Added PYPI files

LICENSE

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+Copyright (c) 2018 The Python Packaging Authority
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

pyproject.toml

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+[project]
+name = "sparselib"
+version = "1.0.0"
+authors = [
+  { name="Parker Ewen", email="[email protected]" },
+]
+description = "A library for invariant preserving, sparse uncertainty propagation."
+readme = "README.md"
+requires-python = ">=3.10"
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+
+[project.urls]
+Homepage = "https://github.com/roahmlab/sparselib"
+Issues = "https://github.com/roahmlab/sparselib/issues"

scripts/1D_L1_preservation.py

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+import sparselib
+import matplotlib
+import numpy as np
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+
+def dynamics(x):
+	"""
+	Dynamics for x
+	
+	:param      x:    Coordinates
+	:type       x:    np.array
+	
+	:returns:   vector field at coords
+	:rtype:     np.array
+	"""
+	return -np.sin(2 * x)
+
+def uniform_dense(x):
+	"""
+	Initial Uniform uncertainty, independent of dimension
+	
+	:param      x:     Coordinates
+	:type       x:     np.array
+	:param      mu:    Optional mean
+	:type       mu:    np.array
+
+	:returns:   initial uncertainty at coords
+	:rtype:     np.array
+	"""
+	return np.ones_like(x[:,0]) * 1 / (2 * np.pi)
+
+def uniform_sparse(x):
+	"""
+	Initial Uniform uncertainty, independent of dimension
+	
+	:param      x:     Coordinates
+	:type       x:     np.array
+	:param      mu:    Optional mean
+	:type       mu:    np.array
+
+	:returns:   initial uncertainty at coords
+	:rtype:     np.array
+	"""
+	return np.sqrt(np.ones_like(x[:,0]) * 1 / (2 * np.pi))
+
+
+class SolverParamsDense():
+	max_level: int = 6
+	dim: int = 1
+	domain: np.ndarray = np.array([0, 2*np.pi])
+	funcs: list = [uniform_dense, dynamics]
+
+
+class SolverParamsSparse():
+	max_level: int = 6
+	dim: int = 1
+	domain: np.ndarray = np.array([0, 2*np.pi])
+	funcs: list = [uniform_sparse, dynamics]
+
+
+def L1_vs_time():
+	# Initialize solver parameters
+	paramsDense = SolverParamsDense()
+	paramsSparse = SolverParamsSparse()
+
+	# Standard Galerkin method
+	specgalDense = sparselib.SpectralGalerkin(paramsDense)
+
+	# Our sparse method
+	specgalSparse = sparselib.SpectralGalerkin(paramsSparse)
+
+	# Evaluate results
+	N = 1000
+	xs = np.linspace(paramsDense.domain[0], paramsDense.domain[1], N)
+	xs = np.expand_dims(xs, axis=1)
+
+	# Compute the propagated uncertainty for our proposed sparse, half-density
+	# method, a standard Galerkin approach, and the ground-truth distribution.
+	interpDense = np.real(specgalDense.container.grids[0].eval(xs))
+	interpSparse = np.power(np.real(specgalSparse.container.grids[0].eval(xs)), 2)
+
+	L1sparse = []
+	L1dense = []
+
+	L1sparse.append(np.sum(2 * np.pi * np.abs(interpSparse) / N))
+	L1dense.append(np.sum(2 * np.pi * np.abs(interpDense) / N))
+
+	total_time = 1.5
+	M = 200
+	t = 0
+	dt = total_time / M
+
+	print("Computing Lp errors ...")
+	pbar = tqdm(total=M)
+	for i in range(M):
+		t += dt
+		specgalSparse.solve(dt)
+		specgalDense.solve(dt)
+
+		interpDense = np.real(specgalDense.container.grids[0].eval(xs))
+		interpSparse = np.power(np.real(specgalSparse.container.grids[0].eval(xs)), 2)
+
+		L1sparse.append(np.sum(2 * np.pi * np.abs(interpSparse) / N))
+
+		L1dense.append(np.sum(2 * np.pi * np.abs(interpDense) / N))
+
+		pbar.update(1)
+	pbar.close()
+
+	L1sparse = np.array(L1sparse)
+	L1dense = np.array(L1dense)
+
+	ts = np.linspace(0, total_time, M+1)
+
+	matplotlib.rcParams.update({'font.size': 18})
+	fig, ax = plt.subplots(figsize=(18, 6))
+	ax.plot(ts, L1sparse, color='#189ab4', linestyle='-', linewidth=3)
+	ax.plot(ts, L1dense, color='#fd7f20',  linestyle='--', linewidth=3)
+	ax.set_xlabel("Time [s]")
+	ax.set_ylabel("$L^1$-norm")
+	ax.set_xlim([0, total_time])
+	ax.set_ylim([0,1.2])
+	plt.gca().legend(('Sparse (Ours)',
+					  'Galerkin'))
+	fig.tight_layout()
+	# plt.grid()
+	plt.show()
+
+
+L1_vs_time()