From 79199729fa5f3d94e4002470b0b358ee2945fbf6 Mon Sep 17 00:00:00 2001 From: Kerem Turgutlu Date: Fri, 19 Dec 2025 13:18:28 +0300 Subject: [PATCH] catch errors before exec --- execnb/shell.py | 3 +- nbs/02_shell.ipynb | 226 ++++++++++++++++++++++++++++++++++++++------- 2 files changed, 194 insertions(+), 35 deletions(-) diff --git a/execnb/shell.py b/execnb/shell.py index 2b026ce..be60b72 100644 --- a/execnb/shell.py +++ b/execnb/shell.py @@ -73,7 +73,8 @@ def _run(self, raw_cell, store_history=False, silent=False, shell_futures=True, with capture_output(display=display, stdout=stdout and not verbose, stderr=stderr and not verbose) as c: result = super().run_cell(raw_cell, store_history, silent, shell_futures=shell_futures, cell_id=cell_id) return AttrDict(result=result, stdout='' if semic else c.stdout, stderr=c.stderr, - display_objects=c.outputs, exception=result.error_in_exec, quiet=semic) + display_objects=c.outputs, + exception=result.error_in_exec or result.error_before_exec, quiet=semic) def set_path(self, path): "Add `path` to python path, or `path.parent` if it's a file" diff --git a/nbs/02_shell.ipynb b/nbs/02_shell.ipynb index a05d868..96fd37a 100644 --- a/nbs/02_shell.ipynb +++ b/nbs/02_shell.ipynb @@ -3,6 +3,7 @@ { "cell_type": "code", "execution_count": null, + "id": "294f736b", "metadata": {}, "outputs": [], "source": [ @@ -12,6 +13,7 @@ }, { "cell_type": "markdown", + "id": "d00572b2", "metadata": {}, "source": [ "# shell\n", @@ -22,6 +24,7 @@ { "cell_type": "code", "execution_count": null, + "id": "535003cf", "metadata": {}, "outputs": [], "source": [ @@ -59,6 +62,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f5f6279b", "metadata": {}, "outputs": [], "source": [ @@ -70,6 +74,7 @@ }, { "cell_type": "markdown", + "id": "62042b9e", "metadata": {}, "source": [ "## CaptureShell -" @@ -78,6 +83,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6913382c", "metadata": {}, "outputs": [], "source": [ @@ -97,6 +103,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e3fb2bee", "metadata": {}, "outputs": [], "source": [ @@ -124,7 +131,8 @@ " with capture_output(display=display, stdout=stdout and not verbose, stderr=stderr and not verbose) as c:\n", " result = super().run_cell(raw_cell, store_history, silent, shell_futures=shell_futures, cell_id=cell_id)\n", " return AttrDict(result=result, stdout='' if semic else c.stdout, stderr=c.stderr,\n", - " display_objects=c.outputs, exception=result.error_in_exec, quiet=semic)\n", + " display_objects=c.outputs, \n", + " exception=result.error_in_exec or result.error_before_exec, quiet=semic)\n", " \n", " def set_path(self, path):\n", " \"Add `path` to python path, or `path.parent` if it's a file\"\n", @@ -138,6 +146,7 @@ { "cell_type": "code", "execution_count": null, + "id": "93adf867", "metadata": {}, "outputs": [], "source": [ @@ -159,6 +168,7 @@ { "cell_type": "code", "execution_count": null, + "id": "5fd515b4", "metadata": {}, "outputs": [], "source": [ @@ -168,8 +178,35 @@ { "cell_type": "code", "execution_count": null, + "id": "dc2a7c14", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "```python\n", + "{ 'display_objects': [],\n", + " 'exception': None,\n", + " 'quiet': False,\n", + " 'result': result: None; err: None; info: ,\n", + " 'stderr': '',\n", + " 'stdout': ''}\n", + "```" + ], + "text/plain": [ + "{'result': result: None; err: None; info: ,\n", + " 'stdout': '',\n", + " 'stderr': '',\n", + " 'display_objects': [],\n", + " 'exception': None,\n", + " 'quiet': False}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s.run_cell('a=1');" ] @@ -177,12 +214,13 @@ { "cell_type": "code", "execution_count": null, + "id": "c6bace5f", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", + "```python\n", "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': False,\n", @@ -213,12 +251,13 @@ { "cell_type": "code", "execution_count": null, + "id": "8006658b", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", + "```python\n", "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': False,\n", @@ -250,12 +289,13 @@ { "cell_type": "code", "execution_count": null, + "id": "42b54423", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", + "```python\n", "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': False,\n", @@ -286,13 +326,14 @@ { "cell_type": "code", "execution_count": null, + "id": "fb95968f", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", - "{ 'display_objects': [ ],\n", + "```python\n", + "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': False,\n", " 'result': result: ; err: None; info: ,\n", @@ -322,6 +363,7 @@ { "cell_type": "code", "execution_count": null, + "id": "24b04efe", "metadata": {}, "outputs": [ { @@ -345,6 +387,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e72dc4c9", "metadata": {}, "outputs": [ { @@ -368,12 +411,13 @@ { "cell_type": "code", "execution_count": null, + "id": "e5c9a6c2", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", + "```python\n", "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': True,\n", @@ -404,13 +448,14 @@ { "cell_type": "code", "execution_count": null, + "id": "68b196f0", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", - "{ 'display_objects': [ ],\n", + "```python\n", + "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': False,\n", " 'result': result: []; err: None; info: ,\n", @@ -440,6 +485,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0d83721a", "metadata": {}, "outputs": [ { @@ -460,11 +506,12 @@ { "cell_type": "code", "execution_count": null, + "id": "d425b8ca", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -486,12 +533,13 @@ { "cell_type": "code", "execution_count": null, + "id": "e6228c38", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", + "```python\n", "{ 'display_objects': [],\n", " 'exception': None,\n", " 'quiet': False,\n", @@ -532,6 +580,7 @@ { "cell_type": "code", "execution_count": null, + "id": "166d86be", "metadata": {}, "outputs": [ { @@ -592,12 +641,13 @@ { "cell_type": "code", "execution_count": null, + "id": "525cdb94", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "```json\n", + "```python\n", "{ 'display_objects': [],\n", " 'exception': ZeroDivisionError('division by zero'),\n", " 'quiet': False,\n", @@ -633,29 +683,51 @@ "o" ] }, + { + "cell_type": "markdown", + "id": "2d978d9a", + "metadata": {}, + "source": [ + "Testing errors caught after exec:" + ] + }, { "cell_type": "code", "execution_count": null, + "id": "5858155e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "TimeoutError()" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "o = s.run_cell('import time; time.sleep(2)', timeout=1)\n", - "o['exception']" + "test_eq(type(o['exception']), TimeoutError)" + ] + }, + { + "cell_type": "markdown", + "id": "f7aefa86", + "metadata": {}, + "source": [ + "Testing errors caught before exec:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58e485f0", + "metadata": {}, + "outputs": [], + "source": [ + "o = s.run_cell('print(', timeout=1)\n", + "test_eq(isinstance(o['exception'], SyntaxError), True)\n", + "o = s.run_cell(\"def foo():\\npass\")\n", + "test_eq(isinstance(o['exception'], IndentationError), True)\n", + "o = s.run_cell(\"if True:\\n\\tpass\\n pass\")\n", + "test_eq(isinstance(o['exception'], TabError), True)\n" ] }, { "cell_type": "markdown", + "id": "dfd6b6a9", "metadata": {}, "source": [ "### Cells / run" @@ -664,6 +736,7 @@ { "cell_type": "code", "execution_count": null, + "id": "a1eb7703", "metadata": {}, "outputs": [], "source": [ @@ -676,6 +749,7 @@ { "cell_type": "code", "execution_count": null, + "id": "8ba0ae59", "metadata": {}, "outputs": [], "source": [ @@ -686,6 +760,7 @@ { "cell_type": "code", "execution_count": null, + "id": "26bf9161", "metadata": {}, "outputs": [], "source": [ @@ -726,6 +801,7 @@ { "cell_type": "code", "execution_count": null, + "id": "242f732f", "metadata": {}, "outputs": [], "source": [ @@ -747,6 +823,7 @@ { "cell_type": "code", "execution_count": null, + "id": "db9afaa6", "metadata": {}, "outputs": [], "source": [ @@ -756,6 +833,7 @@ { "cell_type": "code", "execution_count": null, + "id": "125948f2", "metadata": {}, "outputs": [ { @@ -775,6 +853,7 @@ }, { "cell_type": "markdown", + "id": "769aeb73", "metadata": {}, "source": [ "Code can include magics and `!` shell commands:" @@ -783,6 +862,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6cb3fd41", "metadata": {}, "outputs": [ { @@ -810,6 +890,7 @@ }, { "cell_type": "markdown", + "id": "987c7f9d", "metadata": {}, "source": [ "The result of the last successful execution is stored in `result`:" @@ -818,6 +899,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6ac75a6c", "metadata": {}, "outputs": [ { @@ -837,6 +919,7 @@ }, { "cell_type": "markdown", + "id": "be9e444b", "metadata": {}, "source": [ "A trailing `;` stops the result from being captured:" @@ -845,6 +928,7 @@ { "cell_type": "code", "execution_count": null, + "id": "25dfd264", "metadata": {}, "outputs": [ { @@ -865,6 +949,7 @@ { "cell_type": "code", "execution_count": null, + "id": "60294a50", "metadata": {}, "outputs": [ { @@ -899,6 +984,7 @@ }, { "cell_type": "markdown", + "id": "3fea8ed4", "metadata": {}, "source": [ "This is how IPython formats exceptions internally:" @@ -907,6 +993,7 @@ { "cell_type": "code", "execution_count": null, + "id": "bf763821", "metadata": {}, "outputs": [], "source": [ @@ -916,6 +1003,7 @@ { "cell_type": "code", "execution_count": null, + "id": "06031a14", "metadata": {}, "outputs": [], "source": [ @@ -927,6 +1015,7 @@ { "cell_type": "code", "execution_count": null, + "id": "33fb2116", "metadata": {}, "outputs": [ { @@ -954,6 +1043,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ac097335", "metadata": {}, "outputs": [ { @@ -974,6 +1064,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c0e31a21", "metadata": {}, "outputs": [ { @@ -996,6 +1087,7 @@ { "cell_type": "code", "execution_count": null, + "id": "519841ce", "metadata": {}, "outputs": [ { @@ -1026,6 +1118,7 @@ { "cell_type": "code", "execution_count": null, + "id": "eaa11df9", "metadata": {}, "outputs": [], "source": [ @@ -1043,6 +1136,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0078cdda", "metadata": {}, "outputs": [ { @@ -1066,6 +1160,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f698a432", "metadata": {}, "outputs": [], "source": [ @@ -1108,6 +1203,7 @@ { "cell_type": "code", "execution_count": null, + "id": "567545b5", "metadata": {}, "outputs": [ { @@ -1139,6 +1235,7 @@ }, { "cell_type": "markdown", + "id": "db569c54", "metadata": {}, "source": [ "We can use `ansi2html` to convert from ANSI to HTML for color rendering. You need some [css styles](https://github.com/fastai/fastcore/blob/master/examples/ansi.css) for the colors to render properly. Jupyter already has these built in so it's not neccessary here, but if you plan on using this in another web app you will need to ensure that css styling is included." @@ -1147,6 +1244,7 @@ { "cell_type": "code", "execution_count": null, + "id": "5269f826", "metadata": {}, "outputs": [ { @@ -1178,6 +1276,7 @@ }, { "cell_type": "markdown", + "id": "48b6dce3", "metadata": {}, "source": [ "Images and matplotlib figures are captured:" @@ -1186,6 +1285,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ecec6fac", "metadata": {}, "outputs": [ { @@ -1212,6 +1312,7 @@ }, { "cell_type": "markdown", + "id": "9ad54512", "metadata": {}, "source": [ "If an exception is raised then the exception type, object, and stacktrace are stored in `exc`:" @@ -1220,6 +1321,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d31a62eb", "metadata": {}, "outputs": [ { @@ -1255,6 +1357,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7366a7e6", "metadata": {}, "outputs": [ { @@ -1275,6 +1378,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c6176b9e", "metadata": {}, "outputs": [], "source": [ @@ -1291,6 +1395,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d3fc3628", "metadata": {}, "outputs": [ { @@ -1331,6 +1436,7 @@ { "cell_type": "code", "execution_count": null, + "id": "158cbc4e", "metadata": {}, "outputs": [ { @@ -1356,6 +1462,7 @@ { "cell_type": "code", "execution_count": null, + "id": "008c0cef", "metadata": {}, "outputs": [], "source": [ @@ -1370,6 +1477,7 @@ { "cell_type": "code", "execution_count": null, + "id": "2029888b", "metadata": {}, "outputs": [ { @@ -1395,6 +1503,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f0dbb335", "metadata": {}, "outputs": [ { @@ -1415,6 +1524,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6e677414", "metadata": {}, "outputs": [], "source": [ @@ -1428,6 +1538,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1e34216e", "metadata": {}, "outputs": [ { @@ -1448,6 +1559,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c7352a2b", "metadata": {}, "outputs": [], "source": [ @@ -1461,6 +1573,7 @@ { "cell_type": "code", "execution_count": null, + "id": "b1e8f387", "metadata": {}, "outputs": [ { @@ -1481,6 +1594,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7d52326c", "metadata": {}, "outputs": [], "source": [ @@ -1493,6 +1607,7 @@ }, { "cell_type": "markdown", + "id": "69265ba0", "metadata": {}, "source": [ "### NBs -" @@ -1501,6 +1616,7 @@ { "cell_type": "code", "execution_count": null, + "id": "60a25f27", "metadata": {}, "outputs": [], "source": [ @@ -1529,6 +1645,7 @@ { "cell_type": "code", "execution_count": null, + "id": "801e6817", "metadata": {}, "outputs": [ { @@ -1549,6 +1666,7 @@ { "cell_type": "code", "execution_count": null, + "id": "92f08b1a", "metadata": {}, "outputs": [ { @@ -1573,6 +1691,7 @@ }, { "cell_type": "markdown", + "id": "6985dc41", "metadata": {}, "source": [ "With `exc_stop=False` (the default), execution continues after exceptions, and exception details are stored into the appropriate cell's output:" @@ -1581,6 +1700,7 @@ { "cell_type": "code", "execution_count": null, + "id": "20dcc0a6", "metadata": {}, "outputs": [ { @@ -1601,6 +1721,7 @@ { "cell_type": "code", "execution_count": null, + "id": "8685b93f", "metadata": {}, "outputs": [ { @@ -1634,6 +1755,7 @@ }, { "cell_type": "markdown", + "id": "09afaf8f", "metadata": {}, "source": [ "With `exc_stop=True`, exceptions in a cell are raised and no further processing occurs:" @@ -1642,6 +1764,7 @@ { "cell_type": "code", "execution_count": null, + "id": "832c25c0", "metadata": {}, "outputs": [ { @@ -1659,6 +1782,7 @@ }, { "cell_type": "markdown", + "id": "53154f9f", "metadata": {}, "source": [ "We can pass a function to `preproc` to have it run on every cell. It can modify the cell as needed. If the function returns `True`, then that cell will not be executed. For instance, to skip the cell which raises an exception:" @@ -1667,6 +1791,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7ded2410", "metadata": {}, "outputs": [], "source": [ @@ -1676,6 +1801,7 @@ }, { "cell_type": "markdown", + "id": "c33cd9eb", "metadata": {}, "source": [ "This cell will contain no output, since it was skipped." @@ -1684,6 +1810,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d5c52794", "metadata": {}, "outputs": [ { @@ -1704,6 +1831,7 @@ { "cell_type": "code", "execution_count": null, + "id": "b34f55f2", "metadata": {}, "outputs": [ { @@ -1727,6 +1855,7 @@ }, { "cell_type": "markdown", + "id": "d501502d", "metadata": {}, "source": [ "You can also pass a function to `postproc` to modify a cell after it is executed." @@ -1735,6 +1864,7 @@ { "cell_type": "code", "execution_count": null, + "id": "70808010", "metadata": {}, "outputs": [], "source": [ @@ -1763,6 +1893,7 @@ }, { "cell_type": "markdown", + "id": "82fac474", "metadata": {}, "source": [ "This is a shortcut for the combination of `read_nb`, `CaptureShell.run_all`, and `write_nb`." @@ -1771,6 +1902,7 @@ { "cell_type": "code", "execution_count": null, + "id": "085dc4ac", "metadata": {}, "outputs": [ { @@ -1792,6 +1924,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ae254c2f", "metadata": {}, "outputs": [], "source": [ @@ -1802,6 +1935,7 @@ { "cell_type": "code", "execution_count": null, + "id": "694161cb", "metadata": {}, "outputs": [], "source": [ @@ -1820,6 +1954,7 @@ }, { "cell_type": "markdown", + "id": "f2ab7602", "metadata": {}, "source": [ "If an error occurs while running a notebook, you can retrieve a pretty version of the error with the `prettytb` method: " @@ -1828,6 +1963,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7dff07b6", "metadata": {}, "outputs": [ { @@ -1869,6 +2005,7 @@ }, { "cell_type": "markdown", + "id": "cb6a1f25", "metadata": {}, "source": [ "### Tests -" @@ -1877,6 +2014,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e269789c", "metadata": {}, "outputs": [], "source": [ @@ -1894,6 +2032,7 @@ { "cell_type": "code", "execution_count": null, + "id": "8ab45961", "metadata": {}, "outputs": [], "source": [ @@ -1906,6 +2045,7 @@ { "cell_type": "code", "execution_count": null, + "id": "464fc090", "metadata": {}, "outputs": [], "source": [ @@ -1918,6 +2058,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c71b30b6", "metadata": {}, "outputs": [], "source": [ @@ -1935,6 +2076,7 @@ { "cell_type": "code", "execution_count": null, + "id": "be14cd69", "metadata": {}, "outputs": [], "source": [ @@ -1949,6 +2091,7 @@ { "cell_type": "code", "execution_count": null, + "id": "867dab53", "metadata": {}, "outputs": [], "source": [ @@ -1961,6 +2104,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d547dc83", "metadata": {}, "outputs": [], "source": [ @@ -1974,6 +2118,7 @@ { "cell_type": "code", "execution_count": null, + "id": "aa8ff46b", "metadata": {}, "outputs": [], "source": [ @@ -1988,6 +2133,7 @@ }, { "cell_type": "markdown", + "id": "1047fa89", "metadata": {}, "source": [ "## Params -" @@ -1995,6 +2141,7 @@ }, { "cell_type": "markdown", + "id": "f16127c9", "metadata": {}, "source": [ "If you pass `inject_code` to `CaptureShell.execute` or `CaptureShell.run_all`, the source of `nb.cells[inject_idx]` will be replaced with `inject_code`. By default, the first cell is replaced. For instance consider this notebook:" @@ -2003,6 +2150,7 @@ { "cell_type": "code", "execution_count": null, + "id": "bae2726b", "metadata": {}, "outputs": [ { @@ -2021,6 +2169,7 @@ }, { "cell_type": "markdown", + "id": "fc2f302a", "metadata": {}, "source": [ "We can replace the first cell with `a=2` by passing that as `inject_code`, and the notebook will run with that change:" @@ -2029,6 +2178,7 @@ { "cell_type": "code", "execution_count": null, + "id": "006785e8", "metadata": {}, "outputs": [ { @@ -2063,6 +2213,7 @@ }, { "cell_type": "markdown", + "id": "2b0328b5", "metadata": {}, "source": [ "This can be used with `CaptureShell.execute` to parameterise runs of models in notebooks. Place any defaults for configuration code needed in the first cell, and then when running `execute` pass in new parameters as needed in `inject_code`. To replace only some of the defaults, leave an empty cell as the second cell, and inject code using `inject_idx=1` to replace the empty second cell with code that overrides some of the defaults set in the first cell. When using `execute` you can pass `inject_path` instead of `inject_code` to read the injected code from a file." @@ -2070,6 +2221,7 @@ }, { "cell_type": "markdown", + "id": "b5137218", "metadata": {}, "source": [ "## cli -" @@ -2078,6 +2230,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1227c8b1", "metadata": {}, "outputs": [], "source": [ @@ -2099,6 +2252,7 @@ }, { "cell_type": "markdown", + "id": "f4ad7caf", "metadata": {}, "source": [ "This is the command-line version of `CaptureShell.execute`. Run `exec_nb -h` from the command line to see how to pass arguments. If you don't pass `dest` then the output notebook won't be saved; this is mainly useful for running tests." @@ -2106,6 +2260,7 @@ }, { "cell_type": "markdown", + "id": "47d208a2", "metadata": {}, "source": [ "## Completions -" @@ -2114,6 +2269,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c44963a0", "metadata": {}, "outputs": [], "source": [ @@ -2141,6 +2297,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f3b82fd2", "metadata": {}, "outputs": [], "source": [ @@ -2169,6 +2326,7 @@ { "cell_type": "code", "execution_count": null, + "id": "6256bf35", "metadata": {}, "outputs": [ { @@ -2193,6 +2351,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7d7c852c", "metadata": {}, "outputs": [], "source": [ @@ -2206,6 +2365,7 @@ { "cell_type": "code", "execution_count": null, + "id": "68f8d111", "metadata": {}, "outputs": [ { @@ -2228,6 +2388,7 @@ { "cell_type": "code", "execution_count": null, + "id": "95a4f195", "metadata": {}, "outputs": [ { @@ -2248,6 +2409,7 @@ }, { "cell_type": "markdown", + "id": "18579b12", "metadata": {}, "source": [ "## export -" @@ -2256,6 +2418,7 @@ { "cell_type": "code", "execution_count": null, + "id": "83fc7b03", "metadata": {}, "outputs": [], "source": [ @@ -2266,18 +2429,13 @@ { "cell_type": "code", "execution_count": null, + "id": "42e4790a", "metadata": {}, "outputs": [], "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, + "metadata": {}, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 }