wildcard():
"""Test regex replacing notebook values."""
notebook = create_notebook()
notebook.cells.extend([
prepare_cell({
"metadata": {},
"outputs": [],
"source": ["print(1)\n", "print(2)"]
}),
prepare_cell({
"metadata": {},
"outputs": [],
"source": ["print(1)\n", "print(2)"]
}),
])
new_notebook = regex_replace_nb(notebook, [("/cells/*/source", "p", "s"),
("/cells/0/source", "t", "y")])
output = mapping_to_dict(new_notebook)
output.pop("nbformat_minor")
assert output == {
"cells": [
{
"metadata": {},
"outputs": [],
"source": "sriny(1)\nsriny(2)"
},
{
"metadata": {},
"outputs": [],
"source": "srint(1)\nsrint(2)"
},
],
"metadata": {},
"nbformat":
4,
}
def test_blacken_code(data_regression):
"""Test blacken unformatted code."""
notebook = create_notebook()
notebook.cells.append(
prepare_cell({
"cell_type":
"code",
"execution_count":
1,
"metadata": {},
"outputs": [],
"source":
textwrap.dedent("""\
for i in range(5):
x=i
a ='123'# comment
"""),
}))
new_notebook, _ = blacken_code(notebook, {})
output = mapping_to_dict(new_notebook)
output["nbformat_minor"] = 2
data_regression.check(output)
def get_test_cell(type_name, variable="hallo"):
cells = {
"stdout": {
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{"name": "stdout", "output_type": "stream", "text": [f"{variable}\n"]}
],
"source": "".join(["# code cell + stdout\n", f"print('{variable}')"]),
},
"stderr": {
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{"name": "stderr", "output_type": "stream", "text": [f"{variable}\n"]}
],
"source": [
"# code cell + stderr\n",
f"print('{variable}', file=sys.stderr)",
],
},
"stdout_stderr": {
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{"name": "stdout", "output_type": "stream", "text": ["hallo2\n"]},
{"name": "stderr", "output_type": "stream", "text": [f"{variable}\n"]},
],
"source": [
"# code cell + stderr + stdout\n",
"print('hallo1', file=sys.stderr)\n",
"print('hallo2')",
],
},
"text/latex": {
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [f"\\textit{{{variable}}}"],
"text/plain": ["<IPython.core.display.Latex object>"],
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result",
}
],
"source": [
"# code cell + latex\n",
f"display.Latex('\\\\textit{{{variable}}}')",
],
},
"text/html": {
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
'<div class="section" id="submodules">\n',
f' <h2>{variable}<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>\n', # noqa: E501
"</div>",
],
"text/plain": ["<IPython.core.display.HTML object>"],
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result",
}
],
"source": [
"# code cell + html\n",
'display.HTML("""\n',
'<div class="section" id="submodules">\n',
f' <h2>{variable}<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>\n', # noqa: E501
'</div>""")',
],
},
"application/json": {
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"application/json": {
"a": [1, 2, 3, 4],
"b": {"inner1": f"{variable}", "inner2": "foobar"},
},
"text/plain": ["<IPython.core.display.JSON object>"],
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result",
}
],
"source": [
"# code cell + json\n",
(
f"display.JSON({{'a': [1, 2, 3, 4,], 'b': "
f"{{'inner1': '{variable}', 'inner2': 'foobar'}}}})"
),
],
},
"image/png": {
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n", # noqa: E501
"text/plain": ["<IPython.core.display.Image object>"],
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result",
}
],
"source": [
"from IPython.display import Image\n",
'Image(filename="128x128.png")',
],
},
"image/png_altered": {
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n", # noqa: E501
"text/plain": ["<IPython.core.display.Image object>"],
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result",
}
],
"source": [
"from IPython.display import Image\n",
'Image(filename="128x128_altered.png")',
],
},
"image/jpeg": {
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": ["<IPython.core.display.Image object>"],
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result",
}
],
"source": ['Image(filename="128x128.jpg")'],
},
"image/jpeg_altered": {
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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# noqa: E501
"text/plain": ["<IPython.core.display.Image object>"],
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result",
}
],
"source": ['Image(filename="128x128_altered.jpg")'],
},
}
return prepare_cell(cells[type_name])