Comments (6)
By the way gotta use "Fixes #34" not "Fix for #34" to get autoclose. :)
from tensor-sensor.
Weird. is Consolas font installed on your system? Is it Intel or M1?
from tensor-sensor.
Consolas wasn't installed in my environment (Intel). The fallback font is "Dejavu Sans", which fails to display the code as seen above.
Advising users to install the font may involve recreating the environment etc. An alternative solution to consider is to explicitly specify a fallback font. This is much more user friendly in my opinion at nearly no burden on the package. For instance, "DejaVu Sans Mono" works well. This can be trivially achieved providing a sequence of font names:
from tensor-sensor.
agreed this is a good change. I guess your PR only sets the arg default, but there's no code to fall back to the indicated font. Font name is now a tuple by default, which is really a comment for the developer. Shouldn't there be logic to check for font installations and use one that works?
from tensor-sensor.
Matplotlib handles all that logic, this PR just changes the default:
WARNING:matplotlib.font_manager:findfont: Font family ('Consolas', 'Consolas Sans Mono') not found. Falling back to DejaVu Sans.
from tensor-sensor.
You are kidding me!? Wow. that's insane. You just took me to school!
from tensor-sensor.
Related Issues (20)
- Graphviz errors for abstract syntax tree (AST) vizualisation HOT 2
- Power `**` operator not handled HOT 1
- explain() likely affects PyTorch autograd operations
- Unhandled statements cause exceptions (Was: Nested calls to clarify can raise stacked Exceptions) HOT 3
- Seem a problem with np.ones() function HOT 3
- Supporting JAX HOT 8
- Identify nested clarify() calls and ignored nested ones. HOT 1
- can't handle `a = b = np.ones(3)` statements
- Can't visualize operations in pytorch modules HOT 1
- Suppress visualisation of () as operator in tree HOT 3
- Add tensor element type info HOT 3
- Improvement: See into nn.Sequential models
- Showing too many matrices for complicated operands
- Boxes for operands packed too tightly
- Not an issue - livepython fyi HOT 2
- executing and pure_eval HOT 5
- Contribution Guidelines HOT 3
- Optional dependencies not working properly HOT 10
- Remove hard torch dependecies for keras/tensorflow user HOT 5
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