diff --git a/capellambse/diagram/capstyle.py b/capellambse/diagram/capstyle.py index 931ca28c6..15662b830 100644 --- a/capellambse/diagram/capstyle.py +++ b/capellambse/diagram/capstyle.py @@ -141,25 +141,20 @@ class in the form:: The type can be: ``Box``, ``Edge``. The style class can be any known style class. """ + if "." not in objectclass: + raise ValueError(f"Malformed objectclass: {objectclass}") + if "symbol" in objectclass.lower(): return {} - if ( - objectclass not in STYLES["__GLOBAL__"] - and diagramclass - and objectclass not in STYLES.get(diagramclass, {}) - ): - LOGGER.debug( - "No default style for %r in %r", objectclass, diagramclass - ) - try: - retval = STYLES["__GLOBAL__"][objectclass].copy() - except KeyError: - retval = {} - - if diagramclass: - retval.update(STYLES.get(diagramclass, {}).get(objectclass, {})) - return retval + obj_type: str = objectclass.split(".", 1)[0] + styles = { + **STYLES["__GLOBAL__"].get(obj_type, {}), + **STYLES.get(diagramclass or "", {}).get(obj_type, {}), + **STYLES["__GLOBAL__"].get(objectclass, {}), + **STYLES.get(diagramclass or "", {}).get(objectclass, {}), + } + return styles #: This dict maps the color names used by Capella to RGB tuples. @@ -295,6 +290,8 @@ class in the form:: }, "Edge": { "stroke-width": 1, + "fill": None, + "stroke": COLORS["black"], }, "Edge.Connector": { "stroke": RGB(176, 176, 176), diff --git a/capellambse/model/__init__.py b/capellambse/model/__init__.py index 601f16143..88e70f76f 100644 --- a/capellambse/model/__init__.py +++ b/capellambse/model/__init__.py @@ -102,7 +102,7 @@ def __init__( | None ) = None, diagram_cache_subdir: str | pathlib.PurePosixPath | None = None, - jupyter_untrusted: bool = False, + jupyter_untrusted: bool | None = None, fallback_render_aird: bool = False, **kwargs: t.Any, ) -> None: @@ -234,12 +234,23 @@ def __init__( capellambse.filehandler.http.HTTPFileHandler : A simple ``http(s)://`` file handler. """ + import warnings + + if jupyter_untrusted is not None: + warnings.warn( + ( + "The 'jupyter_untrusted' argument is no longer needed and" + " will be removed soon. Please remove it from your calls." + ), + DeprecationWarning, + stacklevel=2, + ) + capellambse.load_model_extensions() self._constructed = False self._loader = loader.MelodyLoader(path, **kwargs) self._fallback_render_aird = fallback_render_aird - self.jupyter_untrusted = jupyter_untrusted try: self._pvext = capellambse.pvmt.load_pvmt_from_model(self._loader) diff --git a/capellambse/model/diagram.py b/capellambse/model/diagram.py index 7ea3c3b07..28e42642d 100644 --- a/capellambse/model/diagram.py +++ b/capellambse/model/diagram.py @@ -184,10 +184,6 @@ def _repr_mimebundle_( if not mime or mime not in include or mime in exclude: continue - # XXX Hack to fix diagram previews on Github-rendered notebooks - if self._model.jupyter_untrusted and mime != "image/png": - continue - formats[mime] = conv if not formats: return None diff --git a/capellambse/svg/drawing.py b/capellambse/svg/drawing.py index f2c852e66..8c29eb705 100644 --- a/capellambse/svg/drawing.py +++ b/capellambse/svg/drawing.py @@ -16,6 +16,7 @@ from capellambse import diagram from capellambse import helpers as chelpers +from capellambse.diagram import capstyle from . import decorations, generate, helpers, style, symbols @@ -44,17 +45,24 @@ class Drawing: """The main container that stores all svg elements.""" def __init__(self, metadata: generate.DiagramMetadata): + base_css = ( + "shape-rendering: geometricPrecision;" + " font-family: 'Segoe UI';" + " font-size: 8pt;" + " cursor: pointer;" + ) superparams = { "filename": f"{metadata.name}.svg", "size": metadata.size, "viewBox": metadata.viewbox, + "style": base_css, } if metadata.class_: superparams["class_"] = re.sub(r"\s+", "", metadata.class_) self.__drawing = drawing.Drawing(**superparams) self.diagram_class = metadata.class_ - self.stylesheet = self.make_stylesheet() + self.add_static_decorations() self.add_backdrop(pos=metadata.pos, size=metadata.size) self.obj_cache: dict[str | None, t.Any] = {} @@ -93,17 +101,13 @@ def add_backdrop( ) self.__drawing.add(self.__backdrop) - def make_stylesheet(self) -> style.SVGStylesheet: - """Return created stylesheet and add sheet and decorations to defs.""" - stylesheet = style.SVGStylesheet(class_=self.diagram_class or "") - self.__drawing.defs.add(stylesheet.sheet) - for name in stylesheet.static_deco: - self.__drawing.defs.add(decorations.deco_factories[name]()) - - for grad in stylesheet.yield_gradients(): - self.__drawing.defs.add(grad) + def add_static_decorations(self) -> None: + static_deco = style.STATIC_DECORATIONS[ + "__GLOBAL__" + ] + style.STATIC_DECORATIONS.get(self.diagram_class or "", ()) - return stylesheet + for name in static_deco: + self.__drawing.defs.add(decorations.deco_factories[name]()) def __repr__(self) -> str: return self.__drawing._repr_svg_() @@ -249,45 +253,12 @@ def _draw_feature_text( def _draw_box_label(self, builder: LabelBuilder) -> container.Group: """Draw label text on given object and return label position.""" - x, text_height, _, y_margin = self._draw_label(builder) + _, _, _, y_margin = self._draw_label(builder) if DEBUG: assert builder.label is not None assert y_margin is not None - debug_y = builder.label["y"] + y_margin - debug_y1 = ( - builder.label["y"] - + (builder.label["height"] - decorations.icon_size) / 2 - ) - x = ( - builder.label["x"] - + decorations.icon_size - + 2 * decorations.icon_padding - ) - if text_height >= decorations.icon_size: - debug_height = text_height - else: - debug_height = decorations.icon_size - - bbox: LabelDict = { - "x": ( - x - if builder.text_anchor == "start" - else x - builder.label["width"] / 2 - ), - "y": debug_y if debug_y <= debug_y1 else debug_y1, - "width": builder.label["width"], - "height": debug_height, - } - labelstyle = style.Styling( - self.diagram_class, - "Box", - stroke="rgb(239, 41, 41)", - fill="none", - ) - self._draw_label_bbox( - bbox, builder.group, "Box", obj_style=labelstyle - ) + self._draw_circle( center_=(builder.label["x"], builder.label["y"]), radius_=3, @@ -390,11 +361,10 @@ def add_port( parent_id: str | None, *, class_: str, - obj_style: style.Styling | None = None, + obj_style: style.Styling, label: LabelDict | None = None, id_: str | None = None, ) -> container.Group: - styles = None if obj_style is None else obj_style[""] grp = self.__drawing.g(class_=f"Box {class_}", id_=id_) if class_ in decorations.all_directed_ports: port_id = "#ErrorSymbol" @@ -403,7 +373,6 @@ def add_port( elif class_ in decorations.component_ports: port_id = "#ComponentPortSymbol" - grp.style = styles grp.add( self.__drawing.use( href=port_id, @@ -413,6 +382,7 @@ def add_port( pos, size, class_, parent_id ), class_=class_, + style=obj_style, ) ) else: @@ -424,7 +394,7 @@ def add_port( transform=self.get_port_transformation( pos, size, class_, parent_id ), - style=styles, + style=obj_style, ) ) @@ -451,38 +421,49 @@ def draw_object(self, obj: cabc.Mapping[str, t.Any]) -> None: obj The (decoded) JSON-dict of a single diagram object. """ + mobj = copy.deepcopy(obj) + del obj + type_mapping: dict[str, tuple[t.Any, str]] = { + "box": (self._draw_box, "Box"), + "edge": (self._draw_edge, "Edge"), + "circle": (self._draw_circle, "Edge"), + "symbol": (self._draw_symbol, "Box"), + "box_symbol": (self._draw_box_symbol, "Box"), + } + try: - drawfunc: t.Any = getattr(self, f'_draw_{obj["type"]}') - except AttributeError: - raise ValueError(f'Invalid object type: {obj["type"]}') from None + drawfunc, style_type = type_mapping[mobj["type"]] + except KeyError: + raise ValueError(f"Invalid object type: {mobj['type']}") from None - mobj = copy.deepcopy(obj) objparams = { - f"{k}_": v for k, v in obj.items() if k not in {"type", "style"} + f"{k}_": v for k, v in mobj.items() if k not in {"type", "style"} } - if obj["class"] in decorations.all_ports: + if mobj["class"] in decorations.all_ports: mobj["type"] = "box" # type: ignore[index] - class_: str = mobj["type"].capitalize() + ( - f".{mobj['class']}" if "class" in obj else "" + class_: str = style_type + ( + f".{mobj['class']}" if "class" in mobj else "" ) + my_styles: dict[str, t.Any] = { + **capstyle.get_style(self.diagram_class, class_), + **mobj.get("style", {}), + } obj_style = style.Styling( self.diagram_class, class_, - **{k: v for k, v in obj.get("style", {}).items() if "_" not in k}, + **{k: v for k, v in my_styles.items() if "_" not in k}, ) text_style = style.Styling( self.diagram_class, class_, "text", **{ - k[len("text_") :]: v - for k, v in obj.get("style", {}).items() - if k.startswith("text_") + k[5:]: v for k, v in my_styles.items() if k.startswith("text_") }, ) - self.obj_cache[mobj["id"]] = obj + self.obj_cache[mobj["id"]] = mobj drawfunc(**objparams, obj_style=obj_style, text_style=text_style) @@ -553,7 +534,7 @@ def _draw_symbol( text_style: style.Styling, **kw: t.Any, ): - del obj_style, context_ # FIXME Add context to SVG + del context_ # FIXME Add context to SVG del kw # Dismiss additional info from json assert isinstance(label_, (dict, type(None))) pos = (x_ + 0.5, y_ + 0.5) @@ -566,6 +547,7 @@ def _draw_symbol( text_style, parent_, class_=class_, + obj_style=obj_style, label=label_, id_=id_, ) @@ -577,6 +559,7 @@ def _draw_symbol( insert=pos, size=size, class_=class_, + style=obj_style, ) ) @@ -687,7 +670,8 @@ def _draw_circle( ): del text_style # No label for circles center_ = tuple(i + 0.5 for i in center_) - + obj_style.fill = obj_style.stroke or diagram.RGB(0, 0, 0) + del obj_style.stroke grp = self.__drawing.g(class_=f"Circle {class_}", id_=id_) grp.add( self.__drawing.circle( @@ -719,7 +703,6 @@ def _draw_edge( # Received text space doesn't allow for anything else than the text for label_ in labels_: label_["class"] = "Annotation" - self._draw_label_bbox(label_, grp, "AnnotationBB") self._draw_edge_label( label_, grp, @@ -764,35 +747,6 @@ def _draw_edge_label( return group - def _draw_label_bbox( - self, - label: LabelDict, - group: container.Group | None = None, - class_: str | None = None, - obj_style: style.Styling | None = None, - ) -> None: - """Draw a bounding box for given label.""" - if DEBUG: - if obj_style is not None: - setattr(obj_style, "stroke", "rgb(239, 41, 41);") - else: - obj_style = style.Styling( - self.diagram_class, - "AnnotationBB", - stroke="rgb(239, 41, 41);", - ) - - bbox = self.__drawing.rect( - insert=(label["x"], label["y"]), - size=(label["width"], label["height"]), - class_=class_, - style=obj_style, - ) - if group is None: - return bbox - - return group.add(bbox) - def _draw_line( self, data: dict[str, float | int], diff --git a/capellambse/svg/style.py b/capellambse/svg/style.py index ddb34a514..769ccbcb9 100644 --- a/capellambse/svg/style.py +++ b/capellambse/svg/style.py @@ -4,22 +4,14 @@ from __future__ import annotations import collections.abc as cabc -import io import itertools import logging import operator import re -import textwrap import typing as t -from lxml import etree -from svgwrite import base, gradients - from capellambse import diagram -if t.TYPE_CHECKING: - from .drawing import Drawing - logger = logging.getLogger(__name__) RE_ELMCLASS = re.compile(r"^([A-Z][a-z_]*)(\.[A-Za-z][A-Za-z0-9_]*)?(:.+)?$") CUSTOM_STYLE_ATTRS = {"marker-fill"} @@ -261,309 +253,5 @@ def _style_name(self, attr: str) -> str: return "_".join((self._prefix, attr)) - -class Style(base.BaseElement): - """An embedded cascading style sheet.""" - - elementname = "style" - - def __init__(self, text: str, **extra: dict[str, t.Any]) -> None: - """Initialize Style class. - - Parameters - ---------- - text - The stylesheet - extra - Additional attributes as keyword arguments - """ - super().__init__(**extra) - self.text = text - - def get_xml(self) -> etree._Element: - xml = super().get_xml() - xml.text = self.text - return xml - - -class SVGStylesheet: - """CSS stylesheet for SVG.""" - - def __init__(self, class_: str): - if not isinstance(class_, str): - raise TypeError( - f"Invalid type for class_ '{type(class_).__name__}'. This" - " needs to be a str." - ) - - self.drawing_class = class_ - self.builder = StyleBuilder(class_) - self.create() - - @property - def sheet(self) -> Style: - """Return Sheet str from builder.""" - return Style(text=self.builder.sheet.getvalue()) - - def create(self) -> None: - """Generate a stylesheet for the given drawing.""" - self.builder.write_styles() - self.static_deco = STATIC_DECORATIONS[ - "__GLOBAL__" - ] + STATIC_DECORATIONS.get(self.drawing_class or "", ()) - - def yield_gradients(self) -> cabc.Iterator[gradients.LinearGradient]: - """Yield an svgwrite LinearGradient for all gradients in this sheet.""" - for gradname, stopcolors in self.builder.gradients.items(): - grad = gradients.LinearGradient( - id_=gradname, start=("0%", "0%"), end=("0%", "100%") - ) - for offset, color in zip((0, 1), stopcolors): - grad.add_stop_color(offset=offset, color=color) - - yield grad - - def __str__(self) -> str: - return self.sheet.text - - -class StyleBuilder: - """Helper class that bundles together all needed objects.""" - - _base = textwrap.dedent( - """\ - {cls}* {{ shape-rendering: geometricPrecision; }} - {cls}text {{ font-family: "Segoe UI"; font-size: 8pt; }} - {cls}g {{ cursor: pointer; }} - {cls}g.Edge > path {{ fill: none; stroke: rgb(0, 0, 0); }} - """ - ) - _highlight_on_hover = { - "ComponentExchange": diagram.RGB(8, 138, 189), - "ExchangeItemElement": diagram.RGB(0, 0, 0), - "FIPAllocation": diagram.RGB(255, 0, 0), - "FOPAllocation": diagram.RGB(255, 0, 0), - "FunctionalExchange": diagram.RGB(0, 0, 255), - "PhysicalLink": diagram.RGB(239, 41, 41), - } - - sheet: io.StringIO - styles: dict[str, dict[str, diagram.CSSdef]] - - def __init__(self, class_: str | None): - self.class_ = class_ - self.sheetclass = re.sub(r"\s+", "", class_ or "") - self.stylewriters = { - "Box": self._write_styles_box, - "Edge": self._write_styles_edge, - } - self.gradients: dict[str, str | tuple[str, ...]] = {} - self.create() - - def write_styles(self) -> None: - """Write edge and box styles to sheet.""" - for key, styles in self.styles.items(): - if not styles: # pragma: no cover - continue - elmtype_match = RE_ELMCLASS.match(key) - if elmtype_match is None: # pragma: no cover - logger.error("Invalid style key: %s", key) - continue - - elmtype, elmclass, pseudoclass = elmtype_match.groups() - if elmtype not in self.stylewriters: - continue - write_styles_real = self.stylewriters[elmtype] - write_styles_real(elmclass or "", pseudoclass or "", styles) - - def create(self) -> None: - """Create style builder and all needed components. - - Create sheet string buffer from _base string template. Copy - global styles from aird.STYLES and update with class-specific - styles. Write styles to sheet. - """ - self.sheet = io.StringIO( - self._base.format( - cls=f".{self.sheetclass} " if self.sheetclass else "" - ) - ) - self.sheet.seek(0, io.SEEK_END) - self.styles = self._make_styles() - - def _make_styles(self) -> dict[str, dict[str, diagram.CSSdef]]: - styles = diagram.STYLES["__GLOBAL__"].copy() - try: - deep_update_dict( - styles, diagram.STYLES[self.class_] # type: ignore[index] - ) - except KeyError: - logger.error( - "No styling defined for diagram class %s", self.class_ - ) - - return styles - - def _write_styles_box( - self, - elmclass: str, - pseudo: str, - styles: dict[str, diagram.CSSdef], - ) -> None: - selectors = [ - f"g.Box{elmclass}{pseudo} > {tag}" for tag in ("rect", "use") - ] - selector_text = f"g.Box{elmclass}{pseudo} > text" - if "stroke" in styles: - self._write_styledict( - elmclass, - f"g.Box{elmclass}{pseudo} > line", - {"stroke": styles["stroke"]}, - ) - self._write_styles_common(elmclass, selectors, selector_text, styles) - - def _write_styles_edge( - self, - elmclass: str, - pseudo: str, - styles: dict[str, diagram.CSSdef], - ) -> None: - selector = f"g.Edge{elmclass}{pseudo} > path" - selector_text = f"g.Edge{elmclass}{pseudo} > text" - self._write_styledict( - elmclass, - f"g.Edge{elmclass}{pseudo} > rect", - {"fill": None, "stroke": None}, - ) - self._write_styledict( - elmclass, - f"g.Circle{elmclass}{pseudo} > circle", - { - "fill": styles.get("stroke", diagram.RGB(0, 0, 0)), - "stroke": None, - }, - ) - - if not pseudo and elmclass in self._highlight_on_hover: - self._write_styledict( - elmclass, - f"g.Edge{elmclass}:hover > path", - { - "stroke": self._highlight_on_hover[elmclass], - "stroke-width": 2, - }, - ) - self._write_styledict( - elmclass, - f"g.Edge{elmclass}:hover > rect", - {"stroke": self._highlight_on_hover[elmclass]}, - ) - self._write_styles_common(elmclass, selector, selector_text, styles) - - def _write_styles_common( - self, - elmclass: str, - sel_obj: str | list[str], - sel_text: str, - allstyles: dict[str, diagram.CSSdef], - ) -> None: - for selector, styles in zip( - [sel_obj, sel_text], _splitstyles(allstyles) - ): - if styles: - self._write_styledict(elmclass, selector, styles) - - def _write_styledict( - self, - elmclass: str, - selector: str | list[str], - styles: dict[str, diagram.CSSdef], - ) -> None: - if isinstance(selector, str): - selector = f".{self.sheetclass} {selector}" - else: - selector = ", ".join(f".{self.sheetclass} {i}" for i in selector) - self.sheet.write(f"{selector} {{ ") - self.sheet.write( - " ".join( - self._serialize_value(k, v, elmclass) - for k, v in styles.items() - if k not in CUSTOM_STYLE_ATTRS - ) - ) - self.sheet.write(" }\n") - - def _serialize_value( - self, key: str, value: diagram.CSSdef, class_: str - ) -> str: - if key in {"marker-start", "marker-end"}: - diagram_class = self.class_ - mystyle = diagram.get_style(diagram_class, f"Edge{class_}") - if "stroke" not in mystyle: - mystyle["stroke"] = "#f00" - - marker_id = Styling._generate_id( - str(value), - [mystyle["stroke"]], - ) - value = f"url(#{marker_id})" - elif value is None: - value = "none" - elif isinstance(value, diagram.RGB): - value = str(value) - elif isinstance(value, cabc.Sequence) and len(value) == 2: - gradname = f"{class_}{key.capitalize()}Gradient" - gradcolors = tuple(str(v) for v in value) - if gradname in self.gradients: - assert self.gradients[gradname] == gradcolors - else: - self.gradients[gradname] = gradcolors - value = f'url("#{gradname}")' - elif not isinstance(value, (float, int, str)): - raise ValueError(f"Invalid stylesheet value: {value}") - return f"{key}: {value};" - - -def _splitstyles( - styles: dict[str, diagram.CSSdef] -) -> tuple[dict[str, diagram.CSSdef], dict[str, diagram.CSSdef]]: - objstyles: dict[str, diagram.CSSdef] = {} - textstyles: dict[str, diagram.CSSdef] = {} - for key, value in styles.items(): - if key.startswith("text_"): - textstyles[key[len("text_") :]] = value - else: - objstyles[key] = value - return objstyles, textstyles - - -def deep_update_dict( - target: cabc.MutableMapping, updates: cabc.Mapping -) -> cabc.MutableMapping: - """Apply the ``updates`` to the nested ``target`` dict recursively. - - Parameters - ---------- - target - The target dict, which will be modified in place. - updates - A dict containing the updates to apply. If one of the values is - the special object ``deep_update_dict.delete``, the - corresponding key will be deleted from the target. - - Returns - ------- - dict - The target dict, after modifying it in place. - """ - for key, value in list(updates.items()): - if value is deep_update_dict.delete: # type: ignore - del target[key] - elif isinstance(value, cabc.Mapping): - target[key] = deep_update_dict(target.get(key, {}), value) - else: - target[key] = value - return target - - -deep_update_dict.delete = object() # type: ignore + def __contains__(self, obj: t.Any) -> bool: + return hasattr(self, obj) diff --git a/docs/source/examples/01 Introduction.ipynb b/docs/source/examples/01 Introduction.ipynb index 983c7661c..6c8f0c8d3 100644 --- a/docs/source/examples/01 Introduction.ipynb +++ b/docs/source/examples/01 Introduction.ipynb @@ -32,7 +32,7 @@ "source": [ "import capellambse\n", "path_to_model = \"../../../tests/data/melodymodel/5_0/Melody Model Test.aird\"\n", - "model = capellambse.MelodyModel(path_to_model, jupyter_untrusted=True)\n", + "model = capellambse.MelodyModel(path_to_model)\n", "model" ] }, @@ -41,9 +41,7 @@ "id": "48ce0c1e", "metadata": {}, "source": [ - "The additional `jupyter_untrusted` parameter is currently needed to render diagrams correctly. In untrusted notebooks, all CSS elements (and JavaScript too) are removed for security reasons - however, capellambse-generated SVGs use a stylesheet to make the implementation a bit cleaner.\n", - "\n", - "Having said that, lets go to the first practical example of working with the library!" + "Let's go to the first practical example of working with the library!" ] }, { @@ -456,6 +454,12 @@ { "data": { "image/png": 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+ "image/svg+xml": [ + "LFHogwartsproduce GreatWizardsprotectStudentsagainst theDeath EatersCampusSchooleducate WizardsWhomping Willowdefend thesurrounding areaagainst IntrudersProf. A. P. W. B.Dumbledoremanage theschooladviseHarryProf. S. SnapeTeachingmaintain alayer ofdefense fortheSorcerer'sStoneHarry J. Potterkill He WhoMust Not BeNamedR. Weasleyassist Harrybreak schoolruleswizardryHeadmasterResponsibilitiesTeacherResponsibilitiesHelp for HarryKnowledgePunishmentLearningeducate & maturefriendshipassistanceCarepunisheducate" + ], + "text/html": [ + "" + ], "text/plain": [ "" ] @@ -487,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "VehicleEquipment compartmentServerCompute Card 1Card 1 OSCamera Driver SWCApp 1 SWCCooling FanX2Compute Card 2Card 2 OSApp 2 SWCX2Network SwitchSwitch FirmwareSwitch ConfigurationP1P3P2Sensor compartmentCamera AssemblyCameraFirmwarecard...PL 1Eth Cable 2Eth Cable 3C 1C 2C 3C 4C 5C 6card1 - card2 connectionD 7D 8PL 1C 10" + ], + "text/html": [ + "" + ], "text/plain": [ "" ] diff --git a/docs/source/examples/03 Data Values.ipynb b/docs/source/examples/03 Data Values.ipynb index 78dd996c9..cb55718ab 100644 --- a/docs/source/examples/03 Data Values.ipynb +++ b/docs/source/examples/03 Data Values.ipynb @@ -39,7 +39,7 @@ "source": [ "import capellambse\n", "path_to_model = \"../../../tests/data/melodymodel/5_2/Melody Model Test.aird\"\n", - "model = capellambse.MelodyModel(path_to_model, jupyter_untrusted=True)\n", + "model = capellambse.MelodyModel(path_to_model)\n", "model" ] }, diff --git a/docs/source/examples/04 Intro to Jinja templating.ipynb b/docs/source/examples/04 Intro to Jinja templating.ipynb index fbbe8125e..bfc6cbd0e 100644 --- a/docs/source/examples/04 Intro to Jinja templating.ipynb +++ b/docs/source/examples/04 Intro to Jinja templating.ipynb @@ -48,7 +48,7 @@ "from IPython.core.display import HTML\n", "\n", "path_to_model = \"../../../tests/data/melodymodel/5_0/Melody Model Test.aird\"\n", - "model = capellambse.MelodyModel(path_to_model, jupyter_untrusted=True)\n", + "model = capellambse.MelodyModel(path_to_model)\n", "env = jinja2.Environment()" ] }, @@ -481,80 +481,7 @@ "\n", "\n", " Figure 1: [LAB] Wizzard Education\n", - " LFHogwartsproduce GreatWizardsprotectStudentsagainst theDeath EatersCampusSchooleducate WizardsWhomping Willowdefend thesurrounding areaagainst IntrudersProf. A. P. W. B.Dumbledoremanage theschooladviseHarryProf. S. SnapeTeachingmaintain alayer ofdefense fortheSorcerer'sStoneHarry J. Potterkill He WhoMust Not BeNamedR. Weasleyassist Harrybreak schoolruleswizardryHeadmasterResponsibilitiesTeacherResponsibilitiesHelp for HarryKnowledgePunishmentLearningeducate & maturefriendshipassistanceCarepunisheducate\n", + " LFHogwartsproduce GreatWizardsprotectStudentsagainst theDeath EatersCampusSchooleducate WizardsWhomping Willowdefend thesurrounding areaagainst IntrudersProf. A. P. W. B.Dumbledoremanage theschooladviseHarryProf. S. SnapeTeachingmaintain alayer ofdefense fortheSorcerer'sStoneHarry J. Potterkill He WhoMust Not BeNamedR. Weasleyassist Harrybreak schoolruleswizardryHeadmasterResponsibilitiesTeacherResponsibilitiesHelp for HarryKnowledgePunishmentLearningeducate & maturefriendshipassistanceCarepunisheducate\n", "" ], "text/plain": [ @@ -645,80 +572,7 @@ "\n", "\n", " Figure 1: [LAB] Wizzard Education\n", - " LFHogwartsproduce GreatWizardsprotectStudentsagainst theDeath EatersCampusSchooleducate WizardsWhomping Willowdefend thesurrounding areaagainst IntrudersProf. A. P. W. B.Dumbledoremanage theschooladviseHarryProf. S. SnapeTeachingmaintain alayer ofdefense fortheSorcerer'sStoneHarry J. Potterkill He WhoMust Not BeNamedR. Weasleyassist Harrybreak schoolruleswizardryHeadmasterResponsibilitiesTeacherResponsibilitiesHelp for HarryKnowledgePunishmentLearningeducate & maturefriendshipassistanceCarepunisheducate\n", + " LFHogwartsproduce GreatWizardsprotectStudentsagainst theDeath EatersCampusSchooleducate WizardsWhomping Willowdefend thesurrounding areaagainst IntrudersProf. A. P. W. B.Dumbledoremanage theschooladviseHarryProf. S. SnapeTeachingmaintain alayer ofdefense fortheSorcerer'sStoneHarry J. Potterkill He WhoMust Not BeNamedR. Weasleyassist Harrybreak schoolruleswizardryHeadmasterResponsibilitiesTeacherResponsibilitiesHelp for HarryKnowledgePunishmentLearningeducate & maturefriendshipassistanceCarepunisheducate\n", "\n", "\n", "