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GNU Affero General Public License
=================================
_Version 3, 19 November 2007_
_Copyright © 2007 Free Software Foundation, Inc. &lt;<http://fsf.org/>&gt;_
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
## Preamble
The GNU Affero General Public License is a free, copyleft license for
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The precise terms and conditions for copying, distribution and
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## TERMS AND CONDITIONS
### 0. Definitions
“This License” refers to version 3 of the GNU Affero General Public License.
“Copyright” also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
“The Program” refers to any copyrightable work licensed under this
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To “propagate” a work means to do anything with it that, without
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public, and in some countries other activities as well.
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### 1. Source Code
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The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
### 2. Basic Permissions
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
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for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
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Conveying under any other circumstances is permitted solely under
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### 3. Protecting Users' Legal Rights From Anti-Circumvention Law
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similar laws prohibiting or restricting circumvention of such
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When you convey a covered work, you waive any legal power to forbid
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### 4. Conveying Verbatim Copies
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### 5. Conveying Modified Source Versions
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* **b)** Convey the object code in, or embodied in, a physical product
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If you convey an object code work under this section in, or with, or
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The requirement to provide Installation Information does not include a
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unpacking, reading or copying.
### 7. Additional Terms
“Additional permissions” are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
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All other non-permissive additional terms are considered “further
restrictions” within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
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a further restriction but permits relicensing or conveying under this
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Additional terms, permissive or non-permissive, may be stated in the
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the above requirements apply either way.
### 8. Termination
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
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However, if you cease all violation of this License, then your
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finally terminates your license, and **(b)** permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
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violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
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Termination of your rights under this section does not terminate the
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material under section 10.
### 9. Acceptance Not Required for Having Copies
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
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not accept this License. Therefore, by modifying or propagating a
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### 10. Automatic Licensing of Downstream Recipients
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
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Corresponding Source of the work from the predecessor in interest, if
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You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
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rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
### 11. Patents
A “contributor” is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's “contributor version”.
A contributor's “essential patent claims” are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
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purposes of this definition, “control” includes the right to grant
patent sublicenses in a manner consistent with the requirements of
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Each contributor grants you a non-exclusive, worldwide, royalty-free
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In the following three paragraphs, a “patent license” is any express
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sue for patent infringement). To “grant” such a patent license to a
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If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
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available, or **(2)** arrange to deprive yourself of the benefit of the
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consistent with the requirements of this License, to extend the patent
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covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
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receiving the covered work authorizing them to use, propagate, modify
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you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is “discriminatory” if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license **(a)** in connection with copies of the covered work
conveyed by you (or copies made from those copies), or **(b)** primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
### 12. No Surrender of Others' Freedom
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
### 13. Remote Network Interaction; Use with the GNU General Public License
Notwithstanding any other provision of this License, if you modify the
Program, your modified version must prominently offer all users
interacting with it remotely through a computer network (if your version
supports such interaction) an opportunity to receive the Corresponding
Source of your version by providing access to the Corresponding Source
from a network server at no charge, through some standard or customary
means of facilitating copying of software. This Corresponding Source
shall include the Corresponding Source for any work covered by version 3
of the GNU General Public License that is incorporated pursuant to the
following paragraph.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the work with which it is combined will remain governed by version
3 of the GNU General Public License.
### 14. Revised Versions of this License
The Free Software Foundation may publish revised and/or new versions of
the GNU Affero General Public License from time to time. Such new versions
will be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU Affero General
Public License “or any later version” applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU Affero General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU Affero General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
### 15. Disclaimer of Warranty
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
### 16. Limitation of Liability
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
### 17. Interpretation of Sections 15 and 16
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
_END OF TERMS AND CONDITIONS_
## How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the “copyright” line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a “Source” link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a “copyright disclaimer” for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
&lt;<http://www.gnu.org/licenses/>&gt;.

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# Masque README
Masque is a Python module for designing lithography masks.
The general idea is to implement something resembling the GDSII file-format, but
with some vectorized element types (eg. circles, not just polygons), better support for
E-beam doses, and the ability to output to multiple formats.
- [Source repository](https://mpxd.net/code/jan/masque)
- [PyPi](https://pypi.org/project/masque)
## Installation
Requirements:
* python >= 3.5 (written and tested with 3.6)
* numpy
* matplotlib (optional, used for visualization functions and text)
* python-gdsii (optional, used for gdsii i/o)
* svgwrite (optional, used for svg output)
* freetype (optional, used for text)
Install with pip:
```bash
pip3 install masque
```
Alternatively, install from git
```bash
pip3 install git+https://mpxd.net/code/jan/masque.git@release
```
## TODO
* Mirroring
* Polygon de-embedding
### Maybe
* Construct from bitmap
* Boolean operations on polygons (using pyclipper)
* Output to OASIS (using fatamorgana)

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# Quick script for testing arcs
import numpy
import masque
import masque.file.gdsii
from masque import shapes
def main():
pat = masque.Pattern(name='ellip_grating')
for rmin in numpy.arange(10, 15, 0.5):
pat.shapes.append(shapes.Arc(
radii=(rmin, rmin),
width=0.1,
angles=(-numpy.pi/4, numpy.pi/4)
))
pat.scale_by(1000)
# pat.visualize()
pat2 = masque.Pattern(name='p2')
pat2.name = 'ellip_grating'
pat2.subpatterns += [
masque.SubPattern(pattern=pat, offset=(20e3, 0)),
masque.SubPattern(pattern=pat, offset=(0, 20e3)),
]
masque.file.gdsii.write_dose2dtype((pat, pat2, pat2.copy(), pat2.copy()), 'out.gds', 1e-9, 1e-3)
if __name__ == '__main__':
main()

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"""
masque 2D CAD library
masque is an attempt to make a relatively small library for designing lithography
masks. The general idea is to implement something resembling the GDSII file-format, but
with some vectorized element types (eg. circles, not just polygons), better support for
E-beam doses, and the ability to output to multiple formats.
Pattern is a basic object containing a 2D lithography mask, composed of a list of Shape
objects and a list of SubPattern objects.
SubPattern provides basic support for nesting Pattern objects within each other, by adding
offset, rotation, scaling, and other such properties to a Pattern reference.
Note that the methods for these classes try to avoid copying wherever possible, so unless
otherwise noted, assume that arguments are stored by-reference.
Dependencies:
- numpy
- matplotlib [Pattern.visualize(...)]
- python-gdsii [masque.file.gdsii]
- svgwrite [masque.file.svg]
"""
from .error import PatternError
from .shapes import Shape
from .label import Label
from .subpattern import SubPattern
from .repetition import GridRepetition
from .pattern import Pattern
__author__ = 'Jan Petykiewicz'
version = '0.5'

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class PatternError(Exception):
"""
Simple Exception for Pattern objects and their contents
"""
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.value)

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"""
Functions for reading from and writing to various file formats.
"""

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"""
GDSII file format readers and writers
"""
# python-gdsii
import gdsii.library
import gdsii.structure
import gdsii.elements
from typing import List, Any, Dict, Tuple
import re
import numpy
import base64
import struct
import logging
from .utils import mangle_name, make_dose_table
from .. import Pattern, SubPattern, GridRepetition, PatternError, Label, Shape
from ..shapes import Polygon
from ..utils import rotation_matrix_2d, get_bit, set_bit, vector2, is_scalar
from ..utils import remove_colinear_vertices
__author__ = 'Jan Petykiewicz'
logger = logging.getLogger(__name__)
def write(patterns: Pattern or List[Pattern],
filename: str,
meters_per_unit: float,
logical_units_per_unit: float = 1,
library_name: str = 'masque-gdsii-write'):
"""
Write a Pattern or list of patterns to a GDSII file, by first calling
.polygonize() to change the shapes into polygons, and then writing patterns
as GDSII structures, polygons as boundary elements, and subpatterns as structure
references (sref).
For each shape,
layer is chosen to be equal to shape.layer if it is an int,
or shape.layer[0] if it is a tuple
datatype is chosen to be shape.layer[1] if available,
otherwise 0
Note that this function modifies the Pattern.
It is often a good idea to run pattern.subpatternize() prior to calling this function,
especially if calling .polygonize() will result in very many vertices.
If you want pattern polygonized with non-default arguments, just call pattern.polygonize()
prior to calling this function.
:param patterns: A Pattern or list of patterns to write to file. Modified by this function.
:param filename: Filename to write to.
:param meters_per_unit: Written into the GDSII file, meters per (database) length unit.
All distances are assumed to be an integer multiple of this unit, and are stored as such.
:param logical_units_per_unit: Written into the GDSII file. Allows the GDSII to specify a
"logical" unit which is different from the "database" unit, for display purposes.
Default 1.
:param library_name: Library name written into the GDSII file.
Default 'masque-gdsii-write'.
"""
# Create library
lib = gdsii.library.Library(version=600,
name=library_name.encode('ASCII'),
logical_unit=logical_units_per_unit,
physical_unit=meters_per_unit)
if isinstance(patterns, Pattern):
patterns = [patterns]
# Get a dict of id(pattern) -> pattern
patterns_by_id = {id(pattern): pattern for pattern in patterns}
for pattern in patterns:
patterns_by_id.update(pattern.referenced_patterns_by_id())
_disambiguate_pattern_names(patterns_by_id.values())
# Now create a structure for each pattern, and add in any Boundary and SREF elements
for pat in patterns_by_id.values():
structure = gdsii.structure.Structure(name=pat.name)
lib.append(structure)
# Add a Boundary element for each shape
structure += _shapes_to_boundaries(pat.shapes)
structure += _labels_to_texts(pat.labels)
# Add an SREF / AREF for each subpattern entry
structure += _subpatterns_to_refs(pat.subpatterns)
with open(filename, mode='wb') as stream:
lib.save(stream)
def write_dose2dtype(patterns: Pattern or List[Pattern],
filename: str,
meters_per_unit: float,
*args,
**kwargs,
) -> List[float]:
"""
Write a Pattern or list of patterns to a GDSII file, by first calling
.polygonize() to change the shapes into polygons, and then writing patterns
as GDSII structures, polygons as boundary elements, and subpatterns as structure
references (sref).
For each shape,
layer is chosen to be equal to shape.layer if it is an int,
or shape.layer[0] if it is a tuple
datatype is chosen arbitrarily, based on calcualted dose for each shape.
Shapes with equal calcualted dose will have the same datatype.
A list of doses is retured, providing a mapping between datatype
(list index) and dose (list entry).
Note that this function modifies the Pattern(s).
It is often a good idea to run pattern.subpatternize() prior to calling this function,
especially if calling .polygonize() will result in very many vertices.
If you want pattern polygonized with non-default arguments, just call pattern.polygonize()
prior to calling this function.
:param patterns: A Pattern or list of patterns to write to file. Modified by this function.
:param filename: Filename to write to.
:param meters_per_unit: Written into the GDSII file, meters per (database) length unit.
All distances are assumed to be an integer multiple of this unit, and are stored as such.
:param args: passed to masque.file.gdsii.write().
:param kwargs: passed to masque.file.gdsii.write().
:returns: A list of doses, providing a mapping between datatype (int, list index)
and dose (float, list entry).
"""
patterns, dose_vals = dose2dtype(patterns)
write(patterns, filename, meters_per_unit, *args, **kwargs)
return dose_vals
def dose2dtype(patterns: Pattern or List[Pattern],
) -> Tuple[List[Pattern], List[float]]:
"""
For each shape in each pattern, set shape.layer to the tuple
(base_layer, datatype), where:
layer is chosen to be equal to the original shape.layer if it is an int,
or shape.layer[0] if it is a tuple
datatype is chosen arbitrarily, based on calcualted dose for each shape.
Shapes with equal calcualted dose will have the same datatype.
A list of doses is retured, providing a mapping between datatype
(list index) and dose (list entry).
Note that this function modifies the input Pattern(s).
:param patterns: A Pattern or list of patterns to write to file. Modified by this function.
:returns: (patterns, dose_list)
patterns: modified input patterns
dose_list: A list of doses, providing a mapping between datatype (int, list index)
and dose (float, list entry).
"""
if isinstance(patterns, Pattern):
patterns = [patterns]
# Get a dict of id(pattern) -> pattern
patterns_by_id = {id(pattern): pattern for pattern in patterns}
for pattern in patterns:
patterns_by_id.update(pattern.referenced_patterns_by_id())
# Get a table of (id(pat), written_dose) for each pattern and subpattern
sd_table = make_dose_table(patterns)
# Figure out all the unique doses necessary to write this pattern
# This means going through each row in sd_table and adding the dose values needed to write
# that subpattern at that dose level
dose_vals = set()
for pat_id, pat_dose in sd_table:
pat = patterns_by_id[pat_id]
[dose_vals.add(shape.dose * pat_dose) for shape in pat.shapes]
if len(dose_vals) > 256:
raise PatternError('Too many dose values: {}, maximum 256 when using dtypes.'.format(len(dose_vals)))
# Create a new pattern for each non-1-dose entry in the dose table
# and update the shapes to reflect their new dose
new_pats = {} # (id, dose) -> new_pattern mapping
for pat_id, pat_dose in sd_table:
if pat_dose == 1:
new_pats[(pat_id, pat_dose)] = patterns_by_id[pat_id]
continue
pat = patterns_by_id[pat_id].deepcopy()
encoded_name = mangle_name(pat, pat_dose).encode('ASCII')
if len(encoded_name) == 0:
raise PatternError('Zero-length name after mangle+encode, originally "{}"'.format(pat.name))
for shape in pat.shapes:
data_type = dose_vals_list.index(shape.dose * pat_dose)
if is_scalar(shape.layer):
layer = (shape.layer, data_type)
else:
layer = (shape.layer[0], data_type)
new_pats[(pat_id, pat_dose)] = pat
# Go back through all the dose-specific patterns and fix up their subpattern entries
for (pat_id, pat_dose), pat in new_pats.items():
for subpat in pat.subpatterns:
dose_mult = subpat.dose * pat_dose
subpat.pattern = new_pats[(id(subpat.pattern), dose_mult)]
return patterns, list(dose_vals)
def read_dtype2dose(filename: str) -> (List[Pattern], Dict[str, Any]):
"""
Alias for read(filename, use_dtype_as_dose=True)
"""
return read(filename, use_dtype_as_dose=True)
def read(filename: str,
use_dtype_as_dose: bool = False,
clean_vertices: bool = True,
) -> (Dict[str, Pattern], Dict[str, Any]):
"""
Read a gdsii file and translate it into a dict of Pattern objects. GDSII structures are
translated into Pattern objects; boundaries are translated into polygons, and srefs and arefs
are translated into SubPattern objects.
Additional library info is returned in a dict, containing:
'name': name of the library
'meters_per_unit': number of meters per database unit (all values are in database units)
'logical_units_per_unit': number of "logical" units displayed by layout tools (typically microns)
per database unit
:param filename: Filename specifying a GDSII file to read from.
:param use_dtype_as_dose: If false, set each polygon's layer to (gds_layer, gds_datatype).
If true, set the layer to gds_layer and the dose to gds_datatype.
Default False.
:param clean_vertices: If true, remove any redundant vertices when loading polygons.
The cleaning process removes any polygons with zero area or <3 vertices.
Default True.
:return: Tuple: (Dict of pattern_name:Patterns generated from GDSII structures, Dict of GDSII library info)
"""
with open(filename, mode='rb') as stream:
lib = gdsii.library.Library.load(stream)
library_info = {'name': lib.name.decode('ASCII'),
'meters_per_unit': lib.physical_unit,
'logical_units_per_unit': lib.logical_unit,
}
patterns = []
for structure in lib:
pat = Pattern(name=structure.name.decode('ASCII'))
for element in structure:
# Switch based on element type:
if isinstance(element, gdsii.elements.Boundary):
if use_dtype_as_dose:
shape = Polygon(vertices=element.xy[:-1],
dose=element.data_type,
layer=element.layer)
else:
shape = Polygon(vertices=element.xy[:-1],
layer=(element.layer, element.data_type))
if clean_vertices:
try:
shape.clean_vertices()
except PatternError:
continue
pat.shapes.append(shape)
if isinstance(element, gdsii.elements.Path):
if element.path_type == 0:
extension = 0.0
elif element.path_type in (1, 4):
raise PatternError('Round-ended and custom paths (types 1 and 4) are not implemented yet')
elif element.path_type == 2:
extension = element.width / 2
else:
raise PatternError('Unrecognized path type: {}'.format(element.path_type))
if element.width == 0:
continue
#TODO add extension
v = remove_colinear_vertices(numpy.array(element.xy, dtype=float), closed_path=False)
dv = numpy.diff(v, axis=0)
dvdir = dv / numpy.sqrt((dv * dv).sum(axis=1))[:, None]
v[0] -= dvdir[0] * extension
v[-1] += dvdir[-1] * extension
perp = dvdir[:, ::-1] * [1, -1] * element.width / 2
o0 = [v[0] + perp[0]]
o1 = [v[0] - perp[0]]
for i in range(1, dv.shape[0]):
towards_perp = numpy.dot(perp[i - 1], dv[i]) > 0 # bends towards previous perp
#straight = numpy.dot(perp[i - 1], dv[i]) == 0 # TODO maybe run without cleaning?
acute = numpy.dot(dv[i - 1], dv[i]) < 0
A = numpy.column_stack((dv[i - 1], -dv[i]))
ab = numpy.linalg.solve(A, v[i] + perp[i] - v[i - 1] - perp[i - 1])
cd = numpy.linalg.solve(A, v[i] - perp[i] - v[i - 1] + perp[i - 1])
perpside_intersection = v[i - 1] + ab[0] * dv[i - 1] + perp[i - 1]
otherside_intersection = v[i - 1] + cd[0] * dv[i - 1] - perp[i - 1]
if towards_perp:
o0.append(perpside_intersection)
if acute:
# Opposite is 180 > angle > 270
o1.append(otherside_intersection)
else:
# Opposite is >270
pt0 = v[i] - perp[i - 1] + dvdir[i - 1] * element.width / 2
pt1 = v[i] - perp[i] - dvdir[i] * element.width / 2
o1 += [pt0, pt1]
else:
# > 180, opposite is <180
o1.append(otherside_intersection)
print('oi', otherside_intersection)
if acute:
# > 270, opposite is <90
pt0 = v[i] + perp[i - 1] + dvdir[i - 1] * element.width / 2
pt1 = v[i] + perp[i] - dvdir[i] * element.width / 2
o0 += [pt0, pt1]
else:
# 180 > angle >270
o0.append(perpside_intersection)
o0.append(v[-1] + perp[-1])
o1.append(v[-1] - perp[-1])
verts = numpy.vstack((o0, o1[::-1]))
if use_dtype_as_dose:
shape = Polygon(vertices=verts,
dose=element.data_type,
layer=element.layer)
else:
shape = Polygon(vertices=verts,
layer=(element.layer, element.data_type))
if clean_vertices:
try:
shape.clean_vertices()
except PatternError as err:
print('error cleaning! {}'.format(err))
continue
pat.shapes.append(shape)
elif isinstance(element, gdsii.elements.Text):
label = Label(offset=element.xy,
layer=(element.layer, element.text_type),
string=element.string.decode('ASCII'))
pat.labels.append(label)
elif isinstance(element, gdsii.elements.SRef):
pat.subpatterns.append(_sref_to_subpat(element))
elif isinstance(element, gdsii.elements.ARef):
pat.subpatterns.append(_aref_to_gridrep(element))
patterns.append(pat)
# Create a dict of {pattern.name: pattern, ...}, then fix up all subpattern.pattern entries
# according to the subpattern.ref_name (which is deleted after use).
patterns_dict = dict(((p.name, p) for p in patterns))
for p in patterns_dict.values():
for sp in p.subpatterns:
sp.pattern = patterns_dict[sp.ref_name.decode('ASCII')]
del sp.ref_name
return patterns_dict, library_info
def _mlayer2gds(mlayer):
if is_scalar(mlayer):
layer = mlayer
data_type = 0
else:
layer = mlayer[0]
if len(mlayer) > 1:
data_type = mlayer[1]
else:
data_type = 0
return layer, data_type
def _sref_to_subpat(element: gdsii.elements.SRef) -> SubPattern:
# Helper function to create a SubPattern from an SREF. Sets subpat.pattern to None
# and sets the instance attribute .ref_name to the struct_name.
#
# BUG: "Absolute" means not affected by parent elements.
# That's not currently supported by masque at all, so need to either tag it and
# undo the parent transformations, or implement it in masque.
subpat = SubPattern(pattern=None, offset=element.xy)
subpat.ref_name = element.struct_name
if element.strans is not None:
if element.mag is not None:
subpat.scale = element.mag
# Bit 13 means absolute scale
if get_bit(element.strans, 15 - 13):
#subpat.offset *= subpat.scale
raise PatternError('Absolute scale is not implemented yet!')
if element.angle is not None:
subpat.rotation = element.angle * numpy.pi / 180
# Bit 14 means absolute rotation
if get_bit(element.strans, 15 - 14):
#subpat.offset = numpy.dot(rotation_matrix_2d(subpat.rotation), subpat.offset)
raise PatternError('Absolute rotation is not implemented yet!')
# Bit 0 means mirror x-axis
if get_bit(element.strans, 15 - 0):
subpat.mirror(axis=0)
return subpat
def _aref_to_gridrep(element: gdsii.elements.ARef) -> GridRepetition:
# Helper function to create a GridRepetition from an AREF. Sets gridrep.pattern to None
# and sets the instance attribute .ref_name to the struct_name.
#
# BUG: "Absolute" means not affected by parent elements.
# That's not currently supported by masque at all, so need to either tag it and
# undo the parent transformations, or implement it in masque.i
rotation = 0
offset = numpy.array(element.xy[0])
scale = 1
mirror_signs = numpy.ones(2)
if element.strans is not None:
if element.mag is not None:
scale = element.mag
# Bit 13 means absolute scale
if get_bit(element.strans, 15 - 13):
raise PatternError('Absolute scale is not implemented yet!')
if element.angle is not None:
rotation = element.angle * numpy.pi / 180
# Bit 14 means absolute rotation
if get_bit(element.strans, 15 - 14):
raise PatternError('Absolute rotation is not implemented yet!')
# Bit 0 means mirror x-axis
if get_bit(element.strans, 15 - 0):
mirror_signs[0] = -1
counts = [element.cols, element.rows]
vec_a0 = element.xy[1] - offset
vec_b0 = element.xy[2] - offset
a_vector = numpy.dot(rotation_matrix_2d(-rotation), vec_a0 / scale / counts[0]) * mirror_signs
b_vector = numpy.dot(rotation_matrix_2d(-rotation), vec_b0 / scale / counts[1]) * mirror_signs
gridrep = GridRepetition(pattern=None,
a_vector=a_vector,
b_vector=b_vector,
a_count=counts[0],
b_count=counts[1],
offset=offset,
rotation=rotation,
scale=scale,
mirrored=(mirror_signs == -1))
gridrep.ref_name = element.struct_name
return gridrep
def _subpatterns_to_refs(subpatterns: List[SubPattern or GridRepetition]
) -> List[gdsii.elements.ARef or gdsii.elements.SRef]:
# strans must be set for angle and mag to take effect
refs = []
for subpat in subpatterns:
encoded_name = subpat.pattern.name
if isinstance(subpat, GridRepetition):
mirror_signs = (-1) ** numpy.array(subpat.mirrored)
xy = numpy.array(subpat.offset) + [
[0, 0],
numpy.dot(rotation_matrix_2d(subpat.rotation), subpat.a_vector * mirror_signs) * subpat.scale * subpat.a_count,
numpy.dot(rotation_matrix_2d(subpat.rotation), subpat.b_vector * mirror_signs) * subpat.scale * subpat.b_count,
]
ref = gdsii.elements.ARef(struct_name=encoded_name,
xy=numpy.round(xy).astype(int),
cols=numpy.round(subpat.a_count).astype(int),
rows=numpy.round(subpat.b_count).astype(int))
else:
ref = gdsii.elements.SRef(struct_name=encoded_name,
xy=numpy.round([subpat.offset]).astype(int))
ref.strans = 0
ref.angle = subpat.rotation * 180 / numpy.pi
mirror_x, mirror_y = subpat.mirrored
if mirror_x and mirror_y:
ref.angle += 180
elif mirror_x:
ref.strans = set_bit(ref.strans, 15 - 0, True)
elif mirror_y:
ref.angle += 180
ref.strans = set_bit(ref.strans, 15 - 0, True)
ref.angle %= 360
ref.mag = subpat.scale
refs.append(ref)
return refs
def _shapes_to_boundaries(shapes: List[Shape]
) -> List[gdsii.elements.Boundary]:
# Add a Boundary element for each shape
boundaries = []
for shape in shapes:
layer, data_type = _mlayer2gds(shape.layer)
for polygon in shape.to_polygons():
xy_open = numpy.round(polygon.vertices + polygon.offset).astype(int)
xy_closed = numpy.vstack((xy_open, xy_open[0, :]))
boundaries.append(gdsii.elements.Boundary(layer=layer,
data_type=data_type,
xy=xy_closed))
return boundaries
def _labels_to_texts(labels: List[Label]) -> List[gdsii.elements.Text]:
texts = []
for label in labels:
layer, text_type = _mlayer2gds(label.layer)
xy = numpy.round([label.offset]).astype(int)
texts.append(gdsii.elements.Text(layer=layer,
text_type=text_type,
xy=xy,
string=label.string.encode('ASCII')))
return texts
def _disambiguate_pattern_names(patterns):
used_names = []
for pat in patterns:
sanitized_name = re.compile('[^A-Za-z0-9_\?\$]').sub('_', pat.name)
i = 0
suffixed_name = sanitized_name
while suffixed_name in used_names or suffixed_name == '':
suffix = base64.b64encode(struct.pack('>Q', i), b'$?').decode('ASCII')
suffixed_name = sanitized_name + '$' + suffix[:-1].lstrip('A')
i += 1
if sanitized_name == '':
logger.warning('Empty pattern name saved as "{}"'.format(suffixed_name))
elif suffixed_name != sanitized_name:
logger.warning('Pattern name "{}" appears multiple times; renaming to "{}"'.format(pat.name, suffixed_name))
encoded_name = suffixed_name.encode('ASCII')
if len(encoded_name) == 0:
# Should never happen since zero-length names are replaced
raise PatternError('Zero-length name after sanitize+encode, originally "{}"'.format(pat.name))
if len(encoded_name) > 32:
raise PatternError('Pattern name "{}" length > 32 after encode, originally "{}"'.format(encoded_name, pat.name))
pat.name = encoded_name
used_names.append(suffixed_name)

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"""
SVG file format readers and writers
"""
import svgwrite
import numpy
from .utils import mangle_name
from .. import Pattern
__author__ = 'Jan Petykiewicz'
def write(pattern: Pattern,
filename: str,
custom_attributes: bool=False):
"""
Write a Pattern to an SVG file, by first calling .polygonize() on it
to change the shapes into polygons, and then writing patterns as SVG
groups (<g>, inside <defs>), polygons as paths (<path>), and subpatterns
as <use> elements.
Note that this function modifies the Pattern.
If custom_attributes is True, non-standard pattern_layer and pattern_dose attributes
are written to the relevant elements.
It is often a good idea to run pattern.subpatternize() on pattern prior to
calling this function, especially if calling .polygonize() will result in very
many vertices.
If you want pattern polygonized with non-default arguments, just call pattern.polygonize()
prior to calling this function.
:param pattern: Pattern to write to file. Modified by this function.
:param filename: Filename to write to.
:param custom_attributes: Whether to write non-standard pattern_layer and
pattern_dose attributes to the SVG elements.
"""
# Polygonize pattern
pattern.polygonize()
[bounds_min, bounds_max] = pattern.get_bounds()
viewbox = numpy.hstack((bounds_min - 1, (bounds_max - bounds_min) + 2))
viewbox_string = '{:g} {:g} {:g} {:g}'.format(*viewbox)
# Create file
svg = svgwrite.Drawing(filename, profile='full', viewBox=viewbox_string,
debug=(not custom_attributes))
# Get a dict of id(pattern) -> pattern
patterns_by_id = {**(pattern.referenced_patterns_by_id()), id(pattern): pattern}
# Now create a group for each row in sd_table (ie, each pattern + dose combination)
# and add in any Boundary and Use elements
for pat in patterns_by_id.values():
svg_group = svg.g(id=mangle_name(pat), fill='blue', stroke='red')
for shape in pat.shapes:
for polygon in shape.to_polygons():
path_spec = poly2path(polygon.vertices + polygon.offset)
path = svg.path(d=path_spec)
if custom_attributes:
path['pattern_layer'] = polygon.layer
path['pattern_dose'] = polygon.dose
svg_group.add(path)
for subpat in pat.subpatterns:
transform = 'scale({:g}) rotate({:g}) translate({:g},{:g})'.format(
subpat.scale, subpat.rotation, subpat.offset[0], subpat.offset[1])
use = svg.use(href='#' + mangle_name(subpat.pattern), transform=transform)
if custom_attributes:
use['pattern_dose'] = subpat.dose
svg_group.add(use)
svg.defs.add(svg_group)
svg.add(svg.use(href='#' + mangle_name(pattern)))
svg.save()
def write_inverted(pattern: Pattern, filename: str):
"""
Write an inverted Pattern to an SVG file, by first calling .polygonize() and
.flatten() on it to change the shapes into polygons, then drawing a bounding
box and drawing the polygons with reverse vertex order inside it, all within
one <path> element.
Note that this function modifies the Pattern.
If you want pattern polygonized with non-default arguments, just call pattern.polygonize()
prior to calling this function.
:param pattern: Pattern to write to file. Modified by this function.
:param filename: Filename to write to.
"""
# Polygonize and flatten pattern
pattern.polygonize().flatten()
[bounds_min, bounds_max] = pattern.get_bounds()
viewbox = numpy.hstack((bounds_min - 1, (bounds_max - bounds_min) + 2))
viewbox_string = '{:g} {:g} {:g} {:g}'.format(*viewbox)
# Create file
svg = svgwrite.Drawing(filename, profile='full', viewBox=viewbox_string)
# Draw bounding box
slab_edge = [[bounds_min[0] - 1, bounds_max[1] + 1],
[bounds_max[0] + 1, bounds_max[1] + 1],
[bounds_max[0] + 1, bounds_min[1] - 1],
[bounds_min[0] - 1, bounds_min[1] - 1]]
path_spec = poly2path(slab_edge)
# Draw polygons with reversed vertex order
for shape in pattern.shapes:
for polygon in shape.to_polygons():
path_spec += poly2path(polygon.vertices[::-1] + polygon.offset)
svg.add(svg.path(d=path_spec, fill='blue', stroke='red'))
svg.save()
def poly2path(vertices: numpy.ndarray) -> str:
"""
Create an SVG path string from an Nx2 list of vertices.
:param vertices: Nx2 array of vertices.
:return: SVG path-string.
"""
commands = 'M{:g},{:g} '.format(vertices[0][0], vertices[0][1])
for vertex in vertices[1:]:
commands += 'L{:g},{:g}'.format(vertex[0], vertex[1])
commands += ' Z '
return commands

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"""
Helper functions for file reading and writing
"""
import re
from typing import Set, Tuple, List
from masque.pattern import Pattern
__author__ = 'Jan Petykiewicz'
def mangle_name(pattern: Pattern, dose_multiplier: float=1.0) -> str:
"""
Create a name using pattern.name, id(pattern), and the dose multiplier.
:param pattern: Pattern whose name we want to mangle.
:param dose_multiplier: Dose multiplier to mangle with.
:return: Mangled name.
"""
expression = re.compile('[^A-Za-z0-9_\?\$]')
full_name = '{}_{}_{}'.format(pattern.name, dose_multiplier, id(pattern))
sanitized_name = expression.sub('_', full_name)
return sanitized_name
def make_dose_table(patterns: List[Pattern], dose_multiplier: float=1.0) -> Set[Tuple[int, float]]:
"""
Create a set containing (id(pat), written_dose) for each pattern (including subpatterns)
:param pattern: Source Patterns.
:param dose_multiplier: Multiplier for all written_dose entries.
:return: {(id(subpat.pattern), written_dose), ...}
"""
dose_table = {(id(pattern), dose_multiplier) for pattern in patterns}
for pattern in patterns:
for subpat in pattern.subpatterns:
subpat_dose_entry = (id(subpat.pattern), subpat.dose * dose_multiplier)
if subpat_dose_entry not in dose_table:
subpat_dose_table = make_dose_table([subpat.pattern], subpat.dose * dose_multiplier)
dose_table = dose_table.union(subpat_dose_table)
return dose_table

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from typing import List, Tuple
import copy
import numpy
from numpy import pi
from . import PatternError
from .utils import is_scalar, vector2, rotation_matrix_2d
__author__ = 'Jan Petykiewicz'
class Label:
"""
A circle, which has a position and radius.
"""
# [x_offset, y_offset]
_offset = numpy.array([0.0, 0.0]) # type: numpy.ndarray
# Layer (integer >= 0)
_layer = 0 # type: int or Tuple
# Label string
_string = None # type: str
# ---- Properties
# offset property
@property
def offset(self) -> numpy.ndarray:
"""
[x, y] offset
:return: [x_offset, y_offset]
"""
return self._offset
@offset.setter
def offset(self, val: vector2):
if not isinstance(val, numpy.ndarray):
val = numpy.array(val, dtype=float)
if val.size != 2:
raise PatternError('Offset must be convertible to size-2 ndarray')
self._offset = val.flatten()
# layer property
@property
def layer(self) -> int or Tuple[int]:
"""
Layer number (int or tuple of ints)
:return: Layer
"""
return self._layer
@layer.setter
def layer(self, val: int or List[int]):
self._layer = val
# string property
@property
def string(self) -> str:
"""
Label string (str)
:return: string
"""
return self._string
@string.setter
def string(self, val: str):
self._string = val
def __init__(self,
string: str,
offset: vector2=(0.0, 0.0),
layer: int=0):
self.string = string
self.offset = numpy.array(offset, dtype=float)
self.layer = layer
# ---- Non-abstract methods
def copy(self) -> 'Label':
"""
Returns a deep copy of the shape.
:return: Deep copy of self
"""
return copy.deepcopy(self)
def translate(self, offset: vector2) -> 'Label':
"""
Translate the shape by the given offset
:param offset: [x_offset, y,offset]
:return: self
"""
self.offset += offset
return self
def rotate_around(self, pivot: vector2, rotation: float) -> 'Label':
"""
Rotate the shape around a point.
:param pivot: Point (x, y) to rotate around
:param rotation: Angle to rotate by (counterclockwise, radians)
:return: self
"""
pivot = numpy.array(pivot, dtype=float)
self.translate(-pivot)
self.offset = numpy.dot(rotation_matrix_2d(rotation), self.offset)
self.translate(+pivot)
return self
def get_bounds(self) -> numpy.ndarray:
"""
Return the bounds of the label.
Labels are assumed to take up 0 area, i.e.
bounds = [self.offset,
self.offset]
:return: Bounds [[xmin, xmax], [ymin, ymax]]
"""
return numpy.array([self.offset, self.offset])

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"""
Base object for containing a lithography mask.
"""
from typing import List, Callable, Tuple, Dict, Union
import copy
import itertools
import pickle
from collections import defaultdict
import numpy
# .visualize imports matplotlib and matplotlib.collections
from .subpattern import SubPattern
from .repetition import GridRepetition
from .shapes import Shape, Polygon
from .label import Label
from .utils import rotation_matrix_2d, vector2
from .error import PatternError
__author__ = 'Jan Petykiewicz'
class Pattern:
"""
2D layout consisting of some set of shapes and references to other Pattern objects
(via SubPattern). Shapes are assumed to inherit from .shapes.Shape or provide equivalent
functions.
:var shapes: List of all shapes in this Pattern. Elements in this list are assumed to inherit
from Shape or provide equivalent functions.
:var subpatterns: List of all SubPattern objects in this Pattern. Multiple SubPattern objects
may reference the same Pattern object.
:var name: An identifier for this object. Not necessarily unique.
"""
shapes = None # type: List[Shape]
labels = None # type: List[Labels]
subpatterns = None # type: List[SubPattern or GridRepetition]
name = None # type: str
def __init__(self,
shapes: List[Shape]=(),
labels: List[Label]=(),
subpatterns: List[SubPattern]=(),
name: str='',
):
"""
Basic init; arguments get assigned to member variables.
Non-list inputs for shapes and subpatterns get converted to lists.
:param shapes: Initial shapes in the Pattern
:param labels: Initial labels in the Pattern
:param subpatterns: Initial subpatterns in the Pattern
:param name: An identifier for the Pattern
"""
if isinstance(shapes, list):
self.shapes = shapes
else:
self.shapes = list(shapes)
if isinstance(labels, list):
self.labels = labels
else:
self.labels = list(labels)
if isinstance(subpatterns, list):
self.subpatterns = subpatterns
else:
self.subpatterns = list(subpatterns)
self.name = name
def append(self, other_pattern: 'Pattern') -> 'Pattern':
"""
Appends all shapes, labels and subpatterns from other_pattern to self's shapes,
labels, and supbatterns.
:param other_pattern: The Pattern to append
:return: self
"""
self.subpatterns += other_pattern.subpatterns
self.shapes += other_pattern.shapes
self.labels += other_pattern.labels
return self
def subset(self,
shapes_func: Callable[[Shape], bool]=None,
labels_func: Callable[[Label], bool]=None,
subpatterns_func: Callable[[SubPattern], bool]=None,
recursive: bool=False,
) -> 'Pattern':
"""
Returns a Pattern containing only the entities (e.g. shapes) for which the
given entity_func returns True.
Self is _not_ altered, but shapes, labels, and subpatterns are _not_ copied.
:param shapes_func: Given a shape, returns a boolean denoting whether the shape is a member
of the subset. Default always returns False.
:param labels_func: Given a label, returns a boolean denoting whether the label is a member
of the subset. Default always returns False.
:param subpatterns_func: Given a subpattern, returns a boolean denoting if it is a member
of the subset. Default always returns False.
:param recursive: If True, also calls .subset() recursively on patterns referenced by this
pattern.
:return: A Pattern containing all the shapes and subpatterns for which the parameter
functions return True
"""
def do_subset(src):
pat = Pattern(name=src.name)
if shapes_func is not None:
pat.shapes = [s for s in src.shapes if shapes_func(s)]
if labels_func is not None:
pat.labels = [s for s in src.labels if labels_func(s)]
if subpatterns_func is not None:
pat.subpatterns = [s for s in src.subpatterns if subpatterns_func(s)]
return pat
if recursive:
pat = self.apply(do_subset)
else:
pat = do_subset(self)
return pat
def apply(self,
func: Callable[['Pattern'], 'Pattern'],
memo: Dict[int, 'Pattern']=None,
) -> 'Pattern':
"""
Recursively apply func() to this pattern and any pattern it references.
func() is expected to take and return a Pattern.
func() is first applied to the pattern as a whole, then any referenced patterns.
It is only applied to any given pattern once, regardless of how many times it is
referenced.
:param func: Function which accepts a Pattern, and returns a pattern.
:param memo: Dictionary used to avoid re-running on multiply-referenced patterns.
Stores {id(pattern): func(pattern)} for patterns which have already been processed.
Default None (no already-processed patterns).
:return: The result of applying func() to this pattern and all subpatterns.
:raises: PatternError if called on a pattern containing a circular reference.
"""
if memo is None:
memo = {}
pat_id = id(self)
if pat_id not in memo:
memo[pat_id] = None
pat = func(self)
for subpat in pat.subpatterns:
subpat.pattern = subpat.pattern.apply(func, memo)
memo[pat_id] = pat
elif memo[pat_id] is None:
raise PatternError('.apply() called on pattern with circular reference')
else:
pat = memo[pat_id]
return pat
def polygonize(self,
poly_num_points: int=None,
poly_max_arclen: float=None
) -> 'Pattern':
"""
Calls .to_polygons(...) on all the shapes in this Pattern and any referenced patterns,
replacing them with the returned polygons.
Arguments are passed directly to shape.to_polygons(...).
:param poly_num_points: Number of points to use for each polygon. Can be overridden by
poly_max_arclen if that results in more points. Optional, defaults to shapes'
internal defaults.
:param poly_max_arclen: Maximum arclength which can be approximated by a single line
segment. Optional, defaults to shapes' internal defaults.
:return: self
"""
old_shapes = self.shapes
self.shapes = list(itertools.chain.from_iterable(
(shape.to_polygons(poly_num_points, poly_max_arclen)
for shape in old_shapes)))
for subpat in self.subpatterns:
subpat.pattern.polygonize(poly_num_points, poly_max_arclen)
return self
def manhattanize(self,
grid_x: numpy.ndarray,
grid_y: numpy.ndarray
) -> 'Pattern':
"""
Calls .polygonize() and .flatten on the pattern, then calls .manhattanize() on all the
resulting shapes, replacing them with the returned Manhattan polygons.
:param grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
:param grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
:return: self
"""
self.polygonize().flatten()
old_shapes = self.shapes
self.shapes = list(itertools.chain.from_iterable(
(shape.manhattanize(grid_x, grid_y) for shape in old_shapes)))
return self
def subpatternize(self,
recursive: bool=True,
norm_value: int=1e6,
exclude_types: Tuple[Shape]=(Polygon,)
) -> 'Pattern':
"""
Iterates through this Pattern and all referenced Patterns. Within each Pattern, it iterates
over all shapes, calling .normalized_form(norm_value) on them to retrieve a scale-,
offset-, dose-, and rotation-independent form. Each shape whose normalized form appears
more than once is removed and re-added using subpattern objects referencing a newly-created
Pattern containing only the normalized form of the shape.
Note that the default norm_value was chosen to give a reasonable precision when converting
to GDSII, which uses integer values for pixel coordinates.
:param recursive: Whether to call recursively on self's subpatterns. Default True.
:param norm_value: Passed to shape.normalized_form(norm_value). Default 1e6 (see function
note about GDSII)
:param exclude_types: Shape types passed in this argument are always left untouched, for
speed or convenience. Default: (Shapes.Polygon,)
:return: self
"""
if exclude_types is None:
exclude_types = ()
if recursive:
for subpat in self.subpatterns:
subpat.pattern.subpatternize(recursive=True,
norm_value=norm_value,
exclude_types=exclude_types)
# Create a dict which uses the label tuple from .normalized_form() as a key, and which
# stores (function_to_create_normalized_shape, [(index_in_shapes, values), ...]), where
# values are the (offset, scale, rotation, dose) values as calculated by .normalized_form()
shape_table = defaultdict(lambda: [None, list()])
for i, shape in enumerate(self.shapes):
if not any((isinstance(shape, t) for t in exclude_types)):
label, values, func = shape.normalized_form(norm_value)
shape_table[label][0] = func
shape_table[label][1].append((i, values))
# Iterate over the normalized shapes in the table. If any normalized shape occurs more than
# once, create a Pattern holding a normalized shape object, and add self.subpatterns
# entries for each occurrence in self. Also, note down that we should delete the
# self.shapes entries for which we made SubPatterns.
shapes_to_remove = []
for label in shape_table:
if len(shape_table[label][1]) > 1:
shape = shape_table[label][0]()
pat = Pattern(shapes=[shape])
for i, values in shape_table[label][1]:
(offset, scale, rotation, dose) = values
subpat = SubPattern(pattern=pat, offset=offset, scale=scale,
rotation=rotation, dose=dose)
self.subpatterns.append(subpat)
shapes_to_remove.append(i)
# Remove any shapes for which we have created subpatterns.
for i in sorted(shapes_to_remove, reverse=True):
del self.shapes[i]
return self
def as_polygons(self) -> List[numpy.ndarray]:
"""
Represents the pattern as a list of polygons.
Deep-copies the pattern, then calls .polygonize() and .flatten() on the copy in order to
generate the list of polygons.
:return: A list of (Ni, 2) numpy.ndarrays specifying vertices of the polygons. Each ndarray
is of the form [[x0, y0], [x1, y1],...].
"""
pat = copy.deepcopy(self).polygonize().flatten()
return [shape.vertices + shape.offset for shape in pat.shapes]
def referenced_patterns_by_id(self) -> Dict[int, 'Pattern']:
"""
Create a dictionary of {id(pat): pat} for all Pattern objects referenced by this
Pattern (operates recursively on all referenced Patterns as well)
:return: Dictionary of {id(pat): pat} for all referenced Pattern objects
"""
ids = {}
for subpat in self.subpatterns:
if id(subpat.pattern) not in ids:
ids[id(subpat.pattern)] = subpat.pattern
ids.update(subpat.pattern.referenced_patterns_by_id())
return ids
def get_bounds(self) -> Union[numpy.ndarray, None]:
"""
Return a numpy.ndarray containing [[x_min, y_min], [x_max, y_max]], corresponding to the
extent of the Pattern's contents in each dimension.
Returns None if the Pattern is empty.
:return: [[x_min, y_min], [x_max, y_max]] or None
"""
entries = self.shapes + self.subpatterns + self.labels
if not entries:
return None
init_bounds = entries[0].get_bounds()
min_bounds = init_bounds[0, :]
max_bounds = init_bounds[1, :]
for entry in entries[1:]:
bounds = entry.get_bounds()
min_bounds = numpy.minimum(min_bounds, bounds[0, :])
max_bounds = numpy.maximum(max_bounds, bounds[1, :])
return numpy.vstack((min_bounds, max_bounds))
def flatten(self) -> 'Pattern':
"""
Removes all subpatterns and adds equivalent shapes.
:return: self
"""
subpatterns = copy.deepcopy(self.subpatterns)
self.subpatterns = []
for subpat in subpatterns:
subpat.pattern.flatten()
p = subpat.as_pattern()
self.shapes += p.shapes
self.labels += p.labels
return self
def translate_elements(self, offset: vector2) -> 'Pattern':
"""
Translates all shapes, label, and subpatterns by the given offset.
:param offset: Offset to translate by
:return: self
"""
for entry in self.shapes + self.subpatterns + self.labels:
entry.translate(offset)
return self
def scale_elements(self, scale: float) -> 'Pattern':
""""
Scales all shapes and subpatterns by the given value.
:param scale: value to scale by
:return: self
"""
for entry in self.shapes + self.subpatterns:
entry.scale(scale)
return self
def scale_by(self, c: float) -> 'Pattern':
"""
Scale this Pattern by the given value
(all shapes and subpatterns and their offsets are scaled)
:param c: value to scale by
:return: self
"""
for entry in self.shapes + self.subpatterns:
entry.offset *= c
entry.scale_by(c)
return self
def rotate_around(self, pivot: vector2, rotation: float) -> 'Pattern':
"""
Rotate the Pattern around the a location.
:param pivot: Location to rotate around
:param rotation: Angle to rotate by (counter-clockwise, radians)
:return: self
"""
pivot = numpy.array(pivot)
self.translate_elements(-pivot)
self.rotate_elements(rotation)
self.rotate_element_centers(rotation)
self.translate_elements(+pivot)
return self
def rotate_element_centers(self, rotation: float) -> 'Pattern':
"""
Rotate the offsets of all shapes, labels, and subpatterns around (0, 0)
:param rotation: Angle to rotate by (counter-clockwise, radians)
:return: self
"""
for entry in self.shapes + self.subpatterns + self.labels:
entry.offset = numpy.dot(rotation_matrix_2d(rotation), entry.offset)
return self
def rotate_elements(self, rotation: float) -> 'Pattern':
"""
Rotate each shape and subpattern around its center (offset)
:param rotation: Angle to rotate by (counter-clockwise, radians)
:return: self
"""
for entry in self.shapes + self.subpatterns:
entry.rotate(rotation)
return self
def mirror_element_centers(self, axis: int) -> 'Pattern':
"""
Mirror the offsets of all shapes, labels, and subpatterns across an axis
:param axis: Axis to mirror across
:return: self
"""
for entry in self.shapes + self.subpatterns + self.labels:
entry.offset[axis - 1] *= -1
return self
def mirror_elements(self, axis: int) -> 'Pattern':
"""
Mirror each shape and subpattern across an axis, relative to its
center (offset)
:param axis: Axis to mirror across
:return: self
"""
for entry in self.shapes + self.subpatterns:
entry.mirror(axis)
return self
def mirror(self, axis: int) -> 'Pattern':
"""
Mirror the Pattern across an axis
:param axis: Axis to mirror across
:return: self
"""
self.mirror_elements(axis)
self.mirror_element_centers(axis)
return self
def scale_element_doses(self, factor: float) -> 'Pattern':
"""
Multiply all shape and subpattern doses by a factor
:param factor: Factor to multiply doses by
:return: self
"""
for entry in self.shapes + self.subpatterns:
entry.dose *= factor
return self
def copy(self) -> 'Pattern':
"""
Return a copy of the Pattern, deep-copying shapes and copying subpattern entries, but not
deep-copying any referenced patterns.
See also: Pattern.deepcopy()
:return: A copy of the current Pattern.
"""
cp = copy.copy(self)
cp.shapes = copy.deepcopy(cp.shapes)
cp.labels = copy.deepcopy(cp.labels)
cp.subpatterns = [copy.copy(subpat) for subpat in cp.subpatterns]
return cp
def deepcopy(self) -> 'Pattern':
"""
Convenience method for copy.deepcopy(pattern)
:return: A deep copy of the current Pattern.
"""
return copy.deepcopy(self)
@staticmethod
def load(filename: str) -> 'Pattern':
"""
Load a Pattern from a file
:param filename: Filename to load from
:return: Loaded Pattern
"""
with open(filename, 'rb') as f:
tmp_dict = pickle.load(f)
pattern = Pattern()
pattern.__dict__.update(tmp_dict)
return pattern
def save(self, filename: str) -> 'Pattern':
"""
Save the Pattern to a file
:param filename: Filename to save to
:return: self
"""
with open(filename, 'wb') as f:
pickle.dump(self.__dict__, f, protocol=2)
return self
def visualize(self,
offset: vector2=(0., 0.),
line_color: str='k',
fill_color: str='none',
overdraw: bool=False):
"""
Draw a picture of the Pattern and wait for the user to inspect it
Imports matplotlib.
:param offset: Coordinates to offset by before drawing
:param line_color: Outlines are drawn with this color (passed to matplotlib PolyCollection)
:param fill_color: Interiors are drawn with this color (passed to matplotlib PolyCollection)
:param overdraw: Whether to create a new figure or draw on a pre-existing one
"""
# TODO: add text labels to visualize()
from matplotlib import pyplot
import matplotlib.collections
offset = numpy.array(offset, dtype=float)
if not overdraw:
figure = pyplot.figure()
pyplot.axis('equal')
else:
figure = pyplot.gcf()
axes = figure.gca()
polygons = []
for shape in self.shapes:
polygons += [offset + s.offset + s.vertices for s in shape.to_polygons()]
mpl_poly_collection = matplotlib.collections.PolyCollection(polygons,
facecolors=fill_color,
edgecolors=line_color)
axes.add_collection(mpl_poly_collection)
pyplot.axis('equal')
for subpat in self.subpatterns:
subpat.as_pattern().visualize(offset=offset, overdraw=True,
line_color=line_color, fill_color=fill_color)
if not overdraw:
pyplot.show()

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"""
Repetitions provides support for efficiently nesting multiple identical
instances of a Pattern in the same parent Pattern.
"""
from typing import Union, List
import copy
import numpy
from numpy import pi
from .error import PatternError
from .utils import is_scalar, rotation_matrix_2d, vector2
__author__ = 'Jan Petykiewicz'
# TODO need top-level comment about what order rotation/scale/offset/mirror/array are applied
class GridRepetition:
"""
GridRepetition provides support for efficiently embedding multiple copies of a Pattern
into another Pattern at regularly-spaced offsets.
"""
pattern = None # type: Pattern
_offset = (0.0, 0.0) # type: numpy.ndarray
_rotation = 0.0 # type: float
_dose = 1.0 # type: float
_scale = 1.0 # type: float
_mirrored = None # type: List[bool]
_a_vector = None # type: numpy.ndarray
_b_vector = None # type: numpy.ndarray
a_count = None # type: int
b_count = 1 # type: int
def __init__(self,
pattern: 'Pattern',
a_vector: numpy.ndarray,
a_count: int,
b_vector: numpy.ndarray = None,
b_count: int = 1,
offset: vector2 = (0.0, 0.0),
rotation: float = 0.0,
mirrored: List[bool] = None,
dose: float = 1.0,
scale: float = 1.0):
"""
:param a_vector: First lattice vector, of the form [x, y].
Specifies center-to-center spacing between adjacent elements.
:param a_count: Number of elements in the a_vector direction.
:param b_vector: Second lattice vector, of the form [x, y].
Specifies center-to-center spacing between adjacent elements.
Can be omitted when specifying a 1D array.
:param b_count: Number of elements in the b_vector direction.
Should be omitted if b_vector was omitted.
:raises: InvalidDataError if b_* inputs conflict with each other
or a_count < 1.
"""
if b_vector is None:
if b_count > 1:
raise PatternError('Repetition has b_count > 1 but no b_vector')
else:
b_vector = numpy.array([0.0, 0.0])
if a_count < 1:
raise InvalidDataError('Repetition has too-small a_count: '
'{}'.format(a_count))
if b_count < 1:
raise InvalidDataError('Repetition has too-small b_count: '
'{}'.format(b_count))
self.a_vector = a_vector
self.b_vector = b_vector
self.a_count = a_count
self.b_count = b_count
self.pattern = pattern
self.offset = offset
self.rotation = rotation
self.dose = dose
self.scale = scale
if mirrored is None:
mirrored = [False, False]
self.mirrored = mirrored
# offset property
@property
def offset(self) -> numpy.ndarray:
return self._offset
@offset.setter
def offset(self, val: vector2):
if not isinstance(val, numpy.ndarray):
val = numpy.array(val, dtype=float)
if val.size != 2:
raise PatternError('Offset must be convertible to size-2 ndarray')
self._offset = val.flatten().astype(float)
# dose property
@property
def dose(self) -> float:
return self._dose
@dose.setter
def dose(self, val: float):
if not is_scalar(val):
raise PatternError('Dose must be a scalar')
if not val >= 0:
raise PatternError('Dose must be non-negative')
self._dose = val
# scale property
@property
def scale(self) -> float:
return self._scale
@scale.setter
def scale(self, val: float):
if not is_scalar(val):
raise PatternError('Scale must be a scalar')
if not val > 0:
raise PatternError('Scale must be positive')
self._scale = val
# Rotation property [ccw]
@property
def rotation(self) -> float:
return self._rotation
@rotation.setter
def rotation(self, val: float):
if not is_scalar(val):
raise PatternError('Rotation must be a scalar')
self._rotation = val % (2 * pi)
# Mirrored property
@property
def mirrored(self) -> List[bool]:
return self._mirrored
@mirrored.setter
def mirrored(self, val: List[bool]):
if is_scalar(val):
raise PatternError('Mirrored must be a 2-element list of booleans')
self._mirrored = val
# a_vector property
@property
def a_vector(self) -> numpy.ndarray:
return self._a_vector
@a_vector.setter
def a_vector(self, val: vector2):
if not isinstance(val, numpy.ndarray):
val = numpy.array(val, dtype=float)
if val.size != 2:
raise PatternError('a_vector must be convertible to size-2 ndarray')
self._a_vector = val.flatten()
# b_vector property
@property
def b_vector(self) -> numpy.ndarray:
return self._b_vector
@b_vector.setter
def b_vector(self, val: vector2):
if not isinstance(val, numpy.ndarray):
val = numpy.array(val, dtype=float)
if val.size != 2:
raise PatternError('b_vector must be convertible to size-2 ndarray')
self._b_vector = val.flatten()
def as_pattern(self) -> 'Pattern':
"""
Returns a copy of self.pattern which has been scaled, rotated, etc. according to this
SubPattern's properties.
:return: Copy of self.pattern that has been altered to reflect the SubPattern's properties.
"""
#xy = numpy.array(element.xy)
#origin = xy[0]
#col_spacing = (xy[1] - origin) / element.cols
#row_spacing = (xy[2] - origin) / element.rows
patterns = []
for a in range(self.a_count):
for b in range(self.b_count):
offset = a * self.a_vector + b * self.b_vector
newPat = self.pattern.deepcopy()
newPat.translate_elements(offset)
patterns.append(newPat)
combined = patterns[0]
for p in patterns[1:]:
combined.append(p)
combined.scale_by(self.scale)
[combined.mirror(ax) for ax, do in enumerate(self.mirrored) if do]
combined.rotate_around((0.0, 0.0), self.rotation)
combined.translate_elements(self.offset)
combined.scale_element_doses(self.dose)
return combined
def translate(self, offset: vector2) -> 'GridRepetition':
"""
Translate by the given offset
:param offset: Translate by this offset
:return: self
"""
self.offset += offset
return self
def rotate_around(self, pivot: vector2, rotation: float) -> 'GridRepetition':
"""
Rotate around a point
:param pivot: Point to rotate around
:param rotation: Angle to rotate by (counterclockwise, radians)
:return: self
"""
pivot = numpy.array(pivot, dtype=float)
self.translate(-pivot)
self.offset = numpy.dot(rotation_matrix_2d(rotation), self.offset)
self.rotate(rotation)
self.translate(+pivot)
return self
def rotate(self, rotation: float) -> 'GridRepetition':
"""
Rotate around (0, 0)
:param rotation: Angle to rotate by (counterclockwise, radians)
:return: self
"""
self.rotation += rotation
return self
def mirror(self, axis: int) -> 'GridRepetition':
"""
Mirror the subpattern across an axis.
:param axis: Axis to mirror across.
:return: self
"""
self.mirrored[axis] = not self.mirrored[axis]
return self
def get_bounds(self) -> numpy.ndarray or None:
"""
Return a numpy.ndarray containing [[x_min, y_min], [x_max, y_max]], corresponding to the
extent of the SubPattern in each dimension.
Returns None if the contained Pattern is empty.
:return: [[x_min, y_min], [x_max, y_max]] or None
"""
return self.as_pattern().get_bounds()
def scale_by(self, c: float) -> 'GridRepetition':
"""
Scale the subpattern by a factor
:param c: scaling factor
"""
self.scale *= c
return self
def copy(self) -> 'GridRepetition':
"""
Return a shallow copy of the repetition.
:return: copy.copy(self)
"""
return copy.copy(self)
def deepcopy(self) -> 'SubPattern':
"""
Return a deep copy of the repetition.
:return: copy.copy(self)
"""
return copy.deepcopy(self)

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"""
Shapes for use with the Pattern class, as well as the Shape abstract class from
which they are derived.
"""
from .shape import Shape, normalized_shape_tuple, DEFAULT_POLY_NUM_POINTS
from .polygon import Polygon
from .circle import Circle
from .ellipse import Ellipse
from .arc import Arc
from .text import Text

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from typing import List
import math
import numpy
from numpy import pi
from . import Shape, Polygon, normalized_shape_tuple, DEFAULT_POLY_NUM_POINTS
from .. import PatternError
from ..utils import is_scalar, vector2
__author__ = 'Jan Petykiewicz'
class Arc(Shape):
"""
An elliptical arc, formed by cutting off an elliptical ring with two rays which exit from its
center. It has a position, two radii, a start and stop angle, a rotation, and a width.
The radii define an ellipse; the ring is formed with radii +/- width/2.
The rotation gives the angle from x-axis, counterclockwise, to the first (x) radius.
The start and stop angle are measured counterclockwise from the first (x) radius.
"""
_radii = None # type: numpy.ndarray
_angles = None # type: numpy.ndarray
_width = 1.0 # type: float
_rotation = 0.0 # type: float
# Defaults for to_polygons
poly_num_points = DEFAULT_POLY_NUM_POINTS # type: int
poly_max_arclen = None # type: float
# radius properties
@property
def radii(self) -> numpy.ndarray:
"""
Return the radii [rx, ry]
:return: [rx, ry]
"""
return self._radii
@radii.setter
def radii(self, val: vector2):
val = numpy.array(val, dtype=float).flatten()
if not val.size == 2:
raise PatternError('Radii must have length 2')
if not val.min() >= 0:
raise PatternError('Radii must be non-negative')
self._radii = val
@property
def radius_x(self) -> float:
return self._radii[0]
@radius_x.setter
def radius_x(self, val: float):
if not val >= 0:
raise PatternError('Radius must be non-negative')
self._radii[0] = val
@property
def radius_y(self) -> float:
return self._radii[1]
@radius_y.setter
def radius_y(self, val: float):
if not val >= 0:
raise PatternError('Radius must be non-negative')
self._radii[1] = val
# arc start/stop angle properties
@property
def angles(self) -> vector2:
"""
Return the start and stop angles [a_start, a_stop].
Angles are measured from x-axis after rotation
:return: [a_start, a_stop]
"""
return self._angles
@angles.setter
def angles(self, val: vector2):
val = numpy.array(val, dtype=float).flatten()
if not val.size == 2:
raise PatternError('Angles must have length 2')
self._angles = val
@property
def start_angle(self) -> float:
return self.angles[0]
@start_angle.setter
def start_angle(self, val: float):
self.angles = (val, self.angles[1])
@property
def stop_angle(self) -> float:
return self.angles[1]
@stop_angle.setter
def stop_angle(self, val: float):
self.angles = (self.angles[0], val)
# Rotation property
@property
def rotation(self) -> float:
"""
Rotation of radius_x from x_axis, counterclockwise, in radians. Stored mod 2*pi
:return: rotation counterclockwise in radians
"""
return self._rotation
@rotation.setter
def rotation(self, val: float):
if not is_scalar(val):
raise PatternError('Rotation must be a scalar')
self._rotation = val % (2 * pi)
# Width
@property
def width(self) -> float:
"""
Width of the arc (difference between inner and outer radii)
:return: width
"""
return self._width
@width.setter
def width(self, val: float):
if not is_scalar(val):
raise PatternError('Width must be a scalar')
if not val > 0:
raise PatternError('Width must be positive')
self._width = val
def __init__(self,
radii: vector2,
angles: vector2,
width: float,
rotation: float=0,
poly_num_points: int=DEFAULT_POLY_NUM_POINTS,
poly_max_arclen: float=None,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0):
self.offset = offset
self.layer = layer
self.dose = dose
self.radii = radii
self.angles = angles
self.width = width
self.rotation = rotation
self.poly_num_points = poly_num_points
self.poly_max_arclen = poly_max_arclen
def to_polygons(self, poly_num_points: int=None, poly_max_arclen: float=None) -> List[Polygon]:
if poly_num_points is None:
poly_num_points = self.poly_num_points
if poly_max_arclen is None:
poly_max_arclen = self.poly_max_arclen
if (poly_num_points is None) and (poly_max_arclen is None):
raise PatternError('Max number of points and arclength left unspecified' +
' (default was also overridden)')
r0, r1 = self.radii
# Convert from polar angle to ellipse parameter (for [rx*cos(t), ry*sin(t)] representation)
a_ranges = self._angles_to_parameters()
# Approximate perimeter
# Ramanujan, S., "Modular Equations and Approximations to ,"
# Quart. J. Pure. Appl. Math., vol. 45 (1913-1914), pp. 350-372
a0, a1 = a_ranges[1] # use outer arc
h = ((r1 - r0) / (r1 + r0)) ** 2
ellipse_perimeter = pi * (r1 + r0) * (1 + 3 * h / (10 + math.sqrt(4 - 3 * h)))
perimeter = abs(a0 - a1) / (2 * pi) * ellipse_perimeter # TODO: make this more accurate
n = []
if poly_num_points is not None:
n += [poly_num_points]
if poly_max_arclen is not None:
n += [perimeter / poly_max_arclen]
thetas_inner = numpy.linspace(a_ranges[0][1], a_ranges[0][0], max(n), endpoint=True)
thetas_outer = numpy.linspace(a_ranges[1][0], a_ranges[1][1], max(n), endpoint=True)
sin_th_i, cos_th_i = (numpy.sin(thetas_inner), numpy.cos(thetas_inner))
sin_th_o, cos_th_o = (numpy.sin(thetas_outer), numpy.cos(thetas_outer))
wh = self.width / 2.0
xs1 = (r0 + wh) * cos_th_o
ys1 = (r1 + wh) * sin_th_o
xs2 = (r0 - wh) * cos_th_i
ys2 = (r1 - wh) * sin_th_i
xs = numpy.hstack((xs1, xs2))
ys = numpy.hstack((ys1, ys2))
xys = numpy.vstack((xs, ys)).T
poly = Polygon(xys, dose=self.dose, layer=self.layer, offset=self.offset)
poly.rotate(self.rotation)
return [poly]
def get_bounds(self) -> numpy.ndarray:
'''
Equation for rotated ellipse is
x = x0 + a * cos(t) * cos(rot) - b * sin(t) * sin(phi)
y = y0 + a * cos(t) * sin(rot) + b * sin(t) * cos(rot)
where t is our parameter.
Differentiating and solving for 0 slope wrt. t, we find
tan(t) = -+ b/a cot(phi)
where -+ is for x, y cases, so that's where the extrema are.
If the extrema are innaccessible due to arc constraints, check the arc endpoints instead.
'''
a_ranges = self._angles_to_parameters()
mins = []
maxs = []
for a, sgn in zip(a_ranges, (-1, +1)):
wh = sgn * self.width/2
rx = self.radius_x + wh
ry = self.radius_y + wh
a0, a1 = a
a0_offset = a0 - (a0 % (2 * pi))
sin_r = numpy.sin(self.rotation)
cos_r = numpy.cos(self.rotation)
sin_a = numpy.sin(a)
cos_a = numpy.cos(a)
# Cutoff angles
xpt = (-self.rotation) % (2 * pi) + a0_offset
ypt = (pi/2 - self.rotation) % (2 * pi) + a0_offset
xnt = (xpt - pi) % (2 * pi) + a0_offset
ynt = (ypt - pi) % (2 * pi) + a0_offset
# Points along coordinate axes
rx2_inv = 1 / (rx * rx)
ry2_inv = 1 / (ry * ry)
xr = numpy.abs(cos_r * cos_r * rx2_inv + sin_r * sin_r * ry2_inv) ** -0.5
yr = numpy.abs(-sin_r * -sin_r * rx2_inv + cos_r * cos_r * ry2_inv) ** -0.5
# Arc endpoints
xn, xp = sorted(rx * cos_r * cos_a - ry * sin_r * sin_a)
yn, yp = sorted(rx * sin_r * cos_a + ry * cos_r * sin_a)
# If our arc subtends a coordinate axis, use the extremum along that axis
if a0 < xpt < a1 or a0 < xpt + 2 * pi < a1:
xp = xr
if a0 < xnt < a1 or a0 < xnt + 2 * pi < a1:
xn = -xr
if a0 < ypt < a1 or a0 < ypt + 2 * pi < a1:
yp = yr
if a0 < ynt < a1 or a0 < ynt + 2 * pi < a1:
yn = -yr
mins.append([xn, yn])
maxs.append([xp, yp])
return numpy.vstack((numpy.min(mins, axis=0) + self.offset,
numpy.max(maxs, axis=0) + self.offset))
def rotate(self, theta: float) -> 'Arc':
self.rotation += theta
return self
def mirror(self, axis: int) -> 'Arc':
self.offset[axis - 1] *= -1
self.rotation *= -1
self.angles *= -1
return self
def scale_by(self, c: float) -> 'Arc':
self.radii *= c
self.width *= c
return self
def normalized_form(self, norm_value: float) -> normalized_shape_tuple:
if self.radius_x < self.radius_y:
radii = self.radii / self.radius_x
scale = self.radius_x
rotation = self.rotation
angles = self.angles
else: # rotate by 90 degrees and swap radii
radii = self.radii[::-1] / self.radius_y
scale = self.radius_y
rotation = self.rotation + pi / 2
angles = self.angles - pi / 2
delta_angle = angles[1] - angles[0]
start_angle = angles[0] % (2 * pi)
if start_angle >= pi:
start_angle -= pi
rotation += pi
angles = (start_angle, start_angle + delta_angle)
rotation %= 2 * pi
width = self.width
return (type(self), radii, angles, width, self.layer), \
(self.offset, scale/norm_value, rotation, self.dose), \
lambda: Arc(radii=radii*norm_value, angles=angles, width=width, layer=self.layer)
def get_cap_edges(self) -> numpy.ndarray:
'''
:returns: [[[x0, y0], [x1, y1]], array of 4 points, specifying the two cuts which
[[x2, y2], [x3, y3]]], would create this arc from its corresponding ellipse.
'''
a_ranges = self._angles_to_parameters()
mins = []
maxs = []
for a, sgn in zip(a_ranges, (-1, +1)):
wh = sgn * self.width/2
rx = self.radius_x + wh
ry = self.radius_y + wh
sin_r = numpy.sin(self.rotation)
cos_r = numpy.cos(self.rotation)
sin_a = numpy.sin(a)
cos_a = numpy.cos(a)
# arc endpoints
xn, xp = sorted(rx * cos_r * cos_a - ry * sin_r * sin_a)
yn, yp = sorted(rx * sin_r * cos_a + ry * cos_r * sin_a)
mins.append([xn, yn])
maxs.append([xp, yp])
return numpy.array([mins, maxs]) + self.offset
def _angles_to_parameters(self) -> numpy.ndarray:
'''
:return: "Eccentric anomaly" parameter ranges for the inner and outer edges, in the form
[[a_min_inner, a_max_inner], [a_min_outer, a_max_outer]]
'''
a = []
for sgn in (-1, +1):
wh = sgn * self.width/2
rx = self.radius_x + wh
ry = self.radius_y + wh
# create paremeter 'a' for parametrized ellipse
a0, a1 = (numpy.arctan2(rx*numpy.sin(a), ry*numpy.cos(a)) for a in self.angles)
sign = numpy.sign(self.angles[1] - self.angles[0])
if sign != numpy.sign(a1 - a0):
a1 += sign * 2 * pi
a.append((a0, a1))
return numpy.array(a)

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from typing import List
import numpy
from numpy import pi
from . import Shape, Polygon, normalized_shape_tuple, DEFAULT_POLY_NUM_POINTS
from .. import PatternError
from ..utils import is_scalar, vector2
__author__ = 'Jan Petykiewicz'
class Circle(Shape):
"""
A circle, which has a position and radius.
"""
_radius = None # type: float
# Defaults for to_polygons
poly_num_points = DEFAULT_POLY_NUM_POINTS # type: int
poly_max_arclen = None # type: float
# radius property
@property
def radius(self) -> float:
"""
Circle's radius (float, >= 0)
:return: radius
"""
return self._radius
@radius.setter
def radius(self, val: float):
if not is_scalar(val):
raise PatternError('Radius must be a scalar')
if not val >= 0:
raise PatternError('Radius must be non-negative')
self._radius = val
def __init__(self,
radius: float,
poly_num_points: int=DEFAULT_POLY_NUM_POINTS,
poly_max_arclen: float=None,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0):
self.offset = numpy.array(offset, dtype=float)
self.layer = layer
self.dose = dose
self.radius = radius
self.poly_num_points = poly_num_points
self.poly_max_arclen = poly_max_arclen
def to_polygons(self, poly_num_points: int=None, poly_max_arclen: float=None) -> List[Polygon]:
if poly_num_points is None:
poly_num_points = self.poly_num_points
if poly_max_arclen is None:
poly_max_arclen = self.poly_max_arclen
if (poly_num_points is None) and (poly_max_arclen is None):
raise PatternError('Number of points and arclength left '
'unspecified (default was also overridden)')
n = []
if poly_num_points is not None:
n += [poly_num_points]
if poly_max_arclen is not None:
n += [2 * pi * self.radius / poly_max_arclen]
thetas = numpy.linspace(2 * pi, 0, max(n), endpoint=False)
xs = numpy.cos(thetas) * self.radius
ys = numpy.sin(thetas) * self.radius
xys = numpy.vstack((xs, ys)).T
return [Polygon(xys, offset=self.offset, dose=self.dose, layer=self.layer)]
def get_bounds(self) -> numpy.ndarray:
return numpy.vstack((self.offset - self.radius,
self.offset + self.radius))
def rotate(self, theta: float) -> 'Circle':
return self
def mirror(self, axis: int) -> 'Circle':
self.offset *= -1
return self
def scale_by(self, c: float) -> 'Circle':
self.radius *= c
return self
def normalized_form(self, norm_value) -> normalized_shape_tuple:
rotation = 0.0
magnitude = self.radius / norm_value
return (type(self), self.layer), \
(self.offset, magnitude, rotation, self.dose), \
lambda: Circle(radius=norm_value, layer=self.layer)

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from typing import List
import math
import numpy
from numpy import pi
from . import Shape, Polygon, normalized_shape_tuple, DEFAULT_POLY_NUM_POINTS
from .. import PatternError
from ..utils import is_scalar, rotation_matrix_2d, vector2
__author__ = 'Jan Petykiewicz'
class Ellipse(Shape):
"""
An ellipse, which has a position, two radii, and a rotation.
The rotation gives the angle from x-axis, counterclockwise, to the first (x) radius.
"""
_radii = None # type: numpy.ndarray
_rotation = 0.0 # type: float
# Defaults for to_polygons
poly_num_points = DEFAULT_POLY_NUM_POINTS # type: int
poly_max_arclen = None # type: float
# radius properties
@property
def radii(self) -> numpy.ndarray:
"""
Return the radii [rx, ry]
:return: [rx, ry]
"""
return self._radii
@radii.setter
def radii(self, val: vector2):
val = numpy.array(val).flatten()
if not val.size == 2:
raise PatternError('Radii must have length 2')
if not val.min() >= 0:
raise PatternError('Radii must be non-negative')
self._radii = val
@property
def radius_x(self) -> float:
return self.radii[0]
@radius_x.setter
def radius_x(self, val: float):
if not val >= 0:
raise PatternError('Radius must be non-negative')
self.radii[0] = val
@property
def radius_y(self) -> float:
return self.radii[1]
@radius_y.setter
def radius_y(self, val: float):
if not val >= 0:
raise PatternError('Radius must be non-negative')
self.radii[1] = val
# Rotation property
@property
def rotation(self) -> float:
"""
Rotation of rx from the x axis. Uses the interval [0, pi) in radians (counterclockwise
is positive)
:return: counterclockwise rotation in radians
"""
return self._rotation
@rotation.setter
def rotation(self, val: float):
if not is_scalar(val):
raise PatternError('Rotation must be a scalar')
self._rotation = val % pi
def __init__(self,
radii: vector2,
rotation: float=0,
poly_num_points: int=DEFAULT_POLY_NUM_POINTS,
poly_max_arclen: float=None,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0):
self.offset = offset
self.layer = layer
self.dose = dose
self.radii = radii
self.rotation = rotation
self.poly_num_points = poly_num_points
self.poly_max_arclen = poly_max_arclen
def to_polygons(self,
poly_num_points: int=None,
poly_max_arclen: float=None
) -> List[Polygon]:
if poly_num_points is None:
poly_num_points = self.poly_num_points
if poly_max_arclen is None:
poly_max_arclen = self.poly_max_arclen
if (poly_num_points is None) and (poly_max_arclen is None):
raise PatternError('Number of points and arclength left unspecified'
' (default was also overridden)')
r0, r1 = self.radii
# Approximate perimeter
# Ramanujan, S., "Modular Equations and Approximations to ,"
# Quart. J. Pure. Appl. Math., vol. 45 (1913-1914), pp. 350-372
h = ((r1 - r0) / (r1 + r0)) ** 2
perimeter = pi * (r1 + r0) * (1 + 3 * h / (10 + math.sqrt(4 - 3 * h)))
n = []
if poly_num_points is not None:
n += [poly_num_points]
if poly_max_arclen is not None:
n += [perimeter / poly_max_arclen]
thetas = numpy.linspace(2 * pi, 0, max(n), endpoint=False)
sin_th, cos_th = (numpy.sin(thetas), numpy.cos(thetas))
xs = r0 * cos_th
ys = r1 * sin_th
xys = numpy.vstack((xs, ys)).T
poly = Polygon(xys, dose=self.dose, layer=self.layer, offset=self.offset)
poly.rotate(self.rotation)
return [poly]
def get_bounds(self) -> numpy.ndarray:
rot_radii = numpy.dot(rotation_matrix_2d(self.rotation), self.radii)
return numpy.vstack((self.offset - rot_radii[0],
self.offset + rot_radii[1]))
def rotate(self, theta: float) -> 'Ellipse':
self.rotation += theta
return self
def mirror(self, axis: int) -> 'Ellipse':
self.offset[axis - 1] *= -1
self.rotation *= -1
return self
def scale_by(self, c: float) -> 'Ellipse':
self.radii *= c
return self
def normalized_form(self, norm_value: float) -> normalized_shape_tuple:
if self.radius_x < self.radius_y:
radii = self.radii / self.radius_x
scale = self.radius_x
angle = self.rotation
else:
radii = self.radii[::-1] / self.radius_y
scale = self.radius_y
angle = (self.rotation + pi / 2) % pi
return (type(self), radii, self.layer), \
(self.offset, scale/norm_value, angle, self.dose), \
lambda: Ellipse(radii=radii*norm_value, layer=self.layer)

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from typing import List
import copy
import numpy
from numpy import pi
from . import Shape, normalized_shape_tuple
from .. import PatternError
from ..utils import is_scalar, rotation_matrix_2d, vector2
from ..utils import remove_colinear_vertices, remove_duplicate_vertices
__author__ = 'Jan Petykiewicz'
class Polygon(Shape):
"""
A polygon, consisting of a bunch of vertices (Nx2 ndarray) along with an offset.
A normalized_form(...) is available, but can be quite slow with lots of vertices.
"""
_vertices = None # type: numpy.ndarray
# vertices property
@property
def vertices(self) -> numpy.ndarray:
"""
Vertices of the polygon (Nx2 ndarray: [[x0, y0], [x1, y1], ...]
:return: vertices
"""
return self._vertices
@vertices.setter
def vertices(self, val: numpy.ndarray):
val = numpy.array(val, dtype=float)
if len(val.shape) < 2 or val.shape[1] != 2:
raise PatternError('Vertices must be an Nx2 array')
if val.shape[0] < 3:
raise PatternError('Must have at least 3 vertices (Nx2, N>3)')
self._vertices = val
# xs property
@property
def xs(self) -> numpy.ndarray:
"""
All x vertices in a 1D ndarray
"""
return self.vertices[:, 0]
@xs.setter
def xs(self, val: numpy.ndarray):
val = numpy.array(val, dtype=float).flatten()
if val.size != self.vertices.shape[0]:
raise PatternError('Wrong number of vertices')
self.vertices[:, 0] = val
# ys property
@property
def ys(self) -> numpy.ndarray:
"""
All y vertices in a 1D ndarray
"""
return self.vertices[:, 1]
@ys.setter
def ys(self, val: numpy.ndarray):
val = numpy.array(val, dtype=float).flatten()
if val.size != self.vertices.shape[0]:
raise PatternError('Wrong number of vertices')
self.vertices[:, 1] = val
def __init__(self,
vertices: numpy.ndarray,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0):
self.offset = offset
self.layer = layer
self.dose = dose
self.vertices = vertices
@staticmethod
def square(side_length: float,
rotation: float=0.0,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0
) -> 'Polygon':
"""
Draw a square given side_length, centered on the origin.
:param side_length: Length of one side
:param rotation: Rotation counterclockwise, in radians
:param offset: Offset, default (0, 0)
:param layer: Layer, default 0
:param dose: Dose, default 1.0
:return: A Polygon object containing the requested square
"""
norm_square = numpy.array([[-1, -1],
[-1, +1],
[+1, +1],
[+1, -1]], dtype=float)
vertices = 0.5 * side_length * norm_square
poly = Polygon(vertices, offset, layer, dose)
poly.rotate(rotation)
return poly
@staticmethod
def rectangle(lx: float,
ly: float,
rotation: float=0,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0
) -> 'Polygon':
"""
Draw a rectangle with side lengths lx and ly, centered on the origin.
:param lx: Length along x (before rotation)
:param ly: Length along y (before rotation)
:param rotation: Rotation counterclockwise, in radians
:param offset: Offset, default (0, 0)
:param layer: Layer, default 0
:param dose: Dose, default 1.0
:return: A Polygon object containing the requested rectangle
"""
vertices = 0.5 * numpy.array([[-lx, -ly],
[-lx, +ly],
[+lx, +ly],
[+lx, -ly]], dtype=float)
poly = Polygon(vertices, offset, layer, dose)
poly.rotate(rotation)
return poly
@staticmethod
def rect(xmin: float = None,
xctr: float = None,
xmax: float = None,
lx: float = None,
ymin: float = None,
yctr: float = None,
ymax: float = None,
ly: float = None,
layer: int = 0,
dose: float = 1.0
) -> 'Polygon':
"""
Draw a rectangle by specifying side/center positions.
Must provide 2 of (xmin, xctr, xmax, lx),
and 2 of (ymin, yctr, ymax, ly).
:param xmin: Minimum x coordinate
:param xctr: Center x coordinate
:param xmax: Maximum x coordinate
:param lx: Length along x direction
:param ymin: Minimum y coordinate
:param yctr: Center y coordinate
:param ymax: Maximum y coordinate
:param ly: Length along y direction
:param layer: Layer, default 0
:param dose: Dose, default 1.0
:return: A Polygon object containing the requested rectangle
"""
if lx is None:
if xctr is None:
xctr = 0.5 * (xmax + xmin)
lx = xmax - xmin
elif xmax is None:
lx = 2 * (xctr - xmin)
elif xmin is None:
lx = 2 * (xmax - xctr)
else:
raise PatternError('Two of xmin, xctr, xmax, lx must be None!')
else:
if xctr is not None:
pass
elif xmax is None:
xctr = xmin + 0.5 * lx
elif xmin is None:
xctr = xmax - 0.5 * lx
else:
raise PatternError('Two of xmin, xctr, xmax, lx must be None!')
if ly is None:
if yctr is None:
yctr = 0.5 * (ymax + ymin)
ly = ymax - ymin
elif ymax is None:
ly = 2 * (yctr - ymin)
elif ymin is None:
ly = 2 * (ymax - yctr)
else:
raise PatternError('Two of ymin, yctr, ymax, ly must be None!')
else:
if yctr is not None:
pass
elif ymax is None:
yctr = ymin + 0.5 * ly
elif ymin is None:
yctr = ymax - 0.5 * ly
else:
raise PatternError('Two of ymin, yctr, ymax, ly must be None!')
poly = Polygon.rectangle(lx, ly, offset=(xctr, yctr),
layer=layer, dose=dose)
return poly
def to_polygons(self,
_poly_num_points: int=None,
_poly_max_arclen: float=None,
) -> List['Polygon']:
return [copy.deepcopy(self)]
def get_bounds(self) -> numpy.ndarray:
return numpy.vstack((self.offset + numpy.min(self.vertices, axis=0),
self.offset + numpy.max(self.vertices, axis=0)))
def rotate(self, theta: float) -> 'Polygon':
self.vertices = numpy.dot(rotation_matrix_2d(theta), self.vertices.T).T
return self
def mirror(self, axis: int) -> 'Polygon':
self.vertices[:, axis - 1] *= -1
return self
def scale_by(self, c: float) -> 'Polygon':
self.vertices *= c
return self
def normalized_form(self, norm_value: float) -> normalized_shape_tuple:
# Note: this function is going to be pretty slow for many-vertexed polygons, relative to
# other shapes
offset = self.vertices.mean(axis=0) + self.offset
zeroed_vertices = self.vertices - offset
scale = zeroed_vertices.std()
normed_vertices = zeroed_vertices / scale
_, _, vertex_axis = numpy.linalg.svd(zeroed_vertices)
rotation = numpy.arctan2(vertex_axis[0][1], vertex_axis[0][0]) % (2 * pi)
rotated_vertices = numpy.vstack([numpy.dot(rotation_matrix_2d(-rotation), v)
for v in normed_vertices])
# Reorder the vertices so that the one with lowest x, then y, comes first.
x_min = rotated_vertices[:, 0].argmin()
if not is_scalar(x_min):
y_min = rotated_vertices[x_min, 1].argmin()
x_min = x_min[y_min]
reordered_vertices = numpy.roll(rotated_vertices, -x_min, axis=0)
return (type(self), reordered_vertices.data.tobytes(), self.layer), \
(offset, scale/norm_value, rotation, self.dose), \
lambda: Polygon(reordered_vertices*norm_value, layer=self.layer)
def clean_vertices(self) -> 'Polygon':
"""
Removes duplicate, co-linear and otherwise redundant vertices.
:returns: self
"""
self.remove_colinear_vertices()
return self
def remove_duplicate_vertices(self) -> 'Polygon':
'''
Removes all consecutive duplicate (repeated) vertices.
:returns: self
'''
self.vertices = remove_duplicate_vertices(self.vertices, closed_path=True)
return self
def remove_colinear_vertices(self) -> 'Polygon':
'''
Removes consecutive co-linear vertices.
:returns: self
'''
self.vertices = remove_colinear_vertices(self.vertices, closed_path=True)
return self

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from typing import List, Tuple, Callable
from abc import ABCMeta, abstractmethod
import copy
import numpy
from .. import PatternError
from ..utils import is_scalar, rotation_matrix_2d, vector2
__author__ = 'Jan Petykiewicz'
# Type definitions
normalized_shape_tuple = Tuple[Tuple,
Tuple[numpy.ndarray, float, float, float],
Callable[[], 'Shape']]
# ## Module-wide defaults
# Default number of points per polygon for shapes
DEFAULT_POLY_NUM_POINTS = 24
class Shape(metaclass=ABCMeta):
"""
Abstract class specifying functions common to all shapes.
"""
# [x_offset, y_offset]
_offset = numpy.array([0.0, 0.0]) # type: numpy.ndarray
# Layer (integer >= 0 or tuple)
_layer = 0 # type: int or Tuple
# Dose
_dose = 1.0 # type: float
# --- Abstract methods
@abstractmethod
def to_polygons(self, num_vertices: int, max_arclen: float) -> List['Polygon']:
"""
Returns a list of polygons which approximate the shape.
:param num_vertices: Number of points to use for each polygon. Can be overridden by
max_arclen if that results in more points. Optional, defaults to shapes'
internal defaults.
:param max_arclen: Maximum arclength which can be approximated by a single line
segment. Optional, defaults to shapes' internal defaults.
:return: List of polygons equivalent to the shape
"""
pass
@abstractmethod
def get_bounds(self) -> numpy.ndarray:
"""
Returns [[x_min, y_min], [x_max, y_max]] which specify a minimal bounding box for the shape.
:return: [[x_min, y_min], [x_max, y_max]]
"""
pass
@abstractmethod
def rotate(self, theta: float) -> 'Shape':
"""
Rotate the shape around its center (0, 0), ignoring its offset.
:param theta: Angle to rotate by (counterclockwise, radians)
:return: self
"""
pass
@abstractmethod
def mirror(self, axis: int) -> 'Shape':
"""
Mirror the shape across an axis.
:param axis: Axis to mirror across.
:return: self
"""
pass
@abstractmethod
def scale_by(self, c: float) -> 'Shape':
"""
Scale the shape's size (eg. radius, for a circle) by a constant factor.
:param c: Factor to scale by
:return: self
"""
pass
@abstractmethod
def normalized_form(self, norm_value: int) -> normalized_shape_tuple:
"""
Writes the shape in a standardized notation, with offset, scale, rotation, and dose
information separated out from the remaining values.
:param norm_value: This value is used to normalize lengths intrinsic to teh shape;
eg. for a circle, the returned magnitude value will be (radius / norm_value), and
the returned callable will create a Circle(radius=norm_value, ...). This is useful
when you find it important for quantities to remain in a certain range, eg. for
GDSII where vertex locations are stored as integers.
:return: The returned information takes the form of a 3-element tuple,
(intrinsic, extrinsic, constructor). These are further broken down as:
extrinsic: ([x_offset, y_offset], scale, rotation, dose)
intrinsic: A tuple of basic types containing all information about the instance that
is not contained in 'extrinsic'. Usually, intrinsic[0] == type(self).
constructor: A callable (no arguments) which returns an instance of type(self) with
internal state equivalent to 'intrinsic'.
"""
pass
# ---- Non-abstract properties
# offset property
@property
def offset(self) -> numpy.ndarray:
"""
[x, y] offset
:return: [x_offset, y_offset]
"""
return self._offset
@offset.setter
def offset(self, val: vector2):
if not isinstance(val, numpy.ndarray):
val = numpy.array(val, dtype=float)
if val.size != 2:
raise PatternError('Offset must be convertible to size-2 ndarray')
self._offset = val.flatten()
# layer property
@property
def layer(self) -> int or Tuple[int]:
"""
Layer number (int or tuple of ints)
:return: Layer
"""
return self._layer
@layer.setter
def layer(self, val: int or List[int]):
self._layer = val
# dose property
@property
def dose(self) -> float:
"""
Dose (float >= 0)
:return: Dose value
"""
return self._dose
@dose.setter
def dose(self, val: float):
if not is_scalar(val):
raise PatternError('Dose must be a scalar')
if not val >= 0:
raise PatternError('Dose must be non-negative')
self._dose = val
# ---- Non-abstract methods
def copy(self) -> 'Shape':
"""
Returns a deep copy of the shape.
:return: Deep copy of self
"""
return copy.deepcopy(self)
def translate(self, offset: vector2) -> 'Shape':
"""
Translate the shape by the given offset
:param offset: [x_offset, y,offset]
:return: self
"""
self.offset += offset
return self
def rotate_around(self, pivot: vector2, rotation: float) -> 'Shape':
"""
Rotate the shape around a point.
:param pivot: Point (x, y) to rotate around
:param rotation: Angle to rotate by (counterclockwise, radians)
:return: self
"""
pivot = numpy.array(pivot, dtype=float)
self.translate(-pivot)
self.rotate(rotation)
self.offset = numpy.dot(rotation_matrix_2d(rotation), self.offset)
self.translate(+pivot)
return self
def manhattanize_fast(self, grid_x: numpy.ndarray, grid_y: numpy.ndarray) -> List['Polygon']:
"""
Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape.
This function works by
1) Converting the shape to polygons using .to_polygons()
2) Approximating each edge with an equivalent Manhattan edge
This process results in a reasonable Manhattan representation of the shape, but is
imprecise near non-Manhattan or off-grid corners.
:param grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
:param grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
:return: List of Polygon objects with grid-aligned edges.
"""
from . import Polygon
grid_x = numpy.unique(grid_x)
grid_y = numpy.unique(grid_y)
polygon_contours = []
for polygon in self.to_polygons():
mins, maxs = polygon.get_bounds()
vertex_lists = []
p_verts = polygon.vertices + polygon.offset
for v, v_next in zip(p_verts, numpy.roll(p_verts, -1, axis=0)):
dv = v_next - v
# Find x-index bounds for the line # TODO: fix this and err_xmin/xmax for grids smaller than the line / shape
gxi_range = numpy.digitize([v[0], v_next[0]], grid_x)
gxi_min = numpy.min(gxi_range - 1).clip(0, len(grid_x) - 1)
gxi_max = numpy.max(gxi_range).clip(0, len(grid_x))
err_xmin = (min(v[0], v_next[0]) - grid_x[gxi_min]) / (grid_x[gxi_min + 1] - grid_x[gxi_min])
err_xmax = (max(v[0], v_next[0]) - grid_x[gxi_max - 1]) / (grid_x[gxi_max] - grid_x[gxi_max - 1])
if err_xmin >= 0.5:
gxi_min += 1
if err_xmax >= 0.5:
gxi_max += 1
if abs(dv[0]) < 1e-20:
# Vertical line, don't calculate slope
xi = [gxi_min, gxi_max - 1]
ys = numpy.array([v[1], v_next[1]])
yi = numpy.digitize(ys, grid_y).clip(1, len(grid_y) - 1)
err_y = (ys - grid_y[yi]) / (grid_y[yi] - grid_y[yi - 1])
yi[err_y < 0.5] -= 1
segment = numpy.column_stack((grid_x[xi], grid_y[yi]))
vertex_lists.append(segment)
continue
m = dv[1]/dv[0]
def get_grid_inds(xes):
ys = m * (xes - v[0]) + v[1]
# (inds - 1) is the index of the y-grid line below the edge's intersection with the x-grid
inds = numpy.digitize(ys, grid_y).clip(1, len(grid_y) - 1)
# err is what fraction of the cell upwards we have to go to reach our y
# (can be negative at bottom edge due to clip above)
err = (ys - grid_y[inds - 1]) / (grid_y[inds] - grid_y[inds - 1])
# now set inds to the index of the nearest y-grid line
inds[err < 0.5] -= 1
return inds
# Find the y indices on all x gridlines
xs = grid_x[gxi_min:gxi_max]
inds = get_grid_inds(xs)
# Find y-intersections for x-midpoints
xs2 = (xs[:-1] + xs[1:]) / 2
inds2 = get_grid_inds(xs2)
xinds = numpy.round(numpy.arange(gxi_min, gxi_max - 0.99, 1/3)).astype(int)
# interleave the results
yinds = xinds.copy()
yinds[0::3] = inds
yinds[1::3] = inds2
yinds[2::3] = inds2
vlist = numpy.column_stack((grid_x[xinds], grid_y[yinds]))
if dv[0] < 0:
vlist = vlist[::-1]
vertex_lists.append(vlist)
polygon_contours.append(numpy.vstack(vertex_lists))
manhattan_polygons = []
for contour in polygon_contours:
manhattan_polygons.append(Polygon(
vertices=contour,
layer=self.layer,
dose=self.dose))
return manhattan_polygons
def manhattanize(self, grid_x: numpy.ndarray, grid_y: numpy.ndarray) -> List['Polygon']:
"""
Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape.
This function works by
1) Converting the shape to polygons using .to_polygons()
2) Accurately rasterizing each polygon on a grid,
where the edges of each grid cell correspond to the allowed coordinates
3) Thresholding the (anti-aliased) rasterized image
4) Finding the contours which outline the filled areas in the thresholded image
This process results in a fairly accurate Manhattan representation of the shape. Possible
caveats include:
a) If high accuracy is important, perform any polygonization and clipping operations
prior to calling this function. This allows you to specify any arguments you may
need for .to_polygons(), and also avoids calling .manhattanize() multiple times for
the same grid location (which causes inaccuracies in the final representation).
b) If the shape is very large or the grid very fine, memory requirements can be reduced
by breaking the shape apart into multiple, smaller shapes.
c) Inaccuracies in edge shape can result from Manhattanization of edges which are
equidistant from allowed edge location.
Implementation notes:
i) Rasterization is performed using float_raster, giving a high-precision anti-aliased
rasterized image.
ii) To find the exact polygon edges, the thresholded rasterized image is supersampled
prior to calling skimage.measure.find_contours(), which uses marching squares
to find the contours. This is done because find_contours() performs interpolation,
which has to be undone in order to regain the axis-aligned contours. A targetted
rewrite of find_contours() for this specific application, or use of a different
boundary tracing method could remove this requirement, but for now this seems to
be the most performant approach.
:param grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
:param grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
:return: List of Polygon objects with grid-aligned edges.
"""
from . import Polygon
import skimage.measure
import float_raster
grid_x = numpy.unique(grid_x)
grid_y = numpy.unique(grid_y)
polygon_contours = []
for polygon in self.to_polygons():
# Get rid of unused gridlines (anything not within 2 lines of the polygon bounds)
mins, maxs = polygon.get_bounds()
keep_x = numpy.logical_and(grid_x > mins[0], grid_x < maxs[0])
keep_y = numpy.logical_and(grid_y > mins[1], grid_y < maxs[1])
for k in (keep_x, keep_y):
for s in (1, 2):
k[s:] += k[:-s]
k[:-s] += k[s:]
k = k > 0
gx = grid_x[keep_x]
gy = grid_y[keep_y]
if len(gx) == 0 or len(gy) == 0:
continue
offset = (numpy.where(keep_x)[0][0],
numpy.where(keep_y)[0][0])
rastered = float_raster.raster((polygon.vertices + polygon.offset).T, gx, gy)
binary_rastered = (numpy.abs(rastered) >= 0.5)
supersampled = binary_rastered.repeat(2, axis=0).repeat(2, axis=1)
contours = skimage.measure.find_contours(supersampled, 0.5)
polygon_contours.append((offset, contours))
manhattan_polygons = []
for offset_i, contours in polygon_contours:
for contour in contours:
# /2 deals with supersampling
# +.5 deals with the fact that our 0-edge becomes -.5 in the super-sampled contour output
snapped_contour = numpy.round((contour + .5) / 2).astype(int)
vertices = numpy.hstack((grid_x[snapped_contour[:, None, 0] + offset_i[0]],
grid_y[snapped_contour[:, None, 1] + offset_i[1]]))
manhattan_polygons.append(Polygon(
vertices=vertices,
layer=self.layer,
dose=self.dose))
return manhattan_polygons

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masque/shapes/text.py Normal file
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from typing import List, Tuple
import numpy
from numpy import pi, inf
from . import Shape, Polygon, normalized_shape_tuple
from .. import PatternError
from ..utils import is_scalar, vector2, get_bit
# Loaded on use:
# from freetype import Face
# from matplotlib.path import Path
__author__ = 'Jan Petykiewicz'
class Text(Shape):
_string = ''
_height = 1.0
_rotation = 0.0
_mirrored = None
font_path = ''
# vertices property
@property
def string(self) -> str:
return self._string
@string.setter
def string(self, val: str):
self._string = val
# Rotation property
@property
def rotation(self) -> float:
return self._rotation
@rotation.setter
def rotation(self, val: float):
if not is_scalar(val):
raise PatternError('Rotation must be a scalar')
self._rotation = val % (2 * pi)
# Height property
@property
def height(self) -> float:
return self._height
@height.setter
def height(self, val: float):
if not is_scalar(val):
raise PatternError('Height must be a scalar')
self._height = val
# Mirrored property
@property
def mirrored(self) -> List[bool]:
return self._mirrored
@mirrored.setter
def mirrored(self, val: List[bool]):
if is_scalar(val):
raise PatternError('Mirrored must be a 2-element list of booleans')
self._mirrored = val
def __init__(self,
string: str,
height: float,
font_path: str,
mirrored: List[bool]=None,
rotation: float=0.0,
offset: vector2=(0.0, 0.0),
layer: int=0,
dose: float=1.0):
self.offset = offset
self.layer = layer
self.dose = dose
self.string = string
self.height = height
self.rotation = rotation
self.font_path = font_path
if mirrored is None:
mirrored = [False, False]
self.mirrored = mirrored
def to_polygons(self,
_poly_num_points: int=None,
_poly_max_arclen: float=None
) -> List[Polygon]:
all_polygons = []
total_advance = 0
for char in self.string:
raw_polys, advance = get_char_as_polygons(self.font_path, char)
# Move these polygons to the right of the previous letter
for xys in raw_polys:
poly = Polygon(xys, dose=self.dose, layer=self.layer)
[poly.mirror(ax) for ax, do in enumerate(self.mirrored) if do]
poly.scale_by(self.height)
poly.offset = self.offset + [total_advance, 0]
poly.rotate_around(self.offset, self.rotation)
all_polygons += [poly]
# Update the list of all polygons and how far to advance
total_advance += advance * self.height
return all_polygons
def rotate(self, theta: float) -> 'Text':
self.rotation += theta
return self
def mirror(self, axis: int) -> 'Text':
self.mirrored[axis] = not self.mirrored[axis]
return self
def scale_by(self, c: float) -> 'Text':
self.height *= c
return self
def normalized_form(self, norm_value: float) -> normalized_shape_tuple:
return (type(self), self.string, self.font_path, self.mirrored, self.layer), \
(self.offset, self.height / norm_value, self.rotation, self.dose), \
lambda: Text(string=self.string,
height=self.height * norm_value,
font_path=self.font_path,
mirrored=self.mirrored,
layer=self.layer)
def get_bounds(self) -> numpy.ndarray:
# rotation makes this a huge pain when using slot.advance and glyph.bbox(), so
# just convert to polygons instead
bounds = [[+inf, +inf], [-inf, -inf]]
polys = self.to_polygons()
for poly in polys:
poly_bounds = poly.get_bounds()
bounds[0, :] = numpy.minimum(bounds[0, :], poly_bounds[0, :])
bounds[1, :] = numpy.maximum(bounds[1, :], poly_bounds[1, :])
return bounds
def get_char_as_polygons(font_path: str,
char: str,
resolution: float=48*64,
) -> Tuple[List[List[List[float]]], float]:
from freetype import Face
from matplotlib.path import Path
"""
Get a list of polygons representing a single character.
The output is normalized so that the font size is 1 unit.
:param font_path: File path specifying a font loadable by freetype
:param char: Character to convert to polygons
:param resolution: Internal resolution setting (used for freetype
Face.set_font_size(resolution)). Modify at your own peril!
:return: List of polygons [[[x0, y0], [x1, y1], ...], ...] and 'advance' distance (distance
from the start of this glyph to the start of the next one)
"""
if len(char) != 1:
raise Exception('get_char_as_polygons called with non-char')
face = Face(font_path)
face.set_char_size(resolution)
face.load_char(char)
slot = face.glyph
outline = slot.outline
start = 0
all_verts, all_codes = [], []
for end in outline.contours:
points = outline.points[start:end + 1]
points.append(points[0])
tags = outline.tags[start:end + 1]
tags.append(tags[0])
segments = []
for j, point in enumerate(points):
# If we already have a segment, add this point to it
if j > 0:
segments[-1].append(point)
# If not bezier control point, start next segment
if get_bit(tags[j], 0) and j < (len(points) - 1):
segments.append([point])
verts = [points[0]]
codes = [Path.MOVETO]
for segment in segments:
if len(segment) == 2:
verts.extend(segment[1:])
codes.extend([Path.LINETO])
elif len(segment) == 3:
verts.extend(segment[1:])
codes.extend([Path.CURVE3, Path.CURVE3])
else:
verts.append(segment[1])
codes.append(Path.CURVE3)
for i in range(1, len(segment) - 2):
a, b = segment[i], segment[i + 1]
c = ((a[0] + b[0]) / 2.0, (a[1] + b[1]) / 2.0)
verts.extend([c, b])
codes.extend([Path.CURVE3, Path.CURVE3])
verts.append(segment[-1])
codes.append(Path.CURVE3)
all_verts.extend(verts)
all_codes.extend(codes)
start = end + 1
all_verts = numpy.array(all_verts) / resolution
advance = slot.advance.x / resolution
if len(all_verts) == 0:
polygons = []
else:
path = Path(all_verts, all_codes)
path.should_simplify = False
polygons = path.to_polygons()
return polygons, advance

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"""
SubPattern provides basic support for nesting Pattern objects within each other, by adding
offset, rotation, scaling, and other such properties to the reference.
"""
from typing import Union, List
import copy
import numpy
from numpy import pi
from .error import PatternError
from .utils import is_scalar, rotation_matrix_2d, vector2
__author__ = 'Jan Petykiewicz'
class SubPattern:
"""
SubPattern provides basic support for nesting Pattern objects within each other, by adding
offset, rotation, scaling, and associated methods.
"""
pattern = None # type: Pattern
_offset = (0.0, 0.0) # type: numpy.ndarray
_rotation = 0.0 # type: float
_dose = 1.0 # type: float
_scale = 1.0 # type: float
_mirrored = None # type: List[bool]
def __init__(self,
pattern: 'Pattern',
offset: vector2=(0.0, 0.0),
rotation: float=0.0,
mirrored: List[bool]=None,
dose: float=1.0,
scale: float=1.0):
self.pattern = pattern
self.offset = offset
self.rotation = rotation
self.dose = dose
self.scale = scale
if mirrored is None:
mirrored = [False, False]
self.mirrored = mirrored
# offset property
@property
def offset(self) -> numpy.ndarray:
return self._offset
@offset.setter
def offset(self, val: vector2):
if not isinstance(val, numpy.ndarray):
val = numpy.array(val, dtype=float)
if val.size != 2:
raise PatternError('Offset must be convertible to size-2 ndarray')
self._offset = val.flatten().astype(float)
# dose property
@property
def dose(self) -> float:
return self._dose
@dose.setter
def dose(self, val: float):
if not is_scalar(val):
raise PatternError('Dose must be a scalar')
if not val >= 0:
raise PatternError('Dose must be non-negative')
self._dose = val
# scale property
@property
def scale(self) -> float:
return self._scale
@scale.setter
def scale(self, val: float):
if not is_scalar(val):
raise PatternError('Scale must be a scalar')
if not val > 0:
raise PatternError('Scale must be positive')
self._scale = val
# Rotation property [ccw]
@property
def rotation(self) -> float:
return self._rotation
@rotation.setter
def rotation(self, val: float):
if not is_scalar(val):
raise PatternError('Rotation must be a scalar')
self._rotation = val % (2 * pi)
# Mirrored property
@property
def mirrored(self) -> List[bool]:
return self._mirrored
@mirrored.setter
def mirrored(self, val: List[bool]):
if is_scalar(val):
raise PatternError('Mirrored must be a 2-element list of booleans')
self._mirrored = val
def as_pattern(self) -> 'Pattern':
"""
Returns a copy of self.pattern which has been scaled, rotated, etc. according to this
SubPattern's properties.
:return: Copy of self.pattern that has been altered to reflect the SubPattern's properties.
"""
pattern = self.pattern.deepcopy()
pattern.scale_by(self.scale)
[pattern.mirror(ax) for ax, do in enumerate(self.mirrored) if do]
pattern.rotate_around((0.0, 0.0), self.rotation)
pattern.translate_elements(self.offset)
pattern.scale_element_doses(self.dose)
return pattern
def translate(self, offset: vector2) -> 'SubPattern':
"""
Translate by the given offset
:param offset: Translate by this offset
:return: self
"""
self.offset += offset
return self
def rotate_around(self, pivot: vector2, rotation: float) -> 'SubPattern':
"""
Rotate around a point
:param pivot: Point to rotate around
:param rotation: Angle to rotate by (counterclockwise, radians)
:return: self
"""
pivot = numpy.array(pivot, dtype=float)
self.translate(-pivot)
self.offset = numpy.dot(rotation_matrix_2d(rotation), self.offset)
self.rotate(rotation)
self.translate(+pivot)
return self
def rotate(self, rotation: float) -> 'SubPattern':
"""
Rotate around (0, 0)
:param rotation: Angle to rotate by (counterclockwise, radians)
:return: self
"""
self.rotation += rotation
return self
def mirror(self, axis: int) -> 'SubPattern':
"""
Mirror the subpattern across an axis.
:param axis: Axis to mirror across.
:return: self
"""
self.mirrored[axis] = not self.mirrored[axis]
return self
def get_bounds(self) -> numpy.ndarray or None:
"""
Return a numpy.ndarray containing [[x_min, y_min], [x_max, y_max]], corresponding to the
extent of the SubPattern in each dimension.
Returns None if the contained Pattern is empty.
:return: [[x_min, y_min], [x_max, y_max]] or None
"""
return self.as_pattern().get_bounds()
def scale_by(self, c: float) -> 'SubPattern':
"""
Scale the subpattern by a factor
:param c: scaling factor
"""
self.scale *= c
return self
def copy(self) -> 'SubPattern':
"""
Return a shallow copy of the subpattern.
:return: copy.copy(self)
"""
return copy.copy(self)
def deepcopy(self) -> 'SubPattern':
"""
Return a deep copy of the subpattern.
:return: copy.copy(self)
"""
return copy.deepcopy(self)

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"""
Various helper functions
"""
from typing import Any, Union, Tuple
import numpy
# Type definitions
vector2 = Union[numpy.ndarray, Tuple[float, float]]
def is_scalar(var: Any) -> bool:
"""
Alias for 'not hasattr(var, "__len__")'
:param var: Checks if var has a length.
"""
return not hasattr(var, "__len__")
def get_bit(bit_string: Any, bit_id: int) -> bool:
"""
Returns true iff bit number 'bit_id' from the right of 'bit_string' is 1
:param bit_string: Bit string to test
:param bit_id: Bit number, 0-indexed from the right (lsb)
:return: value of the requested bit (bool)
"""
return bit_string & (1 << bit_id) != 0
def set_bit(bit_string: Any, bit_id: int, value: bool) -> Any:
"""
Returns 'bit_string' with bit number 'bit_id' set to 'value'.
:param bit_string: Bit string to alter
:param bit_id: Bit number, 0-indexed from right (lsb)
:param value: Boolean value to set bit to
:return: Altered 'bit_string'
"""
mask = (1 << bit_id)
bit_string &= ~mask
if value:
bit_string |= mask
return bit_string
def rotation_matrix_2d(theta: float) -> numpy.ndarray:
"""
2D rotation matrix for rotating counterclockwise around the origin.
:param theta: Angle to rotate, in radians
:return: rotation matrix
"""
return numpy.array([[numpy.cos(theta), -numpy.sin(theta)],
[numpy.sin(theta), +numpy.cos(theta)]])
def remove_duplicate_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
duplicates = (vertices == numpy.roll(vertices, 1, axis=0)).all(axis=1)
if not closed_path:
duplicates[0] = False
return vertices[~duplicates]
def remove_colinear_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
'''
Given a list of vertices, remove any superflous vertices (i.e.
those which lie along the line formed by their neighbors)
:param vertices: Nx2 ndarray of vertices
:param closed_path: If True, the vertices are assumed to represent an implicitly
closed path. If False, the path is assumed to be open. Default True.
:return:
'''
# Check for dx0/dy0 == dx1/dy1
dv = numpy.roll(vertices, 1, axis=0) - vertices #[y0 - yn1, y1-y0, ...]
dxdy = dv * numpy.roll(dv, 1, axis=0)[:, ::-1] # [[dx1*dy0, dx1*dy0], ...]
dxdy_diff = numpy.abs(numpy.diff(dxdy, axis=1))[:, 0]
err_mult = 2 * numpy.abs(dxdy).sum(axis=1) + 1e-40
slopes_equal = (dxdy_diff / err_mult) < 1e-15
if not closed_path:
slopes_equal[[0, -1]] = False
return vertices[~slopes_equal]

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#!/usr/bin/env python3
from setuptools import setup, find_packages
import masque
with open('README.md', 'r') as f:
long_description = f.read()
setup(name='masque',
version=masque.version,
description='Lithography mask library',
long_description=long_description,
long_description_content_type='text/markdown',
author='Jan Petykiewicz',
author_email='anewusername@gmail.com',
url='https://mpxd.net/code/jan/masque',
packages=find_packages(),
install_requires=[
'numpy',
],
extras_require={
'visualization': ['matplotlib'],
'gdsii': ['python-gdsii'],
'svg': ['svgwrite'],
'text': ['freetype-py', 'matplotlib'],
},
classifiers=[
'Programming Language :: Python :: 3',
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Intended Audience :: Information Technology',
'Intended Audience :: Manufacturing',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: GNU Affero General Public License v3',
'Operating System :: POSIX :: Linux',
'Operating System :: Microsoft :: Windows',
'Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)',
'Topic :: Scientific/Engineering :: Visualization',
],
)