# expressive
A library for quickly applying symbolic expressions to NumPy arrays
Enabling callers to front-load and validate sample data, developers can move the runtime cost of Numba's JIT to applications' initial loading and avoid `exec` during user-interactable runtime (otherwise needed when "lambdifying" SymPy expressions) .. additionally, Expressive can identify and handle indexing (`x[i]`, `x[i-1]`) during input parsing, which allows expressions to have offset data references, which can be annoying and isn't automatically handled by SymPy's `parse_expr()` et al.
Inspired in part by this Stack Overflow Question [Using numba.autojit on a lambdify'd sympy expression](https://stackoverflow.com/questions/22793601/using-numba-autojit-on-a-lambdifyd-sympy-expression)
Internally this relies heavily on [SymPy](https://www.sympy.org), [NumPy](https://numpy.org), and [Numba](https://numba.pydata.org), along with [coverage.py](https://coverage.readthedocs.io) to maintain its 100% coverage test suite and [MathJax](https://www.mathjax.org) ([jsDelivr CDN](https://www.jsdelivr.com)) for LaTeX rendering in Notebooks
#### major features
* feedback and result seeding via result array passing and referencing `a[n] + result[n-1]`
* automatic indexer detection and offsetting `a[i+1] + b[i-1]` (`i -> Idx('i')` and `result[0]` and `[-1]` ignored)
* result array type discovery and creation if not passed
* support for unevaluated summation function `Sum(f(x), (x, start, end))` (both via loop codegen and attempted algebraic decomposition)
* global and per-instance config tunables (detailed in [`src/exressive/config.py`](https://gitlab.com/expressive-py/expressive/-/blob/main/src/expressive/config.py))
* expr pretty print display in Notebooks
* validation to help discover type overflowing and more during builds - optionally sample data results from NumPy, SymPy, and build expr are compared, which slows the initial build, but provides good coverage, especially if data extremas are included
## installation
install via pip https://pypi.org/project/expressive/
```shell
pip install expressive
```
## usage
refer to tests for examples for now
when using, follow a workflow like
* create instance `E = Expressive("log(a + log(b))")`
* build instance `E.build(sample_data)`
* directly use callable `E(full_data)`
`data` should be provided as dict of NumPy arrays and the types and shapes of sample data must match the expected runtime data
```python
data_sample = { # simplified data to build and test expr
"a": numpy.array([1,2,3,4], dtype="int64"),
"b": numpy.array([4,3,2,1], dtype="int64"),
}
data = { # real data user wants to process
"a": numpy.array(range(1_000_000), dtype="int64"),
"b": numpy.array(range(1_000_000), dtype="int64"),
}
E = Expressive(expr) # string or SymPy expr
E.build(data_sample) # types used to compile a fast version
E(data) # very fast callable
```
simple demo
```python
import time
import contextlib
import numpy
import matplotlib.pyplot as plt
from expressive import Expressive
# simple projectile motion in a plane
E_position = Expressive("y = v0*t*sin(a0) + 1/2(g*t^2)")
# expr is built early in the process runtime by user
def build():
# create some sample data and build with it
# the types are used to compile a fast version for full data
data_example = {
"v0": 100, # initial velocity m/s
"g": -9.81, # earth gravity m/s/s
"a0": .785, # starting angle ~45° in radians
"t": numpy.linspace(0, 15, dtype="float64"), # 15 seconds is probably enough
}
assert len(data_example["t"]) == 50 # linspace default
time_start = time.perf_counter()
E_position.build(data_example) # verify is implied with little data
time_run = time.perf_counter() - time_start
# provide some extra display details
count = len(data_example["t"])
print(f"built in {time_run*1000:.2f}ms on {count:,} points")
print(f" {E_position}")
def load_data(
point_count=10**8, # 100 million points (*count of angles), maybe 4GiB here
initial_velocity=100, # m/s
):
# manufacture lots of data, which would be loaded in a real example
time_array = numpy.linspace(0, 15, point_count, dtype="float64")
# collect the results
data_collections = []
# process much more data than the build sample
for angle in (.524, .785, 1.047): # initial angles (30°, 45°, 60°)
data = { # data is just generated in this case
"v0": initial_velocity, # NOTE type must match example data
"g": -9.81, # earth gravity m/s/s
"a0": angle, # radians
"t": time_array, # just keep re-using the times for this example
}
data_collections.append(data)
# data collections are now loaded (created)
return data_collections
# later during the process runtime
# user calls the object directly with new data
def runtime(data_collections):
""" whatever the program is normally up to """
# create equivalent function for numpy compare
def numpy_cmp(v0, g, a0, t):
return v0*t*numpy.sin(a0) + 1/2*(g*t**2)
# TODO also compare numexpr demo
# call already-built object directly on each data
results = []
for data in data_collections:
# expressive run
t_start_e = time.perf_counter() # just to show time, prefer timeit for perf
results.append(E_position(data))
t_run_e = time.perf_counter() - t_start_e
# simple numpy run
t_start_n = time.perf_counter()
result_numpy = numpy_cmp(**data)
t_run_n = time.perf_counter() - t_start_n
# provide some extra display details
angle = data["a0"]
count = len(data["t"])
t_run_e = t_run_e * 1000 # convert to ms
t_run_n = t_run_n * 1000
print(f"initial angle {angle}rad ran in {t_run_e:.2f}ms on {count:,} points (numpy:{t_run_n:.2f}ms)")
# decimate to avoid very long matplotlib processing
def sketchy_downsample(ref, count=500):
offset = len(ref) // count
return ref[::offset]
# display results to show it worked
for result, data in zip(results, data_collections):
x = sketchy_downsample(data["t"])
y = sketchy_downsample(result)
plt.scatter(x, y)
plt.xlabel("time (s)")
plt.ylabel("position (m)")
plt.show()
def main():
build()
data_collections = load_data()
runtime(data_collections)
main()
```
## compatibility matrix
generally this strives to only rely on high-level support from SymPy and Numba, though Numba has stricter requirements for NumPy and llvmlite
=== FINAL REPORT =======================================================
| Python | Numba | NumPy | SymPy | commit | coverage | ran |
| --- | --- | --- | --- | --- | --- | --- |
| 3.7.17 | 0.56.4 | 1.21.6 | 1.6 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,575,590'} 🟢 100% (10path) | 112s |
| 3.8.20 | 0.58.1 | 1.24.4 | 1.7 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,575,590'} 🟢 100% (10path) | 112s |
| 3.9.19 | 0.53.1 | 1.23.5 | 1.7 | d5fa3b5 | 🔴 99% `test_modulo_indexed` fails (see note) | 105s |
| 3.9.19 | 0.60.0 | 2.0.1 | 1.13.2 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590'} 🟢 100% (10path) | 110s |
| 3.10.16 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590'} 🟢 100% (10path) | 109s |
| 3.11.11 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590'} 🟢 100% (10path) | 114s |
| 3.12.7 | 0.59.1 | 1.26.4 | 1.13.1 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590', 'test.py': '🟠 99% m 1262'} 🟢 100% (9path) | 106s |
| 3.12.8 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590'} 🟢 100% (10path) | 123s |
| 3.13.1 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590'} 🟢 100% (10path) | 126s |
| 3.13.1 | 0.61.2 | 2.2.6 | 1.14.0 | d5fa3b5 | {'codegen.py': '🟠 99% m 544,590'} 🟢 100% (10path) | 129s |
NOTE differences in test run times are not an indicator of built expr speed, more likely the opposite and _more_ time spent represents additional build step effort, likely improving runtime execution! please consider the values arbitrary and just for development reasons
The one current test failure in py3.9 with old packages is due to a bad array result ordering `'A'`, passing a result array fixes this and very likely any similar issues, and a future version should improve internal result array creation
#### further compatibility notes
these runs build the package themselves internally, while my publishing environment is currently Python 3.11.2
though my testing indicates that this works under a wide variety of quite old versions of Python/Numba/SymPy, upgrading to the highest dependency versions you can will generally be best
* Python 3 major version status https://devguide.python.org/versions/
* https://numba.readthedocs.io/en/stable/release-notes-overview.html
NumPy 1.x and 2.0 saw some major API changes, so older environments may need to adjust or discover working combinations themselves
* some versions of Numba rely on `numpy.MachAr`, which has been [deprecated since at least NumPy 1.22](https://numpy.org/doc/stable/release/1.22.0-notes.html#the-np-machar-class-has-been-deprecated) and may result in warnings
TBD publish multi-version test tool
## testing
Only `docker` is required in the host and used to generate and host testing
```shell
sudo apt install docker.io # debian/ubuntu
sudo usermod -aG docker $USER
sudo su -l $USER # login shell to self (reboot for all shells)
```
Run the test script from the root of the repository and it will build the docker test environment and run itself inside it automatically
```shell
./test/runtests.sh
```
## build + install locally
Follows the generic build and publish process
* https://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archives
* build (builder) https://pypi.org/project/build/
```shell
python3 -m build
python3 -m pip install ./dist/*.whl
```
## contributing
The development process is currently private (though most fruits are available here!), largely due to this being my first public project with the potential for other users than myself, and so the potential for more public gaffes is far greater
Please refer to [CONTRIBUTING.md](https://gitlab.com/expressive-py/expressive/-/blob/main/CONTRIBUTING.md) and [LICENSE.txt](https://gitlab.com/expressive-py/expressive/-/blob/main/LICENSE.txt) and feel free to provide feedback, bug reports, etc. via [Issues](https://gitlab.com/expressive-py/expressive/-/issues), subject to the former
#### additional future intentions for contributing
* ~~improve internal development history as time, popularity, and practicality allows~~
* ~~move to parallel, multi-version CI over all-in-1, single-version dev+test container~~
* ~~greatly relax dependency version requirements to improve compatibility~~
* publish majority of ticket ("Issue") history
## version history
##### v3.6.20250717
* support for modulo `%` and `Mod`
* new string functions `p%q` or `mod(p,q)` are transformed to `Mod(p,q)`, while existing non-function name `mod` are left unchanged
* fixed a bug where internal result array builder could choose a name from `extend` or other 0-dim Symbol when determining its length
##### v3.5.20250711
* support for `Sum` to `Piecewise` transforms, ideally avoiding an additional loop for each row
* initial support for adding additional values or functions via new `extend` arg (`dict` mapping `{str: number or function}`)
* numbers can be any reasonable number-like, while functions must be strings or callables
* all functions are embedded into the template as closures, with callables stringified via `inspect.getsource()`, even if they're some Numba instance via `.py_func` (for now)
##### v3.4.20250523
* basic/experimental version of `native_threadpool` parallelization model
* `Sum` simplifications which results in `Piecewise` are ignored (for now)
##### v3.3.20250508
* improved README with major features and links to major dependency projects
* explicitly name `translate_simplify.build.sum.try_algebraic_convert` tunable in stuck `Sum()` builder condition warning
##### v3.2.20250425
* improved smaller types handling
* automatic dtype determination with `Pow()` is improved
* give a dedicated warning when an exception related to setting `dtype_result` to a type with a small width that a function (such as `Pow()`) automatically promotes occurs
* improve autobuilding experience with new config tunables
* easily enable autobuild globally `builder.autobuild.allow_autobuild`
* option to disable build-time usage warning `builder.autobuild.usage_warn_nag`
* minor version is now a datestamp
##### v3.1.0
* instances of `Expressive` now have individual configurations
* further config changes
* all configuration keys are now flattened and `.`-separated
* warn and still handle legacy keys
* include per-instance builder settings
* new UNSET value singleton
##### v3.0.0
* cutover splitting project into numerous files (dbd89cd+)
* improved MathJax reference copy handling
* split out changelog too (truncated view in README)
* add tunables for parallelization and `numba.prange()` support
* improved a bug where the name literally "row" couldn't be used in `Sum()` (now has a dedicated error and uses "rowscalar" name, a future version should avoid this entirely via by-ref handling and/or name mangling)
* testing changes
* various pathing and import changes to accomodate file changes
* downgrade to non-root user in test containers
* essential argument features (verbose, `trap` EXIT to shell, subset to single `TestCase` or `TestCase.test_`)
##### v2.2.0
* added support for the `Sum` function (SymPy unevaluated summation)
* attempts to evaluate/decompose `Sum` into an algebraic expression during building `.build()`
* creates a custom function to manage `Sum` instances which can't be simplified
* spawn a thread to warn user when attempting to simplify a `Sum`s is taking an excessive amount of time (duration and even halting are unknown, so the user may not know where the issue is .. 20s default)
* added basic configuration system `CONFIG`
* API is unstable and largely featureless, but needed to control/disable `Sum` simplifying
* currently a singleton `dict` shared by all `Expressive` instances, but a future version/design will accept per-instance configurations and combine them with global defaults
* generally much better handling for scalars in data
* scalar values are no longer coerced into a 0-dim array
* NumPy scalars (not just Python numbers) are now allowed
[complete at CHANGELOG.md](https://gitlab.com/expressive-py/expressive/-/blob/main/CHANGELOG.md)
Raw data
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"name": "expressive",
"maintainer": "Russell Fordyce",
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"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "sympy numba numpy codegen",
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"platform": null,
"description": "# expressive\n\nA library for quickly applying symbolic expressions to NumPy arrays\n\nEnabling callers to front-load and validate sample data, developers can move the runtime cost of Numba's JIT to applications' initial loading and avoid `exec` during user-interactable runtime (otherwise needed when \"lambdifying\" SymPy expressions) .. additionally, Expressive can identify and handle indexing (`x[i]`, `x[i-1]`) during input parsing, which allows expressions to have offset data references, which can be annoying and isn't automatically handled by SymPy's `parse_expr()` et al.\n\nInspired in part by this Stack Overflow Question [Using numba.autojit on a lambdify'd sympy expression](https://stackoverflow.com/questions/22793601/using-numba-autojit-on-a-lambdifyd-sympy-expression)\n\nInternally this relies heavily on [SymPy](https://www.sympy.org), [NumPy](https://numpy.org), and [Numba](https://numba.pydata.org), along with [coverage.py](https://coverage.readthedocs.io) to maintain its 100% coverage test suite and [MathJax](https://www.mathjax.org) ([jsDelivr CDN](https://www.jsdelivr.com)) for LaTeX rendering in Notebooks\n\n#### major features\n\n* feedback and result seeding via result array passing and referencing `a[n] + result[n-1]`\n* automatic indexer detection and offsetting `a[i+1] + b[i-1]` (`i -> Idx('i')` and `result[0]` and `[-1]` ignored)\n* result array type discovery and creation if not passed\n* support for unevaluated summation function `Sum(f(x), (x, start, end))` (both via loop codegen and attempted algebraic decomposition)\n* global and per-instance config tunables (detailed in [`src/exressive/config.py`](https://gitlab.com/expressive-py/expressive/-/blob/main/src/expressive/config.py))\n* expr pretty print display in Notebooks\n* validation to help discover type overflowing and more during builds - optionally sample data results from NumPy, SymPy, and build expr are compared, which slows the initial build, but provides good coverage, especially if data extremas are included\n\n## installation\n\ninstall via pip https://pypi.org/project/expressive/\n\n```shell\npip install expressive\n```\n\n## usage\n\nrefer to tests for examples for now\n\nwhen using, follow a workflow like\n* create instance `E = Expressive(\"log(a + log(b))\")`\n* build instance `E.build(sample_data)`\n* directly use callable `E(full_data)`\n\n`data` should be provided as dict of NumPy arrays and the types and shapes of sample data must match the expected runtime data\n\n ```python\ndata_sample = { # simplified data to build and test expr\n \"a\": numpy.array([1,2,3,4], dtype=\"int64\"),\n \"b\": numpy.array([4,3,2,1], dtype=\"int64\"),\n}\ndata = { # real data user wants to process\n \"a\": numpy.array(range(1_000_000), dtype=\"int64\"),\n \"b\": numpy.array(range(1_000_000), dtype=\"int64\"),\n}\nE = Expressive(expr) # string or SymPy expr\nE.build(data_sample) # types used to compile a fast version\nE(data) # very fast callable\n```\n\nsimple demo\n\n```python\nimport time\nimport contextlib\nimport numpy\nimport matplotlib.pyplot as plt\nfrom expressive import Expressive\n\n# simple projectile motion in a plane\nE_position = Expressive(\"y = v0*t*sin(a0) + 1/2(g*t^2)\")\n\n# expr is built early in the process runtime by user\ndef build():\n # create some sample data and build with it\n # the types are used to compile a fast version for full data\n data_example = {\n \"v0\": 100, # initial velocity m/s\n \"g\": -9.81, # earth gravity m/s/s\n \"a0\": .785, # starting angle ~45\u00b0 in radians\n \"t\": numpy.linspace(0, 15, dtype=\"float64\"), # 15 seconds is probably enough\n }\n assert len(data_example[\"t\"]) == 50 # linspace default\n time_start = time.perf_counter()\n E_position.build(data_example) # verify is implied with little data\n time_run = time.perf_counter() - time_start\n\n # provide some extra display details\n count = len(data_example[\"t\"])\n print(f\"built in {time_run*1000:.2f}ms on {count:,} points\")\n print(f\" {E_position}\")\n\ndef load_data(\n point_count=10**8, # 100 million points (*count of angles), maybe 4GiB here\n initial_velocity=100, # m/s\n):\n # manufacture lots of data, which would be loaded in a real example\n time_array = numpy.linspace(0, 15, point_count, dtype=\"float64\")\n # collect the results\n data_collections = []\n # process much more data than the build sample\n for angle in (.524, .785, 1.047): # initial angles (30\u00b0, 45\u00b0, 60\u00b0)\n data = { # data is just generated in this case\n \"v0\": initial_velocity, # NOTE type must match example data\n \"g\": -9.81, # earth gravity m/s/s\n \"a0\": angle, # radians\n \"t\": time_array, # just keep re-using the times for this example\n }\n data_collections.append(data)\n\n # data collections are now loaded (created)\n return data_collections\n\n# later during the process runtime\n# user calls the object directly with new data\ndef runtime(data_collections):\n \"\"\" whatever the program is normally up to \"\"\"\n\n # create equivalent function for numpy compare\n def numpy_cmp(v0, g, a0, t):\n return v0*t*numpy.sin(a0) + 1/2*(g*t**2)\n\n # TODO also compare numexpr demo\n\n # call already-built object directly on each data\n results = []\n for data in data_collections:\n # expressive run\n t_start_e = time.perf_counter() # just to show time, prefer timeit for perf\n results.append(E_position(data))\n t_run_e = time.perf_counter() - t_start_e\n\n # simple numpy run\n t_start_n = time.perf_counter()\n result_numpy = numpy_cmp(**data)\n t_run_n = time.perf_counter() - t_start_n\n\n # provide some extra display details\n angle = data[\"a0\"]\n count = len(data[\"t\"])\n t_run_e = t_run_e * 1000 # convert to ms\n t_run_n = t_run_n * 1000\n print(f\"initial angle {angle}rad ran in {t_run_e:.2f}ms on {count:,} points (numpy:{t_run_n:.2f}ms)\")\n\n # decimate to avoid very long matplotlib processing\n def sketchy_downsample(ref, count=500):\n offset = len(ref) // count\n return ref[::offset]\n\n # display results to show it worked\n for result, data in zip(results, data_collections):\n x = sketchy_downsample(data[\"t\"])\n y = sketchy_downsample(result)\n plt.scatter(x, y)\n plt.xlabel(\"time (s)\")\n plt.ylabel(\"position (m)\")\n plt.show()\n\ndef main():\n build()\n data_collections = load_data()\n runtime(data_collections)\n\nmain()\n```\n\n\n\n## compatibility matrix\n\ngenerally this strives to only rely on high-level support from SymPy and Numba, though Numba has stricter requirements for NumPy and llvmlite\n\n=== FINAL REPORT =======================================================\n| Python | Numba | NumPy | SymPy | commit | coverage | ran |\n| --- | --- | --- | --- | --- | --- | --- |\n| 3.7.17 | 0.56.4 | 1.21.6 | 1.6 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,575,590'} \ud83d\udfe2 100% (10path) | 112s |\n| 3.8.20 | 0.58.1 | 1.24.4 | 1.7 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,575,590'} \ud83d\udfe2 100% (10path) | 112s |\n| 3.9.19 | 0.53.1 | 1.23.5 | 1.7 | d5fa3b5 | \ud83d\udd34 99% `test_modulo_indexed` fails (see note) | 105s |\n| 3.9.19 | 0.60.0 | 2.0.1 | 1.13.2 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590'} \ud83d\udfe2 100% (10path) | 110s |\n| 3.10.16 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590'} \ud83d\udfe2 100% (10path) | 109s |\n| 3.11.11 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590'} \ud83d\udfe2 100% (10path) | 114s |\n| 3.12.7 | 0.59.1 | 1.26.4 | 1.13.1 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590', 'test.py': '\ud83d\udfe0 99% m 1262'} \ud83d\udfe2 100% (9path) | 106s |\n| 3.12.8 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590'} \ud83d\udfe2 100% (10path) | 123s |\n| 3.13.1 | 0.61.0 | 2.1.3 | 1.13.3 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590'} \ud83d\udfe2 100% (10path) | 126s |\n| 3.13.1 | 0.61.2 | 2.2.6 | 1.14.0 | d5fa3b5 | {'codegen.py': '\ud83d\udfe0 99% m 544,590'} \ud83d\udfe2 100% (10path) | 129s |\n\nNOTE differences in test run times are not an indicator of built expr speed, more likely the opposite and _more_ time spent represents additional build step effort, likely improving runtime execution! please consider the values arbitrary and just for development reasons\n\nThe one current test failure in py3.9 with old packages is due to a bad array result ordering `'A'`, passing a result array fixes this and very likely any similar issues, and a future version should improve internal result array creation\n\n#### further compatibility notes\n\nthese runs build the package themselves internally, while my publishing environment is currently Python 3.11.2\n\nthough my testing indicates that this works under a wide variety of quite old versions of Python/Numba/SymPy, upgrading to the highest dependency versions you can will generally be best\n* Python 3 major version status https://devguide.python.org/versions/\n* https://numba.readthedocs.io/en/stable/release-notes-overview.html\n\nNumPy 1.x and 2.0 saw some major API changes, so older environments may need to adjust or discover working combinations themselves\n* some versions of Numba rely on `numpy.MachAr`, which has been [deprecated since at least NumPy 1.22](https://numpy.org/doc/stable/release/1.22.0-notes.html#the-np-machar-class-has-been-deprecated) and may result in warnings\n\nTBD publish multi-version test tool\n\n## testing\n\nOnly `docker` is required in the host and used to generate and host testing\n\n```shell\nsudo apt install docker.io # debian/ubuntu\nsudo usermod -aG docker $USER\nsudo su -l $USER # login shell to self (reboot for all shells)\n```\n\nRun the test script from the root of the repository and it will build the docker test environment and run itself inside it automatically\n\n```shell\n./test/runtests.sh\n```\n\n## build + install locally\n\nFollows the generic build and publish process\n* https://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archives\n* build (builder) https://pypi.org/project/build/\n\n```shell\npython3 -m build\npython3 -m pip install ./dist/*.whl\n```\n\n## contributing\n\nThe development process is currently private (though most fruits are available here!), largely due to this being my first public project with the potential for other users than myself, and so the potential for more public gaffes is far greater\n\nPlease refer to [CONTRIBUTING.md](https://gitlab.com/expressive-py/expressive/-/blob/main/CONTRIBUTING.md) and [LICENSE.txt](https://gitlab.com/expressive-py/expressive/-/blob/main/LICENSE.txt) and feel free to provide feedback, bug reports, etc. via [Issues](https://gitlab.com/expressive-py/expressive/-/issues), subject to the former\n\n#### additional future intentions for contributing\n* ~~improve internal development history as time, popularity, and practicality allows~~\n* ~~move to parallel, multi-version CI over all-in-1, single-version dev+test container~~\n* ~~greatly relax dependency version requirements to improve compatibility~~\n* publish majority of ticket (\"Issue\") history\n\n## version history\n\n##### v3.6.20250717\n* support for modulo `%` and `Mod`\n* new string functions `p%q` or `mod(p,q)` are transformed to `Mod(p,q)`, while existing non-function name `mod` are left unchanged\n* fixed a bug where internal result array builder could choose a name from `extend` or other 0-dim Symbol when determining its length\n\n##### v3.5.20250711\n* support for `Sum` to `Piecewise` transforms, ideally avoiding an additional loop for each row\n* initial support for adding additional values or functions via new `extend` arg (`dict` mapping `{str: number or function}`)\n * numbers can be any reasonable number-like, while functions must be strings or callables\n * all functions are embedded into the template as closures, with callables stringified via `inspect.getsource()`, even if they're some Numba instance via `.py_func` (for now)\n\n##### v3.4.20250523\n* basic/experimental version of `native_threadpool` parallelization model\n* `Sum` simplifications which results in `Piecewise` are ignored (for now)\n\n##### v3.3.20250508\n* improved README with major features and links to major dependency projects\n* explicitly name `translate_simplify.build.sum.try_algebraic_convert` tunable in stuck `Sum()` builder condition warning\n\n##### v3.2.20250425\n* improved smaller types handling\n * automatic dtype determination with `Pow()` is improved\n * give a dedicated warning when an exception related to setting `dtype_result` to a type with a small width that a function (such as `Pow()`) automatically promotes occurs\n* improve autobuilding experience with new config tunables\n * easily enable autobuild globally `builder.autobuild.allow_autobuild`\n * option to disable build-time usage warning `builder.autobuild.usage_warn_nag`\n* minor version is now a datestamp\n\n##### v3.1.0\n* instances of `Expressive` now have individual configurations\n* further config changes\n * all configuration keys are now flattened and `.`-separated\n * warn and still handle legacy keys\n * include per-instance builder settings\n * new UNSET value singleton\n\n##### v3.0.0\n* cutover splitting project into numerous files (dbd89cd+)\n * improved MathJax reference copy handling\n * split out changelog too (truncated view in README)\n* add tunables for parallelization and `numba.prange()` support\n* improved a bug where the name literally \"row\" couldn't be used in `Sum()` (now has a dedicated error and uses \"rowscalar\" name, a future version should avoid this entirely via by-ref handling and/or name mangling)\n* testing changes\n * various pathing and import changes to accomodate file changes\n * downgrade to non-root user in test containers\n * essential argument features (verbose, `trap` EXIT to shell, subset to single `TestCase` or `TestCase.test_`)\n\n##### v2.2.0\n* added support for the `Sum` function (SymPy unevaluated summation)\n * attempts to evaluate/decompose `Sum` into an algebraic expression during building `.build()`\n * creates a custom function to manage `Sum` instances which can't be simplified\n * spawn a thread to warn user when attempting to simplify a `Sum`s is taking an excessive amount of time (duration and even halting are unknown, so the user may not know where the issue is .. 20s default)\n* added basic configuration system `CONFIG`\n * API is unstable and largely featureless, but needed to control/disable `Sum` simplifying\n * currently a singleton `dict` shared by all `Expressive` instances, but a future version/design will accept per-instance configurations and combine them with global defaults\n* generally much better handling for scalars in data\n * scalar values are no longer coerced into a 0-dim array\n * NumPy scalars (not just Python numbers) are now allowed\n\n[complete at CHANGELOG.md](https://gitlab.com/expressive-py/expressive/-/blob/main/CHANGELOG.md)\n",
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