Optimize Font Scaling With FontTools: A Code Refactor
In the world of digital typography, the units-per-em (UPEM) of a font is a fundamental value that dictates the resolution at which the font is rendered. It's essentially the number of design units in a font's em square. When this value needs to be changed, often for compatibility or rendering purposes, it requires a careful adjustment of various font data to maintain the font's integrity. The fontTools library in Python provides powerful tools for manipulating font files, and one of its capabilities is scaling the UPEM. This article delves into a specific refactoring effort within the scaleUpem.py script, focusing on optimizing code duplication related to variable font data. We'll explore the necessity of this change and how it improves the maintainability and efficiency of the scaling process, especially when dealing with complex variable font features.
Understanding the Challenge of Scaling UPEM
Scaling the units-per-em (UPEM) of a font isn't just about changing a single number in the 'head' table. It involves systematically adjusting coordinates, advances, and other numerical values across numerous font tables to reflect the new scaling factor. This process is crucial for ensuring that the font remains visually consistent and functionally correct after the UPEM adjustment. For instance, the 'glyf' table contains outlines for each glyph, and their coordinates must be scaled. Similarly, metrics like advance width and side bearings in the 'hmtx' and 'vmtx' tables need proportional scaling. Even tables like 'OS/2', 'hhea', and 'vhea', which store various font metrics and typographical properties, require updates. The fontTools library's ScalerVisitor class is designed to abstract this complex traversal and modification process. It allows developers to define how different parts of the font structure should be handled during scaling by registering specific visitor methods for various table types and data structures. This visitor pattern is highly effective for ensuring that all relevant parts of the font are accounted for, reducing the risk of errors and inconsistencies.
However, the challenge becomes significantly more intricate when dealing with variable fonts. Variable fonts, unlike static fonts, can have multiple axes of variation (e.g., weight, width, slant) that allow for a continuous range of styles within a single font file. This variability is often managed through sophisticated data structures, particularly the 'avar' and 'gvar' tables for TrueType fonts, and the 'CFF ' or 'CFF2' tables with their associated variation stores for OpenType fonts. When scaling the UPEM of a variable font, not only do the base coordinates need adjustment, but the variation data itself, which defines how glyphs change across different design axes, must also be scaled accurately. This includes scaling the delta values that represent the differences from a default or master design. The complexity arises from how this variation data is stored and accessed, especially within the MultiVarStore, which can hold complex nested structures of variation data for different masters and axes. Ensuring that scaling operations are applied consistently to both static and variable font data is paramount for maintaining the quality and usability of the modified font file. The scaleUpem.py script, therefore, needs to handle these intricate details meticulously.
Identifying and Addressing Code Duplication
The specific refactoring discussed in the provided code snippet targets a section within the ScalerVisitor.visit(visitor, obj, attr, varc) method, which handles the scaling of VARC (Variable Composite Glyphs) tables in TrueType variable fonts. This method iterates through composite glyphs and their components, scaling transformations and variation indices. It's within the handling of component.axisValuesVarIndex and component.transformVarIndex that the duplication was identified. The code block responsible for retrieving and processing variation data from the MultiVarStore was being repeated almost verbatim for both indices.
The duplicated logic involves several steps: first, extracting the major and minor indices from the combined varIdx. Then, accessing the relevant varData from the store.MultiVarData. Subsequently, it retrieves the specific vec (vector of variation data) from varData.Item using the minor index. Crucially, it also calls storeBuilder.setSupports(store.get_supports(major, fvar.axes)) to ensure the builder knows the structure of the variation data it's dealing with. Finally, it processes this `vec` into a list of Vector objects, potentially scaling individual values within these vectors, and then uses the storeBuilder.storeDeltas() method to store the processed data and obtain a new index. If the `vec` is empty, it assigns otTables.NO_VARIATION_INDEX. This entire sequence was being executed twice, leading to:
- Increased Code Size: Redundant code makes the script longer and harder to read.
- Maintenance Overhead: If a bug is found or an improvement is needed in this logic, it would have to be fixed in two places, increasing the chance of errors and inconsistencies.
- Reduced Readability: Repetitive code blocks obscure the core logic and make it harder for developers to understand the overall process.
The proposed solution is to move this duplicated code into a dedicated helper method, either within the MultiVarStore class itself (e.g., as a __getitem__ method that handles the retrieval and processing of variation data) or as a separate utility function like getDeltasAndSupports(). This approach would encapsulate the logic, making the visit method cleaner and more focused on its primary task of visiting and scaling. The helper method would take the necessary indices and context (like the `storeBuilder` and font axes) and return the processed variation data or the new index. This adheres to the DRY (Don't Repeat Yourself) principle, a cornerstone of good software engineering.
Implementing the Refactoring
The refactoring effort aims to consolidate the repeated logic for handling variation data within the ScalerVisitor's visit method for VARC tables. Currently, the code for processing component.axisValuesVarIndex and component.transformVarIndex involves a significant overlap. Let's examine the core of the duplicated section:
if varIdx != otTables.NO_VARIATION_INDEX:
major = varIdx >> 16
minor = varIdx & 0xFFFF
varData = store.MultiVarData[major]
vec = varData.Item[minor]
storeBuilder.setSupports(store.get_supports(major, fvar.axes))
if vec:
m = len(vec) // varData.VarRegionCount
vec = list(batched(vec, m))
vec = [Vector(v) for v in vec]
# The result of storeDeltas is assigned to the respective index
# component.axisValuesVarIndex = storeBuilder.storeDeltas(vec)
# OR
# component.transformVarIndex = storeBuilder.storeDeltas(vec)
else:
# assigned to the respective index if vec is None
# component.axisValuesVarIndex = otTables.NO_VARIATION_INDEX
# OR
# component.transformVarIndex = otTables.NO_VARIATION_INDEX
The goal is to extract this logic into a single, reusable unit. A potential implementation could involve a new method, say _process_variation_data(self, varIdx, store, storeBuilder, fvar), within the ScalerVisitor class itself, or perhaps a more integrated approach within the OnlineMultiVarStoreBuilder.
Consider a helper method that encapsulates the core processing:
def _process_variation_data(self, varIdx, store, storeBuilder, fvar, component, is_transform_index):
if varIdx == otTables.NO_VARIATION_INDEX:
if is_transform_index:
component.transformVarIndex = otTables.NO_VARIATION_INDEX
else:
component.axisValuesVarIndex = otTables.NO_VARIATION_INDEX
return
major = varIdx >> 16
minor = varIdx & 0xFFFF
varData = store.MultiVarData[major]
vec_data = varData.Item[minor]
storeBuilder.setSupports(store.get_supports(major, fvar.axes))
if vec_data:
m = len(vec_data) // varData.VarRegionCount
processed_vec = list(batched(vec_data, m))
# Scaling logic specific to axisValuesVarIndex vs transformVarIndex
if is_transform_index:
flags = component.flags
scaled_vec = []
for v_list in processed_vec:
v = list(v_list)
i = 0
# Scale translate & tCenter for transform components
if flags & otTables.VarComponentFlags.HAVE_TRANSLATE_X:
v[i] = self.scale(v[i])
i += 1
if flags & otTables.VarComponentFlags.HAVE_TRANSLATE_Y:
v[i] = self.scale(v[i])
i += 1
# Skip rotation, scale_x, scale_y, skew_x, skew_y as they are not directly scaled here
if flags & otTables.VarComponentFlags.HAVE_ROTATION:
i += 1
if flags & otTables.VarComponentFlags.HAVE_SCALE_X:
i += 1
if flags & otTables.VarComponentFlags.HAVE_SCALE_Y:
i += 1
if flags & otTables.VarComponentFlags.HAVE_SKEW_X:
i += 1
if flags & otTables.VarComponentFlags.HAVE_SKEW_Y:
i += 1
if flags & otTables.VarComponentFlags.HAVE_TCENTER_X:
v[i] = self.scale(v[i])
i += 1
if flags & otTables.VarComponentFlags.HAVE_TCENTER_Y:
v[i] = self.scale(v[i])
i += 1
scaled_vec.append(Vector(v))
processed_vec = scaled_vec
else:
# Direct scaling for axis values
processed_vec = [Vector(v) for v in processed_vec]
new_index = storeBuilder.storeDeltas(processed_vec)
if is_transform_index:
component.transformVarIndex = new_index
else:
component.axisValuesVarIndex = new_index
else:
if is_transform_index:
component.transformVarIndex = otTables.NO_VARIATION_INDEX
else:
component.axisValuesVarIndex = otTables.NO_VARIATION_INDEX
This approach separates the logic for handling variation indices. The main visit method would then call this helper twice, passing the appropriate component and a flag indicating whether it's processing the transform index or the axis values index. This significantly cleans up the code, making it more readable and maintainable. The specific scaling logic for transform components, which involves checking flags to determine which delta values to scale, remains distinct, ensuring that different types of variation data are handled appropriately.
Benefits of the Refactoring
Implementing the proposed refactoring, by moving the duplicated code into a more modular structure, yields several significant benefits for the fontTools library and its users. The most immediate advantage is the enhancement of code maintainability. When logic is duplicated, fixing a bug or adding a new feature requires modifying multiple code sections. This increases the likelihood of introducing regressions or overlooking one of the instances, leading to inconsistencies. By consolidating the variation data processing into a single, well-defined function or method, any future updates or bug fixes only need to be applied in one place. This makes the codebase more robust and less prone to errors, ultimately saving development time and effort.
Furthermore, this refactoring significantly improves code readability and clarity. The original code, with its repeated blocks, could be challenging to follow. Developers had to mentally parse the same logic twice to understand what was happening. Once the duplication is removed, the visit method for VARC tables becomes much more concise. It clearly delegates the complex task of processing variation data to a helper function, allowing the main method to focus on its core responsibilities: iterating through components and orchestrating the scaling process. This improved clarity makes it easier for new contributors to understand the codebase and for experienced developers to quickly grasp the functionality.
Another key benefit is the adherence to the DRY (Don't Repeat Yourself) principle. This fundamental software development principle promotes the idea that
