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A library for interacting with TI-(e)z80 (82/83/84 series) calculator files

Project description

tivars_lib_py

tivars_lib_py is a Python package for interacting with TI-(e)z80 (82/83/84 series) calculator files, i.e. lists, programs, matrices, appvars, etc.

Much of the functionality of this package has been ported over from tivars_lib_cpp. However, a number of changes have made to the API to better suit Python's strengths and capabilities as a language (e.g. scripting).

Installation

The current release version is v0.8.0. All versions require Python 3.10+ to run.

As a Package

Install the tivars package from PyPI using pip:

pip install tivars

Alternatively, you can clone this repository or download a release and extract the tivars directory to include it in your next project. Once downloaded, you can also use pip to install it locally.

As a Submodule

Include this repository in your next project as a submodule using git submodule or manually cloning and writing the following in .gitmodules:

[submodule "tivars_lib_py"]
	path = path/to/tivars_lib_py
	url = https://github.com/TI-Toolkit/tivars_lib_py.git

Then, add the following to any file which imports tivars:

import sys

sys.path.insert(1, 'tivars_lib_py/')

Check out this tool for an example.

Unit Testing

You can run the test suite via __main__.py, or run individual tests found in tests/ with unittest. Tests for optional package extensions (e.g. PIL) will be skipped if the package cannot be found.

Note that the PyPI distribution does not include the test suite.

How to Use

Creating vars

Every var file has two parts: a header and a number of entries, where an entry contains the data for a single variable. Usually, var files contain just one entry; in these cases, there's not much distinction between a var and an entry for the purposes of messing with its data.

To create an empty entry, instantiate its corresponding type from tivars.types. You can specify additional parameters as you like:

from tivars.models import *
from tivars.types import *

my_program = TIProgram(name="HELLO")

If you're not sure of an entry's type, instantiate a base TIEntry:

my_entry = TIEntry()

If you want to create an entire var or just a header, use TIVar or TIHeader instead:

from tivars.var import *

my_var = TIVar()
my_var_for84pce = TIVar(model=TI_84PCE)

my_header = TIHeader()
my_header_with_a_cool_comment = TIHeader(comment="Wow! I'm a comment!")

Reading and writing

Vars can be loaded from files or raw bytes:

my_var = TIVar.open("HELLO.8xp")

# Note binary mode!
with open("HELLO.8xp", 'rb') as file:
    my_var.load_var_file(file)
    
    file.seek(0)
    my_var.load_bytes(file.read())

Entries can be loaded from files or raw bytes. When loading from a file, you may specify which entry to load if there are multiple:

# Raises an error if the var has multiple entries; use load_from_file instead
my_program = TIProgram.open("HELLO.8xp")

with open("HELLO.8xp", 'rb') as file:
    # Offset counts the number of entries to skip; defaults to zero
    my_program.load_from_file(file, offset=1)
    
    file.seek(0)
    my_program.load_bytes(file.read())

Most entry types also support loading from other natural data types. Any data can be passed to the constructor directly and be delegated to the correct loader:

my_program = TIProgram("Disp \"HELLO WORLD!\"")
my_program.load_string("Disp \"HELLO WORLD!\"")

my_real = TIReal(1.23)
my_real.load_float(1.23)

Base TIEntry objects, as well other parent types like TIGDB, will be automatically coerced to the correct type:

# Coerces to a TIProgram
my_entry = TIEntry.open("HELLO.8xp")

Export a var as bytes or straight to a file:

my_var.save("HELLO.8xp")

# Infer the filename and extension
my_var.save()

with open("HELLO.8xp", 'wb+') as file:
    file.write(my_var.bytes())

Entries can be passed an explicit header to attach or model to target when exporting:

my_program.save("HELLO.8xp")
my_program.save()

with open("HELLO.8xp", 'wb+') as file:
    file.write(my_program.export(header=my_header).bytes())

Any input data type can also be exported to:

assert my_program.string() == "Disp \"HELLO WORLD!\""

assert my_real.float() == 1.23

Data types corresponding to built-in Python types can be obtained from the built-in constructors:

assert str(my_program) == "Disp \"HELLO WORLD!\""

assert float(my_real) == 1.23

Data Sections

Vars are comprised of individual sections which represent different forms of data, split across the header and entries. The var itself also contains the total entry length and checksum sections, but these are read-only to prevent file corruption.

You can read and write to individual sections of an entry or header as their "canonical" type:

my_header.comment = "This is my (even cooler) comment!"
my_program.archived = True

assert my_program.type_id == 0x05

Data sections can also be other entry types:

my_gdb = TIGDB()
my_gdb.Xmin = TIReal(0)

assert my_gdb.Xmax == TIReal(10)

Each section is annotated with the expected type.

Models

All TI-82/83/84 series calcs are represented as TIModel objects stored in tivars.models. Each model contains its name, metadata, and features; use has on a TIFeature to check that a model has a given a feature. Models are also used to determine var file extensions and token sheets.

For these reasons, it is not recommended to instantiate your own models.

Other Functionalities

PIL

The tivars.PIL package can be used to interface with PIL, the Python Imaging Library. Simply import the package to register codecs for each of the TI image types. You can then open such images directly into a PIL Image:

from PIL import Image
from tivars.PIL import *

img = Image.open("Pic1.8ci")
img.show()

Tokenization

Functions to decode and encode strings into tokens can be found in tivars.tokenizer. Support currently exists for all models in the 82/83/84 series as well as the TI-73; PR's concerning the sheets themselves should be directed upstream to TI-Toolkit/tokens.

Documentation

Library documentation can be found on GitHub Pages.

The var file format(s) and data sections can be found in a readable format on the repository wiki. Much of the information is copied from the TI-83 Link Guide, though has been updated to account for color models.

Examples

You can find more sample code in examples that details common operations on each of the entry types. There are also examples for interfacing with popular external libraries (e.g. NumPy, PIL). Contributions welcome!

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