Add tox and CI

.github/workflows/python-tox.yml

@@ -0,0 +1,52 @@
+name: tox
+
+on: [
+ push,
+ pull_request,
+]
+
+jobs:
+ build:
+
+ runs-on: ${{ matrix.os }}
+ strategy:
+ matrix:
+ os: [
+ ubuntu-latest,
+ macos-12,
+ windows-latest,
+ ]
+
+ steps:
+ - uses: actions/checkout@v4
+ - name: Setup Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: "3.12"
+ cache: "pip"
+ - name: Install dependencies
+ run: pip install -r requirements.txt
+ - name: Install package
+ run: pip install -e .
+
+ tox:
+
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python: [
+ "3.10",
+ "3.12",
+ ]
+
+ steps:
+ - uses: actions/checkout@v4
+ - name: Setup Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: ${{ matrix.python }}
+ cache: "pip"
+ - name: Install tox
+ run: pip install tox
+ - name: Run tox
+ run: tox

CHANGELOG.md

@@ -1,6 +1,17 @@
 # Changelog
 
-## 2027/08/09 - 00 - v0.0.7 - Update Callback
+## 2024/10/14 - 00 - v0.0.8 - Instantiation and GitHub CI
+* Instantiated backends and solvers with different numerical libraries:
+ * Added `context_manager` modules to `quantrl.backends` and `quantrl.solvers` packages.
+ * Added support for JIT-compilation in `quantrl.backends` package.
+ * Changed naming convention for GPU-based options as `gpu` from `cuda`.
+* Added `tox`-based testing and continuous integration with GitHub workflows.
+* Updated all modules with PEP-based code styling and added `pylintrc`.
+* Updated `requirements.txt` and added version to `quantrl.__init__`.
+* Replaced `setup.py` with `pyproject.toml`.
+* Updated `docs` and `README`.
+
+## 2024/08/09 - 00 - v0.0.7 - Update Callback
 * Fixed issue with cache indexing in `quantrl.envs.base` module.
 * Updated best reward callback in `quantrl.utils` module.
 * Added `seaborn` to `requirements` and `setup`.

README.md

@@ -1,6 +1,6 @@
 # QuantRL: Quantum Control using Reinforcement Learning
 
-![Latest Version](https://img.shields.io/badge/version-0.0.7-red?style=for-the-badge)
+![Latest Version](https://img.shields.io/badge/version-0.0.8-red?style=for-the-badge)
 
 > A library of modules to interface deterministic and stochastic quantum models for reinforcement learning.
 

docs/source/quantrl.backends.rst

@@ -10,6 +10,15 @@ quantrl.backends.base module
  :undoc-members:
  :show-inheritance:
 
+quantrl.backends.context_manager module
+---------------------------------------
+
+.. automodule:: quantrl.backends.context_manager
+ :members:
+ :private-members:
+ :undoc-members:
+ :show-inheritance:
+
 quantrl.backends.jax module
 ---------------------------
 

docs/source/quantrl.solvers.rst

@@ -10,6 +10,15 @@ quantrl.solvers.base module
  :undoc-members:
  :show-inheritance:
 
+quantrl.backends.context_manager module
+---------------------------------------
+
+.. automodule:: quantrl.backends.context_manager
+ :members:
+ :private-members:
+ :undoc-members:
+ :show-inheritance:
+
 quantrl.solvers.jax module
 --------------------------
 

pylintrc

@@ -0,0 +1,26 @@
+[MESSAGES CONTROL]
+disable=too-many-lines,
+ too-many-locals,
+ too-many-arguments,
+ too-many-statements,
+ too-few-public-methods,
+ too-many-public-methods,
+ too-many-instance-attributes,
+ too-many-positional-arguments,
+ line-too-long, # no bounded lines
+ redefined-builtin, # catches __name__
+ duplicate-code, # catches __init__
+ unused-argument, # catches Gym.Env methods
+ import-outside-toplevel, # for numerical libraries
+ not-callable, # catches JAX JIT functions
+ fixme
+
+[BASIC]
+attr-naming-style=any
+variable-naming-style=any
+argument-naming-style=any
+const-naming-style=UPPER_CASE
+method-naming-style=any
+function-naming-style=any
+class-naming-style=PascalCase
+class-attribute-naming-style=snake_case

pyproject.toml

@@ -0,0 +1,87 @@
+[build-system]
+requires = ["cython", "setuptools>=61", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+dynamic = ["version"]
+name = "quantrl"
+authors = [
+ {name = "Sampreet Kalita", email = "[email protected]"},
+]
+maintainers = [
+ {name = "Sampreet Kalita", email = "[email protected]"},
+]
+description = "Quantum Control with Reinforcement Learning"
+keywords = [
+ "quantum",
+ "toolbox",
+ "reinforcement learning",
+ "python3",
+]
+readme = "README.md"
+license = {file = "LICENSE.txt"}
+requires-python = ">=3.10"
+dependencies = [
+ "numpy<2.0.0",
+ "scipy",
+ "matplotlib",
+ "tqdm",
+ "pillow",
+ "pandas",
+ "gymnasium",
+ "stable-baselines3",
+]
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "Development Status :: 2 - Pre-Alpha",
+ "Intended Audience :: Science/Research",
+ "License :: OSI Approved :: BSD License",
+ "Operating System :: OS Independent",
+ "Topic :: Scientific/Engineering",
+]
+
+[project.optional-dependencies]
+jax-cpu = [
+ "jax",
+ "diffrax",
+]
+jax-gpu = [
+ "jax[cuda12]",
+ "diffrax",
+]
+torch = [
+ "torch",
+ "torchvision",
+ "torchaudio",
+ "torchdiffeq",
+]
+
+[project.urls]
+Homepage = "https://github.com/sampreet/quantrl"
+Repository = "https://github.com/sampreet/quantrl"
+Issues = "https://github.com/sampreet/quantrl/issues"
+Changelog = "https://github.com/sampreet/quantrl/blob/master/CHANGELOG.md"
+
+[tool.setuptools.packages.find]
+include = ["quantrl"]
+namespaces = false
+
+[tool.setuptools.dynamic]
+version = {attr = "quantrl.__version__"}
+
+[tool.tox]
+legacy_tox_ini = """
+[tox]
+requires =
+ tox>=4
+ virtualenv>=20
+env_list =
+ lint
+
+[testenv:lint]
+description = run pylint under {base_python}
+deps =
+ -r requirements_tox.txt
+commands =
+ pylint quantrl
+"""

quantrl/__init__.py

@@ -0,0 +1,2 @@
+"""Module to initialize QuantRL."""
+__version__ = "0.0.8"

quantrl/backends/base.py

@@ -6,10 +6,11 @@
 __name__ = 'quantrl.backends.base'
 __authors__ = ["Sampreet Kalita"]
 __created__ = "2024-03-10"
-__updated__ = "2024-10-09"
+__updated__ = "2024-10-13"
 
 # dependencies
 from abc import ABC, abstractmethod
+
 import numpy as np
 
 class BaseBackend(ABC):
@@ -29,9 +30,9 @@ class BaseBackend(ABC):
 
  def __init__(
  self,
- name,
- library,
- tensor_type,
+ name:str='numpy',
+ library:np=np,
+ tensor_type:np.ndarray=np.ndarray,
  precision:str='double'
  ):
  # validate parameters
@@ -68,6 +69,7 @@ def __init__(
  }
  }
  }
+ self.seed_sequence = None
 
  def is_typed(self,
  tensor,
@@ -91,11 +93,32 @@ def is_typed(self,
  _dtype = self.dtype_from_str(
  dtype=dtype
  )
- if type(tensor) == self.tensor_type:
+ if isinstance(tensor, self.tensor_type):
  if dtype is None or (dtype is not None and tensor.dtype == _dtype):
  return True
  return False
 
+ def get_seedsequence(self,
+ seed:int=None
+ ) -> np.random.SeedSequence:
+ """Method to obtain a SeedSequence object.
+ 
+ Parameters
+ ----------
+ seed: int
+ Initial seed to obtain the entropy.
+ 
+ Returns
+ -------
+ seed_sequence: :class:`numpy.random.SeedSequence`
+ The SeedSequence object.
+ """
+ if seed is None:
+ entropy = np.random.randint(1234567890)
+ else:
+ entropy = np.random.default_rng(seed).integers(0, 1234567890, (1, ))[0]
+ return np.random.SeedSequence(entropy)
+
  @abstractmethod
  def convert_to_typed(self,
  tensor,
@@ -546,37 +569,41 @@ def dtype_from_str(self,
  dtype: type
  Selected data-type.
  """
-
- # validate params
- assert dtype is None or dtype in ['integer', 'real', 'complex'], "parameter ``dtype`` can be either ``'integer'``, ``'real'`` or ``'complex'``."
-
  # default dtype is the real data-type
- if dtype is None:
+ if dtype is None or dtype not in ['integer', 'real', 'complex']:
  dtype = 'real'
  return self.dtypes['numpy' if numpy else 'typed'][self.precision][dtype]
- 
+
  def jit_transpose(self, tensor, axis_0, axis_1):
+ """Method to JIT-compile transposition."""
  return self.transpose(tensor, axis_0, axis_1)
- 
+
  def jit_repeat(self, tensor, repeats, axis):
+ """Method to JIT-compile repitition."""
  return self.repeat(tensor, repeats, axis)
- 
+
  def jit_add(self, tensor_0, tensor_1, out):
+ """Method to JIT-compile addition."""
  return self.add(tensor_0, tensor_1, out=out)
- 
+
  def jit_matmul(self, tensor_0, tensor_1, out):
+ """Method to JIT-compile matrix multiplication."""
  return self.matmul(tensor_0, tensor_1, out)
- 
+
  def jit_dot(self, tensor_0, tensor_1, out):
+ """Method to JIT-compile dot product."""
  return self.dot(tensor_0, tensor_1, out)
- 
+
  def jit_concatenate(self, tensors, axis, out):
+ """Method to JIT-compile concatenation."""
  return self.concatenate(tensors, axis, out)
- 
+
  def jit_stack(self, tensors, axis, out):
+ """Method to JIT-compile stacking."""
  return self.stack(tensors, axis, out)
- 
+
  def jit_update(self, tensor, indices, values):
+ """Method to JIT-compile updation."""
  return self.update(tensor, indices, values)
 
  def empty(self,
@@ -1003,4 +1030,4 @@ def argmax(self,
 
  return self.library.argmax(self.convert_to_typed(
  tensor=tensor
- ))
+ ))