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Author SHA1 Message Date
156019fdac chore(deps): update dependency scipy to ~1.10
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2023-01-04 01:31:57 +00:00
c49b8eb034
chore(release): 0.8.7
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2022-09-17 16:19:02 -05:00
c034ae81fd Merge pull request 'xy' (#23) from xy into master
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Reviewed-on: #23
2022-09-17 21:13:43 +00:00
5acf0ac347
fix: Correctly generates monte carlo version of xy model dipoles
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2022-09-17 16:08:11 -05:00
2a1ae3b1a7
feat: moves xy model up to model package
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2022-09-17 15:34:45 -05:00
0447b5d3a7
test: adds test for xy model 2022-09-17 15:34:01 -05:00
e894c89702
feat: adds xy model for convenience to pdme
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2022-09-17 14:39:29 -05:00
6 changed files with 366 additions and 12 deletions

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@ -2,6 +2,19 @@
All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
### [0.8.7](https://gitea.deepak.science:2222/physics/pdme/compare/0.8.6...0.8.7) (2022-09-17)
### Features
* adds xy model for convenience to pdme ([e894c89](https://gitea.deepak.science:2222/physics/pdme/commit/e894c897029c05a1d4754e7930ae9ba2be7a1cfd))
* moves xy model up to model package ([2a1ae3b](https://gitea.deepak.science:2222/physics/pdme/commit/2a1ae3b1a7f7e10469b7fd2930fee0b338f0c03f))
### Bug Fixes
* Correctly generates monte carlo version of xy model dipoles ([5acf0ac](https://gitea.deepak.science:2222/physics/pdme/commit/5acf0ac347382705674bb596440d27cba3730bac))
### [0.8.6](https://gitea.deepak.science:2222/physics/pdme/compare/0.8.5...0.8.6) (2022-06-13)

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@ -11,6 +11,10 @@ from pdme.model.log_spaced_random_choice_model import (
LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
)
from pdme.model.log_spaced_random_choice_xy_model import (
LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
)
from pdme.model.log_spaced_random_choice_fixed_orientation_model import (
LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
)
@ -21,5 +25,6 @@ __all__ = [
"MultipleDipoleFixedMagnitudeModel",
"RandomCountMultipleDipoleFixedMagnitudeModel",
"LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel",
"LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel",
"LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel",
]

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@ -0,0 +1,125 @@
import numpy
import numpy.random
from pdme.model.model import DipoleModel
from pdme.measurement import (
OscillatingDipole,
OscillatingDipoleArrangement,
)
class LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(DipoleModel):
"""
Model of multiple oscillating dipoles with a fixed magnitude, but free rotation in XY plane. Spaced logarithmically.
Parameters
----------
wexp_min: log-10 lower bound for dipole frequency
wexp_min: log-10 upper bound for dipole frequency
pfixed : float
The fixed dipole magnitude.
n_max : int
The maximum number of dipoles.
prob_occupancy : float
The probability of dipole occupancy
"""
def __init__(
self,
xmin: float,
xmax: float,
ymin: float,
ymax: float,
zmin: float,
zmax: float,
wexp_min: float,
wexp_max: float,
pfixed: float,
n_max: int,
prob_occupancy: float,
) -> None:
self.xmin = xmin
self.xmax = xmax
self.ymin = ymin
self.ymax = ymax
self.zmin = zmin
self.zmax = zmax
self.wexp_min = wexp_min
self.wexp_max = wexp_max
self.pfixed = pfixed
self.rng = numpy.random.default_rng()
self.n_max = n_max
if prob_occupancy >= 1 or prob_occupancy <= 0:
raise ValueError(
f"The probability of a dipole site occupancy must be between 0 and 1, got {prob_occupancy}"
)
self.prob_occupancy = prob_occupancy
def __repr__(self) -> str:
return f"LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel({self.xmin}, {self.xmax}, {self.ymin}, {self.ymax}, {self.zmin}, {self.zmax}, {self.wexp_min}, {self.wexp_max}, {self.pfixed}, {self.n_max}, {self.prob_occupancy})"
def get_dipoles(
self, max_frequency: float, rng_to_use: numpy.random.Generator = None
) -> OscillatingDipoleArrangement:
rng: numpy.random.Generator
if rng_to_use is None:
rng = self.rng
else:
rng = rng_to_use
dipoles = []
n = rng.binomial(self.n_max, self.prob_occupancy)
for i in range(n):
phi = rng.uniform(0, 2 * numpy.pi)
px = self.pfixed * numpy.cos(phi)
py = self.pfixed * numpy.sin(phi)
pz = 0
s_pts = numpy.array(
(
rng.uniform(self.xmin, self.xmax),
rng.uniform(self.ymin, self.ymax),
rng.uniform(self.zmin, self.zmax),
)
)
frequency = 10 ** rng.uniform(self.wexp_min, self.wexp_max)
dipoles.append(
OscillatingDipole(numpy.array([px, py, pz]), s_pts, frequency)
)
return OscillatingDipoleArrangement(dipoles)
def get_monte_carlo_dipole_inputs(
self,
monte_carlo_n: int,
_: float,
rng_to_use: numpy.random.Generator = None,
) -> numpy.ndarray:
rng: numpy.random.Generator
if rng_to_use is None:
rng = self.rng
else:
rng = rng_to_use
shape = (monte_carlo_n, self.n_max)
phi = 2 * numpy.pi * rng.random(shape)
p_mask = rng.binomial(1, self.prob_occupancy, shape)
p_magnitude = self.pfixed * p_mask
px = p_magnitude * numpy.cos(phi)
py = p_magnitude * numpy.sin(phi)
pz = p_magnitude * 0
sx = rng.uniform(self.xmin, self.xmax, shape)
sy = rng.uniform(self.ymin, self.ymax, shape)
sz = rng.uniform(self.zmin, self.zmax, shape)
w = 10 ** rng.uniform(self.wexp_min, self.wexp_max, shape)
return numpy.stack([px, py, pz, sx, sy, sz, w], axis=-1)

25
poetry.lock generated
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@ -93,7 +93,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "coverage"
version = "6.4.2"
version = "7.0.3"
description = "Code coverage measurement for Python"
category = "dev"
optional = false
@ -316,8 +316,8 @@ optional = false
python-versions = ">=3.6"
[package.extras]
testing = ["pytest-benchmark", "pytest"]
dev = ["tox", "pre-commit"]
dev = ["pre-commit", "tox"]
testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "prompt-toolkit"
@ -426,18 +426,23 @@ coverage = {version = ">=5.2.1", extras = ["toml"]}
pytest = ">=4.6"
[package.extras]
testing = ["virtualenv", "pytest-xdist", "six", "process-tests", "hunter", "fields"]
testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"]
[[package]]
name = "scipy"
version = "1.9.0"
description = "SciPy: Scientific Library for Python"
version = "1.10.0"
description = "Fundamental algorithms for scientific computing in Python"
category = "main"
optional = false
python-versions = ">=3.8,<3.12"
python-versions = "<3.12,>=3.8"
[package.dependencies]
numpy = ">=1.18.5,<1.25.0"
numpy = ">=1.19.5,<1.27.0"
[package.extras]
test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"]
doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"]
dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"]
[[package]]
name = "six"
@ -461,7 +466,7 @@ executing = "*"
pure-eval = "*"
[package.extras]
tests = ["cython", "littleutils", "pygments", "typeguard", "pytest"]
tests = ["pytest", "typeguard", "pygments", "littleutils", "cython"]
[[package]]
name = "toml"
@ -509,7 +514,7 @@ python-versions = "*"
[metadata]
lock-version = "1.1"
python-versions = "^3.8,<3.10"
content-hash = "ba562365abd9c6b04b231a9f307c8ae109fdb50b99e793a28a33b1438374bfbb"
content-hash = "f5614947eb2f77e9cd8bd9dc026a924f4cb7394c3fef9cb175ab5afbe201ca73"
[metadata.files]
appnope = []

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@ -1,6 +1,6 @@
[tool.poetry]
name = "pdme"
version = "0.8.6"
version = "0.8.7"
description = "Python dipole model evaluator"
authors = ["Deepak <dmallubhotla+github@gmail.com>"]
license = "GPL-3.0-only"
@ -9,7 +9,7 @@ readme = "README.md"
[tool.poetry.dependencies]
python = "^3.8,<3.10"
numpy = "^1.22.3"
scipy = "~1.9"
scipy = "~1.10"
[tool.poetry.dev-dependencies]
pytest = ">=6"

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@ -0,0 +1,206 @@
from pdme.model import LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel
import numpy
import logging
import pytest
_logger = logging.getLogger(__name__)
def test_random_count_multiple_dipole_xy_wrong_probability():
with pytest.raises(ValueError):
LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
-10, 10, -5, 5, 2, 3, 1, 2, 10, 5, 2
)
def test_repr_random_count_multiple_dipole_fixed_mag_xy():
model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
-10, 10, -5, 5, 2, 3, 1, 2, 10, 5, 0.5
)
assert (
repr(model)
== "LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(-10, 10, -5, 5, 2, 3, 1, 2, 10, 5, 0.5)"
), "Repr should be what I want."
def test_random_count_multiple_dipole_fixed_mag_model_get_dipoles_multiple_xy():
p_fixed = 10
dipole_count = 5
model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
-10, 10, -5, 5, 2, 3, 0, 5, p_fixed, dipole_count, 0.5
)
dipole_arrangement = model.get_dipoles(20, numpy.random.default_rng(1234))
dipoles = dipole_arrangement.dipoles
assert (
len(dipoles) == dipole_count
), "Should have had multiple dipole based on count generated."
def test_random_count_multiple_dipole_fixed_mag_model_get_dipoles_invariant_xy():
x_min = -10
x_max = 10
y_min = -5
y_max = 5
z_min = 2
z_max = 3
p_fixed = 10
max_frequency = 5
model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
x_min, x_max, y_min, y_max, z_min, z_max, 0, max_frequency, p_fixed, 1, 0.5
)
model.rng = numpy.random.default_rng(1234)
dipole_arrangement = model.get_dipoles(5)
dipoles = dipole_arrangement.dipoles
assert len(dipoles) == 1, "Should have only had one dipole generated."
for i in range(10):
dipole_arrangement = model.get_dipoles(max_frequency)
dipoles = dipole_arrangement.dipoles
assert len(dipoles) in (
0,
1,
), "Should have either zero or one dipole generated."
if len(dipoles) > 0:
min_s = numpy.array([x_min, y_min, z_min])
max_s = numpy.array([x_max, y_max, z_max])
numpy.testing.assert_equal(
numpy.logical_and(min_s < dipoles[0].s, max_s > dipoles[0].s),
True,
f"Dipole location [{dipoles[0].s}] should have been between min [{min_s}] and max [{max_s}] bounds.",
)
assert (
dipoles[0].w < 10 ** max_frequency and dipoles[0].w > 10**0
), "Dipole frequency should have been between 0 and max."
numpy.testing.assert_allclose(
numpy.linalg.norm(dipoles[0].p),
p_fixed,
err_msg="Should have had the expected dipole moment magnitude.",
)
_logger.warning(dipoles[0].p)
numpy.testing.assert_allclose(
dipoles[0].p[2],
0,
err_msg="Should have had zero z magnitude.",
)
def test_random_count_multiple_dipole_fixed_mag_model_get_dipoles_invariant_monte_carlo_xy():
x_min = -10
x_max = 10
y_min = -5
y_max = 5
z_min = 2
z_max = 3
p_fixed = 10
max_frequency = 5
monte_carlo_n = 20
model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
x_min, x_max, y_min, y_max, z_min, z_max, 0, max_frequency, p_fixed, 1, 0.5
)
model.rng = numpy.random.default_rng(1234)
dipole_arrangement = model.get_dipoles(5)
dipoles = dipole_arrangement.dipoles
assert len(dipoles) == 1, "Should have only had one dipole generated."
for i in range(10):
dipoles = model.get_monte_carlo_dipole_inputs(monte_carlo_n, max_frequency)
min_s = numpy.array([x_min, y_min, z_min])
max_s = numpy.array([x_max, y_max, z_max])
_logger.warning(dipoles)
_logger.warning(dipoles[:, 0, 0:3])
_logger.warning(dipoles[:, 0, 3:6])
_logger.warning(dipoles[:, 0, 6])
ps = dipoles[:, 0, 0:3]
ss = dipoles[:, 0, 3:6]
ws = dipoles[:, 0, 6]
numpy.testing.assert_equal(
numpy.logical_and(min_s < ss, max_s > ss).all(),
True,
f"Dipole location [{ss}] should have been between min [{min_s}] and max [{max_s}] bounds.",
)
assert (ws < 10**max_frequency).all() and (
ws > 10**0
).all(), "Dipole frequency should have been between 0 and max."
norms = numpy.linalg.norm(ps, axis=1)
filtered_norms = norms[norms > 0]
numpy.testing.assert_allclose(
filtered_norms,
p_fixed,
err_msg="Should have had the expected dipole moment magnitude.",
)
numpy.testing.assert_allclose(
ps[:, 2],
0,
err_msg="Should have had zero z magnitude.",
)
def test_random_count_multiple_dipole_shape():
x_min = -10
x_max = 10
y_min = -5
y_max = 5
z_min = 2
z_max = 3
p_fixed = 10
max_frequency = 5
num_dipoles = 13
monte_carlo_n = 11
model = LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel(
x_min,
x_max,
y_min,
y_max,
z_min,
z_max,
0,
max_frequency,
p_fixed,
num_dipoles,
0.5,
)
model.rng = numpy.random.default_rng(1234)
actual_shape = model.get_monte_carlo_dipole_inputs(
monte_carlo_n, max_frequency
).shape
numpy.testing.assert_equal(
actual_shape,
(monte_carlo_n, num_dipoles, 7),
err_msg="shape was wrong for monte carlo outputs",
)
actual_shape = model.get_monte_carlo_dipole_inputs(
monte_carlo_n, max_frequency, rng_to_use=numpy.random.default_rng(1515)
).shape
numpy.testing.assert_equal(
actual_shape,
(monte_carlo_n, num_dipoles, 7),
err_msg="shape was wrong for monte carlo outputs",
)