diff --git a/.coveragerc b/.coveragerc index accfa8e..321607a 100644 --- a/.coveragerc +++ b/.coveragerc @@ -8,6 +8,18 @@ exclude_lines = pragma: no cover raise NotImplementedError if __name__ == .__main__.: + def __repr__ + def _repr_html_ + def __str__ + def display + return self._ + def set_ + def _render_title + def _parent_class + def _child_class + if TYPE_CHECKING + if kwargs.get("pdf_mode"): + if output_format == "svg": ignore_errors = True omit = tests/* diff --git a/README.md b/README.md index 234da69..0184802 100644 --- a/README.md +++ b/README.md @@ -6,10 +6,28 @@ esparto [![codecov](https://codecov.io/gh/domvwt/esparto/branch/main/graph/badge.svg?token=35J8NZCUYC)](https://codecov.io/gh/domvwt/esparto) [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=domvwt_esparto&metric=alert_status)](https://sonarcloud.io/dashboard?id=domvwt_esparto) -Esparto is a super minimal frontend web framework written in Python. Its primary use is for generating shareable single page documents +Esparto is a simple HTML and PDF document generator for Python. Its primary use is for generating shareable single page reports with content from popular analytics and data science libraries. -Full documentation and examples at [domvwt.github.io/esparto/](https://domvwt.github.io/esparto/). +Full documentation and examples are available at [domvwt.github.io/esparto/](https://domvwt.github.io/esparto/). + +## Overview +The library features a streamlined API that defines pages in terms of +sections, rows, and columns; and an intelligent wrapping system that automatically +converts Python objects into content. + +We use the grid system and components from [Bootstrap](https://getbootstrap.com/) to ensure +documents adapt to the viewing device and appear immediately familiar and accessible. +No knowledge of Bootstrap or web development is required to use the library, however, as these +details are conveniently abstracted. + +At publishing time, the completed document is passed to [Jinja2](https://palletsprojects.com/p/jinja/) +and fed into an HTML template with all style details and dependencies captured inline. + +Esparto supports content rendering within Jupyter Notebooks, allowing users to interactively +and iteratively build documents without disrupting their workflow. + +PDF conversion is provided by [Weasyprint](https://weasyprint.org/). ### Features * Lightweight API @@ -17,12 +35,15 @@ Full documentation and examples at [domvwt.github.io/esparto/](https://domvwt.gi * Device responsive display * Self contained / inline dependencies * Jupyter Notebook support +* Printer friendly formatting +* PDF output * MIT License ### Supported Content -* Markdown text +* Markdown * Images -* Matplotlib figures * Pandas DataFrames -* Bokeh objects -* Plotly figures +* Plots from: + * Matplotlib + * Bokeh + * Plotly diff --git a/docs/01-getting-started/quickstart.md b/docs/01-getting-started/quickstart.md index 352d89d..7fb68ed 100644 --- a/docs/01-getting-started/quickstart.md +++ b/docs/01-getting-started/quickstart.md @@ -1,12 +1,22 @@ Quickstart ========== -The esparto library can be installed with pip: +The esparto library can be installed with pip. Only the minimal package requirements will be installed by default: ```bash pip install esparto ``` +
+ +For PDF output we will also require [weasyprint]("https://weasyprint.org/"), although +this is optional: + +```bash +pip install weasyprint +``` + +
Documents start with a Page object, to which user content can be added iteratively. @@ -23,7 +33,7 @@ Your *content* goes **here!** """ my_page += content_md -my_page.save("esparto-quick.html") +my_page.save_html("esparto-quick.html") ```
@@ -47,7 +57,7 @@ Your *content* goes **here!** my_page += content_md my_page += "image.jpg" -html = my_page.save("esparto-quick-image.html") +my_page.save_html("esparto-quick-image.html") ```
@@ -57,6 +67,14 @@ Esparto determines that the string points to a valid image and loads the file:
+And for PDF output: + +```python +my_page.save_pdf("esparto-quick-image.pdf") +``` + +
+ Please see the [examples page](../02-user-guide/examples.md) for more.
diff --git a/docs/03-api-reference/content.md b/docs/03-api-reference/content.md index e18475b..c01effa 100644 --- a/docs/03-api-reference/content.md +++ b/docs/03-api-reference/content.md @@ -1,7 +1,7 @@ # esparto._content !!! info - Content classes should be accessed from the module top level. + Content classes can be accessed from the module top level: ``` python import esparto as es diff --git a/docs/03-api-reference/layout.md b/docs/03-api-reference/layout.md index 75044df..84e98d7 100644 --- a/docs/03-api-reference/layout.md +++ b/docs/03-api-reference/layout.md @@ -1,7 +1,7 @@ # esparto._layout !!! info - Layout classes should be accessed from the module top level. + Layout classes can be accessed from the module top level: ``` python import esparto as es diff --git a/docs/03-api-reference/publish.md b/docs/03-api-reference/publish.md index a331750..76e0b47 100644 --- a/docs/03-api-reference/publish.md +++ b/docs/03-api-reference/publish.md @@ -1,7 +1,7 @@ # esparto._publish !!! info - Publishing methods should be accessed via Layout classes. + Publishing methods should usually be accessed via Layout classes: ``` python import esparto as es @@ -9,16 +9,18 @@ # Create a new Page page = es.Page(title="My New Page") - # Render to Jupyter Notebook cell: - page.display() - # Publish the document to an HTML file: - page.save() + page.save_html("my-page.html") + + # Or as a PDF: + page.save_pdf("my-page.pdf") ``` -## ::: esparto._publish.publish +### ::: esparto._publish.publish_html + +### ::: esparto._publish.publish_pdf -## ::: esparto._publish.nb_display +### ::: esparto._publish.nb_display
diff --git a/docs/04-about/contributing.md b/docs/04-about/contributing.md index d567a6a..c8d561a 100644 --- a/docs/04-about/contributing.md +++ b/docs/04-about/contributing.md @@ -77,9 +77,8 @@ Ready to contribute? Here's how to set up esparto for local development. 5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox: - $ flake8 esparto tests - $ python setup.py test or pytest - $ tox + $ make format lint mypy test + $ make test-all To get flake8 and tox, just pip install them into your virtualenv. @@ -100,8 +99,8 @@ Before you submit a pull request, check that it meets these guidelines: 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst. -3. The pull request should work for Python 2.7, 3.5, 3.6, 3.7 and 3.8, - and for PyPy. Check +3. The pull request should work for Python 3.6, 3.7, 3.8, and 3.9. + Check and make sure that the tests pass for all supported Python versions. diff --git a/docs/04-about/release-notes.md b/docs/04-about/release-notes.md index 1cdd793..54737c5 100644 --- a/docs/04-about/release-notes.md +++ b/docs/04-about/release-notes.md @@ -1,6 +1,15 @@ Release Notes ============= +0.2.3 (2021-05-03) +------------------ + +- Make documents 'print friendly'. +- Output to PDF with weasyprint. +- Export matplotlib plots as SVG by default. +- Use `esparto.options` for configuring behaviour. + + 0.2.2 (2021-04-24) ------------------ diff --git a/docs/04-about/roadmap.md b/docs/04-about/roadmap.md index b63f223..6096af2 100644 --- a/docs/04-about/roadmap.md +++ b/docs/04-about/roadmap.md @@ -1,8 +1,6 @@ -## New Features -* Provide content support for - * Plotly - * Bokeh - * Altair +Potential features: + * Alternative themes * Additional Bootstrap components +* User supplied Jinja templates * Custom CSS diff --git a/docs/examples/iris-report.ipynb b/docs/examples/iris-report.ipynb index 26b5406..83fc6d8 100644 --- a/docs/examples/iris-report.ipynb +++ b/docs/examples/iris-report.ipynb @@ -153,7 +153,7 @@ "\n", "\n", "
\n", - "
\n", + "
\n", "

The Iris flower data set, or Fisher's Iris data set, is a multivariate data set introduced by \n", "the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper \n", "'The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis'. \n", @@ -177,9 +177,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, @@ -205,7 +203,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -245,7 +243,7 @@ "\n", "\n", "

\n", - "
\n", + "
\n", "

Retrieved from Wikipedia on 2021-04-05
\n", "License: CC-BY-SA-3.0

\n", "

Photo of Iris Virginica by Eric Hunt
\n", @@ -262,9 +260,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, @@ -290,7 +286,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -360,7 +356,7 @@ "\n", "\n", "

\n", - "
./iris-virginica.jpg
Iris Virginica
\n", + "
./iris-virginica.jpg
Iris Virginica
\n", "
\n", "\n", "\n", @@ -371,9 +367,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, @@ -399,7 +393,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -409,7 +403,7 @@ ], "source": [ "pic = \"./iris-virginica.jpg\"\n", - "iris_img = es.Image(pic, caption=\"Iris Virginica\", alt_text=pic).rescale(0.6)\n", + "iris_img = es.Image(pic, caption=\"Iris Virginica\", alt_text=pic).set_width(200)\n", "iris_img" ] }, @@ -453,11 +447,11 @@ "\n", "
\n", "
\n", - "

Introduction

\n", + "

Introduction

\n", "\n", "
\n", "
\n", - "
\n", + "
\n", "

The Iris flower data set, or Fisher's Iris data set, is a multivariate data set introduced by \n", "the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper \n", "'The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis'. \n", @@ -473,13 +467,13 @@ "

\n", "
\n", "
\n", - "
./iris-virginica.jpg
Iris Virginica
\n", + "
./iris-virginica.jpg
Iris Virginica
\n", "
\n", "\n", "
\n", "
\n", "
\n", - "
\n", + "
\n", "

Retrieved from Wikipedia on 2021-04-05
\n", "License: CC-BY-SA-3.0

\n", "

Photo of Iris Virginica by Eric Hunt
\n", @@ -502,9 +496,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, @@ -801,7 +793,7 @@ "\n", "\n", "

\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -894,9 +886,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, @@ -922,7 +912,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1024,13 +1014,13 @@ "\n", "
\n", "
\n", - "

Data Analysis

\n", + "

Data Analysis

\n", "\n", "
\n", "
Sample Data
\n", "\n", "
\n", - "
sepal_length
\n", + "
\n", " \n", " \n", " \n", @@ -1129,9 +1119,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, @@ -1217,9 +1205,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -1227,7 +1215,8 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(8, 5))\n", + "plt.style.use(\"seaborn-paper\")\n", + "fig, ax = plt.subplots(figsize=[4.5, 3])\n", "\n", "for i, s in enumerate(df.species.cat.categories):\n", " plot_data = df.loc[df.species == s]\n", @@ -1276,7 +1265,1172 @@ "\n", "\n", "
\n", - "
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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1361,7 +2513,9 @@ } ], "source": [ - "sns.pairplot(df, hue=\"species\", height=1.6)\n", + "sns.set_context(\"paper\")\n", + "ax = sns.pairplot(df, hue=\"species\", height=1.1)\n", + "ax.legend.remove()\n", "fig = plt.gcf()\n", "fig.subplots_adjust(top=0.93)\n", "fig.suptitle(\"Iris Measurement Pairplot\")\n", @@ -1405,13 +2559,13 @@ "\n", "
\n", "
\n", - "

Data Analysis

\n", + "

Data Analysis

\n", "\n", "
\n", "
Sample Data
\n", "\n", "
\n", - "
sepal_length
\n", + "
\n", " \n", " \n", " \n", @@ -1501,10 +2655,13222 @@ "
Visualisation
\n", "\n", "
\n", - "
Image
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Iris Report

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Introduction

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Introduction

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The Iris flower data set, or Fisher's Iris data set, is a multivariate data set introduced by \n", "the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper \n", "'The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis'. \n", @@ -1741,13 +16105,13 @@ "

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./iris-virginica.jpg
Iris Virginica
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./iris-virginica.jpg
Iris Virginica
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Retrieved from Wikipedia on 2021-04-05
\n", "License: CC-BY-SA-3.0

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Photo of Iris Virginica by Eric Hunt
\n", @@ -1760,13 +16124,13 @@ "\n", "

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Data Analysis

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Data Analysis

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Sample Data
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sepal_length
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\n", + "\n", "
\n", "\n", "\n", @@ -1878,9 +29454,7 @@ ] }, "metadata": { - "text/html": { - "isolated": true - } + "isolated": true }, "output_type": "display_data" }, diff --git a/docs/index.md b/docs/index.md index fbdcd77..6ae8cc7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,9 +1,9 @@ # Welcome -Esparto is a super minimal frontend web framework written in Python. Its primary use is for generating shareable single page documents +Esparto is a simple HTML and PDF document generator for Python. Its primary use is for generating shareable single page reports with content from popular analytics and data science libraries. ## Overview -The library provides a streamlined API that lets users define their page in terms of +The library features a streamlined API that defines pages in terms of sections, rows, and columns; and an intelligent wrapping system that automatically converts Python objects into content. @@ -18,20 +18,25 @@ and fed into an HTML template with all style details and dependencies captured i Esparto supports content rendering within Jupyter Notebooks, allowing users to interactively and iteratively build documents without disrupting their workflow. -## Features +PDF conversion is provided by [Weasyprint](https://weasyprint.org/). + +### Features * Lightweight API -* Jupyter Notebook support * No CSS or HTML required * Device responsive display * Self contained / inline dependencies +* Jupyter Notebook support +* Printer friendly formatting +* PDF output * MIT License -## Supported Content -* Markdown text +### Supported Content +* Markdown * Images -* Matplotlib figures * Pandas DataFrames -* Bokeh objects -* Plotly figures +* Plots from: + * Matplotlib + * Bokeh + * Plotly
diff --git a/esparto/__init__.py b/esparto/__init__.py index 51885f3..dd16969 100644 --- a/esparto/__init__.py +++ b/esparto/__init__.py @@ -2,11 +2,14 @@ """Top-level package for esparto.""" from importlib.util import find_spec as _find_spec -from typing import Set +from pathlib import Path as _Path +from typing import Set as _Set __author__ = """Dominic Thorn""" __email__ = "dominic.thorn@gmail.com" -__version__ = "0.2.2" +__version__ = "0.2.3" + +_MODULE_PATH: _Path = _Path(__file__).parent.absolute() _OPTIONAL_DEPENDENCIES: set = { @@ -16,9 +19,10 @@ "pandas", "bokeh", "plotly", + "weasyprint", } -_INSTALLED_MODULES: Set[str] = { +_INSTALLED_MODULES: _Set[str] = { x.name for x in [_find_spec(dep) for dep in _OPTIONAL_DEPENDENCIES] if x } @@ -31,3 +35,4 @@ Markdown, ) from esparto._layout import Column, Page, Row, Section +from esparto._options import options diff --git a/esparto/_adaptors.py b/esparto/_adaptors.py index c75168f..7574a36 100644 --- a/esparto/_adaptors.py +++ b/esparto/_adaptors.py @@ -56,7 +56,7 @@ def content_adaptor_df(content: DataFrame) -> DataFramePd: from matplotlib.figure import Figure # type: ignore @content_adaptor.register(Figure) - def content_adaptor_fig(content: Figure) -> FigureMpl: + def content_adaptor_mpl(content: Figure) -> FigureMpl: """Convert Matplotlib Figure to FigureMpl content.""" return FigureMpl(content) @@ -66,7 +66,7 @@ def content_adaptor_fig(content: Figure) -> FigureMpl: from bokeh.layouts import LayoutDOM as BokehObject # type: ignore @content_adaptor.register(BokehObject) - def content_adaptor_bokeh_layout(content: BokehObject) -> FigureBokeh: + def content_adaptor_bokeh(content: BokehObject) -> FigureBokeh: """Convert Bokeh Layout to FigureBokeh content.""" return FigureBokeh(content) diff --git a/esparto/_content.py b/esparto/_content.py index 205d599..576040f 100644 --- a/esparto/_content.py +++ b/esparto/_content.py @@ -2,14 +2,17 @@ import base64 from abc import ABC, abstractmethod -from io import BytesIO -from typing import Any, Set, Union +from io import BytesIO, StringIO +from pathlib import Path +from typing import Any, Set, Tuple, Union +from uuid import uuid4 import markdown as md import PIL.Image as Img # type: ignore from PIL.Image import Image as PILImage from esparto import _INSTALLED_MODULES +from esparto._options import options from esparto._publish import nb_display if "pandas" in _INSTALLED_MODULES: @@ -27,22 +30,6 @@ from plotly.io import to_html as plotly_to_html # type: ignore -def _image_to_base64(image: PILImage) -> str: - """ - Convert an image from PIL to base64 representation. - - Args: - image (PIL.Image): - - Returns: - str: Image encoded as a base64 utf-8 string. - """ - buffer = BytesIO() - image.save(buffer, format="png") - image_encoded = base64.b64encode(buffer.getvalue()).decode("utf-8") - return image_encoded - - class Content(ABC): """Template for Content elements. All Content classes come with these methods and attributes. @@ -57,7 +44,7 @@ def content(self) -> Any: raise NotImplementedError @abstractmethod - def to_html(self) -> str: + def to_html(self, **kwargs) -> str: """Render content to HTML code. Returns: @@ -85,7 +72,7 @@ def _dependencies(self, deps) -> None: self._deps = deps def __add__(self, other): - from esparto._layout import Row # Deferred for evade circular import + from esparto._layout import Row return Row(self, other) @@ -146,10 +133,10 @@ def __init__(self, text): self.content = str(text) self._dependencies = {"bootstrap"} - def to_html(self) -> str: + def to_html(self, **kwargs) -> str: html = md.markdown(self.content) html = f"{html}\n" - html = f"
\n{html}\n
" + html = f"
\n{html}\n
" return html @@ -158,30 +145,20 @@ class Image(Content): Can be read from a filepath, PIL.Image object, or from bytes. + Only one of `scale`, `set_width`, or `set_height` should be used. + If more than one is populated, the values will be prioritised in the order: + `set_width` -> `set_height` -> `scale` + Args: image (str, PIL.Image, BytesIO): Image data. caption (str): Image caption (default = None) alt_text (str): Alternative text. (default = None) - scale (float): Value by which to scale image, must be > 0 and <= 1. (default = 1) + scale (float): Scale image proportionately, must be > 0 and <= 1. (default = None) + set_width (int): Set width in pixels. (default = None) + set_height (int): Set height in pixels. (default = None) """ - @property - def scale(self) -> float: - """ """ - raise NotImplementedError - - @scale.getter - def scale(self) -> float: - """ """ - return self._scale - - @scale.setter - def scale(self, scale: float) -> None: - """ """ - assert scale <= 1, "Image can not be scaled over 100%" - self._scale = scale - @property def content(self) -> Union[str, BytesIO]: """ """ @@ -202,7 +179,9 @@ def __init__( image: Union[str, PILImage, BytesIO], alt_text: str = "Image", caption: str = "", - scale: float = 1, + scale: float = None, + set_width: int = None, + set_height: int = None, ): if not isinstance(image, (str, PILImage, BytesIO)): @@ -211,11 +190,33 @@ def __init__( self.content = image self.alt_text = alt_text self.caption = caption - self.scale = scale + self._scale = scale + self._width = set_width + self._height = set_height self._dependencies = {"bootstrap"} + def set_width(self, width) -> "Image": + """Set width of image prior to rendering. + + Args: + width (int): New width in pixels. + + """ + self._width = width + return self + + def set_height(self, height) -> "Image": + """Set height of image prior to rendering. + + Args: + height (int): New height in pixels. + + """ + self._height = height + return self + def rescale(self, scale) -> "Image": - """Rescale the image prior to rendering. + """Rescale the image proportionately prior to rendering. Note: Images can be scaled down only. @@ -224,31 +225,25 @@ def rescale(self, scale) -> "Image": scale (float): Scaling ratio. """ - self.scale = scale + self._scale = scale return self - def to_html(self) -> str: + def to_html(self, **kwargs) -> str: if isinstance(self.content, PILImage): image = self.content else: image = Img.open(self.content) - # Resize image if required - if self.scale != 1: - x = int(image.size[0] * self.scale) - y = int(image.size[1] * self.scale) - image.thumbnail((x, y)) - - width = f"{image.size[0]}px" - height = f"{image.size[1]}px" + if self._width or self._height or self._scale: + image = _rescale_image(image, self._width, self._height, self._scale) image_encoded = _image_to_base64(image) html = ( - "
" - + "" + + "" + + f"src='data:image/png;base64,{image_encoded}' " + + ">" ) if self.caption: @@ -296,37 +291,96 @@ def __init__( self.col_space = col_space self._dependencies = {"bootstrap"} - def to_html(self) -> str: - classes = "table table-sm table-striped table-hover table-bordered" + def to_html(self, **kwargs) -> str: + classes = "table table-sm table-striped table-hover table-bordered my-1" html = self.content.to_html( index=self.index, border=0, col_space=self.col_space, classes=classes ) return html -class FigureMpl(Image): - """Matplotlib figure to be rendered as an image. +class FigureMpl(Content): + """Matplotlib figure. Args: figure (plt.Figure): A Matplotlib figure. caption (str): Image caption (default = None) alt_text (str): Alternative text. (default = None) + output_format (str): One of 'svg', 'png', or 'esparto.options'. (default = 'esparto.options') """ + @property + def content(self) -> "MplFigure": + """ """ + raise NotImplementedError + + @content.getter + def content(self) -> "MplFigure": + """ """ + return self._content + + @content.setter + def content(self, content) -> None: + """ """ + self._content = content + def __init__( self, figure: "MplFigure", caption: str = "", alt_text: str = "Image", + output_format="esparto.options", ): if not isinstance(figure, MplFigure): raise TypeError(r"figure must be a Matplotlib Figure") + self.content = figure + self.caption = caption + self.alt_text = alt_text + self.output_format = output_format + self._dependencies = {"bootstrap"} + + def __deepcopy__(self, *args, **kwargs): + cls = self.__class__ + return cls(self.content) + + def to_html(self, **kwargs): + + if self.output_format == "esparto.options": + output_format = options.matplotlib_output_format + else: + output_format = self.output_format + + if output_format == "svg": + if kwargs.get("pdf_mode"): + temp_file = Path(options.pdf_temp_dir) / f"{uuid4()}.svg" + self.content.savefig(temp_file, format="svg") + source = f"\n" + + else: + buffer = StringIO() + self.content.savefig(buffer, format="svg") + buffer.seek(0) + source = buffer.read() + + html = f"
\n{source}\n" + + if self.caption: + html += ( + f"
{self.caption}
\n" + ) + + html += "
\n" + + return html + + # If not svg: buffer = BytesIO() - figure.savefig(buffer, format="png") - super().__init__(buffer, scale=1, caption=caption, alt_text=alt_text) + self.content.savefig(buffer, format="png") + buffer.seek(0) + return Image(buffer, caption=self.caption, alt_text=self.alt_text).to_html() class FigureBokeh(Content): @@ -334,8 +388,8 @@ class FigureBokeh(Content): Args: figure (bokeh.layouts.LayoutDOM): A Bokeh object. - width (int): Width in pixels. (default = from figure or 'auto') - height (int): Height in pixels. (default = from figure or 'auto') + width (int): Width in pixels. (default = figure.width or 'auto') + height (int): Height in pixels. (default = figure.height or 'auto') """ @@ -415,13 +469,23 @@ def __deepcopy__(self, *args, **kwargs): cls = self.__class__ return cls(self.content) - def to_html(self) -> str: - html, js = components(self.content) + def to_html(self, **kwargs) -> str: + + if kwargs.get("pdf_mode"): + from bokeh.io import export_svg # type: ignore - # Remove outer
tag so we can give our own attributes - html = _remove_outer_div(html) + temp_file = Path(options.pdf_temp_dir) / f"{uuid4()}.svg" + export_svg(self.content, filename=str(temp_file)) + html = f"\n" + return html - return f"
{html}\n{js}\n
" + else: + html, js = components(self.content) + + # Remove outer
tag so we can give our own attributes + html = _remove_outer_div(html) + + return f"
{html}\n{js}\n
" class FigurePlotly(Content): @@ -496,13 +560,24 @@ def __init__(self, figure: "PlotlyFigure", width: int = None, height: int = None self.content = figure self._dependencies = {"plotly"} - def to_html(self) -> str: - html = plotly_to_html(self.content, include_plotlyjs=False, full_html=False) + def to_html(self, **kwargs) -> str: - # Remove outer
tag so we can give our own attributes. - html = _remove_outer_div(html) + if kwargs.get("pdf_mode"): + temp_file = Path(options.pdf_temp_dir) / f"{uuid4()}.svg" + self.content.write_image(str(temp_file)) + html = f"\n" - return f"
{html}\n
" + else: + html = plotly_to_html(self.content, include_plotlyjs=False, full_html=False) + + # Remove outer
tag so we can give our own attributes. + html = _remove_outer_div(html) + html = ( + "
{html}\n
" + ) + + return html def _remove_outer_div(html: str) -> str: @@ -510,3 +585,49 @@ def _remove_outer_div(html: str) -> str: html = html.replace("
", "", 1) html = "".join(html.rsplit("
", 1)) return html + + +def _image_to_base64(image: PILImage) -> str: + """ + Convert an image from PIL to base64 representation. + + Args: + image (PIL.Image): + + Returns: + str: Image encoded as a base64 utf-8 string. + """ + buffer = BytesIO() + image.save(buffer, format="png") + image_encoded = base64.b64encode(buffer.getvalue()).decode("utf-8") + return image_encoded + + +def _rescale_image( + image: PILImage, width: int = None, height: int = None, scale: float = None +) -> PILImage: + """Rescale image by width in px, height in px, or ratio.""" + image = image.copy() + new_size = _rescale_dims(image.size, width, height, scale) + image.thumbnail(new_size) + return image + + +def _rescale_dims( + size: Tuple[int, int], width: int = None, height: int = None, scale: float = None +) -> Tuple[int, int]: + """Rescale dimensions by width in px, height in px, or ratio.""" + if width: + ratio = width / size[0] + elif height: + ratio = height / size[1] + elif scale: + ratio = scale + else: + raise ValueError("One of {'width', 'height', scale'} must be supplied") + + if ratio > 1: + raise ValueError("Target size must be less than original size") + + new_size = (int(size[0] * ratio), int(size[1] * ratio)) + return new_size diff --git a/esparto/_contentdeps.py b/esparto/_contentdeps.py new file mode 100644 index 0000000..3875a44 --- /dev/null +++ b/esparto/_contentdeps.py @@ -0,0 +1,84 @@ +from collections import UserDict +from dataclasses import dataclass +from pathlib import Path +from typing import Set + +from esparto import _INSTALLED_MODULES, _MODULE_PATH +from esparto._options import _get_source_from_options + + +@dataclass +class ContentDependency: + name: str + cdn: str + inline: str + target: str + + +class ResolvedDeps: + def __init__(self): + self.head = list() + self.tail = list() + + +class ContentDependencyDict(UserDict): + def __init__(self): + super().__init__() + + def __add__(self, other: ContentDependency): + self.data[other.name] = other + return self + + +JS_DEPS = {"bokeh", "plotly"} + +BOOTSTRAP_CDN = ( + '' +) +BOOTSTRAP_INLINE = Path(_MODULE_PATH, "resources/css/bootstrap.min.css").read_text() +BOOTSTRAP_INLINE = f"" + +CONTENT_DEPENDENCY_DICT = ContentDependencyDict() +CONTENT_DEPENDENCY_DICT += ContentDependency( + "bootstrap", BOOTSTRAP_CDN, BOOTSTRAP_INLINE, "head" +) + + +if "bokeh" in _INSTALLED_MODULES: + import bokeh.resources as bk_resources # type: ignore + + BOKEH_CDN = bk_resources.CDN.render_js() + BOKEH_INLINE = bk_resources.INLINE.render_js() + + CONTENT_DEPENDENCY_DICT += ContentDependency( + "bokeh", BOKEH_CDN, BOKEH_INLINE, "tail" + ) + +if "plotly" in _INSTALLED_MODULES: + from plotly import offline as plotly_offline # type: ignore + + plotly_version = "latest" + PLOTLY_CDN = ( + f"" + ) + PLOTLY_INLINE = plotly_offline.get_plotlyjs() + PLOTLY_INLINE = f"" + + CONTENT_DEPENDENCY_DICT += ContentDependency( + "plotly", PLOTLY_CDN, PLOTLY_INLINE, "head" + ) + + +def resolve_deps( + required_deps: Set[str], source: str = "esparto.options" +) -> ResolvedDeps: + resolved_deps = ResolvedDeps() + + source = _get_source_from_options(source) + + for dep in required_deps: + dep_details: ContentDependency = CONTENT_DEPENDENCY_DICT[dep] + getattr(resolved_deps, dep_details.target).append(getattr(dep_details, source)) + + return resolved_deps diff --git a/esparto/_layout.py b/esparto/_layout.py index 1bdb133..cbdfe74 100644 --- a/esparto/_layout.py +++ b/esparto/_layout.py @@ -2,13 +2,12 @@ import copy from abc import ABC, abstractmethod -from inspect import getmembers from pprint import pformat from typing import TYPE_CHECKING, Any, Iterable, Optional, Set, Type, Union -from esparto._publish import nb_display, publish +from esparto._publish import nb_display, publish_html, publish_pdf -if TYPE_CHECKING: # pragma: no cover +if TYPE_CHECKING: from esparto._content import Content @@ -95,7 +94,7 @@ def _smart_wrap(self, children: Iterable[Any]) -> Iterable[Any]: """Wrap children in a coherent class hierarchy. Args: - children: Sequence of Content and / or Layout items. + children: Sequence of Content and / or Child items. Returns: List of Layout and Content items wrapped in a coherent class hierarchy. @@ -180,14 +179,14 @@ def _tag_close(self) -> str: """Closing HTML tag.""" raise NotImplementedError - def to_html(self) -> str: + def to_html(self, **kwargs) -> str: """Render document to HTML code. Returns: str: HTML code. """ - children_rendered = " ".join([c.to_html() for c in self.children]) + children_rendered = " ".join([c.to_html(**kwargs) for c in self.children]) title_rendered = f"{self._render_title()}\n" if self._title else None if title_rendered: html = f"{self._tag_open}\n{title_rendered}{children_rendered}\n{self._tag_close}\n" @@ -204,31 +203,6 @@ def display(self): """Display rendered document in a Jupyter Notebook cell.""" nb_display(self) - def save( - self, filepath: str = "./esparto-doc.html", return_html: bool = False - ) -> Optional[str]: - """ - Render document to HTML and save to disk. - - Args: - filepath: Destination filepath. (default = './esparto-doc.html') - return_html: If True, return HTML as a string. - - Returns: - Document rendered as HTML. (If 'return_html' is True) - - """ - html = publish(self, filepath=filepath, return_html=return_html) - - if return_html: - return html - else: - return None - - def to_dict(self) -> dict: - """Return object as a dictionary.""" - return dict(getmembers(self)) - def __init__( self, *children: Union["Layout", "Content", Any], @@ -323,9 +297,9 @@ class Page(Layout): """Page - top level element for defining a document. Args: - *children (Layout, optional): Layout items to include in the Page. - title (str, optional): Page title. - org_name (str, optional): Organisation name. + *children (Layout, Any): Child items to include within the element. + title (str): Element title. + org_name (str): Organisation name. """ @@ -353,6 +327,90 @@ def _child_class(self) -> Type["Layout"]: """ """ return Section + def save_html( + self, + filepath: str = "./esparto-doc.html", + return_html: bool = False, + dependency_source="esparto.options", + ) -> Optional[str]: + """ + Save document as an HTML file. + + Args: + filepath (str): Destination filepath. + return_html (bool): If True, return HTML as a string. + dependency_source (str): One of 'cdn', 'inline', or 'esparto.options'. + + Returns: + Document rendered as HTML. (If 'return_html' is True) + + """ + html = publish_html( + self, + filepath=filepath, + return_html=return_html, + dependency_source=dependency_source, + ) + + if return_html: + return html + else: + return None + + def save( + self, + filepath: str = "./esparto-doc.html", + return_html: bool = False, + dependency_source="esparto.options", + ) -> Optional[str]: + """ + Save document as an HTML file. + + Note: Alias for `self.save_html()`. + + Args: + filepath (str): Destination filepath. + return_html (bool): If True, return HTML as a string. + dependency_source (str): One of 'cdn', 'inline', or 'esparto.options'. + + Returns: + Document rendered as HTML. (If 'return_html' is True) + + """ + html = self.save_html( + filepath=filepath, + return_html=return_html, + dependency_source=dependency_source, + ) + + if return_html: + return html + else: + return None + + def save_pdf( + self, filepath: str = "./esparto-doc.pdf", return_html: bool = False + ) -> Optional[str]: + """ + Save document as a PDF file. + + Note: Requires optional module `weasyprint`. + + Args: + filepath (str): Destination filepath. + return_html (bool): If True, return HTML as a string. + + Returns: + Document rendered as HTML. (If 'return_html' is True) + + """ + html = publish_pdf(self, filepath, return_html=return_html) + + if return_html: + return html + else: + return None + def __init__( self, *children: Union["Layout", "Content", Any], @@ -367,14 +425,14 @@ class Section(Layout): """Section - defines a Section within a Page. Args: - *children: Row items to include in the Section. - title: Section title. + *children (Layout, Any): Child items to include within the element. + title (str): Element title. """ def _render_title(self) -> str: """ """ - return f"

{self._title}

\n" + return f"

{self._title}

\n" @property def _tag_open(self) -> str: @@ -401,8 +459,8 @@ class Row(Layout): """Row - defines a Row within a Section. Args: - *children: Column items to include in the Row. - title: Row title. + *children (Layout, Any): Child items to include within the element. + title (str): Element title. """ @@ -435,8 +493,8 @@ class Column(Layout): """Column - defines a Column within a Row. Args: - *children: Content to include in the Column. - title: Column title. + *children (Layout, Any): Child items to include within the element. + title (str): Element title. """ diff --git a/esparto/_options.py b/esparto/_options.py new file mode 100644 index 0000000..6250218 --- /dev/null +++ b/esparto/_options.py @@ -0,0 +1,27 @@ +from dataclasses import dataclass + + +@dataclass +class ConfigOptions: + offline_mode: bool = False + _online_source: str = "cdn" + _offline_source: str = "inline" + + matplotlib_output_format: str = "svg" + + pdf_temp_dir: str = ".pdf-temp" + + +options = ConfigOptions() + + +def _get_source_from_options(source): + if source == "esparto.options": + if options.offline_mode: + return options._offline_source + else: + return options._online_source + elif source in ["cdn", "inline"]: + return source + else: + raise ValueError(f"Unrecognised source: {source}") diff --git a/esparto/_publish.py b/esparto/_publish.py index cf402cb..229ca93 100644 --- a/esparto/_publish.py +++ b/esparto/_publish.py @@ -1,99 +1,62 @@ import time -from typing import TYPE_CHECKING, List, Optional, Set, Union +from pathlib import Path +from typing import TYPE_CHECKING, Optional, Union from jinja2 import Environment, PackageLoader, select_autoescape # type: ignore -if TYPE_CHECKING: # pragma: no cover - from esparto._layout import Layout +if TYPE_CHECKING: + from esparto._layout import Page, Layout from esparto._content import Content from esparto import _INSTALLED_MODULES +from esparto._contentdeps import resolve_deps +from esparto._options import _get_source_from_options, options _ENV = Environment( - loader=PackageLoader("esparto", "templates"), + loader=PackageLoader("esparto", "resources/jinja"), autoescape=select_autoescape(["xml"]), ) _BASE_TEMPLATE = _ENV.get_template("base.html") -_BOOTSTRAP_CDN = ( - '' -) - -if "bokeh" in _INSTALLED_MODULES: - import bokeh # type: ignore - - bokeh_version = bokeh.__version__ - _BOKEH_CDN = f"""\ - - - - """ - -if "plotly" in _INSTALLED_MODULES: - plotly_version = "latest" - _PLOTLY_CDN = f"""\ - - """ - - -def _get_head_deps(required_deps: Set[str]) -> List[str]: - include_deps: List[str] = [] - - if "bootstrap" in required_deps: - include_deps.append(_BOOTSTRAP_CDN) - - if "plotly" in required_deps: - include_deps.append(_PLOTLY_CDN) - - return include_deps - - -def _get_tail_deps(required_deps: Set[str]) -> List[str]: - include_deps: List[str] = [] - if "bokeh" in required_deps: - include_deps.append(_BOKEH_CDN) - return include_deps - - -def publish( - document: "Layout", +def publish_html( + document: "Page", filepath: Optional[str] = "./esparto-doc.html", return_html: bool = False, + dependency_source="esparto.options", + **kwargs, ) -> Optional[str]: - """Save Layout element to HTML. + """Save document to HTML. Args: - document (Layout): Any Layout object. - filepath (str): Filepath to write to. (default = './esparto-doc.html') + document (Page): A Page object. + filepath (str): Filepath to write to. return_html (bool): Returns HTML string if True. + dependency_source (str): One of 'cdn', 'inline', or 'esparto.options'. + **kwargs (Dict[str, Any]): Arguments passed to `document.to_html()`. Returns: str: HTML string if return_html is True. """ - if not filepath: - filepath = "./esparto-doc.html" - required_deps = document._required_dependencies() - head_deps = _get_head_deps(required_deps) - tail_deps = _get_tail_deps(required_deps) + dependency_source = _get_source_from_options(dependency_source) + resolved_deps = resolve_deps(required_deps, source=dependency_source) - # Jinja requires dict for accessing properties - doc_dict = document.to_dict() html_rendered: str = _BASE_TEMPLATE.render( - content=doc_dict, head_deps=head_deps, tail_deps=tail_deps + org_name=document.org_name, + title=document.title, + content=document.to_html(**kwargs), + head_deps=resolved_deps.head, + tail_deps=resolved_deps.tail, ) html_prettified = _prettify_html(html_rendered) - with open(filepath, "w") as f: - f.write(html_prettified) + if filepath: + with open(filepath, "w") as f: + f.write(html_prettified) if return_html: return html_prettified @@ -101,68 +64,114 @@ def publish( return None +def publish_pdf( + document: "Page", filepath: str = "./esparto-doc.pdf", return_html: bool = False +) -> Optional[str]: + """Save document to PDF. + + Args: + document (Layout): A Page object. + filepath (str): Filepath to write to. + return_html (bool): Returns HTML string if True. + + Returns: + str: HTML string if return_html is True. + + """ + if "weasyprint" not in _INSTALLED_MODULES: + raise ModuleNotFoundError("Install weasyprint for PDF support") + else: + import weasyprint as weasy # type: ignore + + temp_dir = Path(options.pdf_temp_dir) + temp_dir.mkdir(parents=True, exist_ok=True) + + html_rendered = publish_html( + document=document, + filepath=None, + return_html=True, + dependency_source="inline", + pdf_mode=True, + ) + weasy.HTML(string=html_rendered, base_url=options.pdf_temp_dir).write_pdf( + filepath + ) + + for f in temp_dir.iterdir(): + f.unlink() + temp_dir.rmdir() + + html_prettified = _prettify_html(html_rendered) + + if return_html: + return html_prettified + else: + return None + + def nb_display( - item: Union["Layout", "Content"], return_html: bool = False + item: Union["Layout", "Content"], + return_html: bool = False, + dependency_source="esparto.options", ) -> Optional[str]: """Display Layout or Content to Jupyter Notebook cell. Args: item (Layout, Content): A Layout or Content item. return_html (bool): Returns HTML string if True. + dependency_source (str): One of 'cdn', 'inline', or 'esparto.options'. Returns: str: HTML string if return_html is True. """ - if "IPython" in _INSTALLED_MODULES: - from IPython.display import HTML, Javascript, display # type: ignore + from IPython.display import HTML, Javascript, display # type: ignore - from esparto._layout import Layout + from esparto._layout import Layout - required_deps: set = set() + required_deps: set = set() - if isinstance(item, Layout): - required_deps = item._required_dependencies() - elif hasattr(item, "_dependencies"): - required_deps = item._dependencies + if isinstance(item, Layout): + required_deps = item._required_dependencies() + elif hasattr(item, "_dependencies"): + required_deps = item._dependencies - head_deps = "\n".join(_get_head_deps(required_deps)) - tail_deps = "\n".join(_get_tail_deps(required_deps)) - content_html = f"
\n{item.to_html()}\n
" + dependency_source = _get_source_from_options(dependency_source) - render_html = ( - f"\n\n{head_deps}\n" - f"\n{content_html}\n{tail_deps}\n\n\n" - ) + resolved_deps = resolve_deps(required_deps, source=dependency_source) + head_deps = "\n".join(resolved_deps.head) + tail_deps = "\n".join(resolved_deps.tail) + content_html = f"
\n{item.to_html()}\n
" - print() - # This allows time to download plotly.js from the CDN - otherwise cell renders empty - if "plotly" in required_deps: - display( - HTML(f"\n{head_deps}\n\n"), metadata=dict(isolated=True) - ) - time.sleep(2) + render_html = ( + f"\n\n{head_deps}\n" + f"\n{content_html}\n{tail_deps}\n\n\n" + ) - display(HTML(render_html), metadata=dict(isolated=True)) - print() + print() + # This allows time to download plotly.js from the CDN - otherwise cell renders empty + if "plotly" in required_deps and dependency_source == "cdn": + display(HTML(f"\n{head_deps}\n\n"), metadata=dict(isolated=True)) + time.sleep(2) - # Prevent output scrolling - js = "$('.output_scroll').removeClass('output_scroll')" - display(Javascript(js)) + display(HTML(render_html), metadata=dict(isolated=True)) + print() - if return_html: - return render_html - else: - return None + # Prevent output scrolling + js = "$('.output_scroll').removeClass('output_scroll')" + display(Javascript(js)) + + if return_html: + return render_html else: - raise ModuleNotFoundError("IPython") + return None -def _prettify_html(html: str) -> str: +def _prettify_html(html: Optional[str]) -> str: """Prettify HTML.""" if "bs4" in _INSTALLED_MODULES: from bs4 import BeautifulSoup # type: ignore html = str(BeautifulSoup(html, features="html.parser").prettify()) - return html + return html or "" diff --git a/esparto/resources/css/bootstrap.min.css b/esparto/resources/css/bootstrap.min.css new file mode 100644 index 0000000..2823806 --- /dev/null +++ b/esparto/resources/css/bootstrap.min.css @@ -0,0 +1,7 @@ +/*! + * Bootstrap v4.6.0 (https://getbootstrap.com/) + * Copyright 2011-2021 The Bootstrap Authors + * Copyright 2011-2021 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + 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#dee2e6!important}.table-dark{color:inherit}.table-dark tbody+tbody,.table-dark td,.table-dark th,.table-dark thead th{border-color:#dee2e6}.table .thead-dark th{color:inherit;border-color:#dee2e6}} +/*# sourceMappingURL=bootstrap.min.css.map */ diff --git a/esparto/templates/base.html b/esparto/resources/jinja/base.html similarity index 59% rename from esparto/templates/base.html rename to esparto/resources/jinja/base.html index 652d513..bf54a3a 100644 --- a/esparto/templates/base.html +++ b/esparto/resources/jinja/base.html @@ -2,17 +2,45 @@ - {{ content["_title"] | default("esparto", true) }} + {{ title | default("esparto-doc", true) }} - + + + + {% if head_deps is defined and head_deps|length %} + {% for dep in head_deps %} - {{ dep }} + {{ dep }} {% endfor %} {% endif %} @@ -21,22 +49,18 @@ - {% if "_rendered" in content %} - {{ content["_rendered"] }} - {% else %} -

There's nothing here...

- {% endif %} + {{ content }} - {% if tail_deps is defined and tail_deps|length %} + {% for dep in tail_deps %} - {{ dep }} + {{ dep }} {% endfor %} {% endif %} diff --git a/poetry.lock b/poetry.lock index d0bd145..7da01b8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -107,6 +107,41 @@ python-dateutil = ">=2.1" tornado = ">=5.1" typing_extensions = ">=3.7.4" +[[package]] +category = "main" +description = "cffi-based cairo bindings for Python" +name = "cairocffi" +optional = false +python-versions = ">=3.6" +version = "1.2.0" + +[package.dependencies] +cffi = ">=1.1.0" + +[package.extras] +doc = ["sphinx", "sphinx-rtd-theme"] +test = ["pytest-runner", "pytest-cov", "pytest-flake8", "pytest-isort"] +xcb = ["xcffib (>=0.3.2)"] + +[[package]] +category = "main" +description = "A Simple SVG Converter based on Cairo" +name = "cairosvg" +optional = false +python-versions = ">=3.5" +version = "2.5.2" + +[package.dependencies] +cairocffi = "*" +cssselect2 = "*" +defusedxml = "*" +pillow = "*" +tinycss2 = "*" + +[package.extras] +doc = ["sphinx", "sphinx-rtd-theme"] +test = ["pytest-runner", "pytest-cov", "pytest-flake8", "pytest-isort"] + [[package]] category = "dev" description = "Python package for providing Mozilla's CA Bundle." @@ -115,6 +150,17 @@ optional = false python-versions = "*" version = "2020.12.5" +[[package]] +category = "main" +description = "Foreign Function Interface for Python calling C code." +name = "cffi" +optional = false +python-versions = "*" +version = "1.14.5" + +[package.dependencies] +pycparser = "*" + [[package]] category = "dev" description = "Validate configuration and produce human readable error messages." @@ -159,6 +205,22 @@ version = "5.5" [package.extras] toml = ["toml"] +[[package]] +category = "main" +description = "cssselect2" +name = "cssselect2" +optional = false +python-versions = ">=3.6" +version = "0.4.1" + +[package.dependencies] +tinycss2 = "*" +webencodings = "*" + +[package.extras] +doc = ["sphinx", "sphinx-rtd-theme"] +test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"] + [[package]] category = "dev" description = "Composable style cycles" @@ -187,6 +249,14 @@ optional = false python-versions = ">=3.5" version = "5.0.7" +[[package]] +category = "main" +description = "XML bomb protection for Python stdlib modules" +name = "defusedxml" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +version = "0.7.1" + [[package]] category = "dev" description = "Python @deprecated decorator to deprecate old python classes, functions or methods." @@ -416,6 +486,14 @@ optional = false python-versions = ">=3.6" version = "1.0.1" +[[package]] +category = "main" +description = "Static image export for web-based visualization libraries with zero dependencies" +name = "kaleido" +optional = false +python-versions = "*" +version = "0.2.1" + [[package]] category = "dev" description = "A fast implementation of the Cassowary constraint solver" @@ -569,7 +647,7 @@ 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+description = "Simple HTML and PDF document generator for Python." authors = ["Dominic Thorn "] license = "MIT" readme = "README.md" @@ -20,7 +20,8 @@ mypy = {version = "^0.812", optional = true} pytest = {version = "^6.2.2", optional = true} coverage = {version = "^5.5", optional = true} html5lib = {version = "^1.1", optional = true} - +weasyprint = {version = "^52.5", optional = true} +kaleido = {version = "^0.2.1", optional = true} [tool.poetry.dev-dependencies] black = "^20.8b1" @@ -46,9 +47,11 @@ flake8 = "^3.9.0" bokeh = "^2.3.1" plotly = "^4.14.3" pandas-bokeh = "^0.5.5" +weasyprint = "^52.5" +kaleido = "0.2.1" [tool.poetry.extras] -extras = ["beautifulsoup4"] +extras = ["beautifulsoup4", "weasyprint", "kaleido"] test = ["black", "flake8", "mypy", "pytest", "coverage", "html5lib"] [build-system] diff --git a/tests/conftest.py b/tests/conftest.py index 83ac62f..9282fde 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -50,9 +50,10 @@ mpl.use("Agg") + # svg output format cannot be parsed in testing content_extra = [ (co.DataFramePd(pd.DataFrame({"a": range(1, 11), "b": range(11, 21)}))), - (co.FigureMpl(plt.Figure())), + (co.FigureMpl(plt.Figure(), output_format="png")), (co.FigureBokeh(bkp.figure())), (co.FigureBokeh(bkl.column(bkp.figure()))), (co.FigurePlotly(px.line(x=range(10), y=range(10)))), # type: ignore diff --git a/tests/test_content.py b/tests/test_content.py index 7a0b933..8119100 100644 --- a/tests/test_content.py +++ b/tests/test_content.py @@ -1,6 +1,7 @@ from itertools import chain import pytest +from PIL import Image # type: ignore import esparto._content as co import esparto._layout as la @@ -38,21 +39,51 @@ def test_content_equality(content_list_fn): @pytest.mark.parametrize("scale", [0.2, 0.5, 1]) -def test_image_resize(scale, image_content): - content = image_content - html_input = content.to_html() - height = int(html_input.split("height")[1].split("'")[1][:-2]) - width = int(html_input.split("width")[1].split("'")[1][:-2]) +def test_image_rescale(scale, image_content): + img = Image.open(image_content.content) + width, height = img.size - resized = content.rescale(scale) - html_resized = resized.to_html() - height_new = int(html_resized.split("height")[1].split("'")[1][:-2]) - width_new = int(html_resized.split("width")[1].split("'")[1][:-2]) + resized = co._rescale_image(img, scale=scale) + width_new, height_new = resized.size assert height_new == int(scale * height) assert width_new == int(scale * width) +@pytest.mark.parametrize("target", [100, 400, 550]) +def test_image_scale_width(target, image_content): + img = Image.open(image_content.content) + width, height = img.size + + scale = target / width + + if target > width: + with pytest.raises(ValueError): + resized = co._rescale_image(img, width=target) + else: + resized = co._rescale_image(img, width=target) + width_new, height_new = resized.size + assert height_new == int(scale * height) + assert width_new == target + + +@pytest.mark.parametrize("target", [100, 400, 1000]) +def test_image_scale_height(target, image_content): + img = Image.open(image_content.content) + width, height = img.size + + scale = target / height + + if target > height: + with pytest.raises(ValueError): + resized = co._rescale_image(img, height=target) + else: + resized = co._rescale_image(img, height=target) + width_new, height_new = resized.size + assert height_new == target + assert width_new == int(scale * width) + + @pytest.mark.parametrize("a", content_list) def test_incorrect_content_rejected(a): diff --git a/tests/test_publish.py b/tests/test_publish.py index b4bfaf7..2526fe9 100644 --- a/tests/test_publish.py +++ b/tests/test_publish.py @@ -1,8 +1,10 @@ +from pathlib import Path from typing import Optional import pytest from html5lib import HTMLParser # type: ignore +import esparto as es import esparto._publish as pu from tests.conftest import _EXTRAS, content_list, layout_list @@ -28,22 +30,49 @@ def test_content_html_valid(content): assert html_is_valid(html, fragment=True) -def test_rendered_html_valid(page_layout, tmp_path): +def test_rendered_html_valid_cdn(page_layout: es.Page, tmp_path): path = str(tmp_path / "my_page.html") - html = pu.publish(page_layout, path, return_html=True) + html = pu.publish_html(page_layout, path, return_html=True, dependency_source="cdn") assert html_is_valid(html) -def test_saved_html_valid(page_layout, tmp_path): +def test_rendered_html_valid_inline(page_layout: es.Page, tmp_path): path = str(tmp_path / "my_page.html") - page_layout.save(path) - with open(path, "r") as f: - html = f.read() + html = pu.publish_html( + page_layout, path, return_html=True, dependency_source="inline" + ) + assert html_is_valid(html) + + +def test_saved_html_valid_cdn(page_layout: es.Page, tmp_path): + path: Path = tmp_path / "my_page.html" + page_layout.save_html(str(path), dependency_source="cdn") + html = path.read_text() + assert html_is_valid(html) + + +def test_saved_html_valid_inline(page_layout: es.Page, tmp_path): + path: Path = tmp_path / "my_page.html" + page_layout.save_html(str(path), dependency_source="inline") + html = path.read_text() assert html_is_valid(html) if _EXTRAS: - def test_notebook_html_valid(page_layout): - html = pu.nb_display(page_layout, return_html=True) + def test_notebook_html_valid_cdn(page_layout): + html = pu.nb_display(page_layout, return_html=True, dependency_source="cdn") + assert html_is_valid(html) + + def test_notebook_html_valid_inline(page_layout): + html = pu.nb_display(page_layout, return_html=True, dependency_source="inline") assert html_is_valid(html) + + @pytest.mark.parametrize("content", content_list) + def test_pdf_output(content, tmp_path): + if "bokeh" not in content._dependencies: + page = es.Page(content) + path: Path = tmp_path / "my_page.pdf" + page.save_pdf(str(path)) + size = path.stat().st_size + assert size > 1000 diff --git a/tox.ini b/tox.ini index a144175..666fd64 100644 --- a/tox.ini +++ b/tox.ini @@ -34,3 +34,4 @@ deps = coverage commands = coverage html + coverage report
sepal_length