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4
.gitignore
vendored
4
.gitignore
vendored
@ -144,3 +144,7 @@ cython_debug/
|
||||
|
||||
.csv
|
||||
.idea
|
||||
|
||||
# just a local scripts dir for us to be able to run things in
|
||||
local_scripts/
|
||||
|
||||
|
||||
34
CHANGELOG.md
34
CHANGELOG.md
@ -2,6 +2,40 @@
|
||||
|
||||
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.
|
||||
|
||||
## [1.3.0](https://gitea.deepak.science:2222/physics/tantri/compare/1.2.0...1.3.0) (2024-12-30)
|
||||
|
||||
|
||||
### Features
|
||||
|
||||
* adds real spectrum correction, which might cause problems with older stuff ([0e52cd6](https://gitea.deepak.science:2222/physics/tantri/commit/0e52cd61aecb0ccc51d800fffe7d98551c17ee2e))
|
||||
|
||||
|
||||
### Bug Fixes
|
||||
|
||||
* fixes real spectrum issue (revise these files if problems later...) ([9c3f5aa](https://gitea.deepak.science:2222/physics/tantri/commit/9c3f5aa286ac9cd812fa9e753d94d7955c89851c))
|
||||
|
||||
## [1.2.0](https://gitea.deepak.science:2222/physics/tantri/compare/1.1.0...1.2.0) (2024-09-04)
|
||||
|
||||
|
||||
### Features
|
||||
|
||||
* adding binning feature that handles summary statistics ([a20f0c2](https://gitea.deepak.science:2222/physics/tantri/commit/a20f0c2069ffc6d930aee2fa2c93c6ade701b7f8))
|
||||
* adds apsd cli command ([f91df42](https://gitea.deepak.science:2222/physics/tantri/commit/f91df4227dc86b525b9a53162884f4bfb3a3944c))
|
||||
* adds event based time series calculation ([34e3156](https://gitea.deepak.science:2222/physics/tantri/commit/34e31569dceb581fd31b0b4479ffddbd6e59b724))
|
||||
* adds output binning and revises cli arguments ([f31b6aa](https://gitea.deepak.science:2222/physics/tantri/commit/f31b6aa6b15441cd6c65e2809165d2e9a668883e))
|
||||
* adds psd calculation code and utilities to handle averaging of periodograms ([22bb3d8](https://gitea.deepak.science:2222/physics/tantri/commit/22bb3d876f8994dc81d93b8ff2b1f68e0d91d2b0))
|
||||
* allows use of new event-based method for time series, despite it being slow ([4623af6](https://gitea.deepak.science:2222/physics/tantri/commit/4623af69e7971eb72fdd6509d28ca41d8125a906))
|
||||
|
||||
## [1.1.0](https://gitea.deepak.science:2222/physics/tantri/compare/1.0.1...1.1.0) (2024-05-02)
|
||||
|
||||
|
||||
### Features
|
||||
|
||||
* adds supersampling to better approximate a realistic time series for high frequency noise ([986e42d](https://gitea.deepak.science:2222/physics/tantri/commit/986e42df4e1d27babbb73cb064caedc5833357ba))
|
||||
* allows calling cli commands from libraries with wrapped commands, removes tqdm ([f55bbe6](https://gitea.deepak.science:2222/physics/tantri/commit/f55bbe66ee846215221a3361934cf365bf390860))
|
||||
|
||||
### [1.0.1](https://gitea.deepak.science:2222/physics/tantri/compare/1.0.0...1.0.1) (2024-04-22)
|
||||
|
||||
## 1.0.0 (2024-04-22)
|
||||
|
||||
|
||||
|
||||
12
README.md
12
README.md
@ -1,4 +1,12 @@
|
||||
# tantri - generating telegraph noise
|
||||
# tantri - generating telegraph noise and other Dipole TLS calculations
|
||||
|
||||
[](https://conventionalcommits.org)
|
||||
[](https://pypi.org/project/tantri/)
|
||||
[](https://jenkins.deepak.science/job/gitea-physics/job/tantri/job/master/)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Named as an oblique half-pun on both tantri as a form of generating, and tantri as a telegraph or wire.
|
||||
|
||||
@ -8,4 +16,4 @@ Build with `just`, preferred over `do.sh` I think.
|
||||
|
||||
## CLI
|
||||
|
||||
the json files for dots and dipoles have a specific format that ignores extraneous data but is sad about missing fields.
|
||||
The json files for dots and dipoles have a specific format that ignores extraneous data but is sad about missing fields.
|
||||
|
||||
25
flake.lock
generated
25
flake.lock
generated
@ -23,11 +23,11 @@
|
||||
"systems": "systems_2"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1705309234,
|
||||
"narHash": "sha256-uNRRNRKmJyCRC/8y1RqBkqWBLM034y4qN7EprSdmgyA=",
|
||||
"lastModified": 1710146030,
|
||||
"narHash": "sha256-SZ5L6eA7HJ/nmkzGG7/ISclqe6oZdOZTNoesiInkXPQ=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"rev": "1ef2e671c3b0c19053962c07dbda38332dcebf26",
|
||||
"rev": "b1d9ab70662946ef0850d488da1c9019f3a9752a",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@ -59,15 +59,16 @@
|
||||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1713733131,
|
||||
"narHash": "sha256-wpksn+coLuupYNuvB0CVh6N2ekYWoHk5rgjE1jw5tpk=",
|
||||
"lastModified": 1719468428,
|
||||
"narHash": "sha256-vN5xJAZ4UGREEglh3lfbbkIj+MPEYMuqewMn4atZFaQ=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "b4bcfd112787f0eb79bbfd8306302802ec885003",
|
||||
"rev": "1e3deb3d8a86a870d925760db1a5adecc64d329d",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "NixOS",
|
||||
"ref": "nixpkgs-unstable",
|
||||
"repo": "nixpkgs",
|
||||
"type": "github"
|
||||
}
|
||||
@ -83,11 +84,11 @@
|
||||
"treefmt-nix": "treefmt-nix"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1708589824,
|
||||
"narHash": "sha256-2GOiFTkvs5MtVF65sC78KNVxQSmsxtk0WmV1wJ9V2ck=",
|
||||
"lastModified": 1719549552,
|
||||
"narHash": "sha256-efvBV+45uQA6r7aov48H6MhvKp1QUIyIX5gh9oueUzs=",
|
||||
"owner": "nix-community",
|
||||
"repo": "poetry2nix",
|
||||
"rev": "3c92540611f42d3fb2d0d084a6c694cd6544b609",
|
||||
"rev": "4fd045cdb85f2a0173021a4717dc01d92d7ab2b2",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
@ -155,11 +156,11 @@
|
||||
]
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1708335038,
|
||||
"narHash": "sha256-ETLZNFBVCabo7lJrpjD6cAbnE11eDOjaQnznmg/6hAE=",
|
||||
"lastModified": 1718522839,
|
||||
"narHash": "sha256-ULzoKzEaBOiLRtjeY3YoGFJMwWSKRYOic6VNw2UyTls=",
|
||||
"owner": "numtide",
|
||||
"repo": "treefmt-nix",
|
||||
"rev": "e504621290a1fd896631ddbc5e9c16f4366c9f65",
|
||||
"rev": "68eb1dc333ce82d0ab0c0357363ea17c31ea1f81",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
description = "Application packaged using poetry2nix";
|
||||
|
||||
inputs.flake-utils.url = "github:numtide/flake-utils";
|
||||
inputs.nixpkgs.url = "github:NixOS/nixpkgs";
|
||||
inputs.nixpkgs.url = "github:NixOS/nixpkgs/nixpkgs-unstable";
|
||||
inputs.poetry2nixSrc = {
|
||||
url = "github:nix-community/poetry2nix";
|
||||
inputs.nixpkgs.follows = "nixpkgs";
|
||||
|
||||
9
justfile
9
justfile
@ -65,3 +65,12 @@ release:
|
||||
|
||||
htmlcov:
|
||||
poetry run pytest --cov-report=html
|
||||
|
||||
zsh_completions:
|
||||
#!/usr/bin/env bash
|
||||
set -euxo pipefail
|
||||
if [[ "${DO_NIX_CUSTOM:=0}" -eq 1 ]]; then
|
||||
eval "$(_TANTRI_COMPLETE=zsh_source tantri)"
|
||||
else
|
||||
echo "Nope only nix."
|
||||
fi
|
||||
|
||||
167
poetry.lock
generated
167
poetry.lock
generated
@ -62,63 +62,63 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "coverage"
|
||||
version = "7.4.4"
|
||||
version = "7.6.0"
|
||||
description = "Code coverage measurement for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "coverage-7.4.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e0be5efd5127542ef31f165de269f77560d6cdef525fffa446de6f7e9186cfb2"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ccd341521be3d1b3daeb41960ae94a5e87abe2f46f17224ba5d6f2b8398016cf"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09fa497a8ab37784fbb20ab699c246053ac294d13fc7eb40ec007a5043ec91f8"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b1a93009cb80730c9bca5d6d4665494b725b6e8e157c1cb7f2db5b4b122ea562"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:690db6517f09336559dc0b5f55342df62370a48f5469fabf502db2c6d1cffcd2"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:09c3255458533cb76ef55da8cc49ffab9e33f083739c8bd4f58e79fecfe288f7"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:8ce1415194b4a6bd0cdcc3a1dfbf58b63f910dcb7330fe15bdff542c56949f87"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b91cbc4b195444e7e258ba27ac33769c41b94967919f10037e6355e998af255c"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-win32.whl", hash = "sha256:598825b51b81c808cb6f078dcb972f96af96b078faa47af7dfcdf282835baa8d"},
|
||||
{file = "coverage-7.4.4-cp310-cp310-win_amd64.whl", hash = "sha256:09ef9199ed6653989ebbcaacc9b62b514bb63ea2f90256e71fea3ed74bd8ff6f"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0f9f50e7ef2a71e2fae92774c99170eb8304e3fdf9c8c3c7ae9bab3e7229c5cf"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:623512f8ba53c422fcfb2ce68362c97945095b864cda94a92edbaf5994201083"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0513b9508b93da4e1716744ef6ebc507aff016ba115ffe8ecff744d1322a7b63"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40209e141059b9370a2657c9b15607815359ab3ef9918f0196b6fccce8d3230f"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8a2b2b78c78293782fd3767d53e6474582f62443d0504b1554370bde86cc8227"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:73bfb9c09951125d06ee473bed216e2c3742f530fc5acc1383883125de76d9cd"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:1f384c3cc76aeedce208643697fb3e8437604b512255de6d18dae3f27655a384"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:54eb8d1bf7cacfbf2a3186019bcf01d11c666bd495ed18717162f7eb1e9dd00b"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-win32.whl", hash = "sha256:cac99918c7bba15302a2d81f0312c08054a3359eaa1929c7e4b26ebe41e9b286"},
|
||||
{file = "coverage-7.4.4-cp311-cp311-win_amd64.whl", hash = "sha256:b14706df8b2de49869ae03a5ccbc211f4041750cd4a66f698df89d44f4bd30ec"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:201bef2eea65e0e9c56343115ba3814e896afe6d36ffd37bab783261db430f76"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:41c9c5f3de16b903b610d09650e5e27adbfa7f500302718c9ffd1c12cf9d6818"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d898fe162d26929b5960e4e138651f7427048e72c853607f2b200909794ed978"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3ea79bb50e805cd6ac058dfa3b5c8f6c040cb87fe83de10845857f5535d1db70"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce4b94265ca988c3f8e479e741693d143026632672e3ff924f25fab50518dd51"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:00838a35b882694afda09f85e469c96367daa3f3f2b097d846a7216993d37f4c"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:fdfafb32984684eb03c2d83e1e51f64f0906b11e64482df3c5db936ce3839d48"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:69eb372f7e2ece89f14751fbcbe470295d73ed41ecd37ca36ed2eb47512a6ab9"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-win32.whl", hash = "sha256:137eb07173141545e07403cca94ab625cc1cc6bc4c1e97b6e3846270e7e1fea0"},
|
||||
{file = "coverage-7.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:d71eec7d83298f1af3326ce0ff1d0ea83c7cb98f72b577097f9083b20bdaf05e"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d5ae728ff3b5401cc320d792866987e7e7e880e6ebd24433b70a33b643bb0384"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:cc4f1358cb0c78edef3ed237ef2c86056206bb8d9140e73b6b89fbcfcbdd40e1"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8130a2aa2acb8788e0b56938786c33c7c98562697bf9f4c7d6e8e5e3a0501e4a"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cf271892d13e43bc2b51e6908ec9a6a5094a4df1d8af0bfc360088ee6c684409"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a4cdc86d54b5da0df6d3d3a2f0b710949286094c3a6700c21e9015932b81447e"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ae71e7ddb7a413dd60052e90528f2f65270aad4b509563af6d03d53e979feafd"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:38dd60d7bf242c4ed5b38e094baf6401faa114fc09e9e6632374388a404f98e7"},
|
||||
{file = "coverage-7.4.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:aa5b1c1bfc28384f1f53b69a023d789f72b2e0ab1b3787aae16992a7ca21056c"},
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -129,13 +129,13 @@ toml = ["tomli"]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.2.1"
|
||||
version = "1.2.2"
|
||||
description = "Backport of PEP 654 (exception groups)"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
{file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
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|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -271,13 +271,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "packaging"
|
||||
version = "24.0"
|
||||
version = "24.1"
|
||||
description = "Core utilities for Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -293,18 +293,19 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "platformdirs"
|
||||
version = "4.2.0"
|
||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
|
||||
version = "4.2.2"
|
||||
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
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||||
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|
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|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
|
||||
test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
|
||||
type = ["mypy (>=1.8)"]
|
||||
|
||||
[[package]]
|
||||
name = "pluggy"
|
||||
@ -345,13 +346,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "8.1.1"
|
||||
version = "8.2.2"
|
||||
description = "pytest: simple powerful testing with Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
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|
||||
{file = "pytest-8.2.2-py3-none-any.whl", hash = "sha256:c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343"},
|
||||
{file = "pytest-8.2.2.tar.gz", hash = "sha256:de4bb8104e201939ccdc688b27a89a7be2079b22e2bd2b07f806b6ba71117977"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -359,11 +360,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
|
||||
exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
|
||||
iniconfig = "*"
|
||||
packaging = "*"
|
||||
pluggy = ">=1.4,<2.0"
|
||||
pluggy = ">=1.5,<2.0"
|
||||
tomli = {version = ">=1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
|
||||
dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-cov"
|
||||
@ -446,38 +447,18 @@ files = [
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.2"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
|
||||
{file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[package.extras]
|
||||
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
|
||||
notebook = ["ipywidgets (>=6)"]
|
||||
slack = ["slack-sdk"]
|
||||
telegram = ["requests"]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.11.0"
|
||||
version = "4.12.2"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
|
||||
{file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<3.10"
|
||||
content-hash = "21aaa150d82e4d2583450110d1c16d878fb673a94e3c9eab7eb48a880618243a"
|
||||
content-hash = "39b03182d5b5b2d09ef49c385ed2eb7b943dca3fdb435267143edca72fcb161e"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "tantri"
|
||||
version = "0.0.1"
|
||||
version = "1.3.0"
|
||||
description = "Python dipole model evaluator"
|
||||
authors = ["Deepak <dmallubhotla+github@gmail.com>"]
|
||||
license = "GPL-3.0-only"
|
||||
@ -11,7 +11,6 @@ python = ">=3.8.1,<3.10"
|
||||
numpy = "^1.22.3"
|
||||
scipy = "~1.10"
|
||||
click = "^8.1.7"
|
||||
tqdm = "^4.66.2"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest = ">=6"
|
||||
@ -38,7 +37,9 @@ plugins = "numpy.typing.mypy_plugin"
|
||||
[[tool.mypy.overrides]]
|
||||
module = [
|
||||
"scipy",
|
||||
"scipy.optimize"
|
||||
"scipy.optimize",
|
||||
"scipy.stats",
|
||||
"scipy.fft",
|
||||
]
|
||||
ignore_missing_imports = true
|
||||
|
||||
|
||||
7
tantri/binning/__init__.py
Normal file
7
tantri/binning/__init__.py
Normal file
@ -0,0 +1,7 @@
|
||||
"""
|
||||
Binning data.
|
||||
"""
|
||||
from tantri.binning.binning import bin_lists, BinConfig, BinSummary, BinSummaryValue
|
||||
|
||||
|
||||
__all__ = ["bin_lists", "BinConfig", "BinSummary", "BinSummaryValue"]
|
||||
135
tantri/binning/binning.py
Normal file
135
tantri/binning/binning.py
Normal file
@ -0,0 +1,135 @@
|
||||
import typing
|
||||
import numpy
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class BinConfig:
|
||||
log_scale: bool # true means that our bins of the x coordinate will be in
|
||||
# if linear scale (not log_scale) then the semantics are
|
||||
# min_x, min_x + bin_width, .... min_x + A * bin_width, max_x (and the last bin may not be evenly spaced)
|
||||
# if log_scale then log(min_x), log(min_x) + bin_width, log(min_x) + 2 bin_width etc.
|
||||
# (so essentially the units of bin_width depend on log_scale)
|
||||
bin_width: float
|
||||
# never log, will be logarithmed if needed
|
||||
bin_min: typing.Optional[float] = None
|
||||
|
||||
# note that min_points_required must be >= 2
|
||||
min_points_required: int = 2
|
||||
|
||||
def __post_init__(self):
|
||||
if self.min_points_required < 2:
|
||||
raise ValueError(
|
||||
f"Can't compute summary statistics with bins of size < 2, so {self.min_points_required} is invalid"
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class BinSummaryValue:
|
||||
mean_y: float
|
||||
stdev_y: float
|
||||
|
||||
|
||||
def _summarise_values(ys: numpy.ndarray) -> BinSummaryValue:
|
||||
mean_y = ys.mean(axis=0).item()
|
||||
stdev_y = ys.std(axis=0, ddof=1).item()
|
||||
return BinSummaryValue(mean_y, stdev_y)
|
||||
|
||||
|
||||
@dataclass
|
||||
class BinSummary:
|
||||
mean_x: float
|
||||
summary_values: typing.Dict[str, BinSummaryValue]
|
||||
|
||||
|
||||
@dataclass
|
||||
class Bin:
|
||||
bindex: int # this is going to be very specific to a particular binning but hey let's include it
|
||||
x_min: float
|
||||
# points is a tuple of (freqs, value_dicts: Dict[str, numpy.ndarray])
|
||||
# this conforms well to APSD result
|
||||
point_xs: numpy.ndarray
|
||||
point_y_dict: typing.Dict[str, numpy.ndarray]
|
||||
|
||||
def mean_point(self) -> typing.Tuple[float, typing.Dict[str, float]]:
|
||||
mean_x = self.point_xs.mean(axis=0).item()
|
||||
mean_y_dict = {k: v.mean(axis=0).item() for k, v in self.point_y_dict.items()}
|
||||
return (mean_x, mean_y_dict)
|
||||
|
||||
def summary_point(self) -> BinSummary:
|
||||
mean_x = self.point_xs.mean(axis=0).item()
|
||||
summary_dict = {k: _summarise_values(v) for k, v in self.point_y_dict.items()}
|
||||
return BinSummary(mean_x, summary_dict)
|
||||
|
||||
|
||||
def _construct_bins(xs: numpy.ndarray, bin_config: BinConfig) -> numpy.ndarray:
|
||||
min_x_raw = numpy.min(xs)
|
||||
|
||||
# if the bin config requested bin_min is None, then we can ignore it.
|
||||
|
||||
if bin_config.bin_min is not None:
|
||||
_logger.debug(f"Received a desired bin_min={bin_config.bin_min}")
|
||||
if bin_config.bin_min > min_x_raw:
|
||||
raise ValueError(
|
||||
f"The lowest x value of {xs=} was {min_x_raw=}, which is lower than the requested bin_min={bin_config.bin_min}"
|
||||
)
|
||||
else:
|
||||
_logger.debug(f"Setting minimum to {bin_config.bin_min}")
|
||||
min_x_raw = bin_config.bin_min
|
||||
|
||||
max_x_raw = numpy.max(xs)
|
||||
|
||||
if bin_config.log_scale:
|
||||
min_x = numpy.log10(min_x_raw)
|
||||
max_x = numpy.log10(max_x_raw)
|
||||
else:
|
||||
min_x = min_x_raw
|
||||
max_x = max_x_raw
|
||||
|
||||
num_points = numpy.ceil(1 + (max_x - min_x) / bin_config.bin_width)
|
||||
bins = min_x + (numpy.arange(0, num_points) * bin_config.bin_width)
|
||||
|
||||
if bin_config.log_scale:
|
||||
return 10**bins
|
||||
else:
|
||||
return bins
|
||||
|
||||
|
||||
def _populate_bins(
|
||||
xs: numpy.ndarray, ys: typing.Dict[str, numpy.ndarray], bins: numpy.ndarray
|
||||
) -> typing.Sequence[Bin]:
|
||||
indexes = numpy.digitize(xs, bins) - 1
|
||||
output_bins = []
|
||||
|
||||
seen = set()
|
||||
|
||||
for bindex in indexes:
|
||||
if bindex not in seen:
|
||||
seen.add(bindex)
|
||||
|
||||
matched_x = xs[indexes == bindex]
|
||||
matched_output_dict = {k: v[indexes == bindex] for k, v in ys.items()}
|
||||
output_bins.append(
|
||||
Bin(
|
||||
bindex,
|
||||
x_min=bins[bindex].item(),
|
||||
point_xs=matched_x,
|
||||
point_y_dict=matched_output_dict,
|
||||
)
|
||||
)
|
||||
|
||||
return output_bins
|
||||
|
||||
|
||||
def bin_lists(
|
||||
xs: numpy.ndarray, ys: typing.Dict[str, numpy.ndarray], bin_config: BinConfig
|
||||
) -> typing.Sequence[Bin]:
|
||||
bins = _construct_bins(xs, bin_config)
|
||||
raw_bins = _populate_bins(xs, ys, bins)
|
||||
return [
|
||||
bin for bin in raw_bins if len(bin.point_xs) >= bin_config.min_points_required
|
||||
]
|
||||
@ -4,11 +4,12 @@ import tantri
|
||||
import numpy
|
||||
import tantri.cli.input_files.write_dipoles
|
||||
import tantri.cli.file_importer
|
||||
import tantri.dipoles
|
||||
import tantri.binning
|
||||
import json
|
||||
import tantri.dipoles.generation
|
||||
import tantri.dipoles
|
||||
import tantri.dipoles.event_time_series
|
||||
import pathlib
|
||||
import tqdm # type: ignore
|
||||
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
@ -93,11 +94,14 @@ def cli(log, log_file):
|
||||
@click.option(
|
||||
"output_file",
|
||||
"-o",
|
||||
type=click.File("w"),
|
||||
type=click.Path(path_type=pathlib.Path),
|
||||
help="The output file to write, in csv format",
|
||||
required=True,
|
||||
)
|
||||
@click.option("--header-row/--no-header-row", default=False, help="Write a header row")
|
||||
@click.option(
|
||||
"--event-based/--no-event-based", default=False, help="Use new event-based method"
|
||||
)
|
||||
def write_time_series(
|
||||
dipoles_file,
|
||||
dots_file,
|
||||
@ -107,10 +111,35 @@ def write_time_series(
|
||||
time_series_rng_seed,
|
||||
output_file,
|
||||
header_row,
|
||||
event_based,
|
||||
):
|
||||
"""
|
||||
Generate a time series for the passed in parameters.
|
||||
"""
|
||||
_write_time_series(
|
||||
dipoles_file,
|
||||
dots_file,
|
||||
measurement_type,
|
||||
delta_t,
|
||||
num_iterations,
|
||||
time_series_rng_seed,
|
||||
output_file,
|
||||
header_row,
|
||||
event_based,
|
||||
)
|
||||
|
||||
|
||||
def _write_time_series(
|
||||
dipoles_file,
|
||||
dots_file,
|
||||
measurement_type,
|
||||
delta_t,
|
||||
num_iterations,
|
||||
time_series_rng_seed,
|
||||
output_file,
|
||||
header_row,
|
||||
new_method,
|
||||
):
|
||||
_logger.debug(
|
||||
f"Received parameters [dipoles_file: {dipoles_file}] and [dots_file: {dots_file}]"
|
||||
)
|
||||
@ -126,29 +155,238 @@ def write_time_series(
|
||||
|
||||
_logger.debug(f"Using measurement {measurement_enum.name}")
|
||||
labels = [dot.label for dot in dots]
|
||||
if header_row:
|
||||
value_labels = ", ".join([f"{value_name}_{label}" for label in labels])
|
||||
output_file.write(f"t (s), {value_labels}\n")
|
||||
with output_file.open("w") as out:
|
||||
if header_row:
|
||||
value_labels = ", ".join([f"{value_name}_{label}" for label in labels])
|
||||
out.write(f"t (s), {value_labels}\n")
|
||||
|
||||
_logger.debug(
|
||||
f"Going to simulate {num_iterations} iterations with a delta t of {delta_t}"
|
||||
_logger.debug(
|
||||
f"Going to simulate {num_iterations} iterations with a delta t of {delta_t}"
|
||||
)
|
||||
|
||||
if new_method:
|
||||
_logger.info("Using new method")
|
||||
_logger.debug(f"Got seed {time_series_rng_seed}")
|
||||
if time_series_rng_seed is None:
|
||||
time_series = tantri.dipoles.event_time_series.EventDipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t, num_iterations
|
||||
)
|
||||
else:
|
||||
rng = numpy.random.default_rng(time_series_rng_seed)
|
||||
time_series = tantri.dipoles.event_time_series.EventDipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t, num_iterations, rng
|
||||
)
|
||||
output_series = time_series.create_time_series()
|
||||
for time, time_series_dict in output_series:
|
||||
values = ", ".join(str(time_series_dict[label]) for label in labels)
|
||||
out.write(f"{time}, {values}\n")
|
||||
|
||||
else:
|
||||
# in the old method
|
||||
_logger.debug(f"Got seed {time_series_rng_seed}")
|
||||
if time_series_rng_seed is None:
|
||||
time_series = tantri.dipoles.DipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t
|
||||
)
|
||||
else:
|
||||
rng = numpy.random.default_rng(time_series_rng_seed)
|
||||
time_series = tantri.dipoles.DipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t, rng
|
||||
)
|
||||
|
||||
for i in range(num_iterations):
|
||||
transition = time_series.transition()
|
||||
transition_values = ", ".join(
|
||||
str(transition[label]) for label in labels
|
||||
)
|
||||
out.write(f"{i * delta_t}, {transition_values}\n")
|
||||
|
||||
|
||||
@cli.command()
|
||||
@click.option(
|
||||
"--dipoles-file",
|
||||
default="dipoles.json",
|
||||
show_default=True,
|
||||
type=click.Path(exists=True, path_type=pathlib.Path),
|
||||
help="File with json array of dipoles",
|
||||
)
|
||||
@click.option(
|
||||
"--dots-file",
|
||||
default="dots.json",
|
||||
show_default=True,
|
||||
type=click.Path(exists=True, path_type=pathlib.Path),
|
||||
help="File with json array of dots",
|
||||
)
|
||||
@click.option(
|
||||
"--measurement-type",
|
||||
type=click.Choice([POTENTIAL, X_ELECTRIC_FIELD]),
|
||||
default=POTENTIAL,
|
||||
help="The type of measurement to simulate",
|
||||
show_default=True,
|
||||
)
|
||||
@click.option(
|
||||
"--delta-t",
|
||||
"-t",
|
||||
type=float,
|
||||
default=1,
|
||||
help="The delta t between time series iterations.",
|
||||
show_default=True,
|
||||
)
|
||||
@click.option(
|
||||
"--num-iterations",
|
||||
# Note we're keeping this name to match write-time-series
|
||||
"-n",
|
||||
type=int,
|
||||
default=10,
|
||||
help="The number of time steps per time series, total time is num_iterations * delta_t.",
|
||||
show_default=True,
|
||||
)
|
||||
@click.option(
|
||||
"--num-time-series",
|
||||
type=int,
|
||||
default=20,
|
||||
help="The number of simulated time series, which will be averaged over",
|
||||
show_default=True,
|
||||
)
|
||||
@click.option(
|
||||
"--time-series-rng-seed",
|
||||
"-s",
|
||||
type=int,
|
||||
default=None,
|
||||
help="A seed to use to create an override default rng. You should set this.",
|
||||
)
|
||||
@click.option(
|
||||
"--output-file",
|
||||
"-o",
|
||||
type=click.Path(path_type=pathlib.Path),
|
||||
help="The output file to write, in csv format",
|
||||
required=True,
|
||||
)
|
||||
@click.option(
|
||||
"--binned-output-file",
|
||||
"-b",
|
||||
type=click.Path(path_type=pathlib.Path),
|
||||
help="Optional binned output file",
|
||||
)
|
||||
@click.option(
|
||||
"--bin-widths",
|
||||
type=float,
|
||||
default=1,
|
||||
show_default=True,
|
||||
help="The default log(!) bin width, 1 means widths of a decade",
|
||||
)
|
||||
@click.option("--header-row/--no-header-row", default=False, help="Write a header row")
|
||||
def write_apsd(
|
||||
dipoles_file,
|
||||
dots_file,
|
||||
measurement_type,
|
||||
delta_t,
|
||||
num_iterations,
|
||||
num_time_series,
|
||||
time_series_rng_seed,
|
||||
output_file,
|
||||
binned_output_file,
|
||||
bin_widths,
|
||||
header_row,
|
||||
):
|
||||
"""
|
||||
Generate an APSD for the passed in parameters, averaging over multiple (num_time_series) iterations.
|
||||
"""
|
||||
_write_apsd(
|
||||
dipoles_file,
|
||||
dots_file,
|
||||
measurement_type,
|
||||
delta_t,
|
||||
num_iterations,
|
||||
num_time_series,
|
||||
time_series_rng_seed,
|
||||
output_file,
|
||||
binned_output_file,
|
||||
bin_widths,
|
||||
header_row,
|
||||
)
|
||||
|
||||
_logger.debug(f"Got seed {time_series_rng_seed}")
|
||||
if time_series_rng_seed is None:
|
||||
time_series = tantri.dipoles.DipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t
|
||||
)
|
||||
else:
|
||||
rng = numpy.random.default_rng(time_series_rng_seed)
|
||||
time_series = tantri.dipoles.DipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t, rng
|
||||
|
||||
def _write_apsd(
|
||||
dipoles_file,
|
||||
dots_file,
|
||||
measurement_type,
|
||||
delta_t,
|
||||
num_iterations,
|
||||
num_time_series,
|
||||
time_series_rng_seed,
|
||||
output_file,
|
||||
binned_output_file,
|
||||
bin_widths,
|
||||
header_row,
|
||||
):
|
||||
_logger.debug(
|
||||
f"Received parameters [dipoles_file: {dipoles_file}] and [dots_file: {dots_file}]"
|
||||
)
|
||||
dipoles = tantri.cli.file_importer.read_dipoles_json_file(dipoles_file)
|
||||
dots = tantri.cli.file_importer.read_dots_json_file(dots_file)
|
||||
|
||||
if measurement_type == POTENTIAL:
|
||||
measurement_enum = tantri.dipoles.DipoleMeasurementType.ELECTRIC_POTENTIAL
|
||||
value_name = "APSD_V"
|
||||
elif measurement_type == X_ELECTRIC_FIELD:
|
||||
measurement_enum = tantri.dipoles.DipoleMeasurementType.X_ELECTRIC_FIELD
|
||||
value_name = "APSD_Ex"
|
||||
|
||||
_logger.debug(f"Using measurement {measurement_enum.name}")
|
||||
labels = [dot.label for dot in dots]
|
||||
with output_file.open("w") as out:
|
||||
if header_row:
|
||||
value_labels = ", ".join([f"{value_name}_{label}" for label in labels])
|
||||
out.write(f"f (Hz), {value_labels}\n")
|
||||
|
||||
_logger.debug(
|
||||
f"Going to simulate {num_iterations} iterations with a delta t of {delta_t}"
|
||||
)
|
||||
|
||||
for i in tqdm.trange(num_iterations):
|
||||
transition = time_series.transition()
|
||||
transition_values = ", ".join(str(transition[label]) for label in labels)
|
||||
output_file.write(f"{i * delta_t}, {transition_values}\n")
|
||||
_logger.debug(f"Got seed {time_series_rng_seed}")
|
||||
if time_series_rng_seed is None:
|
||||
time_series = tantri.dipoles.DipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t
|
||||
)
|
||||
else:
|
||||
rng = numpy.random.default_rng(time_series_rng_seed)
|
||||
time_series = tantri.dipoles.DipoleTimeSeries(
|
||||
dipoles, dots, measurement_enum, delta_t, rng
|
||||
)
|
||||
|
||||
apsd = time_series.generate_average_apsd(
|
||||
num_series=num_time_series, num_time_series_points=num_iterations
|
||||
)
|
||||
|
||||
values_list = zip(*[apsd.psd_dict[label] for label in labels])
|
||||
for freq, values in zip(apsd.freqs, values_list):
|
||||
value_string = ", ".join(str(v) for v in values)
|
||||
out.write(f"{freq}, {value_string}\n")
|
||||
if binned_output_file is not None:
|
||||
with binned_output_file.open("w") as out:
|
||||
if header_row:
|
||||
value_labels = ["mean bin f (Hz)"]
|
||||
for label in labels:
|
||||
value_labels.append(f"{value_name}_{label}_mean")
|
||||
value_labels.append(f"{value_name}_{label}_stdev")
|
||||
value_labels_text = ", ".join(value_labels)
|
||||
out.write(value_labels_text + "\n")
|
||||
binned = tantri.binning.bin_lists(
|
||||
apsd.freqs,
|
||||
apsd.psd_dict,
|
||||
tantri.binning.BinConfig(
|
||||
True, bin_width=bin_widths, bin_min=1e-6, min_points_required=2
|
||||
),
|
||||
)
|
||||
for bin_result in binned:
|
||||
summary = bin_result.summary_point()
|
||||
out_list = [str(summary.mean_x)]
|
||||
for label in labels:
|
||||
out_list.append(str(summary.summary_values[label].mean_y))
|
||||
out_list.append(str(summary.summary_values[label].stdev_y))
|
||||
out_string = ", ".join(out_list) + "\n"
|
||||
out.write(out_string)
|
||||
|
||||
|
||||
@cli.command()
|
||||
@ -158,12 +396,16 @@ def write_time_series(
|
||||
)
|
||||
@click.argument(
|
||||
"output_file",
|
||||
type=click.File("w"),
|
||||
type=click.Path(path_type=pathlib.Path),
|
||||
)
|
||||
@click.option(
|
||||
"--override-rng-seed", type=int, help="Seed to override the generation config spec."
|
||||
)
|
||||
def generate_dipoles(generation_config, output_file, override_rng_seed):
|
||||
_generate_dipoles(generation_config, output_file, override_rng_seed)
|
||||
|
||||
|
||||
def _generate_dipoles(generation_config, output_file, override_rng_seed):
|
||||
"""Generate random dipoles as described by GENERATION_CONFIG and output to OUTPUT_FILE.
|
||||
|
||||
GENERATION_CONFIG should be a JSON file that matches the appropriate spec, and OUTPUT_FILE will contain JSON formatted contents.
|
||||
@ -178,18 +420,19 @@ def generate_dipoles(generation_config, output_file, override_rng_seed):
|
||||
|
||||
with open(generation_config, "r") as config_file:
|
||||
data = json.load(config_file)
|
||||
config = tantri.dipoles.generation.DipoleGenerationConfig(**data)
|
||||
config = tantri.dipoles.DipoleGenerationConfig(**data)
|
||||
|
||||
override_rng = None
|
||||
if override_rng_seed is not None:
|
||||
_logger.info(f"Overriding the rng with a new one with seed {override_rng_seed}")
|
||||
override_rng = numpy.random.default_rng(override_rng_seed)
|
||||
_logger.debug(f"generating dipoles with config {config}...")
|
||||
generated = tantri.dipoles.generation.make_dipoles(config, override_rng)
|
||||
generated = tantri.dipoles.make_dipoles(config, override_rng)
|
||||
|
||||
output_file.write(
|
||||
json.dumps(
|
||||
[g.as_dict() for g in generated],
|
||||
cls=tantri.cli.input_files.write_dipoles.NumpyEncoder,
|
||||
with output_file.open("w") as out:
|
||||
out.write(
|
||||
json.dumps(
|
||||
[g.as_dict() for g in generated],
|
||||
cls=tantri.cli.input_files.write_dipoles.NumpyEncoder,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
@ -1,150 +1,18 @@
|
||||
from dataclasses import dataclass
|
||||
import numpy
|
||||
import numpy.random
|
||||
import typing
|
||||
from enum import Enum
|
||||
from tantri.dipoles.types import DipoleTO
|
||||
|
||||
import logging
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DipoleMeasurementType(Enum):
|
||||
ELECTRIC_POTENTIAL = 1
|
||||
X_ELECTRIC_FIELD = 2
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DotPosition:
|
||||
# assume len 3
|
||||
r: numpy.ndarray
|
||||
label: str
|
||||
|
||||
|
||||
@dataclass
|
||||
class WrappedDipole:
|
||||
# assumed len 3
|
||||
p: numpy.ndarray
|
||||
s: numpy.ndarray
|
||||
|
||||
# should be 1/tau up to some pis
|
||||
w: float
|
||||
|
||||
# For caching purposes tell each dipole where the dots are
|
||||
# TODO: This can be done better by only passing into the time series the non-repeated p s and w,
|
||||
# TODO: and then creating a new wrapper type to include all the cached stuff.
|
||||
# TODO: Realistically, the dot positions and measurement type data should live in the time series.
|
||||
dot_positions: typing.Sequence[DotPosition]
|
||||
|
||||
measurement_type: DipoleMeasurementType
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""
|
||||
Coerce the inputs into numpy arrays.
|
||||
"""
|
||||
self.p = numpy.array(self.p)
|
||||
self.s = numpy.array(self.s)
|
||||
|
||||
self.state = 1
|
||||
self.cache = {}
|
||||
for pos in self.dot_positions:
|
||||
if self.measurement_type is DipoleMeasurementType.ELECTRIC_POTENTIAL:
|
||||
self.cache[pos.label] = self.potential(pos)
|
||||
elif self.measurement_type is DipoleMeasurementType.X_ELECTRIC_FIELD:
|
||||
self.cache[pos.label] = self.e_field_x(pos)
|
||||
|
||||
def potential(self, dot: DotPosition) -> float:
|
||||
# let's assume single dot at origin for now
|
||||
r_diff = self.s - dot.r
|
||||
return self.p.dot(r_diff) / (numpy.linalg.norm(r_diff) ** 3)
|
||||
|
||||
def e_field_x(self, dot: DotPosition) -> float:
|
||||
# let's assume single dot at origin for now
|
||||
r_diff = self.s - dot.r
|
||||
norm = numpy.linalg.norm(r_diff)
|
||||
|
||||
return (
|
||||
((3 * self.p.dot(r_diff) * r_diff / (norm**2)) - self.p) / (norm**3)
|
||||
)[0]
|
||||
|
||||
def transition(
|
||||
self, dt: float, rng_to_use: typing.Optional[numpy.random.Generator] = None
|
||||
) -> typing.Dict[str, float]:
|
||||
rng: numpy.random.Generator
|
||||
if rng_to_use is None:
|
||||
rng = numpy.random.default_rng()
|
||||
else:
|
||||
rng = rng_to_use
|
||||
# if on average flipping often, then just return 0, basically this dipole has been all used up.
|
||||
# Facilitates going for different types of noise at very low freq?
|
||||
if dt * 10 >= 1 / self.w:
|
||||
# _logger.warning(
|
||||
# f"delta t {dt} is too long compared to dipole frequency {self.w}"
|
||||
# )
|
||||
self.state = rng.integers(0, 1, endpoint=True)
|
||||
else:
|
||||
prob = dt * self.w
|
||||
if rng.random() < prob:
|
||||
# _logger.debug("flip!")
|
||||
self.flip_state()
|
||||
return {k: self.state * v for k, v in self.cache.items()}
|
||||
|
||||
def flip_state(self):
|
||||
self.state *= -1
|
||||
|
||||
|
||||
def get_wrapped_dipoles(
|
||||
dipole_tos: typing.Sequence[DipoleTO],
|
||||
dots: typing.Sequence[DotPosition],
|
||||
measurement_type: DipoleMeasurementType,
|
||||
) -> typing.Sequence[WrappedDipole]:
|
||||
return [
|
||||
WrappedDipole(
|
||||
p=dipole_to.p,
|
||||
s=dipole_to.s,
|
||||
w=dipole_to.w,
|
||||
dot_positions=dots,
|
||||
measurement_type=measurement_type,
|
||||
)
|
||||
for dipole_to in dipole_tos
|
||||
]
|
||||
|
||||
|
||||
class DipoleTimeSeries:
|
||||
def __init__(
|
||||
self,
|
||||
dipoles: typing.Sequence[DipoleTO],
|
||||
dots: typing.Sequence[DotPosition],
|
||||
measurement_type: DipoleMeasurementType,
|
||||
dt: float,
|
||||
rng_to_use: typing.Optional[numpy.random.Generator] = None,
|
||||
):
|
||||
self.rng: numpy.random.Generator
|
||||
if rng_to_use is None:
|
||||
self.rng = numpy.random.default_rng()
|
||||
else:
|
||||
self.rng = rng_to_use
|
||||
|
||||
self.dipoles = get_wrapped_dipoles(dipoles, dots, measurement_type)
|
||||
self.state = 0
|
||||
self.dt = dt
|
||||
|
||||
def transition(self) -> typing.Dict[str, float]:
|
||||
new_vals = [dipole.transition(self.dt, self.rng) for dipole in self.dipoles]
|
||||
|
||||
ret = {}
|
||||
for transition in new_vals:
|
||||
for k, v in transition.items():
|
||||
if k not in ret:
|
||||
ret[k] = v
|
||||
else:
|
||||
ret[k] += v
|
||||
return ret
|
||||
|
||||
from tantri.dipoles.types import (
|
||||
DipoleTO,
|
||||
DotPosition,
|
||||
DipoleMeasurementType,
|
||||
DipoleGenerationConfig,
|
||||
)
|
||||
from tantri.dipoles.time_series import DipoleTimeSeries, WrappedDipole
|
||||
from tantri.dipoles.generation import make_dipoles
|
||||
|
||||
__all__ = [
|
||||
"WrappedDipole",
|
||||
"DipoleTimeSeries",
|
||||
"DipoleTO",
|
||||
"DotPosition",
|
||||
"DipoleMeasurementType",
|
||||
"make_dipoles",
|
||||
"DipoleGenerationConfig",
|
||||
]
|
||||
|
||||
154
tantri/dipoles/event_time_series.py
Normal file
154
tantri/dipoles/event_time_series.py
Normal file
@ -0,0 +1,154 @@
|
||||
import numpy.random
|
||||
from typing import Callable, Sequence, Tuple, Optional, Dict, List
|
||||
from dataclasses import dataclass
|
||||
from tantri.dipoles.types import DipoleTO, DotPosition, DipoleMeasurementType
|
||||
import logging
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class EventWrappedDipole:
|
||||
# assumed len 3
|
||||
p: numpy.ndarray
|
||||
s: numpy.ndarray
|
||||
|
||||
# should be 1/tau up to some pis
|
||||
w: float
|
||||
|
||||
# For caching purposes tell each dipole where the dots are
|
||||
# TODO: This can be done better by only passing into the time series the non-repeated p s and w,
|
||||
# TODO: and then creating a new wrapper type to include all the cached stuff.
|
||||
# TODO: Realistically, the dot positions and measurement type data should live in the time series.
|
||||
dot_positions: Sequence[DotPosition]
|
||||
|
||||
measurement_type: DipoleMeasurementType
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""
|
||||
Coerce the inputs into numpy arrays.
|
||||
"""
|
||||
self.p = numpy.array(self.p)
|
||||
self.s = numpy.array(self.s)
|
||||
|
||||
self.state = 1
|
||||
self.cache = {}
|
||||
for pos in self.dot_positions:
|
||||
if self.measurement_type is DipoleMeasurementType.ELECTRIC_POTENTIAL:
|
||||
self.cache[pos.label] = self.potential(pos)
|
||||
elif self.measurement_type is DipoleMeasurementType.X_ELECTRIC_FIELD:
|
||||
self.cache[pos.label] = self.e_field_x(pos)
|
||||
|
||||
def potential(self, dot: DotPosition) -> float:
|
||||
# let's assume single dot at origin for now
|
||||
r_diff = self.s - dot.r
|
||||
return self.p.dot(r_diff) / (numpy.linalg.norm(r_diff) ** 3)
|
||||
|
||||
def e_field_x(self, dot: DotPosition) -> float:
|
||||
# let's assume single dot at origin for now
|
||||
r_diff = self.s - dot.r
|
||||
norm = numpy.linalg.norm(r_diff)
|
||||
|
||||
return (
|
||||
((3 * self.p.dot(r_diff) * r_diff / (norm**2)) - self.p) / (norm**3)
|
||||
)[0]
|
||||
|
||||
def get_time_series(
|
||||
self, dt: float, num_samples: int, rng: numpy.random.Generator
|
||||
) -> Sequence[Tuple[float, Dict[str, float]]]:
|
||||
_logger.debug(
|
||||
f"Creating time series with params {dt=}, {num_samples=}, scale={self.w}"
|
||||
)
|
||||
raw_time_series = create_exponential_time_series(rng, dt, num_samples, self.w)
|
||||
output = []
|
||||
|
||||
for time, state in raw_time_series:
|
||||
output.append((time, {k: state * v for k, v in self.cache.items()}))
|
||||
return output
|
||||
|
||||
|
||||
def get_event_wrapped_dipoles(
|
||||
dipole_tos: Sequence[DipoleTO],
|
||||
dots: Sequence[DotPosition],
|
||||
measurement_type: DipoleMeasurementType,
|
||||
) -> Sequence[EventWrappedDipole]:
|
||||
return [
|
||||
EventWrappedDipole(
|
||||
p=dipole_to.p,
|
||||
s=dipole_to.s,
|
||||
w=dipole_to.w,
|
||||
dot_positions=dots,
|
||||
measurement_type=measurement_type,
|
||||
)
|
||||
for dipole_to in dipole_tos
|
||||
]
|
||||
|
||||
|
||||
class EventDipoleTimeSeries:
|
||||
def __init__(
|
||||
self,
|
||||
dipoles: Sequence[DipoleTO],
|
||||
dots: Sequence[DotPosition],
|
||||
measurement_type: DipoleMeasurementType,
|
||||
dt: float,
|
||||
num_samples: int,
|
||||
rng_to_use: Optional[numpy.random.Generator] = None,
|
||||
):
|
||||
self.rng: numpy.random.Generator
|
||||
if rng_to_use is None:
|
||||
self.rng = numpy.random.default_rng()
|
||||
else:
|
||||
self.rng = rng_to_use
|
||||
|
||||
self.dt = dt
|
||||
self.num_samples = num_samples
|
||||
self.dipoles = get_event_wrapped_dipoles(dipoles, dots, measurement_type)
|
||||
|
||||
def create_time_series(self) -> Sequence[Tuple[float, Dict[str, float]]]:
|
||||
collected_dictionary: Dict[float, Dict[str, float]] = {}
|
||||
_logger.debug("Creating time series")
|
||||
|
||||
for dipole in self.dipoles:
|
||||
_logger.debug(f"Doing dipole {dipole}")
|
||||
series = dipole.get_time_series(self.dt, self.num_samples, self.rng)
|
||||
for time, meases in series:
|
||||
if time in collected_dictionary:
|
||||
for k, v in meases.items():
|
||||
|
||||
collected_dictionary[time][k] += v
|
||||
else:
|
||||
collected_dictionary[time] = meases
|
||||
|
||||
return [(k, v) for k, v in collected_dictionary.items()]
|
||||
|
||||
|
||||
def get_num_events_before(
|
||||
rng: numpy.random.Generator, scale: float, total_time: float
|
||||
) -> Callable[[float], int]:
|
||||
|
||||
_logger.debug(
|
||||
f"Creating the events before function for params {scale=} {total_time=}"
|
||||
)
|
||||
event_times: List = []
|
||||
random_size = max(1, int(total_time // scale))
|
||||
while sum(event_times) < total_time:
|
||||
event_times.extend(rng.exponential(scale=scale, size=random_size))
|
||||
|
||||
accumulator = 0
|
||||
scanned_times = [accumulator := accumulator + t for t in event_times]
|
||||
|
||||
def num_events_before(time: float) -> int:
|
||||
return len([t for t in scanned_times if t < time])
|
||||
|
||||
return num_events_before
|
||||
|
||||
|
||||
def create_exponential_time_series(
|
||||
rng: numpy.random.Generator, dt: float, num_samples: int, scale: float
|
||||
) -> Sequence[Tuple[float, int]]:
|
||||
_logger.debug("Creating an exponential time series")
|
||||
total_time = dt * num_samples
|
||||
_logger.debug(f"Have a total time {total_time}")
|
||||
events_before = get_num_events_before(rng, scale, total_time)
|
||||
_logger.debug("Finished getting the events before function")
|
||||
return [(dt * i, (events_before(dt * i) % 2) * 2 - 1) for i in range(num_samples)]
|
||||
@ -1,107 +1,5 @@
|
||||
import numpy
|
||||
from typing import Sequence, Optional
|
||||
from dataclasses import dataclass, asdict
|
||||
from tantri.dipoles.types import DipoleTO
|
||||
from enum import Enum
|
||||
import logging
|
||||
from tantri.dipoles.generation.generate_dipole_config import make_dipoles
|
||||
|
||||
|
||||
# stuff for generating random dipoles from parameters
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Orientation(str, Enum):
|
||||
# Enum for orientation, making string for json serialisation purposes
|
||||
#
|
||||
# Note that htis might not be infinitely extensible?
|
||||
# https://stackoverflow.com/questions/75040733/is-there-a-way-to-use-strenum-in-earlier-python-versions
|
||||
XY = "XY"
|
||||
Z = "Z"
|
||||
RANDOM = "RANDOM"
|
||||
|
||||
|
||||
# A description of the parameters needed to generate random dipoles
|
||||
@dataclass
|
||||
class DipoleGenerationConfig:
|
||||
# note no actual checks anywhere that these are sensibly defined with min less than max etc.
|
||||
x_min: float
|
||||
x_max: float
|
||||
y_min: float
|
||||
y_max: float
|
||||
z_min: float
|
||||
z_max: float
|
||||
|
||||
mag: float
|
||||
|
||||
# these are log_10 of actual value
|
||||
w_log_min: float
|
||||
w_log_max: float
|
||||
|
||||
orientation: Orientation
|
||||
|
||||
dipole_count: int
|
||||
generation_seed: int
|
||||
|
||||
def __post_init__(self):
|
||||
# This allows us to transparently set this with a string, while providing early warning of a type error
|
||||
self.orientation = Orientation(self.orientation)
|
||||
|
||||
def as_dict(self) -> dict:
|
||||
return_dict = asdict(self)
|
||||
|
||||
return_dict["orientation"] = return_dict["orientation"].value
|
||||
|
||||
return return_dict
|
||||
|
||||
|
||||
def make_dipoles(
|
||||
config: DipoleGenerationConfig,
|
||||
rng_override: Optional[numpy.random.Generator] = None,
|
||||
) -> Sequence[DipoleTO]:
|
||||
|
||||
if rng_override is None:
|
||||
_logger.info(
|
||||
f"Using the seed [{config.generation_seed}] provided by configuration for dipole generation"
|
||||
)
|
||||
rng = numpy.random.default_rng(config.generation_seed)
|
||||
else:
|
||||
_logger.info("Using overridden rng, of unknown seed")
|
||||
rng = rng_override
|
||||
|
||||
dipoles = []
|
||||
|
||||
for i in range(config.dipole_count):
|
||||
sx = rng.uniform(config.x_min, config.x_max)
|
||||
sy = rng.uniform(config.y_min, config.y_max)
|
||||
sz = rng.uniform(config.z_min, config.z_max)
|
||||
|
||||
# orientation
|
||||
# 0, 1, 2
|
||||
# xy, z, random
|
||||
|
||||
if config.orientation is Orientation.RANDOM:
|
||||
theta = numpy.arccos(2 * rng.random() - 1)
|
||||
phi = 2 * numpy.pi * rng.random()
|
||||
elif config.orientation is Orientation.Z:
|
||||
theta = 0
|
||||
phi = 0
|
||||
elif config.orientation is Orientation.XY:
|
||||
theta = numpy.pi / 2
|
||||
phi = 2 * numpy.pi * rng.random()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"this shouldn't have happened, orientation index: {config}"
|
||||
)
|
||||
|
||||
px = config.mag * numpy.cos(phi) * numpy.sin(theta)
|
||||
py = config.mag * numpy.sin(phi) * numpy.sin(theta)
|
||||
pz = config.mag * numpy.cos(theta)
|
||||
|
||||
w = 10 ** rng.uniform(config.w_log_min, config.w_log_max)
|
||||
|
||||
dipoles.append(
|
||||
DipoleTO(numpy.array([px, py, pz]), numpy.array([sx, sy, sz]), w)
|
||||
)
|
||||
|
||||
return dipoles
|
||||
__all__ = [
|
||||
"make_dipoles",
|
||||
]
|
||||
|
||||
61
tantri/dipoles/generation/generate_dipole_config.py
Normal file
61
tantri/dipoles/generation/generate_dipole_config.py
Normal file
@ -0,0 +1,61 @@
|
||||
import numpy
|
||||
from typing import Sequence, Optional
|
||||
from tantri.dipoles.types import DipoleTO, DipoleGenerationConfig, Orientation
|
||||
import logging
|
||||
|
||||
|
||||
# stuff for generating random dipoles from parameters
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def make_dipoles(
|
||||
config: DipoleGenerationConfig,
|
||||
rng_override: Optional[numpy.random.Generator] = None,
|
||||
) -> Sequence[DipoleTO]:
|
||||
|
||||
if rng_override is None:
|
||||
_logger.info(
|
||||
f"Using the seed [{config.generation_seed}] provided by configuration for dipole generation"
|
||||
)
|
||||
rng = numpy.random.default_rng(config.generation_seed)
|
||||
else:
|
||||
_logger.info("Using overridden rng, of unknown seed")
|
||||
rng = rng_override
|
||||
|
||||
dipoles = []
|
||||
|
||||
for i in range(config.dipole_count):
|
||||
sx = rng.uniform(config.x_min, config.x_max)
|
||||
sy = rng.uniform(config.y_min, config.y_max)
|
||||
sz = rng.uniform(config.z_min, config.z_max)
|
||||
|
||||
# orientation
|
||||
# 0, 1, 2
|
||||
# xy, z, random
|
||||
|
||||
if config.orientation is Orientation.RANDOM:
|
||||
theta = numpy.arccos(2 * rng.random() - 1)
|
||||
phi = 2 * numpy.pi * rng.random()
|
||||
elif config.orientation is Orientation.Z:
|
||||
theta = 0
|
||||
phi = 0
|
||||
elif config.orientation is Orientation.XY:
|
||||
theta = numpy.pi / 2
|
||||
phi = 2 * numpy.pi * rng.random()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"this shouldn't have happened, orientation index: {config}"
|
||||
)
|
||||
|
||||
px = config.mag * numpy.cos(phi) * numpy.sin(theta)
|
||||
py = config.mag * numpy.sin(phi) * numpy.sin(theta)
|
||||
pz = config.mag * numpy.cos(theta)
|
||||
|
||||
w = 10 ** rng.uniform(config.w_log_min, config.w_log_max)
|
||||
|
||||
dipoles.append(
|
||||
DipoleTO(numpy.array([px, py, pz]), numpy.array([sx, sy, sz]), w)
|
||||
)
|
||||
|
||||
return dipoles
|
||||
33
tantri/dipoles/supersample.py
Normal file
33
tantri/dipoles/supersample.py
Normal file
@ -0,0 +1,33 @@
|
||||
import dataclasses
|
||||
import logging
|
||||
import math
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
# how many times faster than the max frequency we want to be, bigger is more accurate but 10 is probably fine
|
||||
DESIRED_THRESHOLD = 10
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class SuperSample:
|
||||
super_dt: float
|
||||
super_sample_ratio: int
|
||||
|
||||
|
||||
def get_supersample(max_frequency: float, dt: float) -> SuperSample:
|
||||
# now we want to sample at least 10x faster than max_frequency, otherwise we're going to skew our statistics
|
||||
# note that this is why if performance mattered we'd be optimising this to pre-gen our flip times with poisson statistics.
|
||||
# so we want (1/dt) > 10 * max_freq
|
||||
if DESIRED_THRESHOLD * dt * max_frequency < 1:
|
||||
# can return unchanged
|
||||
_logger.debug("no supersampling needed")
|
||||
return SuperSample(super_dt=dt, super_sample_ratio=1)
|
||||
else:
|
||||
# else we want a such that a / dt > 10 * max_freq, or a > 10 * dt * max_freq, a = math.ceil(10 * dt * max_freq)
|
||||
a = math.ceil(DESIRED_THRESHOLD * dt * max_frequency)
|
||||
_logger.debug(
|
||||
f"max_frequency {max_frequency} and delta_t {dt} needs a ratio of {a}"
|
||||
)
|
||||
ret_val = SuperSample(super_dt=dt / a, super_sample_ratio=a)
|
||||
_logger.debug(ret_val)
|
||||
return ret_val
|
||||
261
tantri/dipoles/time_series.py
Normal file
261
tantri/dipoles/time_series.py
Normal file
@ -0,0 +1,261 @@
|
||||
from dataclasses import dataclass
|
||||
import numpy
|
||||
import numpy.random
|
||||
import typing
|
||||
from tantri.dipoles.types import DipoleTO, DotPosition, DipoleMeasurementType
|
||||
import tantri.dipoles.supersample
|
||||
import tantri.util
|
||||
import scipy.stats
|
||||
import scipy.fft
|
||||
import logging
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class APSDResult:
|
||||
psd_dict: typing.Dict[str, numpy.ndarray]
|
||||
freqs: numpy.ndarray
|
||||
|
||||
|
||||
@dataclass
|
||||
class TimeSeriesResult:
|
||||
series_dict: typing.Dict[str, numpy.ndarray]
|
||||
num_points: int
|
||||
delta_t: float
|
||||
|
||||
def get_time_points(self):
|
||||
return [t * self.delta_t for t in range(self.num_points)]
|
||||
|
||||
def get_apsds(self) -> APSDResult:
|
||||
def sq(a):
|
||||
return numpy.real(a * numpy.conjugate(a))
|
||||
|
||||
def psd(v):
|
||||
_logger.debug("Using real part correction and multiplying PSD by 2")
|
||||
return 2 * sq(scipy.fft.rfft(v)[1:]) * self.delta_t / self.num_points
|
||||
|
||||
fft_dict = {k: psd(v) for k, v in self.series_dict.items()}
|
||||
freqs = scipy.fft.rfftfreq(self.num_points, self.delta_t)[1:]
|
||||
|
||||
return APSDResult(fft_dict, freqs)
|
||||
|
||||
|
||||
def average_apsds(apsds: typing.Sequence[APSDResult]) -> APSDResult:
|
||||
def mean(list_of_arrays: typing.Sequence[numpy.ndarray]) -> numpy.ndarray:
|
||||
return numpy.mean(numpy.array(list_of_arrays), axis=0)
|
||||
|
||||
if len(apsds) >= 1:
|
||||
for subsequent in apsds[1:]:
|
||||
if not numpy.array_equal(subsequent.freqs, apsds[0].freqs):
|
||||
raise ValueError(
|
||||
f"Could not average apsds, as {subsequent} does not match the frequencies in {apsds[0]}"
|
||||
)
|
||||
freqs = apsds[0].freqs
|
||||
|
||||
average_dict = tantri.util.dict_reduce([apsd.psd_dict for apsd in apsds], mean)
|
||||
return APSDResult(average_dict, freqs)
|
||||
|
||||
|
||||
@dataclass
|
||||
class WrappedDipole:
|
||||
# assumed len 3
|
||||
p: numpy.ndarray
|
||||
s: numpy.ndarray
|
||||
|
||||
# should be 1/tau up to some pis
|
||||
w: float
|
||||
|
||||
# For caching purposes tell each dipole where the dots are
|
||||
# TODO: This can be done better by only passing into the time series the non-repeated p s and w,
|
||||
# TODO: and then creating a new wrapper type to include all the cached stuff.
|
||||
# TODO: Realistically, the dot positions and measurement type data should live in the time series.
|
||||
dot_positions: typing.Sequence[DotPosition]
|
||||
|
||||
measurement_type: DipoleMeasurementType
|
||||
|
||||
def __post_init__(self) -> None:
|
||||
"""
|
||||
Coerce the inputs into numpy arrays.
|
||||
"""
|
||||
self.p = numpy.array(self.p)
|
||||
self.s = numpy.array(self.s)
|
||||
|
||||
self.state = 1
|
||||
self.cache = {}
|
||||
for pos in self.dot_positions:
|
||||
if self.measurement_type is DipoleMeasurementType.ELECTRIC_POTENTIAL:
|
||||
self.cache[pos.label] = self.potential(pos)
|
||||
elif self.measurement_type is DipoleMeasurementType.X_ELECTRIC_FIELD:
|
||||
self.cache[pos.label] = self.e_field_x(pos)
|
||||
|
||||
def potential(self, dot: DotPosition) -> float:
|
||||
# let's assume single dot at origin for now
|
||||
r_diff = self.s - dot.r
|
||||
return self.p.dot(r_diff) / (numpy.linalg.norm(r_diff) ** 3)
|
||||
|
||||
def e_field_x(self, dot: DotPosition) -> float:
|
||||
# let's assume single dot at origin for now
|
||||
r_diff = self.s - dot.r
|
||||
norm = numpy.linalg.norm(r_diff)
|
||||
|
||||
return (
|
||||
((3 * self.p.dot(r_diff) * r_diff / (norm**2)) - self.p) / (norm**3)
|
||||
)[0]
|
||||
|
||||
def transition(
|
||||
self, dt: float, rng_to_use: typing.Optional[numpy.random.Generator] = None
|
||||
) -> typing.Dict[str, float]:
|
||||
rng: numpy.random.Generator
|
||||
if rng_to_use is None:
|
||||
rng = numpy.random.default_rng()
|
||||
else:
|
||||
rng = rng_to_use
|
||||
# if on average flipping often, then just return 0, basically this dipole has been all used up.
|
||||
# Facilitates going for different types of noise at very low freq?
|
||||
if dt * 10 >= 1 / self.w:
|
||||
# _logger.warning(
|
||||
# f"delta t {dt} is too long compared to dipole frequency {self.w}"
|
||||
# )
|
||||
self.state = rng.integers(0, 1, endpoint=True)
|
||||
else:
|
||||
prob = dt * self.w
|
||||
if rng.random() < prob:
|
||||
# _logger.debug("flip!")
|
||||
self.flip_state()
|
||||
return {k: self.state * v for k, v in self.cache.items()}
|
||||
|
||||
def time_series(
|
||||
self,
|
||||
dt: float,
|
||||
num_points: int,
|
||||
rng_to_use: typing.Optional[numpy.random.Generator] = None,
|
||||
) -> typing.Dict[str, numpy.ndarray]:
|
||||
|
||||
# don't forget to set rng
|
||||
if rng_to_use is None:
|
||||
rng = numpy.random.default_rng()
|
||||
else:
|
||||
rng = rng_to_use
|
||||
|
||||
# scale effective mu by the sample rate.
|
||||
# mu (or w) has units of events/time, so effective rate is events/time * dt, giving mu per dt
|
||||
eff_mu = dt * self.w
|
||||
events = scipy.stats.poisson.rvs(eff_mu, size=num_points, random_state=rng)
|
||||
telegraph_sequence = numpy.cumprod((-1) ** events)
|
||||
|
||||
return {k: telegraph_sequence * v for k, v in self.cache.items()}
|
||||
|
||||
def flip_state(self):
|
||||
self.state *= -1
|
||||
|
||||
|
||||
def get_wrapped_dipoles(
|
||||
dipole_tos: typing.Sequence[DipoleTO],
|
||||
dots: typing.Sequence[DotPosition],
|
||||
measurement_type: DipoleMeasurementType,
|
||||
) -> typing.Sequence[WrappedDipole]:
|
||||
return [
|
||||
WrappedDipole(
|
||||
p=dipole_to.p,
|
||||
s=dipole_to.s,
|
||||
w=dipole_to.w,
|
||||
dot_positions=dots,
|
||||
measurement_type=measurement_type,
|
||||
)
|
||||
for dipole_to in dipole_tos
|
||||
]
|
||||
|
||||
|
||||
class DipoleTimeSeries:
|
||||
def __init__(
|
||||
self,
|
||||
dipoles: typing.Sequence[DipoleTO],
|
||||
dots: typing.Sequence[DotPosition],
|
||||
measurement_type: DipoleMeasurementType,
|
||||
dt: float,
|
||||
rng_to_use: typing.Optional[numpy.random.Generator] = None,
|
||||
):
|
||||
self.rng: numpy.random.Generator
|
||||
if rng_to_use is None:
|
||||
self.rng = numpy.random.default_rng()
|
||||
else:
|
||||
self.rng = rng_to_use
|
||||
|
||||
self.dipoles = get_wrapped_dipoles(dipoles, dots, measurement_type)
|
||||
self.state = 0
|
||||
|
||||
self.real_delta_t = dt
|
||||
|
||||
# we may need to supersample, because of how dumb this process is.
|
||||
# let's find our highest frequency
|
||||
max_frequency = max(d.w for d in self.dipoles)
|
||||
|
||||
super_sample = tantri.dipoles.supersample.get_supersample(max_frequency, dt)
|
||||
self.dt = super_sample.super_dt
|
||||
self.super_sample_ratio = super_sample.super_sample_ratio
|
||||
|
||||
def _sub_transition(self) -> typing.Dict[str, float]:
|
||||
new_vals = [dipole.transition(self.dt, self.rng) for dipole in self.dipoles]
|
||||
|
||||
ret = {}
|
||||
for transition in new_vals:
|
||||
for k, v in transition.items():
|
||||
if k not in ret:
|
||||
ret[k] = v
|
||||
else:
|
||||
ret[k] += v
|
||||
return ret
|
||||
|
||||
def transition(self) -> typing.Dict[str, float]:
|
||||
return [self._sub_transition() for i in range(self.super_sample_ratio)][-1]
|
||||
|
||||
def _generate_series(
|
||||
self, num_points: int, delta_t: float
|
||||
) -> typing.Dict[str, numpy.ndarray]:
|
||||
|
||||
serieses = [
|
||||
dipole.time_series(delta_t, num_points, self.rng) for dipole in self.dipoles
|
||||
]
|
||||
|
||||
result = {}
|
||||
for series in serieses:
|
||||
for k, v in series.items():
|
||||
if k not in result:
|
||||
result[k] = v
|
||||
else:
|
||||
result[k] += v
|
||||
|
||||
return result
|
||||
|
||||
def generate_series(
|
||||
self, num_points: int, override_delta_t: typing.Optional[float] = None
|
||||
) -> TimeSeriesResult:
|
||||
|
||||
delta_t_to_use: float
|
||||
if override_delta_t is not None:
|
||||
delta_t_to_use = override_delta_t
|
||||
else:
|
||||
delta_t_to_use = self.real_delta_t
|
||||
|
||||
series = self._generate_series(num_points, delta_t_to_use)
|
||||
|
||||
return TimeSeriesResult(
|
||||
series_dict=series, num_points=num_points, delta_t=delta_t_to_use
|
||||
)
|
||||
|
||||
def generate_average_apsd(
|
||||
self,
|
||||
num_series: int,
|
||||
num_time_series_points: int,
|
||||
override_delta_t: typing.Optional[float] = None,
|
||||
) -> APSDResult:
|
||||
|
||||
apsds = [
|
||||
self.generate_series(num_time_series_points, override_delta_t).get_apsds()
|
||||
for _ in range(num_series)
|
||||
]
|
||||
|
||||
_logger.debug(f"Averaging {num_series} series")
|
||||
|
||||
return average_apsds(apsds)
|
||||
@ -1,5 +1,6 @@
|
||||
import numpy
|
||||
from dataclasses import dataclass, asdict
|
||||
from enum import Enum
|
||||
|
||||
|
||||
# Lazily just separating this from Dipole where there's additional cached stuff, this is just a thing
|
||||
@ -15,3 +16,59 @@ class DipoleTO:
|
||||
|
||||
def as_dict(self) -> dict:
|
||||
return asdict(self)
|
||||
|
||||
|
||||
class Orientation(str, Enum):
|
||||
# Enum for orientation, making string for json serialisation purposes
|
||||
#
|
||||
# Note that this might not be infinitely extensible?
|
||||
# https://stackoverflow.com/questions/75040733/is-there-a-way-to-use-strenum-in-earlier-python-versions
|
||||
XY = "XY"
|
||||
Z = "Z"
|
||||
RANDOM = "RANDOM"
|
||||
|
||||
|
||||
# A description of the parameters needed to generate random dipoles
|
||||
@dataclass
|
||||
class DipoleGenerationConfig:
|
||||
# note no actual checks anywhere that these are sensibly defined with min less than max etc.
|
||||
x_min: float
|
||||
x_max: float
|
||||
y_min: float
|
||||
y_max: float
|
||||
z_min: float
|
||||
z_max: float
|
||||
|
||||
mag: float
|
||||
|
||||
# these are log_10 of actual value
|
||||
w_log_min: float
|
||||
w_log_max: float
|
||||
|
||||
orientation: Orientation
|
||||
|
||||
dipole_count: int
|
||||
generation_seed: int
|
||||
|
||||
def __post_init__(self):
|
||||
# This allows us to transparently set this with a string, while providing early warning of a type error
|
||||
self.orientation = Orientation(self.orientation)
|
||||
|
||||
def as_dict(self) -> dict:
|
||||
return_dict = asdict(self)
|
||||
|
||||
return_dict["orientation"] = return_dict["orientation"].value
|
||||
|
||||
return return_dict
|
||||
|
||||
|
||||
class DipoleMeasurementType(Enum):
|
||||
ELECTRIC_POTENTIAL = 1
|
||||
X_ELECTRIC_FIELD = 2
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DotPosition:
|
||||
# assume len 3
|
||||
r: numpy.ndarray
|
||||
label: str
|
||||
|
||||
21
tantri/util.py
Normal file
21
tantri/util.py
Normal file
@ -0,0 +1,21 @@
|
||||
import typing
|
||||
|
||||
A = typing.TypeVar("A")
|
||||
B = typing.TypeVar("B")
|
||||
|
||||
|
||||
def dict_reduce(
|
||||
list_of_dicts: typing.Sequence[typing.Dict[str, A]],
|
||||
func: typing.Callable[[typing.Sequence[A]], B],
|
||||
) -> typing.Dict[str, B]:
|
||||
"""
|
||||
Reduce over list of dicts with function that can coalesce list of dict values.
|
||||
Assumes the keys in the first dictionary are the same as the keys for every other passed in dictionary.
|
||||
"""
|
||||
keys = list_of_dicts[0].keys()
|
||||
|
||||
collated = {}
|
||||
for key in keys:
|
||||
collated[key] = [dct[key] for dct in list_of_dicts]
|
||||
|
||||
return {k: func(v) for k, v in collated.items()}
|
||||
0
tests/binning/__init__.py
Normal file
0
tests/binning/__init__.py
Normal file
86
tests/binning/__snapshots__/test_binning.ambr
Normal file
86
tests/binning/__snapshots__/test_binning.ambr
Normal file
@ -0,0 +1,86 @@
|
||||
# serializer version: 1
|
||||
# name: test_group_x_bins
|
||||
list([
|
||||
Bin(bindex=0, x_min=1.0, point_xs=array([1. , 2.8, 8. ]), point_y_dict={'identity_plus_one': array([ 3. , 4.8, 10. ])}),
|
||||
Bin(bindex=1, x_min=9.0, point_xs=array([12.2, 13.6]), point_y_dict={'identity_plus_one': array([14.2, 15.6])}),
|
||||
Bin(bindex=2, x_min=17.0, point_xs=array([17. , 19.71, 20. , 24. ]), point_y_dict={'identity_plus_one': array([19. , 21.71, 22. , 26. ])}),
|
||||
])
|
||||
# ---
|
||||
# name: test_group_x_bins_log
|
||||
list([
|
||||
Bin(bindex=0, x_min=0.0015848899999999994, point_xs=array([0.00158489, 0.00363078, 0.0398107 ]), point_y_dict={'basic_lorentzian': array([0.159154, 0.15915 , 0.158535])}),
|
||||
Bin(bindex=1, x_min=0.15848899999999994, point_xs=array([ 0.275423, 0.524807, 2.51189 , 8.74984 , 10. ]), point_y_dict={'basic_lorentzian': array([0.134062 , 0.0947588 , 0.00960602, 0.00083808, 0.00064243])}),
|
||||
])
|
||||
# ---
|
||||
# name: test_group_x_bins_mean
|
||||
list([
|
||||
tuple(
|
||||
3.9333333333333336,
|
||||
dict({
|
||||
'identity_plus_one': 5.933333333333334,
|
||||
}),
|
||||
),
|
||||
tuple(
|
||||
12.899999999999999,
|
||||
dict({
|
||||
'identity_plus_one': 14.899999999999999,
|
||||
}),
|
||||
),
|
||||
tuple(
|
||||
20.177500000000002,
|
||||
dict({
|
||||
'identity_plus_one': 22.177500000000002,
|
||||
}),
|
||||
),
|
||||
])
|
||||
# ---
|
||||
# name: test_group_x_bins_mean_log
|
||||
list([
|
||||
tuple(
|
||||
0.0423015,
|
||||
dict({
|
||||
'basic_lorentzian': 0.15817799999999999,
|
||||
}),
|
||||
),
|
||||
tuple(
|
||||
0.593058,
|
||||
dict({
|
||||
'basic_lorentzian': 0.09491108333333335,
|
||||
}),
|
||||
),
|
||||
tuple(
|
||||
4.0870750000000005,
|
||||
dict({
|
||||
'basic_lorentzian': 0.004363105,
|
||||
}),
|
||||
),
|
||||
tuple(
|
||||
24.196866666666665,
|
||||
dict({
|
||||
'basic_lorentzian': 0.0001410066333333333,
|
||||
}),
|
||||
),
|
||||
tuple(
|
||||
394.723,
|
||||
dict({
|
||||
'basic_lorentzian': 1.364947e-06,
|
||||
}),
|
||||
),
|
||||
])
|
||||
# ---
|
||||
# name: test_group_x_bins_summary
|
||||
list([
|
||||
BinSummary(mean_x=3.9333333333333336, summary_values={'identity_plus_one': BinSummaryValue(mean_y=5.933333333333334, stdev_y=3.635014901390823)}),
|
||||
BinSummary(mean_x=12.899999999999999, summary_values={'identity_plus_one': BinSummaryValue(mean_y=14.899999999999999, stdev_y=0.9899494936611668)}),
|
||||
BinSummary(mean_x=20.177500000000002, summary_values={'identity_plus_one': BinSummaryValue(mean_y=22.177500000000002, stdev_y=2.884329789280923)}),
|
||||
])
|
||||
# ---
|
||||
# name: test_group_x_bins_summary_log
|
||||
list([
|
||||
BinSummary(mean_x=0.0423015, summary_values={'basic_lorentzian': BinSummaryValue(mean_y=0.15817799999999999, stdev_y=0.001275436787927965)}),
|
||||
BinSummary(mean_x=0.593058, summary_values={'basic_lorentzian': BinSummaryValue(mean_y=0.09491108333333335, stdev_y=0.05205159393153745)}),
|
||||
BinSummary(mean_x=4.0870750000000005, summary_values={'basic_lorentzian': BinSummaryValue(mean_y=0.004363105, stdev_y=0.0025964466030423193)}),
|
||||
BinSummary(mean_x=24.196866666666665, summary_values={'basic_lorentzian': BinSummaryValue(mean_y=0.0001410066333333333, stdev_y=0.00010167601686387665)}),
|
||||
BinSummary(mean_x=394.723, summary_values={'basic_lorentzian': BinSummaryValue(mean_y=1.364947e-06, stdev_y=1.7011900210905307e-06)}),
|
||||
])
|
||||
# ---
|
||||
314
tests/binning/test_binning.py
Normal file
314
tests/binning/test_binning.py
Normal file
@ -0,0 +1,314 @@
|
||||
import pytest
|
||||
import tantri.binning.binning as binning
|
||||
import numpy
|
||||
|
||||
|
||||
def test_bin_config_validation():
|
||||
with pytest.raises(ValueError):
|
||||
binning.BinConfig(log_scale=False, bin_width=1, min_points_required=1)
|
||||
|
||||
|
||||
def test_bin_construction_faulty_min():
|
||||
x_list = numpy.array([5, 6, 7, 8])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=0.8, bin_min=5.5)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
binning._construct_bins(x_list, bin_config)
|
||||
|
||||
|
||||
def test_bin_construction_force_min():
|
||||
x_list = numpy.array([4.5, 5.5, 6.5, 7.5, 8.5])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=1, bin_min=2)
|
||||
|
||||
expected_bins = numpy.array([2, 3, 4, 5, 6, 7, 8, 9])
|
||||
|
||||
actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
|
||||
numpy.testing.assert_allclose(
|
||||
actual_bins, expected_bins, err_msg="The bins were not as expected"
|
||||
)
|
||||
|
||||
|
||||
def test_bin_construction_even():
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=8)
|
||||
expected_bins = numpy.array([1, 9, 17, 25, 33])
|
||||
|
||||
actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
|
||||
numpy.testing.assert_allclose(
|
||||
actual_bins, expected_bins, err_msg="The bins were not as expected"
|
||||
)
|
||||
|
||||
|
||||
def test_bin_construction_uneven():
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=7)
|
||||
expected_bins = numpy.array([1, 8, 15, 22, 29, 36])
|
||||
|
||||
actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
|
||||
numpy.testing.assert_allclose(
|
||||
actual_bins, expected_bins, err_msg="The bins were not as expected"
|
||||
)
|
||||
|
||||
|
||||
def test_bin_construction_uneven_non_integer():
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=7.5)
|
||||
expected_bins = numpy.array([1, 8.5, 16, 23.5, 31, 38.5])
|
||||
|
||||
actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
|
||||
numpy.testing.assert_allclose(
|
||||
actual_bins, expected_bins, err_msg="The bins were not as expected"
|
||||
)
|
||||
|
||||
|
||||
def test_group_x_bins(snapshot):
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
y_dict = {
|
||||
"identity_plus_one": (
|
||||
numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
|
||||
)
|
||||
}
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=8)
|
||||
# expected_bins = numpy.array([1, 9, 17, 25, 33])
|
||||
|
||||
binned = binning.bin_lists(x_list, y_dict, bin_config)
|
||||
|
||||
assert binned == snapshot
|
||||
|
||||
|
||||
def test_group_x_bins_mean(snapshot):
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
y_dict = {
|
||||
"identity_plus_one": (
|
||||
numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
|
||||
)
|
||||
}
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=8)
|
||||
# expected_bins = numpy.array([1, 9, 17, 25, 33])
|
||||
|
||||
binned = binning.bin_lists(x_list, y_dict, bin_config)
|
||||
mean_binned = [bin.mean_point() for bin in binned]
|
||||
|
||||
assert mean_binned == snapshot
|
||||
|
||||
|
||||
def test_group_x_bins_summary(snapshot):
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
y_dict = {
|
||||
"identity_plus_one": (
|
||||
numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33]) + 2
|
||||
)
|
||||
}
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=False, bin_width=8)
|
||||
# expected_bins = numpy.array([1, 9, 17, 25, 33])
|
||||
|
||||
binned = binning.bin_lists(x_list, y_dict, bin_config)
|
||||
summary = [bin.summary_point() for bin in binned]
|
||||
|
||||
assert summary == snapshot
|
||||
|
||||
|
||||
def test_bin_construction_faulty_min_log_scale():
|
||||
x_list = numpy.array([5, 6, 7, 8])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=True, bin_width=0.8, bin_min=5.5)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
binning._construct_bins(x_list, bin_config)
|
||||
|
||||
|
||||
def test_bin_construction_force_min_log():
|
||||
"""
|
||||
This test shows the main use ofthe bin_min parameter, if we want our bins to nicely line up with decades for example,
|
||||
then we can force it by ignoring the provided minimum x.
|
||||
"""
|
||||
x_list = numpy.array([1500, 5000, 10000, 33253, 400000])
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=True, bin_width=1, bin_min=10)
|
||||
|
||||
expected_bins = numpy.array([10, 100, 1000, 10000, 100000, 1000000])
|
||||
|
||||
actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
|
||||
numpy.testing.assert_allclose(
|
||||
actual_bins, expected_bins, err_msg="The bins were not as expected"
|
||||
)
|
||||
|
||||
|
||||
def test_bin_construction_even_log_scale():
|
||||
x_list = numpy.array([1, 2.8, 8, 12.2, 13.6, 17, 19.71, 20, 24, 33])
|
||||
|
||||
# bin width of 0.3 corresponds to 10^0.3 ~= 2, so we're roughly looking at
|
||||
bin_config = binning.BinConfig(log_scale=True, bin_width=0.3)
|
||||
expected_bins = numpy.array(
|
||||
[
|
||||
1.00000000000,
|
||||
1.99526231497,
|
||||
3.98107170553,
|
||||
7.94328234724,
|
||||
15.8489319246,
|
||||
31.6227766017,
|
||||
63.0957344480,
|
||||
]
|
||||
)
|
||||
|
||||
actual_bins = binning._construct_bins(x_list, bin_config=bin_config)
|
||||
numpy.testing.assert_allclose(
|
||||
actual_bins, expected_bins, err_msg="The bins were not as expected"
|
||||
)
|
||||
|
||||
|
||||
def test_group_x_bins_log(snapshot):
|
||||
x_list = numpy.array(
|
||||
[
|
||||
0.00158489,
|
||||
0.00363078,
|
||||
0.0398107,
|
||||
0.275423,
|
||||
0.524807,
|
||||
2.51189,
|
||||
8.74984,
|
||||
10.0,
|
||||
63.0957,
|
||||
3981.07,
|
||||
]
|
||||
)
|
||||
y_dict = {
|
||||
"basic_lorentzian": numpy.array(
|
||||
[
|
||||
0.159154,
|
||||
0.15915,
|
||||
0.158535,
|
||||
0.134062,
|
||||
0.0947588,
|
||||
0.00960602,
|
||||
0.000838084,
|
||||
0.000642427,
|
||||
0.0000162008,
|
||||
4.06987e-9,
|
||||
]
|
||||
)
|
||||
}
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=True, bin_width=2)
|
||||
# expected_bins = numpy.array([1, 9, 17, 25, 33])
|
||||
|
||||
binned = binning.bin_lists(x_list, y_dict, bin_config)
|
||||
|
||||
assert binned == snapshot
|
||||
|
||||
|
||||
def test_group_x_bins_mean_log(snapshot):
|
||||
x_list = numpy.array(
|
||||
[
|
||||
0.0158489,
|
||||
0.0316228,
|
||||
0.0794328,
|
||||
0.158489,
|
||||
0.17378,
|
||||
0.316228,
|
||||
0.944061,
|
||||
0.977237,
|
||||
0.988553,
|
||||
3.16228,
|
||||
5.01187,
|
||||
15.8489,
|
||||
25.1189,
|
||||
31.6228,
|
||||
158.489,
|
||||
630.957,
|
||||
]
|
||||
)
|
||||
y_dict = {
|
||||
"basic_lorentzian": (
|
||||
numpy.array(
|
||||
[
|
||||
0.159056,
|
||||
0.158763,
|
||||
0.156715,
|
||||
0.149866,
|
||||
0.148118,
|
||||
0.127657,
|
||||
0.0497503,
|
||||
0.0474191,
|
||||
0.0466561,
|
||||
0.00619907,
|
||||
0.00252714,
|
||||
0.000256378,
|
||||
0.000102165,
|
||||
0.0000644769,
|
||||
2.56787e-6,
|
||||
1.62024e-7,
|
||||
]
|
||||
)
|
||||
)
|
||||
}
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=True, bin_width=1, bin_min=1e-2)
|
||||
# expected_bins = numpy.array([1, 9, 17, 25, 33])
|
||||
|
||||
binned = binning.bin_lists(x_list, y_dict, bin_config)
|
||||
mean_binned = [bin.mean_point() for bin in binned]
|
||||
|
||||
assert mean_binned == snapshot
|
||||
|
||||
|
||||
def test_group_x_bins_summary_log(snapshot):
|
||||
x_list = numpy.array(
|
||||
[
|
||||
0.0158489,
|
||||
0.0316228,
|
||||
0.0794328,
|
||||
0.158489,
|
||||
0.17378,
|
||||
0.316228,
|
||||
0.944061,
|
||||
0.977237,
|
||||
0.988553,
|
||||
3.16228,
|
||||
5.01187,
|
||||
15.8489,
|
||||
25.1189,
|
||||
31.6228,
|
||||
158.489,
|
||||
630.957,
|
||||
]
|
||||
)
|
||||
y_dict = {
|
||||
"basic_lorentzian": (
|
||||
numpy.array(
|
||||
[
|
||||
0.159056,
|
||||
0.158763,
|
||||
0.156715,
|
||||
0.149866,
|
||||
0.148118,
|
||||
0.127657,
|
||||
0.0497503,
|
||||
0.0474191,
|
||||
0.0466561,
|
||||
0.00619907,
|
||||
0.00252714,
|
||||
0.000256378,
|
||||
0.000102165,
|
||||
0.0000644769,
|
||||
2.56787e-6,
|
||||
1.62024e-7,
|
||||
]
|
||||
)
|
||||
)
|
||||
}
|
||||
|
||||
bin_config = binning.BinConfig(log_scale=True, bin_width=1, bin_min=1e-2)
|
||||
|
||||
binned = binning.bin_lists(x_list, y_dict, bin_config)
|
||||
summary = [bin.summary_point() for bin in binned]
|
||||
|
||||
assert summary == snapshot
|
||||
@ -5,121 +5,82 @@
|
||||
'dot1': 1.1618574890412874,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.02607677019525,
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1114564655272303,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1114564655272303,
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1114564655272303,
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.076477793709307,
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1114564655272303,
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.076477793709307,
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.02607677019525,
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1114564655272303,
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.02607677019525,
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.02607677019525,
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.076477793709307,
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.02607677019525,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1114564655272303,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 1.1618574890412874,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.31962399244019385,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.31962399244019385,
|
||||
@ -128,28 +89,67 @@
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
'dot1': -0.31962399244019385,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
'dot1': -0.31962399244019385,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
'dot1': -0.31962399244019385,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.45540471128623133,
|
||||
'dot1': -0.31962399244019385,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
'dot1': -0.31962399244019385,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.40500368777217416,
|
||||
'dot1': -0.370025015954251,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9406970748632697,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.9910980983773269,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.4903833831041545,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5407844066182117,
|
||||
}),
|
||||
])
|
||||
# ---
|
||||
@ -159,121 +159,82 @@
|
||||
'dot1': 0.4128225535745013,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3931203265687062,
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.42682283788396164,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.42682283788396164,
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.42682283788396164,
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3791200422592459,
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.42682283788396164,
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3791200422592459,
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3931203265687062,
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.42682283788396164,
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3931203265687062,
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3931203265687062,
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3791200422592459,
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3931203265687062,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.42682283788396164,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.4128225535745013,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.574831767413153,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.574831767413153,
|
||||
@ -282,28 +243,67 @@
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
'dot1': -0.574831767413153,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
'dot1': -0.574831767413153,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
'dot1': -0.574831767413153,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.5945339944189482,
|
||||
'dot1': -0.574831767413153,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
'dot1': -0.574831767413153,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6085342787284085,
|
||||
'dot1': -0.5608314831036927,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.35941781525345085,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': 0.3454175309439905,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6422367900436639,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
dict({
|
||||
'dot1': -0.6282365057342035,
|
||||
}),
|
||||
])
|
||||
# ---
|
||||
|
||||
2157
tests/dipoles/__snapshots__/test_event_time_series.ambr
Normal file
2157
tests/dipoles/__snapshots__/test_event_time_series.ambr
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,4 +1,5 @@
|
||||
from tantri.dipoles.generation import DipoleGenerationConfig, make_dipoles, Orientation
|
||||
from tantri.dipoles.types import DipoleGenerationConfig, Orientation
|
||||
from tantri.dipoles.generation import make_dipoles
|
||||
import numpy
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import json
|
||||
from tantri.dipoles.generation import DipoleGenerationConfig, Orientation
|
||||
from tantri.dipoles.types import DipoleGenerationConfig, Orientation
|
||||
|
||||
|
||||
def test_serialise_generation_config_to_json(snapshot):
|
||||
|
||||
120
tests/dipoles/test_event_time_series.py
Normal file
120
tests/dipoles/test_event_time_series.py
Normal file
@ -0,0 +1,120 @@
|
||||
import tantri.dipoles.event_time_series as ets
|
||||
import numpy.random
|
||||
import logging
|
||||
|
||||
from tantri.dipoles.types import (
|
||||
DipoleTO,
|
||||
DotPosition,
|
||||
DipoleMeasurementType,
|
||||
)
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
SEED = 12345
|
||||
|
||||
|
||||
def test_event_time_series(snapshot):
|
||||
|
||||
_logger.info("testing event time series")
|
||||
rng = numpy.random.default_rng(SEED)
|
||||
|
||||
before_func = ets.get_num_events_before(rng=rng, scale=0.5, total_time=10)
|
||||
|
||||
time_series = [(i, before_func(i)) for i in range(10)]
|
||||
|
||||
_logger.warning(time_series)
|
||||
|
||||
assert time_series == snapshot
|
||||
|
||||
|
||||
def test_event_actual_series(snapshot):
|
||||
rng = numpy.random.default_rng(SEED)
|
||||
time_series = ets.create_exponential_time_series(rng, 0.1, 200, 0.5)
|
||||
assert time_series == snapshot
|
||||
|
||||
|
||||
def test_event_dipoles_collection(snapshot):
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [DotPosition(numpy.array([0, 0, 0]), "dot1")]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
d2 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([-2, 3, 2]),
|
||||
0.1,
|
||||
)
|
||||
d3 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([2, 1, 2]),
|
||||
6,
|
||||
)
|
||||
d4 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([-5, -5, 2]),
|
||||
50,
|
||||
)
|
||||
|
||||
ts_gen = ets.EventDipoleTimeSeries(
|
||||
[d1, d2, d3, d4],
|
||||
dots,
|
||||
DipoleMeasurementType.X_ELECTRIC_FIELD,
|
||||
0.01,
|
||||
100,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
time_series = ts_gen.create_time_series()
|
||||
|
||||
assert time_series == snapshot
|
||||
|
||||
|
||||
def test_event_dipoles_collection_larger(snapshot):
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [
|
||||
DotPosition(numpy.array([0, 0, 0]), "dot1"),
|
||||
DotPosition(numpy.array([0, -1, 0]), "dot2"),
|
||||
]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
d2 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([-2, 3, 2]),
|
||||
0.1,
|
||||
)
|
||||
d3 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([2, 1, 2]),
|
||||
6,
|
||||
)
|
||||
d4 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([-5, -5, 2]),
|
||||
50,
|
||||
)
|
||||
d5 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([-2, -2, 1]),
|
||||
0.01,
|
||||
)
|
||||
|
||||
ts_gen = ets.EventDipoleTimeSeries(
|
||||
[d1, d2, d3, d4, d5],
|
||||
dots,
|
||||
DipoleMeasurementType.X_ELECTRIC_FIELD,
|
||||
0.01,
|
||||
100,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
time_series = ts_gen.create_time_series()
|
||||
|
||||
assert time_series == snapshot
|
||||
23
tests/dipoles/test_supersample.py
Normal file
23
tests/dipoles/test_supersample.py
Normal file
@ -0,0 +1,23 @@
|
||||
from tantri.dipoles.supersample import get_supersample, SuperSample
|
||||
|
||||
|
||||
def test_raw_supersample_slow():
|
||||
# let's say our fluctuations are really slow
|
||||
dt = 1
|
||||
max_frequency = 0.0001
|
||||
|
||||
assert get_supersample(max_frequency, dt) == SuperSample(
|
||||
super_dt=1, super_sample_ratio=1
|
||||
)
|
||||
|
||||
|
||||
def test_raw_supersample_fast():
|
||||
# let's say we our fluctuations are fast
|
||||
dt = 0.3
|
||||
max_frequency = 10
|
||||
|
||||
# Our fastest oscillations will then happen every 0.1 s, so we need to drop down to something less than 0.01, so we divide by 30
|
||||
|
||||
assert get_supersample(max_frequency, dt) == SuperSample(
|
||||
super_dt=0.01, super_sample_ratio=30
|
||||
)
|
||||
@ -0,0 +1,173 @@
|
||||
# serializer version: 1
|
||||
# name: test_multiple_apsd
|
||||
APSDResult(psd_dict={'dot1': array([9.73532062e-04, 8.39303246e-04, 9.61582732e-04, 8.40739221e-04,
|
||||
9.37066683e-04, 1.03213670e-03, 9.56134787e-04, 9.93209101e-04,
|
||||
1.08724022e-03, 1.21048571e-03, 9.38615917e-04, 8.52242772e-04,
|
||||
8.42539642e-04, 1.01955480e-03, 8.97868104e-04, 1.00437469e-03,
|
||||
7.07499008e-04, 8.18440147e-04, 9.24010511e-04, 8.24215010e-04,
|
||||
7.12733009e-04, 8.52407925e-04, 7.34862496e-04, 7.63072581e-04,
|
||||
6.73402818e-04, 7.85605329e-04, 6.30735299e-04, 6.84832870e-04,
|
||||
6.55976134e-04, 8.52604367e-04, 6.57998050e-04, 8.58669644e-04,
|
||||
6.31868625e-04, 6.65638673e-04, 6.31591692e-04, 5.74813110e-04,
|
||||
5.69208218e-04, 6.19839130e-04, 6.65732425e-04, 5.62371802e-04,
|
||||
5.76800794e-04, 5.65916924e-04, 6.56932756e-04, 6.14845472e-04,
|
||||
4.99181492e-04, 5.89816624e-04, 5.65243915e-04, 4.67480729e-04,
|
||||
4.92807307e-04, 3.94075520e-04, 4.17672841e-04, 4.48402297e-04,
|
||||
3.64196723e-04, 4.36311422e-04, 3.49511887e-04, 4.19780757e-04,
|
||||
3.77423406e-04, 4.14824457e-04, 3.97342480e-04, 3.68726505e-04,
|
||||
4.10513044e-04, 4.12630532e-04, 3.29064095e-04, 3.46699091e-04,
|
||||
3.44594847e-04, 3.50505438e-04, 3.77803485e-04, 2.87885784e-04,
|
||||
2.93655837e-04, 3.77470181e-04, 3.23308066e-04, 3.20630183e-04,
|
||||
3.22296166e-04, 3.45562066e-04, 2.59242080e-04, 3.25464512e-04,
|
||||
2.69122248e-04, 2.74602169e-04, 2.63152125e-04, 2.46491048e-04,
|
||||
2.24341046e-04, 2.68591099e-04, 2.65104078e-04, 2.60274323e-04,
|
||||
2.33898312e-04, 2.43711653e-04, 2.41226390e-04, 2.17801406e-04,
|
||||
2.17888671e-04, 2.01274978e-04, 1.93877002e-04, 1.96390771e-04,
|
||||
1.87490996e-04, 2.04166379e-04, 2.08714128e-04, 2.29287658e-04,
|
||||
1.69458646e-04, 1.95255536e-04, 1.74110621e-04, 1.96323415e-04,
|
||||
1.88541448e-04, 1.81924188e-04, 1.70966036e-04, 1.57648084e-04,
|
||||
1.60524745e-04, 1.92460519e-04, 1.53432849e-04, 1.55562203e-04,
|
||||
1.61883665e-04, 1.79482805e-04, 1.65771247e-04, 1.99739063e-04,
|
||||
1.58577129e-04, 1.47380404e-04, 1.33846897e-04, 1.66883453e-04,
|
||||
1.51442563e-04, 1.67281574e-04, 1.37008732e-04, 1.55273374e-04,
|
||||
1.33076567e-04, 1.56652477e-04, 1.55555437e-04, 1.27773146e-04,
|
||||
1.40395843e-04, 1.16816771e-04, 1.16899846e-04, 1.38621871e-04,
|
||||
1.24623109e-04, 1.24337619e-04, 1.17615455e-04, 1.12875623e-04,
|
||||
1.07781236e-04, 1.01389801e-04, 1.15571668e-04, 1.01276406e-04,
|
||||
1.22516329e-04, 1.17040309e-04, 1.34030708e-04, 1.08730317e-04,
|
||||
1.18619015e-04, 9.95587258e-05, 1.00906508e-04, 9.29116526e-05,
|
||||
1.00137183e-04, 9.28680987e-05, 8.01445843e-05, 1.05970263e-04,
|
||||
9.45430569e-05, 8.00503584e-05, 1.01166198e-04, 9.32925898e-05,
|
||||
9.08523306e-05, 8.30813940e-05, 8.57761431e-05, 8.50599551e-05,
|
||||
9.42550643e-05, 7.06534307e-05, 9.38109626e-05, 7.97487294e-05,
|
||||
7.87467189e-05, 8.60532142e-05, 8.01127568e-05, 6.55220855e-05,
|
||||
1.10419369e-04, 7.63287410e-05, 9.62261196e-05, 7.80528169e-05,
|
||||
7.12069198e-05, 8.57268166e-05, 6.88077364e-05, 6.17188837e-05,
|
||||
7.17568191e-05, 7.89992143e-05, 7.71412885e-05, 7.68729059e-05,
|
||||
7.75188516e-05, 7.56236336e-05, 6.84518115e-05, 5.78175904e-05,
|
||||
7.72279483e-05, 6.45329149e-05, 7.66020128e-05, 6.71940726e-05,
|
||||
7.73912298e-05, 6.37107478e-05, 6.45922648e-05, 6.80344334e-05,
|
||||
5.48229973e-05, 5.60630527e-05, 6.52106583e-05, 6.47830130e-05,
|
||||
6.56050943e-05, 7.13923758e-05, 6.90215627e-05, 5.64864551e-05,
|
||||
6.86743791e-05, 6.90877021e-05, 6.53444393e-05, 5.64311212e-05,
|
||||
5.47212311e-05, 5.87415418e-05, 5.51011903e-05, 5.96420633e-05,
|
||||
5.09834748e-05, 5.68214547e-05, 6.36463428e-05, 5.90142079e-05,
|
||||
6.37071295e-05, 6.31479191e-05, 5.52106181e-05, 5.82988324e-05,
|
||||
5.12198359e-05, 5.31861871e-05, 4.77621244e-05, 5.82345313e-05,
|
||||
4.33984309e-05, 5.31962634e-05, 6.48731535e-05, 5.99307716e-05,
|
||||
5.74245258e-05, 3.88412941e-05, 4.62934114e-05, 4.80469646e-05,
|
||||
5.03948341e-05, 5.42217253e-05, 4.85038080e-05, 4.91728399e-05,
|
||||
4.91099787e-05, 4.46390228e-05, 5.46773705e-05, 4.59637262e-05,
|
||||
4.93209973e-05, 3.57845793e-05, 4.47877782e-05, 4.40482223e-05,
|
||||
4.84232225e-05, 3.96890804e-05, 5.10627469e-05, 4.53055547e-05,
|
||||
4.30302383e-05, 4.36255600e-05, 4.54364912e-05, 4.54576504e-05,
|
||||
4.10822418e-05, 4.42264057e-05, 4.62352548e-05, 4.80138761e-05,
|
||||
4.01140813e-05, 4.13529669e-05, 4.74170664e-05, 4.77644760e-05,
|
||||
4.01455048e-05, 3.93192684e-05, 3.61658056e-05, 4.26244670e-05,
|
||||
5.20559547e-05, 3.29253435e-05, 3.91772747e-05, 3.93002047e-05,
|
||||
3.82243628e-05, 3.96951610e-05, 4.13726616e-05, 4.21235362e-05,
|
||||
3.20878033e-05, 3.52791576e-05, 3.98647390e-05, 3.67384189e-05,
|
||||
3.95222859e-05, 3.96914281e-05, 3.33599974e-05, 3.13742493e-05,
|
||||
3.77492407e-05, 3.88687449e-05, 4.18502685e-05, 2.75607171e-05,
|
||||
3.49799432e-05, 4.29941601e-05, 3.40874184e-05, 3.31424257e-05,
|
||||
3.23881135e-05, 3.10279467e-05, 2.97600366e-05, 3.38951446e-05,
|
||||
3.69653017e-05, 3.44308555e-05, 3.34807858e-05, 3.32368096e-05,
|
||||
3.24045191e-05, 3.42274586e-05, 3.47226801e-05, 3.33430915e-05,
|
||||
2.79309207e-05, 2.85890056e-05, 3.58595400e-05, 3.56541692e-05,
|
||||
2.97312185e-05, 2.98471656e-05, 2.84603558e-05, 3.38034256e-05,
|
||||
3.14908877e-05, 2.89949398e-05, 3.59356966e-05, 2.96333483e-05,
|
||||
2.91882065e-05, 3.17205845e-05, 3.48880484e-05, 2.61650545e-05,
|
||||
3.15631282e-05, 3.00337860e-05, 2.64494983e-05, 3.10436426e-05,
|
||||
3.57631331e-05, 3.17242386e-05, 3.30394319e-05, 2.98424226e-05,
|
||||
3.10027253e-05, 2.61786697e-05, 3.59797317e-05, 2.72164745e-05,
|
||||
3.21555684e-05, 3.13640141e-05, 2.65379518e-05, 3.16421046e-05,
|
||||
2.32646534e-05, 2.83861205e-05, 3.42693878e-05, 3.06557252e-05,
|
||||
2.81036550e-05, 2.89282012e-05, 3.16763804e-05, 2.61476123e-05,
|
||||
2.85640047e-05, 2.95775793e-05, 2.67187261e-05, 2.92927165e-05,
|
||||
3.26798613e-05, 2.62158112e-05, 3.17973663e-05, 2.95116461e-05,
|
||||
2.69703722e-05, 2.98312826e-05, 3.11931343e-05, 2.58819861e-05,
|
||||
3.16647839e-05, 2.87014896e-05, 2.61242043e-05, 2.57079454e-05,
|
||||
2.58091499e-05, 2.50869160e-05, 2.54276074e-05, 2.44439218e-05,
|
||||
2.52572719e-05, 2.98144631e-05, 2.66277431e-05, 3.03880570e-05,
|
||||
2.78788636e-05, 2.52971662e-05, 2.98456606e-05, 2.75517614e-05,
|
||||
2.47834735e-05, 2.99825369e-05, 2.51399202e-05, 2.51092295e-05,
|
||||
2.67307775e-05, 2.34116817e-05, 2.27620334e-05, 2.66901206e-05,
|
||||
2.05955245e-05, 2.65978777e-05, 2.44415114e-05, 2.44930174e-05,
|
||||
2.51791081e-05, 2.25198507e-05, 2.41412615e-05, 3.01881015e-05,
|
||||
2.25523290e-05, 3.06489814e-05, 2.49761975e-05, 2.29860177e-05,
|
||||
2.67559223e-05, 2.62764172e-05, 2.59503061e-05, 2.48188521e-05,
|
||||
2.77943655e-05, 2.71207179e-05, 3.08783681e-05, 2.56262648e-05,
|
||||
2.84800395e-05, 2.23110420e-05, 2.64520744e-05, 2.45062819e-05,
|
||||
2.28851184e-05, 2.39202185e-05, 2.50554904e-05, 2.46250750e-05,
|
||||
2.45535212e-05, 2.09270435e-05, 2.12506122e-05, 1.78712553e-05,
|
||||
2.00666320e-05, 2.58464272e-05, 2.35016439e-05, 2.32995872e-05,
|
||||
2.46549862e-05, 2.67874896e-05, 2.09833627e-05, 2.18504040e-05,
|
||||
2.50649930e-05, 2.28623793e-05, 2.33069247e-05, 2.42162495e-05,
|
||||
2.87389611e-05, 2.17665913e-05, 2.43584154e-05, 2.69878208e-05,
|
||||
2.18452473e-05, 2.55275529e-05, 2.38911265e-05, 2.16983190e-05,
|
||||
2.37778250e-05, 2.44521271e-05, 2.57933783e-05, 2.06190911e-05,
|
||||
2.43679377e-05, 2.41749073e-05, 2.15623674e-05, 2.38205203e-05,
|
||||
2.13869346e-05, 2.38095406e-05, 2.25544675e-05, 2.15928046e-05,
|
||||
2.54370470e-05, 2.02202610e-05, 2.23601433e-05, 2.59620109e-05,
|
||||
2.51600457e-05, 1.83265793e-05, 2.22440731e-05, 2.22461351e-05,
|
||||
1.98146961e-05, 2.13685791e-05, 2.45735398e-05, 2.37149622e-05,
|
||||
2.09946835e-05, 2.33047596e-05, 2.04236775e-05, 2.28269774e-05,
|
||||
2.49828520e-05, 2.02448525e-05, 1.86535318e-05, 2.45785739e-05,
|
||||
2.40916779e-05, 2.18635797e-05, 2.22628701e-05, 2.05771608e-05,
|
||||
2.42548135e-05, 2.20517232e-05, 2.36364135e-05, 1.99630472e-05,
|
||||
2.61963168e-05, 1.95322214e-05, 1.98486112e-05, 2.10734331e-05,
|
||||
2.11979535e-05, 2.04815286e-05, 2.56003950e-05, 2.40922633e-05,
|
||||
2.35290452e-05, 2.73907962e-05, 2.22326104e-05, 2.34477154e-05,
|
||||
1.76264270e-05, 2.00146122e-05, 2.01626249e-05, 2.34853924e-05,
|
||||
2.09488146e-05, 2.15764152e-05, 2.30110121e-05, 2.09471986e-05,
|
||||
2.41826684e-05, 2.15110636e-05, 2.11039012e-05, 1.98045826e-05,
|
||||
2.60175138e-05, 2.04043463e-05, 2.25670355e-05, 2.19150862e-05,
|
||||
2.12290561e-05, 2.29491312e-05, 2.08250752e-05, 1.94677239e-05,
|
||||
1.99619307e-05, 1.94053737e-05, 2.02162720e-05, 2.03274024e-05,
|
||||
1.82522614e-05, 2.07751154e-05, 2.33879107e-05, 2.47441318e-05])}, freqs=array([ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1,
|
||||
1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2,
|
||||
2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3,
|
||||
3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4,
|
||||
4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5,
|
||||
5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5, 6.6,
|
||||
6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7,
|
||||
7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8,
|
||||
8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9,
|
||||
10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11. ,
|
||||
11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. , 12.1,
|
||||
12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1, 13.2,
|
||||
13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2, 14.3,
|
||||
14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3, 15.4,
|
||||
15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4, 16.5,
|
||||
16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5, 17.6,
|
||||
17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7,
|
||||
18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8,
|
||||
19.9, 20. , 20.1, 20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9,
|
||||
21. , 21.1, 21.2, 21.3, 21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. ,
|
||||
22.1, 22.2, 22.3, 22.4, 22.5, 22.6, 22.7, 22.8, 22.9, 23. , 23.1,
|
||||
23.2, 23.3, 23.4, 23.5, 23.6, 23.7, 23.8, 23.9, 24. , 24.1, 24.2,
|
||||
24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9, 25. , 25.1, 25.2, 25.3,
|
||||
25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 26. , 26.1, 26.2, 26.3, 26.4,
|
||||
26.5, 26.6, 26.7, 26.8, 26.9, 27. , 27.1, 27.2, 27.3, 27.4, 27.5,
|
||||
27.6, 27.7, 27.8, 27.9, 28. , 28.1, 28.2, 28.3, 28.4, 28.5, 28.6,
|
||||
28.7, 28.8, 28.9, 29. , 29.1, 29.2, 29.3, 29.4, 29.5, 29.6, 29.7,
|
||||
29.8, 29.9, 30. , 30.1, 30.2, 30.3, 30.4, 30.5, 30.6, 30.7, 30.8,
|
||||
30.9, 31. , 31.1, 31.2, 31.3, 31.4, 31.5, 31.6, 31.7, 31.8, 31.9,
|
||||
32. , 32.1, 32.2, 32.3, 32.4, 32.5, 32.6, 32.7, 32.8, 32.9, 33. ,
|
||||
33.1, 33.2, 33.3, 33.4, 33.5, 33.6, 33.7, 33.8, 33.9, 34. , 34.1,
|
||||
34.2, 34.3, 34.4, 34.5, 34.6, 34.7, 34.8, 34.9, 35. , 35.1, 35.2,
|
||||
35.3, 35.4, 35.5, 35.6, 35.7, 35.8, 35.9, 36. , 36.1, 36.2, 36.3,
|
||||
36.4, 36.5, 36.6, 36.7, 36.8, 36.9, 37. , 37.1, 37.2, 37.3, 37.4,
|
||||
37.5, 37.6, 37.7, 37.8, 37.9, 38. , 38.1, 38.2, 38.3, 38.4, 38.5,
|
||||
38.6, 38.7, 38.8, 38.9, 39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6,
|
||||
39.7, 39.8, 39.9, 40. , 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7,
|
||||
40.8, 40.9, 41. , 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8,
|
||||
41.9, 42. , 42.1, 42.2, 42.3, 42.4, 42.5, 42.6, 42.7, 42.8, 42.9,
|
||||
43. , 43.1, 43.2, 43.3, 43.4, 43.5, 43.6, 43.7, 43.8, 43.9, 44. ,
|
||||
44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,
|
||||
45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2,
|
||||
46.3, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3,
|
||||
47.4, 47.5, 47.6, 47.7, 47.8, 47.9, 48. , 48.1, 48.2, 48.3, 48.4,
|
||||
48.5, 48.6, 48.7, 48.8, 48.9, 49. , 49.1, 49.2, 49.3, 49.4, 49.5,
|
||||
49.6, 49.7, 49.8, 49.9, 50. ]))
|
||||
# ---
|
||||
@ -0,0 +1,141 @@
|
||||
# serializer version: 1
|
||||
# name: test_single_dipole_time_series_apsd_new_series
|
||||
APSDResult(psd_dict={'dot1': array([1.43141017e-04, 9.56103070e-04, 1.71013658e-04, 1.51108729e-03,
|
||||
1.07724951e-03, 6.99571756e-04, 6.30720766e-03, 4.50084579e-04,
|
||||
4.51029728e-05, 5.55641583e-04, 3.15751279e-03, 2.88808613e-03])}, freqs=array([0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. , 4.4, 4.8]))
|
||||
# ---
|
||||
# name: test_single_dipole_time_series_psd
|
||||
list([
|
||||
list([
|
||||
0j,
|
||||
(1.0245563439837635+0j),
|
||||
]),
|
||||
list([
|
||||
(0.4+0j),
|
||||
(-0.06997893548071246-0.04298727993617441j),
|
||||
]),
|
||||
list([
|
||||
(0.8+0j),
|
||||
(-0.039450404619039356-0.2278123801105552j),
|
||||
]),
|
||||
list([
|
||||
(1.2000000000000002+0j),
|
||||
(0.06879271540452367+0.18112796265494657j),
|
||||
]),
|
||||
list([
|
||||
(1.6+0j),
|
||||
(0.08886792286401579-0.13758873132600968j),
|
||||
]),
|
||||
list([
|
||||
(2+0j),
|
||||
(0.11176347216411753-0.08120091560477474j),
|
||||
]),
|
||||
list([
|
||||
(2.4000000000000004+0j),
|
||||
(0.2691233202491688-0.010346527463152275j),
|
||||
]),
|
||||
list([
|
||||
(2.8000000000000003+0j),
|
||||
(0.2576145129611034-0.3358195628624988j),
|
||||
]),
|
||||
list([
|
||||
(3.2+0j),
|
||||
(-0.0710407429754301+0.08850346984926659j),
|
||||
]),
|
||||
list([
|
||||
(3.6+0j),
|
||||
(-0.22596960267390404+0.010400520410955974j),
|
||||
]),
|
||||
list([
|
||||
(4+0j),
|
||||
(0.016306070833852868-0.05018492576136252j),
|
||||
]),
|
||||
list([
|
||||
(4.4+0j),
|
||||
(0.1514065333713827-0.22893435273278817j),
|
||||
]),
|
||||
list([
|
||||
(4.800000000000001+0j),
|
||||
(-0.0024668424412067763+0.17633416864658075j),
|
||||
]),
|
||||
])
|
||||
# ---
|
||||
# name: test_single_dipole_time_series_psd_new_series
|
||||
TimeSeriesResult(series_dict={'dot1': array([-0.0853797, 0.0853797, -0.0853797, -0.0853797, -0.0853797,
|
||||
0.0853797, 0.0853797, -0.0853797, 0.0853797, -0.0853797,
|
||||
0.0853797, -0.0853797, 0.0853797, -0.0853797, -0.0853797,
|
||||
-0.0853797, 0.0853797, -0.0853797, -0.0853797, 0.0853797,
|
||||
0.0853797, -0.0853797, -0.0853797, 0.0853797, 0.0853797])}, num_points=25, delta_t=0.1)
|
||||
# ---
|
||||
# name: test_two_dipole_time_series_apsd_new_series
|
||||
APSDResult(psd_dict={'dot1': array([4.02710196e-02, 4.57457789e-02, 5.37775476e-02, 1.71538975e-01,
|
||||
6.64050617e-02, 2.19812590e-02, 1.24038690e-02, 1.85105262e-02,
|
||||
5.08871725e-02, 4.12988805e-02, 4.61602125e-02, 2.92425642e-04,
|
||||
9.03014414e-03, 1.95843872e-05, 3.56775508e-03, 3.75290856e-05,
|
||||
1.74192219e-02, 4.21181893e-03, 1.58904512e-04, 6.56289154e-03,
|
||||
7.51516788e-03, 6.47104574e-04, 2.33974923e-03, 7.66894215e-04,
|
||||
2.57584150e-03, 8.72574773e-04, 5.26689844e-03, 4.21974307e-03,
|
||||
8.29932651e-04, 6.98429966e-03, 2.68044842e-03, 2.89487560e-03,
|
||||
1.84343033e-03, 2.88976521e-03, 1.23220130e-03, 2.50296253e-04,
|
||||
1.23659979e-03, 1.45996347e-03, 1.32959234e-03, 1.11989975e-03,
|
||||
3.93904037e-03, 8.07172874e-03, 9.82477322e-04, 5.77348582e-04,
|
||||
8.55728507e-04, 1.37189017e-03, 8.18999874e-04, 4.32091113e-04,
|
||||
3.63993366e-03, 1.16635078e-04]), 'dot2': array([1.56844649e-01, 1.56126016e-01, 1.85730999e-01, 5.73293874e-01,
|
||||
2.21480303e-01, 7.15829959e-02, 3.72699197e-02, 6.42246090e-02,
|
||||
1.62293347e-01, 1.40013278e-01, 1.54181161e-01, 5.95226933e-04,
|
||||
3.09772138e-02, 1.89287067e-04, 1.31537550e-02, 1.68175913e-04,
|
||||
5.65200539e-02, 1.39687483e-02, 4.66137518e-04, 2.00658617e-02,
|
||||
2.46647142e-02, 1.70799346e-03, 7.59034470e-03, 3.35916059e-03,
|
||||
7.54176383e-03, 1.92217501e-03, 1.59281721e-02, 1.38594650e-02,
|
||||
4.20932884e-03, 2.17324808e-02, 8.90924632e-03, 9.76777034e-03,
|
||||
6.63256403e-03, 8.57467451e-03, 3.08820297e-03, 7.17980397e-04,
|
||||
2.75319390e-03, 4.73466029e-03, 3.64705739e-03, 2.82026829e-03,
|
||||
1.06999569e-02, 2.32474190e-02, 3.10842186e-03, 2.05800401e-03,
|
||||
3.55445806e-03, 3.28394200e-03, 3.29915455e-03, 1.81990222e-03,
|
||||
1.29296403e-02, 2.62413383e-04])}, freqs=array([ 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2,
|
||||
2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4,
|
||||
4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. , 6.2, 6.4, 6.6,
|
||||
6.8, 7. , 7.2, 7.4, 7.6, 7.8, 8. , 8.2, 8.4, 8.6, 8.8,
|
||||
9. , 9.2, 9.4, 9.6, 9.8, 10. ]))
|
||||
# ---
|
||||
# name: test_two_dipole_time_series_psd_new_series
|
||||
TimeSeriesResult(series_dict={'dot1': array([ 0.28499068, 0.45575007, 0.45575007, 0.45575007, 0.45575007,
|
||||
-0.28499068, -0.45575007, -0.45575007, -0.45575007, -0.28499068,
|
||||
-0.45575007, -0.45575007, -0.28499068, -0.45575007, -0.28499068,
|
||||
-0.45575007, -0.28499068, -0.28499068, -0.45575007, 0.28499068,
|
||||
0.45575007, 0.28499068, 0.28499068, 0.45575007, 0.28499068,
|
||||
0.28499068, 0.45575007, 0.45575007, 0.28499068, 0.28499068,
|
||||
0.28499068, 0.28499068, 0.28499068, 0.28499068, -0.28499068,
|
||||
-0.45575007, -0.45575007, 0.28499068, 0.28499068, 0.28499068,
|
||||
0.28499068, 0.28499068, 0.28499068, 0.28499068, 0.28499068,
|
||||
0.28499068, 0.28499068, 0.45575007, 0.28499068, 0.45575007,
|
||||
-0.28499068, -0.28499068, -0.45575007, -0.45575007, -0.45575007,
|
||||
-0.45575007, -0.28499068, -0.28499068, -0.28499068, -0.45575007,
|
||||
-0.45575007, -0.28499068, -0.45575007, -0.28499068, -0.28499068,
|
||||
-0.45575007, 0.45575007, 0.28499068, 0.45575007, 0.45575007,
|
||||
0.28499068, 0.45575007, 0.45575007, 0.45575007, -0.28499068,
|
||||
-0.28499068, -0.28499068, -0.28499068, -0.28499068, -0.45575007,
|
||||
-0.28499068, -0.28499068, -0.28499068, -0.45575007, -0.45575007,
|
||||
-0.28499068, -0.28499068, -0.28499068, -0.28499068, -0.28499068,
|
||||
0.45575007, 0.45575007, 0.45575007, 0.45575007, 0.28499068,
|
||||
-0.45575007, -0.45575007, -0.28499068, -0.45575007, -0.28499068]), 'dot2': array([ 0.55234807, 0.80847957, 0.80847957, 0.80847957, 0.80847957,
|
||||
-0.55234807, -0.80847957, -0.80847957, -0.80847957, -0.55234807,
|
||||
-0.80847957, -0.80847957, -0.55234807, -0.80847957, -0.55234807,
|
||||
-0.80847957, -0.55234807, -0.55234807, -0.80847957, 0.55234807,
|
||||
0.80847957, 0.55234807, 0.55234807, 0.80847957, 0.55234807,
|
||||
0.55234807, 0.80847957, 0.80847957, 0.55234807, 0.55234807,
|
||||
0.55234807, 0.55234807, 0.55234807, 0.55234807, -0.55234807,
|
||||
-0.80847957, -0.80847957, 0.55234807, 0.55234807, 0.55234807,
|
||||
0.55234807, 0.55234807, 0.55234807, 0.55234807, 0.55234807,
|
||||
0.55234807, 0.55234807, 0.80847957, 0.55234807, 0.80847957,
|
||||
-0.55234807, -0.55234807, -0.80847957, -0.80847957, -0.80847957,
|
||||
-0.80847957, -0.55234807, -0.55234807, -0.55234807, -0.80847957,
|
||||
-0.80847957, -0.55234807, -0.80847957, -0.55234807, -0.55234807,
|
||||
-0.80847957, 0.80847957, 0.55234807, 0.80847957, 0.80847957,
|
||||
0.55234807, 0.80847957, 0.80847957, 0.80847957, -0.55234807,
|
||||
-0.55234807, -0.55234807, -0.55234807, -0.55234807, -0.80847957,
|
||||
-0.55234807, -0.55234807, -0.55234807, -0.80847957, -0.80847957,
|
||||
-0.55234807, -0.55234807, -0.55234807, -0.55234807, -0.55234807,
|
||||
0.80847957, 0.80847957, 0.80847957, 0.80847957, 0.55234807,
|
||||
-0.80847957, -0.80847957, -0.55234807, -0.80847957, -0.55234807])}, num_points=100, delta_t=0.05)
|
||||
# ---
|
||||
36
tests/dipoles/time_series_psd/test_apsd_methods.py
Normal file
36
tests/dipoles/time_series_psd/test_apsd_methods.py
Normal file
@ -0,0 +1,36 @@
|
||||
import pytest
|
||||
import numpy
|
||||
import tantri.dipoles.time_series
|
||||
|
||||
|
||||
def test_apsd_merge():
|
||||
freqs = numpy.array([0.01, 0.1, 1, 10, 100])
|
||||
dict1 = {"t1": numpy.array([1, 2, 3, 4, 5])}
|
||||
a1 = tantri.dipoles.time_series.APSDResult(dict1, freqs)
|
||||
|
||||
dict2 = {"t1": numpy.array([3, 4, 5, 6, 7])}
|
||||
a2 = tantri.dipoles.time_series.APSDResult(dict2, freqs)
|
||||
|
||||
merged = tantri.dipoles.time_series.average_apsds([a1, a2])
|
||||
|
||||
expected = tantri.dipoles.time_series.APSDResult(
|
||||
psd_dict={
|
||||
"t1": numpy.array([2, 3, 4, 5, 6]),
|
||||
},
|
||||
freqs=freqs,
|
||||
)
|
||||
|
||||
numpy.testing.assert_equal(merged.freqs, expected.freqs)
|
||||
numpy.testing.assert_equal(merged.psd_dict, expected.psd_dict)
|
||||
|
||||
|
||||
def test_apsd_merge_mismatch_freqs():
|
||||
dict = {"t1": numpy.array([1, 2, 3, 4, 5])}
|
||||
freqs1 = numpy.array([0.01, 0.1, 1, 10, 100])
|
||||
a1 = tantri.dipoles.time_series.APSDResult(dict, freqs1)
|
||||
|
||||
freqs2 = numpy.array([1, 3, 5, 7, 9])
|
||||
a2 = tantri.dipoles.time_series.APSDResult(dict, freqs2)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
tantri.dipoles.time_series.average_apsds([a1, a2])
|
||||
83
tests/dipoles/time_series_psd/test_psd_analytic_compare.py
Normal file
83
tests/dipoles/time_series_psd/test_psd_analytic_compare.py
Normal file
@ -0,0 +1,83 @@
|
||||
import numpy
|
||||
|
||||
from tantri.dipoles import (
|
||||
DipoleTO,
|
||||
DipoleTimeSeries,
|
||||
DotPosition,
|
||||
DipoleMeasurementType,
|
||||
)
|
||||
|
||||
import logging
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_multiple_apsd(snapshot):
|
||||
|
||||
dot_name = "dot1"
|
||||
num_series_to_create = 100
|
||||
num_ts_points = 1000
|
||||
delta_t = 0.01
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [DotPosition(numpy.array([0, 0, 0]), dot_name)]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
|
||||
estimated_psd = ts_gen.generate_average_apsd(num_series_to_create, num_ts_points)
|
||||
|
||||
assert estimated_psd == snapshot
|
||||
|
||||
|
||||
def test_multiple_apsd_compare_analytic():
|
||||
|
||||
dot_name = "dot1"
|
||||
num_series_to_create = 500
|
||||
num_ts_points = 10000
|
||||
delta_t = 0.001
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [DotPosition(numpy.array([0, 0, 0]), dot_name)]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
|
||||
def s_analytic_potential(f: float, dot: DotPosition, dipole: DipoleTO):
|
||||
g = dipole.w / ((numpy.pi * f) ** 2 + dipole.w**2)
|
||||
rdiff = dot.r - dipole.s
|
||||
|
||||
return 2 * g * ((rdiff.dot(dipole.p) / (numpy.linalg.norm(rdiff) ** 3)) ** 2)
|
||||
|
||||
estimated_psd = ts_gen.generate_average_apsd(num_series_to_create, num_ts_points)
|
||||
|
||||
_logger.warning(estimated_psd)
|
||||
|
||||
analytic = numpy.array(
|
||||
[s_analytic_potential(f, dots[0], d1) for f in estimated_psd.freqs]
|
||||
)
|
||||
numpy.testing.assert_allclose(
|
||||
estimated_psd.psd_dict[dot_name], analytic, rtol=1.5, atol=1e-7
|
||||
)
|
||||
176
tests/dipoles/time_series_psd/test_time_series.py
Normal file
176
tests/dipoles/time_series_psd/test_time_series.py
Normal file
@ -0,0 +1,176 @@
|
||||
"""
|
||||
Testing whatever related to APSDs who gives a shit.
|
||||
"""
|
||||
|
||||
from tantri.dipoles import (
|
||||
DipoleTO,
|
||||
DipoleTimeSeries,
|
||||
DotPosition,
|
||||
DipoleMeasurementType,
|
||||
)
|
||||
import numpy
|
||||
|
||||
import scipy.fft
|
||||
|
||||
|
||||
def test_single_dipole_time_series_psd(snapshot):
|
||||
|
||||
dot_name = "dot1"
|
||||
num_points = 25
|
||||
delta_t = 0.1
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [DotPosition(numpy.array([0, 0, 0]), dot_name)]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
# time_series = [ts_gen.transition() for i in range(25)]
|
||||
|
||||
# time_series_to_dict_list = {k: [dic[k] for dic in time_series] for k in time_series[0]}
|
||||
# fft_dict = {k: scipy.fft.rfft(v) for k, v in time_series_to_dict_list.items()}
|
||||
time_series = [ts_gen.transition()[dot_name] for i in range(num_points)]
|
||||
fft = scipy.fft.rfft(time_series)
|
||||
freqs = scipy.fft.rfftfreq(num_points, delta_t)
|
||||
|
||||
result = numpy.array([freqs, fft]).transpose().tolist()
|
||||
|
||||
assert result == snapshot
|
||||
|
||||
|
||||
def test_single_dipole_time_series_psd_new_series(snapshot):
|
||||
|
||||
dot_name = "dot1"
|
||||
num_points = 25
|
||||
delta_t = 0.1
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [DotPosition(numpy.array([0, 0, 0]), dot_name)]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
|
||||
result = ts_gen.generate_series(num_points)
|
||||
|
||||
assert result == snapshot
|
||||
|
||||
|
||||
def test_two_dipole_time_series_psd_new_series(snapshot):
|
||||
|
||||
num_points = 100
|
||||
delta_t = 0.05
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [
|
||||
DotPosition(numpy.array([0, 0, 0]), "dot1"),
|
||||
DotPosition(numpy.array([1, 0, 0]), "dot2"),
|
||||
]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
d2 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([2, 2, 1]),
|
||||
2,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1, d2],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
|
||||
result = ts_gen.generate_series(num_points)
|
||||
|
||||
assert result == snapshot
|
||||
|
||||
|
||||
def test_single_dipole_time_series_apsd_new_series(snapshot):
|
||||
|
||||
dot_name = "dot1"
|
||||
num_points = 25
|
||||
delta_t = 0.1
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [DotPosition(numpy.array([0, 0, 0]), dot_name)]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
|
||||
result = ts_gen.generate_series(num_points).get_apsds()
|
||||
|
||||
assert result == snapshot
|
||||
|
||||
|
||||
def test_two_dipole_time_series_apsd_new_series(snapshot):
|
||||
|
||||
num_points = 100
|
||||
delta_t = 0.05
|
||||
|
||||
rng = numpy.random.default_rng(1234)
|
||||
dots = [
|
||||
DotPosition(numpy.array([0, 0, 0]), "dot1"),
|
||||
DotPosition(numpy.array([1, 0, 0]), "dot2"),
|
||||
]
|
||||
|
||||
d1 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([5, 3, 2]),
|
||||
15,
|
||||
)
|
||||
|
||||
d2 = DipoleTO(
|
||||
numpy.array([0, 0, 10]),
|
||||
numpy.array([2, 2, 1]),
|
||||
2,
|
||||
)
|
||||
|
||||
ts_gen = DipoleTimeSeries(
|
||||
[d1, d2],
|
||||
dots,
|
||||
DipoleMeasurementType.ELECTRIC_POTENTIAL,
|
||||
delta_t,
|
||||
rng_to_use=rng,
|
||||
)
|
||||
|
||||
result = ts_gen.generate_series(num_points).get_apsds()
|
||||
|
||||
assert result == snapshot
|
||||
28
tests/test_util.py
Normal file
28
tests/test_util.py
Normal file
@ -0,0 +1,28 @@
|
||||
import typing
|
||||
import tantri.util
|
||||
import numpy
|
||||
|
||||
|
||||
def test_mean_dict():
|
||||
dict1 = {
|
||||
"squares": numpy.array([1, 4, 9, 16]),
|
||||
"linear": numpy.array([1, 2, 3, 4, 5]),
|
||||
}
|
||||
dict2 = {
|
||||
"squares": numpy.array([2, 8, 18, 32]),
|
||||
"linear": numpy.array([2, 4, 6, 8, 10]),
|
||||
}
|
||||
|
||||
def mean(list_of_arrays: typing.Sequence[numpy.ndarray]) -> numpy.ndarray:
|
||||
return numpy.mean(numpy.array(list_of_arrays), axis=0)
|
||||
|
||||
result = tantri.util.dict_reduce([dict1, dict2], mean)
|
||||
|
||||
expected = {
|
||||
"squares": 1.5 * numpy.array([1, 4, 9, 16]),
|
||||
"linear": 1.5 * numpy.array([1, 2, 3, 4, 5]),
|
||||
}
|
||||
|
||||
numpy.testing.assert_equal(
|
||||
result, expected, "The reduced dictionary should have matched the expected"
|
||||
)
|
||||
Loading…
x
Reference in New Issue
Block a user