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convert coalescese_streams function to CoalesceStreamsPreprocessor
`coalescese_streams` was the last remaining "decorated function Preprocessor", and I couldn't find an example of how to use it. Here it is converted to be a Preprocessor subclass, like the others. A top-level --coalesce-streams flag is also added.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,81 +1,44 @@ | ||
| """Preprocessor for merging consecutive stream outputs for easier handling.""" | ||
| import re | ||
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| # Copyright (c) IPython Development Team. | ||
| # Distributed under the terms of the Modified BSD License. | ||
| from nbconvert.preprocessors import Preprocessor | ||
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| import functools | ||
| import re | ||
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| from traitlets.log import get_logger | ||
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| def cell_preprocessor(function): | ||
| """ | ||
| Wrap a function to be executed on all cells of a notebook | ||
| The wrapped function should have these parameters: | ||
| cell : NotebookNode cell | ||
| Notebook cell being processed | ||
| resources : dictionary | ||
| Additional resources used in the conversion process. Allows | ||
| preprocessors to pass variables into the Jinja engine. | ||
| index : int | ||
| Index of the cell being processed | ||
| """ | ||
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| @functools.wraps(function) | ||
| def wrappedfunc(nb, resources): | ||
| get_logger().debug("Applying preprocessor: %s", function.__name__) | ||
| for index, cell in enumerate(nb.cells): | ||
| nb.cells[index], resources = function(cell, resources, index) | ||
| return nb, resources | ||
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| return wrappedfunc | ||
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| CR_PAT = re.compile(r".*\r(?=[^\n])") | ||
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| cr_pat = re.compile(r".*\r(?=[^\n])") | ||
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| @cell_preprocessor | ||
| def coalesce_streams(cell, resources, index): | ||
| class CoalesceStreamsPreprocessor(Preprocessor): | ||
| """ | ||
| Merge consecutive sequences of stream output into single stream | ||
| to prevent extra newlines inserted at flush calls | ||
| Parameters | ||
| ---------- | ||
| cell : NotebookNode cell | ||
| Notebook cell being processed | ||
| resources : dictionary | ||
| Additional resources used in the conversion process. Allows | ||
| transformers to pass variables into the Jinja engine. | ||
| index : int | ||
| Index of the cell being processed | ||
| """ | ||
|
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| outputs = cell.get("outputs", []) | ||
| if not outputs: | ||
| def preprocess_cell(self, cell, resources, cell_index): | ||
| """ | ||
| Apply a transformation on each cell. See base.py for details. | ||
| """ | ||
| outputs = cell.get("outputs", []) | ||
| if not outputs: | ||
| return cell, resources | ||
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| last = outputs[0] | ||
| new_outputs = [last] | ||
| for output in outputs[1:]: | ||
| if ( | ||
| output.output_type == "stream" | ||
| and last.output_type == "stream" | ||
| and last.name == output.name | ||
| ): | ||
| last.text += output.text | ||
| else: | ||
| new_outputs.append(output) | ||
| last = output | ||
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| # process \r characters | ||
| for output in new_outputs: | ||
| if output.output_type == "stream" and "\r" in output.text: | ||
| output.text = CR_PAT.sub("", output.text) | ||
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| cell.outputs = new_outputs | ||
| return cell, resources | ||
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| last = outputs[0] | ||
| new_outputs = [last] | ||
| for output in outputs[1:]: | ||
| if ( | ||
| output.output_type == "stream" | ||
| and last.output_type == "stream" | ||
| and last.name == output.name | ||
| ): | ||
| last.text += output.text | ||
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| else: | ||
| new_outputs.append(output) | ||
| last = output | ||
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| # process \r characters | ||
| for output in new_outputs: | ||
| if output.output_type == "stream" and "\r" in output.text: | ||
| output.text = cr_pat.sub("", output.text) | ||
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| cell.outputs = new_outputs | ||
| return cell, resources |
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