diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 800f23d..486c0be 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -44,8 +44,8 @@ concurrency: # Set the environment variables to be used in all jobs defined in this workflow env: CI_BRANCH: ${{ github.head_ref || github.ref_name }} - PY_VERSIONS: '3.11 3.13' - PIXI_ENVS: 'py-311-env py-313-env' + PY_VERSIONS: '3.12 3.14' + PIXI_ENVS: 'py-312-env py-314-env' jobs: # Job 1: Set up environment variables @@ -149,7 +149,7 @@ jobs: echo echo "🔹🔸🔹🔸🔹 Python: $py_ver 🔹🔸🔹🔸🔹" - env="py-$(echo $py_ver | tr -d .)-env" # Converts 3.11 -> py-311-env + env="py-$(echo $py_ver | tr -d .)-env" # Converts 3.12 -> py-312-env echo "Building wheel in environment: $env" pixi run --environment $env dist-build diff --git a/docs/docs/api-reference/index.md b/docs/docs/api-reference/index.md index c4a12ab..f730a55 100644 --- a/docs/docs/api-reference/index.md +++ b/docs/docs/api-reference/index.md @@ -7,9 +7,20 @@ icon: material/code-braces-box This section contains the reference detailing the functions and modules available in EasyImaging. -- [Measurement](measurement/measurement.md) - Loads and inspects +- [measurement](measurement/measurement.md) - Loads and inspects measurement data. -- [regions_of_interest](regions_of_interest/index.md) - This module - contains all types of Regions Of Interests (ROIs) available: +- [regions_of_interest](regions_of_interest/index.md) - The module + containing all types of Regions Of Interests (ROIs) available: - [RectangleROI](regions_of_interest/rectangle_roi.md) - A rectangular ROI. The most basic and common type of ROI +- [sample_model](sample_model/index.md) - The module containing the + classes and functions related to creating a model of the sample: + - [Lattice](sample_model/lattice.md) - The crystallographic unit cell + of the sample model. + - [AtomSite](sample_model/atom_site.md) - The atomic sites of a + crystallographic lattice. + - [Atoms](sample_model/atoms.md) - The atomic species occupying an + atomic site. + - [crystals](sample_model/crystals.md) - A module containing + convenience functions to easily create and occupy common + crystallographic lattices. diff --git a/docs/docs/api-reference/sample_model/atom_site.md b/docs/docs/api-reference/sample_model/atom_site.md new file mode 100644 index 0000000..5d54a1e --- /dev/null +++ b/docs/docs/api-reference/sample_model/atom_site.md @@ -0,0 +1 @@ +::: easyimaging.sample_model.AtomSite diff --git a/docs/docs/api-reference/sample_model/atoms.md b/docs/docs/api-reference/sample_model/atoms.md new file mode 100644 index 0000000..1d208eb --- /dev/null +++ b/docs/docs/api-reference/sample_model/atoms.md @@ -0,0 +1,23 @@ +::: easyimaging.sample_model.atoms.Atom + + options: + heading_level: 2 + +::: easyimaging.sample_model.atoms.Atoms + + options: + heading_level: 2 + +The full list of pre-defined +[`Atom`][easyimaging.sample_model.atoms.Atom] instances in the +[`Atoms`][easyimaging.sample_model.atoms.Atoms] enum is listed below. +
Isotopes with `nan` values indicate the natually occuring abundant +isotope. + +```python exec="true" +from easyimaging.sample_model import Atoms +import pandas as pd +names = [f'**{atom.name}**' for atom in list(Atoms)] +pretty_table = pd.DataFrame(list(Atoms), index=names).drop('enum_id', axis=1) +print(pretty_table.rename_axis('name').to_markdown()) +``` diff --git a/docs/docs/api-reference/sample_model/crystals.md b/docs/docs/api-reference/sample_model/crystals.md new file mode 100644 index 0000000..8473cf2 --- /dev/null +++ b/docs/docs/api-reference/sample_model/crystals.md @@ -0,0 +1,4 @@ +::: easyimaging.sample_model.crystals + + options: + group_by_category: false diff --git a/docs/docs/api-reference/sample_model/index.md b/docs/docs/api-reference/sample_model/index.md new file mode 100644 index 0000000..a412de7 --- /dev/null +++ b/docs/docs/api-reference/sample_model/index.md @@ -0,0 +1,16 @@ +--- +icon: material/puzzle +--- + +# :fontawesome-solid-puzzle-piece: Sample Model + +This module contains all the objects which makes up the model of the +physical sample. + +- [Lattice](lattice.md) - The crystallographic unit cell of the sample + model. +- [AtomSite](atom_site.md) - The atomic sites of a crystallographic + lattice. +- [Atoms](atoms.md) - The atomic species occupying an atomic site. +- [crystals](crystals.md) - A module containing convenience functions to + easily create and occupy common crystallographic lattices. diff --git a/docs/docs/api-reference/sample_model/lattice.md b/docs/docs/api-reference/sample_model/lattice.md new file mode 100644 index 0000000..8e6ba90 --- /dev/null +++ b/docs/docs/api-reference/sample_model/lattice.md @@ -0,0 +1 @@ +::: easyimaging.sample_model.Lattice diff --git a/docs/docs/assets/stylesheets/extra.css b/docs/docs/assets/stylesheets/extra.css index 3e9d986..5bc1db1 100644 --- a/docs/docs/assets/stylesheets/extra.css +++ b/docs/docs/assets/stylesheets/extra.css @@ -364,36 +364,3 @@ h1 .label-experiment { .label-experiment:hover { color: white !important; } - -/* - * FontAwesome 5 icon fix for Jupyter widget toolbars (ipympl / plopp) - * This is a temporary workaround until this is fixed on Plopps side - * - * Comment on the issue from Claude: - * embed-amd.js injects FA5 SVG-framework CSS inline, which intentionally sets - * content:"" on all icon classes (expecting FA5 JS to replace elements with SVGs). - * Since FA5 JS conflicts with RequireJS (breaking widget rendering), we instead: - * 1. Load FA5 webfonts via in overrides/main.html so the font file is available. - * 2. Use !important below to override the content:"" with the correct FA5 Solid glyphs. - */ -.fa::before { - font-family: "Font Awesome 5 Free" !important; - font-weight: 900 !important; -} -.fa-home::before { content: "\f015" !important; } -.fa-save::before { content: "\f0c7" !important; } -.fa-camera::before { content: "\f030" !important; } -.fa-cube::before { content: "\f1b2" !important; } -.fa-arrows-alt::before { content: "\f0b2" !important; } -.fa-arrows::before { content: "\f0b2" !important; } /* FA5 equivalent: arrows-alt */ -.fa-times::before { content: "\f00d" !important; } -.fa-plus::before { content: "\f067" !important; } -.fa-minus::before { content: "\f068" !important; } -.fa-search-plus::before { content: "\f00e" !important; } -.fa-crosshairs::before { content: "\f05b" !important; } -.fa-arrow-left::before { content: "\f060" !important; } -.fa-arrow-right::before { content: "\f061" !important; } -.fa-expand::before { content: "\f065" !important; } -.fa-compress::before { content: "\f066" !important; } -/* fa-arrows-v was removed in FA5; map to arrows-alt-v (\f338) */ -.fa-arrows-v::before { content: "\f338" !important; } diff --git a/docs/docs/assets/stylesheets/scipp_style.css b/docs/docs/assets/stylesheets/scipp_style.css deleted file mode 100644 index 2727d42..0000000 --- a/docs/docs/assets/stylesheets/scipp_style.css +++ /dev/null @@ -1,495 +0,0 @@ -/* CSS stylesheet for displaying scipp objects in jupyterlab. -* Original source from -* https://github.com/jsignell/xarray/blob/1d960933ab252e0f79f7e050e6c9261d55568057/xarray/static/css/style.css -*/ - -.sc-root { - --sc-background-color0: var(--jp-layout-color0, #fff) !important; - --sc-background-color1: var(--jp-layout-color1, #fcfcfc) !important; - --sc-background-color2: var(--jp-layout-color2, #efefef) !important; - --sc-inverse-background-color0: var(--jp-inverse-layout-color4, #111) !important; - --sc-font-color0: var(--jp-content-font-color0, #000) !important; - --sc-font-color1: var(--jp-content-font-color1, #555) !important; - --sc-font-color2: var(--jp-content-font-color2, #888) !important; - --sc-font-color3: var(--jp-content-font-color3, #ccc) !important; -} - -body.vscode-dark .sc-root { - --sc-font-color0: rgba(255, 255, 255, 1) !important; - --sc-font-color1: rgba(255, 255, 255, 0.70) !important; - --sc-font-color2: rgba(255, 255, 255, 0.54) !important; - --sc-font-color3: rgba(255, 255, 255, 0.38) !important; - --sc-border-color: #1F1F1F !important; - --sc-disabled-color: #515151 !important; - --sc-background-color0: #111111 !important; - --sc-background-color1: #111111 !important; - --sc-background-color2: #313131 !important; -} - -.sc-wrap { - font-size: 14px !important; - min-width: 300px !important; - max-width: 800px !important; -} - -/* The CSS styling for the inline attributes table */ -.sc-var-attrs .sc-wrap{ - padding-left: 3em !important; -} - -.sc-header { - padding-top: 6px !important; - padding-bottom: 6px !important; - margin-bottom: 4px !important; - border-bottom: solid 1px #ddd !important; -} - -.sc-header > div, -.sc-header > ul { - display: inline !important; - margin-top: 0 !important; - margin-bottom: 0 !important; -} - -.sc-obj-type, -.sc-array-name { - margin-left: 2px !important; - margin-right: 10px !important; -} - -.sc-obj-type { - color: var(--sc-font-color1) !important; -} - -.sc-underlying-size { - color: var(--sc-font-color2) !important; -} - -/* Also override settings of reveal.js as introduced when - generating slides using the rise package. - There are selectors for `.reveal ul` and `.reveal ul ul` - which take precedence over ours and mess up the formatting.*/ -.sc-sections, .reveal .sc-sections { - padding-left: 0 !important; - display: grid !important; - grid-template-columns: 150px auto auto auto 1fr 20px 20px !important; -} - -.sc-section-item { - display: contents !important; -} - -.sc-section-item input { - display: none !important; -} - -.sc-section-item input:enabled + label { - cursor: pointer !important; - color: var(--sc-font-color1) !important; -} - -.sc-section-item input:enabled + label:hover { - color: var(--sc-font-color0) !important; -} - -.sc-section-summary { - grid-column: 1 !important; - font-weight: 500 !important; -} - -.sc-section-summary > span { - display: inline-block !important; - padding-left: 0.5em !important; -} - -.sc-section-summary-in:disabled + label { - color: var(--sc-font-color1) !important; -} - -.sc-section-summary-in + label:before { - display: inline-block !important; - content: '►' !important; - font-size: 11px !important; - width: 15px !important; - text-align: center !important; -} - -.sc-section-summary-in:disabled + label:before { - display: none !important; -} - -.sc-section-summary-in:checked + label:before { - content: '▼' !important; -} - -.sc-section-summary-in:checked + label > span { - display: none !important; -} - -.sc-section-summary, -.sc-section-inline-details { - padding-top: 4px !important; - padding-bottom: 4px !important; -} - -.sc-section-inline-details { - grid-column: 2 / 6 !important; -} - -.sc-section-details { - display: none !important; - grid-column: 1 / -1 !important; - margin-bottom: 5px !important; -} - -.sc-section-summary-in:checked ~ .sc-section-details { - display: contents !important; -} - -.sc-array-wrap { - grid-column: 1 / -1 !important; - display: grid !important; - grid-template-columns: 20px auto !important; -} - -.sc-array-wrap > label { - grid-column: 1 !important; - vertical-align: top !important; -} - -.sc-preview { - color: var(--sc-font-color2) !important; -} - -.sc-array-preview, -.sc-array-data { - padding: 0 5px !important; - grid-column: 2 !important; -} - -.sc-array-data, -.sc-array-in:checked ~ .sc-array-preview { - display: none !important; -} - -.sc-array-in:checked ~ .sc-array-data, -.sc-array-preview { - display: inline-block !important; -} - -.sc-dim-list { - display: inline-block !important; - list-style: none !important; - padding: 0 !important; - margin: 0 !important; -} - -.sc-dim-list li { - display: inline-block !important; - padding: 0 !important; - margin: 0 !important; -} - -.sc-dim-list:before { - content: '(' !important; -} - -.sc-dim-list:after { - content: ')' !important; -} - -.sc-dim-list li:not(:last-child):after { - content: ',' !important; - padding-right: 5px !important; -} - -/* -This selector is for avoiding unexpected extra padding -caused by tag wrapping around the dollar-sign in the Jpyter notebook. -*/ -.sc-dim-list li span, -.sc-standalone-var-name > span span, -.sc-var-name > span span { - padding: 0 !important; -} - -.sc-aligned { - font-weight: bold !important; -} - -.sc-var-list, -.sc-var-item, -.reveal .sc-var-list, -.reveal .sc-var-item { - display: contents !important; -} - -.sc-var-item > div, -.sc-var-item label, -.sc-var-item > .sc-var-name span { - background-color: var(--sc-background-color1) !important; - margin-bottom: 0 !important; -} - -.sc-var-item > .sc-var-name:hover span { - padding-right: 5px !important; -} - -.sc-var-list > li:nth-child(odd) > div, -.sc-var-list > li:nth-child(odd) > label, -.sc-var-list > li:nth-child(odd) > .sc-var-name span { - background-color: var(--sc-background-color2) !important; 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- overflow: hidden !important; - text-overflow: ellipsis !important; - padding-right: 10px !important; -} - -.sc-var-name:hover, -.sc-var-dims:hover, -.sc-var-dtype:hover, -.sc-var-unit:hover, -.sc-attrs dt:hover { - overflow: visible !important; - width: auto !important; - z-index: 1 !important; -} - -.sc-var-attrs { - display: block !important; -} -.sc-var-data, .reveal .sc-var-data { - display: none !important; -} -.sc-var-attrs, -.sc-var-data { - background-color: var(--sc-background-color0) !important; - padding-bottom: 5px !important; -} - -.sc-var-attrs-in:checked ~ .sc-var-attrs { - display: none !important; -} -.sc-var-data-in:checked ~ .sc-var-data { - display: block !important; -} - -.sc-var-data > table { - float: right !important; -} - -.sc-var-name span, -.sc-var-data { - padding-left: 25px !important; -} - -.sc-var-attrs, -.sc-var-data { - grid-column: 1 / -1 !important; -} - -dl.sc-attrs { - padding: 0 !important; - margin: 0 !important; - display: grid !important; - grid-template-columns: 125px auto !important; -} - -.sc-attrs dt, dd { - padding: 0 !important; - margin: 0 !important; - float: left !important; - padding-right: 10px !important; - width: auto !important; -} - -.sc-attrs dt { - font-weight: normal !important; - grid-column: 1 !important; -} - -.sc-attrs dt:hover span { - display: inline-block !important; - padding-right: 10px !important; -} - -.sc-attrs dd { - grid-column: 2 !important; - white-space: pre-wrap !important; - word-break: break-all !important; -} - -.sc-icon-database, -.sc-icon-file-text2 { - display: inline-block !important; - vertical-align: middle !important; - width: 1em !important; - height: 1.5em !important; - stroke-width: 0 !important; - stroke: currentColor !important; - fill: currentColor !important; -} - -label.sc-hide-icon svg{ - opacity: 0; -} - -.sc-standalone-var-name{ - grid-column: 1/3 !important; -} - -.sc-standalone-var-name span{ - padding-left: 25px !important; - padding-right:10px !important; -} - -.sc-title { - font-weight:bold !important; - font-size:1.5em !important; -} - -.sc-subtitle { - font-weight:normal !important; - font-style:italic !important; - text-align:left !important; - font-size:1.2em !important; - padding: 1px !important; -} - -.sc-label { - fill: var(--sc-font-color0, #444444) !important; - text-anchor: middle !important; -} - -.sc-name { - fill: var(--sc-font-color0, #111111) !important; -} - -.sc-inset-line { - stroke: var(--sc-font-color1) !important; - stroke-width: 0.05 !important; - stroke-dasharray: 0.2, 0.2 !important; -} - -.sc-log-wrap { - height: 25ex; - resize: vertical; - overflow-y: scroll; - display: flex; - flex-direction: column-reverse; - border: 1px solid !important; - border-color: var(--jp-border-color2) !important; - background-color: var(--sc-background-color1) !important; -} - -div.sc-log { - line-height: 2.5ex !important; -} - -table.sc-log { - table-layout: auto !important; - border-collapse: collapse !important; -} - -tr.sc-log:nth-child(even) { - background-color: var(--sc-background-color0) !important; -} - -tr.sc-log > td { - vertical-align: top !important; - padding-bottom: 0.5ex !important; -} - -.sc-log-time-stamp { - min-width: 22ch !important; - font-family: var(--jp-code-font-family) !important; - color: var(--sc-font-color2) !important; -} - -.sc-log-level { - min-width: 10ch !important; -} - -tr.sc-log-debug td.sc-log-level { - color: var(--jp-accent-color1) !important; -} - -tr.sc-log-info td.sc-log-level { - color: var(--jp-info-color1) !important; -} - -tr.sc-log-warning td.sc-log-level { - color: var(--jp-warn-color1) !important; -} - -tr.sc-log-error td.sc-log-level { - font-weight: bold !important; - color: var(--jp-error-color2) !important; -} - -tr.sc-log-critical td.sc-log-level { - font-weight: bold !important; - color: var(--sc-background-color0) !important; - background-color: var(--jp-error-color1) !important; -} - -.sc-log-message { - white-space: pre-wrap !important; - width: 100% !important; -} - -.sc-log-html-payload { - white-space: normal !important; -} - -.sc-log-name { - padding-right: 0.5em !important; - text-align: right !important; - white-space: pre-wrap !important; - color: var(--sc-font-color3) !important; -} diff --git a/docs/docs/tutorials/measurement.ipynb b/docs/docs/tutorials/measurement.ipynb index d923b87..aad2765 100644 --- a/docs/docs/tutorials/measurement.ipynb +++ b/docs/docs/tutorials/measurement.ipynb @@ -13,7 +13,14 @@ "cell_type": "code", "execution_count": 1, "id": "bfb22357", - "metadata": {}, + "metadata": { + "deletable": true, + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "outputs": [], "source": [ "import scipp as sc\n", @@ -205,7 +212,7 @@ "\n", "\n", "\n", - "
scipp.Variable (324 Bytes)
" @@ -238,7 +245,7 @@ "\n", "\n", "\n", - "
scipp.Variable (324 Bytes)
" @@ -271,7 +278,7 @@ "\n", "\n", "\n", - "
scipp.Variable (11.98 KB)
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" \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1579,7 +1586,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1614,7 +1621,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1654,7 +1661,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1690,7 +1697,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1703,7 +1710,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1716,7 +1723,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1812,12 +1819,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1831,7 +1838,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1845,7 +1852,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1859,7 +1866,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1872,7 +1879,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1885,7 +1892,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1898,7 +1905,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2312,18 +2319,18 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + "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\" id=\"image6636259edd\" transform=\"scale(1 -1) translate(0 -221.76)\" x=\"365.76\" y=\"-22.32\" width=\"12.24\" height=\"221.76\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2348,7 +2355,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2391,7 +2398,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2407,7 +2414,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2435,7 +2442,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2467,7 +2474,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2483,7 +2490,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2499,7 +2506,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2515,7 +2522,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2584,7 +2591,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2642,11 +2649,11 @@ " \n", " \n", " \n", - " 2026-07-02T14:02:51.032981\n", + " 2026-10-20T14:25:32.721580\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.11.0, https://matplotlib.org/\n", + " Matplotlib v3.11.2, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -2673,20 +2680,20 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + "iVBORw0KGgoAAAANSUhEUgAAAaoAAAE0CAYAAAB943fFAAAGAUlEQVR4nO3XS4qdVRSA0XOtW7GSWDEvE1EMItixYVNsCjYchCNwEHacglNwBuIshAh2tBGEUiFQVMpHnphbJToAsZePzVoj+Deb/3znbD779tPzNcw3d99f01y8/mRNdPPw0Zrm/unhmuadWycv+hP4nz5/++s1zUsv+gMA4L8IFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBp2+9O3lzTXLu7XdPsDq6siZ78drimef10t6Y5vn1nTXThj/M1zckXr6xpvKgASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASBMqANKECoA0oQIgTagASNuer3kOHsybanM+b6Z/DZzr2ZW9Nc3Z/mZNNPG/+vLo4zWNFxUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQJpQAZAmVACkCRUAaUIFQNr29OGlNc3enXn9vXpvtyba7W/WNGd7a5zt4/M10cRdPT+bd/7NmwiAUYQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0oQKgDShAiBNqABIEyoA0rbv3b6/pjk6PlzTPL45806x/+h8TfPs+mZNc/F43p7+sXt53q5+PXl1TTPz9ANgDKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIE2oAEgTKgDShAqANKECIG17dr5Z0/x1ed5Mm935mujpjXl3pYOTebva7a+RLj44W9O8+9bPa5p5pwQAowgVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApG1/+fPqmub55TXO9uFmTXTl6Pma5vFre2uaS8e7NdHuwry7+kfXf1zTzNsSAKMIFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBpQgVAmlABkCZUAKQJFQBp22sHT9Y01z45WtO8cfn3NdEPp7fWNDcuPFvTfHjzpzXRV99/sKa593TeP+VFBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBUCaUAGQJlQApAkVAGlCBcAq+xtZDV9c67j1egAAAABJRU5ErkJggg==\" id=\"image96fd009138\" transform=\"scale(1 -1) translate(0 -221.76)\" x=\"43.2\" y=\"-22.358906\" width=\"306.72\" height=\"221.76\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2739,7 +2746,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2774,7 +2781,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2814,7 +2821,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2850,7 +2857,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2863,7 +2870,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2876,7 +2883,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2972,12 +2979,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2991,7 +2998,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3005,7 +3012,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3019,7 +3026,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3032,7 +3039,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3045,7 +3052,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3058,7 +3065,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3472,18 +3479,18 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", + "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\" id=\"imageea9adf69f4\" transform=\"scale(1 -1) translate(0 -221.76)\" x=\"365.76\" y=\"-22.32\" width=\"12.24\" height=\"221.76\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3507,12 +3514,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3522,12 +3529,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3537,12 +3544,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3552,12 +3559,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3766,12 +3773,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb7831d570014313b2eb71b1052aa4a6", + "model_id": "262c43a7c6364e97bf74084222cf4448", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + "WidgetFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', layout=…" ] }, "execution_count": 17, @@ -3820,9 +3827,9 @@ "\n", "\n", "\n", - "
scipp.DataArray (36.84 KB)
    • t: 1500
    • Ltotal
      ()
      int64
      m
      61
      Values:
      array(61)
    • c
      ()
      string
      intensities
      Values:
      'intensities'
    • tof
      (t [bin-edge])
      float64
      s
      0.022, 0.022, ..., 0.092, 0.093
      Values:
      array([0.02158728, 0.02163456, 0.02168185, ..., 0.09242233, 0.09246961,\n",
-       "       0.0925169 ], shape=(1501,))
    • wavelength
      (t [bin-edge])
      float64
      Å
      1.4, 1.403, ..., 5.997, 6.0
      Values:
      array([1.4       , 1.40306667, 1.40613333, ..., 5.99386667, 5.99693333,\n",
-       "       6.        ], shape=(1501,))
    • (t)
      float32
      𝟙
      0.5531428, 0.56375575, ..., 0.63517606, 0.63194895
      σ = 0.002992472, 0.003154994, ..., 0.006249289, 0.006158047
      Values:
      array([0.5531428 , 0.56375575, 0.5592331 , ..., 0.6236488 , 0.63517606,\n",
+       "
      scipp.DataArray (36.84 KB)
        • t: 1500
        • Ltotal
          ()
          int64
          m
          61
          Values:
          array(61)
        • c
          ()
          string
          intensities
          Values:
          'intensities'
        • tof
          (t [bin-edge])
          float64
          s
          0.022, 0.022, ..., 0.092, 0.093
          Values:
          array([0.02158728, 0.02163456, 0.02168185, ..., 0.09242233, 0.09246961,\n",
+       "       0.0925169 ], shape=(1501,))
        • wavelength
          (t [bin-edge])
          float64
          Å
          1.4, 1.403, ..., 5.997, 6.0
          Values:
          array([1.4       , 1.40306667, 1.40613333, ..., 5.99386667, 5.99693333,\n",
+       "       6.        ], shape=(1501,))
        • (t)
          float32
          𝟙
          0.5531428, 0.56375575, ..., 0.63517606, 0.63194895
          σ = 0.002992472, 0.003154994, ..., 0.006249289, 0.006158047
          Values:
          array([0.5531428 , 0.56375575, 0.5592331 , ..., 0.6236488 , 0.63517606,\n",
        "       0.63194895], shape=(1500,), dtype=float32)

          Variances (σ²):
          array([8.9548885e-06, 9.9539866e-06, 9.6888207e-06, ..., 3.6184254e-05,\n",
        "       3.9053615e-05, 3.7921542e-05], shape=(1500,), dtype=float32)
    " ], @@ -3867,12 +3874,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f619e516860447a1ac3bef249886d9e1", + "model_id": "5168a6469c06433c9f36d8000ee6be85", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', la…" + "WidgetFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='home', layout=…" ] }, "execution_count": 19, @@ -3903,12 +3910,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3299198e2104b019fe2b4367512b155", + "model_id": "6cb9d1b133874b13b1da1044bd57424b", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Box(children=(InteractiveFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(i…" + "Box(children=(WidgetFigure(children=(HBar(), HBar(children=(VBar(children=(Toolbar(children=(ButtonTool(icon='…" ] }, "execution_count": 20, @@ -4001,9 +4008,9 @@ "\n", "\n", "\n", - "
    scipp.DataArray (36.84 KB)
      • t: 1500
      • Ltotal
        ()
        int64
        m
        61
        Values:
        array(61)
      • c
        ()
        string
        intensities
        Values:
        'intensities'
      • tof
        (t [bin-edge])
        float64
        s
        0.022, 0.022, ..., 0.092, 0.093
        Values:
        array([0.02158728, 0.02163456, 0.02168185, ..., 0.09242233, 0.09246961,\n",
-       "       0.0925169 ], shape=(1501,))
      • wavelength
        (t [bin-edge])
        float64
        Å
        1.4, 1.403, ..., 5.997, 6.0
        Values:
        array([1.4       , 1.40306667, 1.40613333, ..., 5.99386667, 5.99693333,\n",
-       "       6.        ], shape=(1501,))
      • (t)
        float32
        𝟙
        0.36840674, 0.3425866, ..., 0.46642005, 0.45523798
        σ = 0.0058153444, 0.0056076236, ..., 0.013006445, 0.012870439
        Values:
        array([0.36840674, 0.3425866 , 0.35694158, ..., 0.43976402, 0.46642005,\n",
+       "
        scipp.DataArray (36.84 KB)
          • t: 1500
          • Ltotal
            ()
            int64
            m
            61
            Values:
            array(61)
          • c
            ()
            string
            intensities
            Values:
            'intensities'
          • tof
            (t [bin-edge])
            float64
            s
            0.022, 0.022, ..., 0.092, 0.093
            Values:
            array([0.02158728, 0.02163456, 0.02168185, ..., 0.09242233, 0.09246961,\n",
+       "       0.0925169 ], shape=(1501,))
          • wavelength
            (t [bin-edge])
            float64
            Å
            1.4, 1.403, ..., 5.997, 6.0
            Values:
            array([1.4       , 1.40306667, 1.40613333, ..., 5.99386667, 5.99693333,\n",
+       "       6.        ], shape=(1501,))
          • (t)
            float32
            𝟙
            0.36840674, 0.3425866, ..., 0.46642005, 0.45523798
            σ = 0.0058153444, 0.0056076236, ..., 0.013006445, 0.012870439
            Values:
            array([0.36840674, 0.3425866 , 0.35694158, ..., 0.43976402, 0.46642005,\n",
        "       0.45523798], shape=(1500,), dtype=float32)

            Variances (σ²):
            array([3.3818233e-05, 3.1445445e-05, 3.3600576e-05, ..., 1.4671838e-04,\n",
        "       1.6916762e-04, 1.6564821e-04], shape=(1500,), dtype=float32)
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", 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", 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", 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RpKeXZBWR0SIAESIAESIIH0EaChlD6WzIkESMCLBKgzCZAACcQgQEMpBhwGkQAJkAAJkAAJFDYBGkqFff29WHvqTAIkQAIkQAJZI0BDKWuoWRAJkAAJkAAJkIDXCGTeUPIaEepLAiRAAiRAAiRAAvsI0FDaB4IHEiABEiABEnBCgHEKi0BKhtKyZcvk73//u3Ts2FFOPfVUGTJkiKxfvz4uwf/9739y5plnynHHHSd9+/aVhQsXxk3DCCRAAiRAAiRAAiSQbQIpGUodOnSQqlWryrBhw+TWW2+V9957Tzp37iw7duywrcfEiRPljDPOkBNPPFEeeugh2bx5s7Rv317Wrl1rm4YBJEACJJA8AaYkARIggeQJpGQozZ49W2677TY55ZRTpFu3bvLKK6/I9OnT5csvv7TV6K677pKePXsaPVEnn3yykWbnzp3y+OOP26ZhAAmQAAmQAAmQAAnkgkBKhlKFChUidC4uLjbO9+zZYxzNf7Zt2yZfffWV9OjRIxRUWloqXbt2lY8//jjkR0dhE2Dte7nTtQAAEABJREFUSYAESIAESMAtBFIylMyVGDp0qDRo0EDatWtnDjLOly5dKjCi6tWrZ5yrPzhftGiROo06Yihv48aNoktUJHqQAAmQAAmQAAmQQJoJpMFQKtfo/vvvl7FjxxpDaYFAoNzT9LesrMzwQS+S4dj3Bz1Tu3bt2ncWfRg+fLhUq1YtJI0aNYqORB8SIAESIAESIAESSDOBtBhKI0aMkDvvvFPeeustwbwjOx1r1aplBK1Zs8Y4qj84r127tjqNOt5+++2yYcOGkMTqfYpKTA8SIAESIAESSIQA45KARiBlQ2nkyJFy8803y5tvvhkx90grI+SEMdS4cWOZNm1ayA+OqVOnSuvWreG0FPQ4YXWdLpYR6UkCJEACJEACJEACaSSQkqE0atQoufHGGw0jCUv+rfT6xz/+IWeffXYo6Oqrr5Znn31W5s2bZ/hhpRxWz1155ZXGOf+QAAmQQIIEGJ0ESIAEMkYgaUNp06ZNcu211wp6e7CHUsuWLUUJ5iopjZcsWSK//PKLOpVbbrlFzjnnHGnRooXUr19frrnmGnn66adj9iiFEtNBAiRAAiRAAiRAAlkkkLShVLlyZfnxxx/liy++kDFjxkQIlvurOmC37vHjx6tTwRYCTz31lKxYsUI++eQTWblypVxxxRWhcDoKgACrSAIkQAIkQAIeIZC0oeT3+0M9SKonSR1r1KgRqj56jQ499NDQuXJUr15d4I8eKeXHIwmQAAmQAAmQAAm4iYDfgTKMQgIkQAIkQAIkQAIFSYCGUkFedlaaBEiABAqZAOtOAs4J0FByzooxSYAESIAESIAECowADaUCu+CsLgl4kQB1JgESIIFcEfAPGjRIBlkIvsmWK6VYLgmQAAmQAAmQAAm4gYAfGz+a5dFHHzU+XusGBamDFwlQZxIgARIgARLIDwL+CRMmiFmw9D8/qsdakAAJkAAJkAAJkEDyBIw5Subk+KxIUVGR2ZvnJEACJEACJEACJFBQBCwNpV69eonP5ysoEKwsCZAACZBA3hBgRUggbQQMQ+n555+XZs2aSY8ePWTZsmVpy5wZkQAJkAAJkAAJkICXCfhXrVplfJj2oosuEsxNwgo4L1eIupMACXiQAFUmARIgAZcS8H/44Ydy0kknybBhw+TZZ5+ViRMnulRVqkUCJEACJEACJEAC2SXgh2HUpUsXo9TKlSvLzp07DTf/kEAMAgwiARIgARIggYIg4H/99dflrLPOMir79ddfS9u2bQ03/5AACZAACZAACZBAYRCwr6V/+PDhxkRuRMFGk4MHD4aTQgIkQAIkQAIkQAIFT8B//fXXhyC8/fbb0rFjx9A5HSRAAiRAAiTgRgLUiQSyRYDbA2SLNMshARIgARIgARLwHAFuD+C5S0aFScCLBKgzCZAACXiTALcH8OZ1o9YkQAIkQAIkQAJZIMDtAbIA2YtFUGcSIAESIAESIAERbg/Au4AESIAESIAESCDfCSRdP24PkDQ6JiQBEiABEiABEsh3AtweIN+vMOtHAiRAAl4kQJ1JwCUE/HPnznWJKlSDBEiABEiABEiABNxFwH/EEUdIWVmZoVXv3r1l2rRphpt/SIAESCABAoxKAiRAAnlJwF9UVBSqWN++fWXYsGGhczpIgARIgARIgARIoJAJ+H0+X6j+J5xwgnz88cehczrymACrRgIkQAIkQAIkEJdAhKG0bds22bt3b9xEjEACJEACJEACJEACbiKQKV0iDKVHHnlE2rZtm6mymC8JkAAJkAAJkAAJeIqAf8+ePfLAAw9I586d5f777+ccJU9dPipLAiRAAl4lQL1JwBsE/AcccICgJ6m0tFSmTJkiHTt29Ibm1JIESIAESIAESIAEMkzAv3jxYlm2bJm8//770r59+wwXx+xJgAS8SoB6kwAJkEAhEvAXYqVZZxIgARIgARIgARJwQsDfpEkTaWKS3bt3O0nLOK4mQOVIgARIgARIgARSJeAfPHiwmMXvZ0dTqmCZngRIgARIgARIII0EcpSVv3///mIWny+8CWWO9GKxJEACJEACJEACJJBzAuw6yvkloAIkQAIkkJcEWCkSyAsC/pKSEikuLo4QzlHKi2vLSpAACZAACZAACaRIwD9+/HiZMGFChHCOUopUmZwEvEiAOpMACZAACUQR8Hfv3l3M4vNxjlIUKXqQAAmQAAmQAAkUHAHOUfLuJafmJEACJEACJEACGSZAQynDgJk9CZAACZAACZCAEwLujONv0aKFtGzZMkI4mdudF4takQAJkAAJkAAJZJeA/8orr4zaR4mTubN7EVgaCZAACXiRAHUmgUIg4B80aJAMMonPx8nchXDxWUcSIAESIAESIIHYBPw7duyQ7du3x44VJ3Tu3Lkybtw4WbVqVZyYIj/++KMRF/GVTJo0KW46RiABEkiVANOTAAmQAAkkSsBfqVIlgRx88MHyxBNPyN69ex3n8dlnn0mnTp2M7QXOO+88mTFjRty0Tz75pFxzzTUyevTokLz99ttx0zECCZAACZAACZAACWSbgP+dd94RbDp56aWXyq233ir/+te/HOuAHqQ777xTvvrqK8dpELFt27YRvUqPPfYYvCkmAjwlARIgARIgARLILQH/mWeeKWeccYbcfffdMnbsWBk5cqRjjc4//3w59dRTHcdXETdt2iT/+9//ZNq0abJ582blzSMJkAAJkAAJkED+EvBkzSL2UerQoYMsW7Ys4xX59ttvjZ6rvn37SqNGjeTFF1+MWSbmUW3cuFF0iZmAgSRAAiRAAiRAAiSQBgIRhtIbb7whBx10UBqytc/i3HPPlcWLF8vkyZNlzpw5cscdd0i/fv1k5syZtomGDx8u1apVCwmMK9vIDCABEiABEkgPAeZCAiQg/gcffFAeeughufjii439lIYMGZJRLF26dJEqVaqEyrjpppukRo0aMmHChJCf2XH77bfLhg0bQrJo0SJzFJ6TAAmQAAmQAAmQQNoJ+DEnCZOpMW/oo48+kssvvzzthcTLEL1FK1eutI1WoUIFqVq1aoQI/5EACZgJ8JwESIAESCDNBPwLFiwQyLvvvisdO3ZMc/ZibBmg9knC1gNr1qyJKGPWrFkyf/58OfbYYyP8eUICJEACJEACJEACuSYQMUcpUWUw12jcuHHywQcfGEmnTp1qLPvH3CPDI/hn1KhRcu211wZdYuzR1L59e8FQ2ssvvyz33XefYCgOBhqG/oxIhfSHdSUBEiABEiABEnA1AX+TJk2kiUmcfhQXPUGjR482jKNzzjlHpk+fLjj//vvvQ5Vu1aqVYQzBA9+Q++abb6ROnTqGcQVD69FHH5WJEydKSUkJolBIgARIgARIgAQ8SiAf1fYPHjxYzAKDxkllTznlFMNIQq+SLr179w4lHzBggGAOlPLARO4bbrjB2BJgxIgR0qtXL/H5fCqYRxIgARIgARIgARJwDQF///79jdVu+tHno+HimitERUiABEggYwSYMQmQQDwCxhyl559/Xpo1ayY9evTIyoaT8ZRiOAmQAAmQAAmQAAm4gYAf32vDR2ovuugiwZDboEGD3KAXdSABErAgQC8SIAESIIHsEvB/+OGHctJJJ8mwYcPk2WefNSZWZ1cFlkYCJEACJEACJEAC7iTgx4ozLNGHepUrV5adO3fCSUkLAWZCAiRAAiRAAiTgZQL+119/Xc466yyjDl9//bW0bdvWcPMPCZAACZAACZAACUQQKMAT//Dhw42J3Kg79jTCVgFwU0iABEiABEiABEig0An4r7/++hCDt99+W7BLdsiDDhIgARIgAS8ToO4kQAIpEjC2B0gxDyYnARIgARIgARIggbwk4MenQ4qLi0UXp58wyUsirBQJ5JIAyyYBEiABEnAVAf/48eNlwoQJEYL9lFylJZUhARIgARIgARIggRwQ8Hfv3l3M4vPxEyYOrwWjkQAJkAAJkAAJ5DEBzlHK44vLqpEACZAACZBAYgQY20yAhpKZCM9JgARIgARIgARIYB8Bf4sWLaRly5YRwsnc++jwQAIkQAIuJ0D1SIAEMkvAf+WVV0r//v0jhJO5MwuduZMACZAACZAACXiDgH/QoEEyyCQ+Hydze+PyUUvvEaDGJEACJEACXiLgnzt3rpf0pa4kQAIkQAIkQAIkkDUC/iOOOELKysqMAnv37i3Tpk0z3PxTToB/SYAESIAESIAECpeAv6ioKFT7vn37yrBhw0LndJAACZAACZAACeQVAVYmQQJ+ny88H+mEE06Qjz/+OMEsGJ0ESIAESIAESIAE8pNAhKG0bds22bt3b37WlLUiARIgAS8SoM4kQAI5JRBhKD3yyCPStm3bnCrEwkmABEiABEiABEjALQT8e/bskQceeEA6d+4s999/P+coueXKUA+vEqDeJEACJEACeUTAf8ABBwh6kkpLS2XKlCnSsWPHPKoeq0ICJEACJEACJEACyRPwL168WJYtWybvv/++tG/fPvmcvJqSepMACZAACZAACZCADQF+FNcGDL1JgARIgARIwIsEqHN6Cfg7depkDLdhyE3J7t2701sKcyMBEiABEiABEiABDxLwY7itQ4cOoovPF95byYN1osokQAIk4CECVJUESMDNBPz//Oc/xSx+P0fk3HzRqBsJkAAJkAAJkEB2CNAiyg5nlpJHBFgVEiABEiCBwiFAQ6lwrjVrSgIkQAIkQAIkkCABf+vWreW4446LkPyazJ0gEUYnARIgARIgARIggX0E/BdeeKH07NkzQjhHaR8dHkiABEiABEjAbQSoT1YJ+G+77TYxi8/HVW9ZvQosjARIgARIgARIwJUEOEfJlZeFSpEACeQRAVaFBEjAwwRoKHn44lF1EiABEiABEiCBzBLwt2jRQlq2bBkhnMydWejM3eUEqB4JeIzAtl275cRHvzcEbo+pT3VJwNUE/FdeeaX0798/QjiZ29XXjMqRAAmQAAmQAAlkiYB/0KBBMsgkPp+nJnNnCRWLIQESIAESIAESKDQCnKNUaFec9SUBEiABEnA5AarnJgL+Jk2aSBOTcI6Smy4RdSEBEiABEiABEsgVAf/gwYPFLJyjlKvLwXJJgAS8SIA6kwAJ5C8Bv3kiN859Ps5Ryt9LzpqRAAmQAAmQAAk4JcA5Sk5JMV4eEWBVSIAESIAESMAZAX/dunXFLJyj5AweY5EACZAACZAACeQ3Af+wYcPk7rvvlrPOOkvWr18v2FcpkTlKn3zyiVxyySXSqlUrmTZtmiNakydPlnPPPVdOOOEEY/+m33//PWY6BpIACZAACdgT2Lt3byhwR9mekJsOEiCB1An4r776arnqqqvkqaeekldeeUU+/fRT8fmczVHCJPA777xT2rVrJzNmzJBNmzbF1QhGUrdu3eTYY4+Ve++9V9asWSPt27eXdevWxU3LCCRAAiRAAtEElm7YFfKcs2JryE2HawlQMQ8RiJij1L17d/nuu+8cq3/77bcbhlWvXr0cp4F+WeIAABAASURBVLnrrrvkggsukCFDhsipp54qY8aMkW3btsmoUaMc58GIJEACJEACYQJ7JdyjtCfsDEegiwRIIGkCEYbShAkTpEqVKo4zq1y5suO4iAiDaOrUqdKjRw+cGlKhQgXp2rWrTJo0yTjnHxIgARJwHQEqRAIkULAE/I8++qiMGDFC+vTpI5dffrmxp1KmaCxdulT27Nkj9erViyiifv36smjRogg//WTHjh2ycePGCNHD6SYBEiABEiABEiCBTBDwP/zww/LQQw/JqlWrBD1K1157bSbKMfLctWuXcUQvkuHY9wfnKmyfV8Rh+PDhUq1atZA0atQoIpwnJGAiwFMSIAESIAESSAsB/8KFCwXy3nvvGUNgacnVJpNatWoZIZjAbTj2/cG5CtvnFXHAXKgNGzaIkli9TxEJeUICJEACJEACJEACKRCImKOk8nnyySdFX26q/FM97r///oLeoK+//joiqy+//FJat24d4aefoMepatWqooseTjcJkAAJkAAJkAAJZIKApaE0cOBASdemk/fcc4+cd955Id2xFcGzzz4rCxYsMPzGjh0rP/30k7GfkuHBPyRAAiRAAiTgcQJUP38I+Dt06CBmcWokYbiuVatW0qVLF4PIgAEDBOcjR440zvEHw2Rz5syB05DbbrvNWPV2xBFHSOPGjaVfv37yxBNPyPHHH2+E8w8JkAAJkAAJkAAJuIWAH0aOWbDXkZPdubHR5OjRo+Wll16S77//Xt544w3BOfZJUhXEppTjxo1Tp1JcXCzPPfecYAXchx9+KCtXrjQ2vAxFoIMESIAEsk6ABZIACZCANQH/0KFDZaiFODGUatSoYfQgoRdJF3w7ThXXsGFDadasmToNHTF5G71KgUAg5EcHCZAACZAACZAACbiJgOUcJTcpSF1IwIoA/UiABMIEKpaEH+UVisPucAy6Co3Atl275cRHvzcE7kKrfzrry19UOmkyLxIgARIgAUsCaKzZcFuioafLCWTJUHI5BapHAiRAAiRAAiRAAhYEaChZQKEXCZAACZAACcQkwMCCIUBDqWAuNStKAiRAAiRAAiSQKAEaSokSY3wSIAEvEqDOJEACJJAUARpKSWFjIhIgARJIngAnNifPjilJINsEaChlmzjLc0aAsUiABEiABEjABQRoKLngIlAFEiABEshXAqr3rPOoH/O1ihmtl+KHrRXgzmhhzNySQLoMJcvM6UkCJEACJJC/BNBwowGHwO20plt27HEaNe3xoCf0hcCd9gKYYd4RoKGUd5eUFSIBEihkAjvK9hi7MbvZEPjitw0euETeVhH3gbdr4B7taSi551pQExIgARIoCAI7dueuRymXgNGDBQMWAncmdXl22vJMZl9QedNQKqjLzcqSQP4S8HLN0Gii8YTA7dW6QHfUAQK3V+uRD3p/On99PlTDFXWgoeSKy0AlSIAESIAESIAE3EiAhpIbr0pB6MRKkkDhEliyfmeo8qs37wq588mBVW6Z6FVCnuixgsCdCrNUdETZ0AECdyp6MK27CdBQcvf1oXYkQAJ5SGDD9rJQrS56cY5s25W/c3Z279kbqisdYQKpGGnhXOjKBgHHhlI2lGEZJEACJEACqRHYs9ddhslL361IrUIOUqNHBz07ELgdJGEUEnBMgIaSY1SMSAIkQALuJzB7+ZaQkuu2hnuuQp5pdGzavjuU28ZtYXfIM+j4YO7a4F9X/g8pBeMKRhYE7lBAFh0oF+VD4M5i0SwqDgEaSnEAMZgESIAEskngq982plTc9rJwj1Km99JZvik812rpph0p6c3E8Qlsz+EQLYw3GHEQuONrmz8xaCjlz7VkTUggvwnkQe3QwKChue7teRG1OfPpWaHzEZ8vCbm95li2IWw4uUF3xRvM4bbSCXOFIHoY4iINBG49LBk38kBe5nISzeuM4H2CvBJNFys+8oNukLVbd6W8WameH9yxyvZKGA0lr1wp6kkCJFAQBNZsSXwVnN7TULY73KOUKjA0dGhAIXDHy+/+jxfFi2KElxbZNz0oB+VB4DYS8A8J5JCA/d2aQ6VYdF4QYCVIgASSIJDtudgwRmCUQOBOQuVQkhnLwvOjQp50ZJUAriGu5cotmZ2f5qRS6EGDPk7iujkODSU3Xx3qRgIkQAJZJIBtCtDIQrzQwC3fmHjvWxZxsqgggXwwlsKGUrBC/E8CJEACJOAeAjBWYLRA4E5Us+1le5Kec4IGDmIuE3pAH0imJ4vrZatydZ1enp75rQd0HezcasWf0hFs4LaLn23/QElRtouMWx74gBMEBnrcBDmMQEMph/BZdPYIRP4orZcxZ08blkQCiROAgYD7OPGUzlPok8qdpNINpWvfjJyg7iR9qnFWaqvuVF7YRwqc0ACDmfLP5PGJr5ZmMvuIvHWjQtUTdYU7IiJP0kaAhlLaUDIjEiABEohNQG/kYse0DkXDH69BLC7yhRJXLE7fIx5llzfI2d9FHHVG+aGKmRz6fk4zl201hSZ2usG0HxTKRfmxcvn6tw1Gzx3ixopnDkP8cqbef3kDI9QFdTLXUz93Gk9Pk2t3+n5Fua4JyycBEvA4AaoPAr59dg4aFKtGB8YWGiQI4iDNd4s24ZCwIL1VGQlnFCOBX1UoRhy7IFXXeDrqPVvbdqZmdCxav91Onaz649rgGpvrjhWO8IeAj1IK8cx+KkzlVR6eGh+VZyEdaSgV0tVmXUmABFxPoGmtignruNXhRoTbdu02ej7S1WBWSLLH6t+TFznSY+6K2L1D3y7eLKiTE2AL1jo3gNK5xYIT3WDkQFTcvTGWPvZ8fraK5pkjDDrccxC4zYp3e+qn4HXcY/Z2zTkNJddcCu8pQo1JgATST6BulVLDiNAbznSU8lsChoLT8v46br7TqEnFeyWNk7V/3md0OZnYPGrqsqT0TVeiLk/MFCfXH71LiZaJfMsNlt1B42R3RDkrNztbRaj34DkpP9G5b07yzGYcGkrZpM2yMkIAb5T44UPgzkghzJQEXELAqtHZtTv8Nm7XG/LoZ+nZ8Xuz9n23TCPZlOIwWrL6jZu1Otmklun+WOe+z7vAYILoCt/z0W/6qa37jRmrbMM8HmCpPg0lSyz0zDcCXy7cmG9VYn08SGDLjtzND5m3altaiE2etzYt+eiZ4AUHLzoQq6EZPa4X3fo38dys//w1zgy6n5bHHhJ1cx2T0Y2GUjLUmMZzBLK5fNdzcKhw1gi8O2dN3LI+XZAZo37Lrj3BoZZwzxN6EyBxFTJFWJrmTR6hA8RUjLNTm1ix5vjYJInpDf3KjTjnhi7SwACMmXEGAjdvz/2O3BmoVk6zpKGUU/wsPFsE1m/lwyNbrFmOPYENaR62SrQxXu1wDop9DUQ2JzAc5mQ+UKyykg37eWV079nO4PAkjB1IrHwTnX8TK69chP2x3lmvUCq6TftjkzGPLp2GYDLzrVKpQyJpaSglQotxSYAEUiHAtBkgAGPp+W9XhnLW91EKee5zvJjGydH7sszowclQJepvbmQ3Whikj36W+qaQKAuGVjoNhFQArtnibPJ1KmUgLeqLusOdKcEu8pnKO9V8aSilSpDpSYAESMBFBL5fstlWm0m/rLcNy1RAKvsoJTPMd8f7v8nt7y1Mujq3TIifFkYDRBWyqyw8pKn8snH8bW10z5lVubquVuGp+mXDkEpVx1TS01BKhZ6L0+LGxZsPBO6kVPVIonXasNqWHbl5YHkEFdUsAAK/aJO2L315rudqbLWqL14lKpbktinbnKOR/Xdnr41Cs25rdnqZogrOY4/c3l15DJZVyx6BpRvDY/IrNu+0LFif3Im3q3w3Hi0h0JME0kDg55X2PVbm7PE7W7rR+jdpjpuP55l+1vxosfps1nJnvUzp5J3ISsXdu/daFn352IWydWfmLU7LwuN40lCKA4jBJEACJJAuAmV7rBuJdOWfjXw2bHG+8isb+qCMDdvc2cBCNxgRt78bfzgPcdMhe2Ls6p2O/PU8UDf93Il72u+ZWdXppOxk49BQSpaclg5vTRjigsCtBdHpEgI+n88lmlCNQiZQ7Pf+fViU1To4u1uueuNXZxELIJbbjfF12703NEhDqQB+OKxiNAG8CcGwhdC4jeZDHxJIBwEMPaUjH+bhnEA2DSXMJ8Mz1LzqMJa2+i7yseK5KYyGkpuuRhp10W/czTncDTiNVWJWHiBAFUmABHJLoChNvefYAwsvkTCE0mnwPj0tvJVFbkk5L52GknNWnoq5dFN4AmUmPobpKRgWytpNKLSISi8SIAGNwKZd3p9npVUn4079pTXjhQULSJOdJDCS0mkgBVXz7H8aSp69dOlQvHDyCJiWD3/lwQmFhXO18remJS6c35O/tFkzEkgPARpK6eHIXDJIAG826P6FwG0uqkJx+DauqLn1eEfW308/lXXbwhMK8dZklW9EAp6QQBoI1KpcnIZcmAUJ2BMozoExvsVN+9fZo0k6JNzCJJ0FE+qT09Zuce8yVadXCkYDjBII3E7TuTleLh4ebuZB3UiABPKTwLtzojehzHRN+4zx3samiTBJi6E0a9YsmThxoixZsiRu2T/99JNMmDAhQqZMmRI3nZsjrNK2ZV3gcEt5N9fHzbqx98fNV4e6kUDCBJiABFxPICVDacuWLXLaaafJKaecIkOGDJFDDjlEhg4dKrH+jRo1Svr37y9PPPFESF577bVYSQo2DL056NWBwJ0IiB3at4d0dyJ55HPcJ75cbls9sAZzCNy2ERlAAiRAAiSQNgKbt7tzRCYlQwlG0S+//CI///yzTJ06Vd577z0ZNmyYTJ48OSa4E044IaJHCcZTzAQeCtwZNFDQwEJy2cjq83Z0t4dQhlTVv10V8rRxXP7KXMk1exvVsuaN+w4MIHCntWBmRgIkUBAE0Huf7YouXL0l20U6Ki8lQ+mFF16QK664QmrXrm0U1qlTJ2nTpo3A3/Cw+bN582bDmPruu+9k69atNrG86X3nB7+HFMeNls3GCo0iyoOk0ouUzS3wQ7BiOMb+ELnvBriirjGShIIQDzz+9/O6kF86HSp/lAF3OvO2ywvloDwI3Hbx6O8+AiVFKT1y3VchahSXALciiYvI9RGS/tUuX75cVq5cKUcffXREJVu1aiUzZsyI8DOffPXVV8YQ3cUXXyyNGjWSV1991Rwl4nzHjh2ycePGCImIkMUTNExooCBwZ7HoZIpKKs07s9YklS5Tibbu3JOprC3zXbc13P37+7odlnHc7JltXsmwwG8HvyEI3MnkwTQk4AUCs1e4s5fEC+zcomPShtL69euNOtSsWdM4qj+1atUSFab89OPZZ58tS5culU8//VR+/fVXufnmm6VPnz6CCeF6PN09fPhwqVatWkhgXOnhmXbjQY4HOkRvRPEZjEyXrfJPtMH+y5vzVNKEj1//vimUBnVEvSHgEArIkgNlTvsjrI8qFnohDHpd/br1d54Qjt4nlcbpcaO2k/n6reEF9jDaAAAQAElEQVRtBKzS658LQFnQB+VaxU2X3+44H1Yd8Xn8RRXp0oX5kAAJxCawaUf4xSt2TIY6I5D9WEkbSqWlpYa2W01DZzhXYUYE0x9M/q5atWrI99Zbb5Xq1asbc5ZCnibH7bffLhs2bAjJokWLTDFyc6q+c7MhCxPQnvpyWdKVxDAcGnBIphvxpJVMMGG2d7u1U+/pGJPC7dJY+eO64ProAj+ruN8v2RzytuKQqWHGUKF05DWBq9vVzev6sXIkkCiBpA2lhg0bSklJiSxevDiiTBgxTZs2jfCLdeLz+QxDCUN5dvEqVKggMK50ERf9izdZWu9pQOOnGkO4nVZj6cb4Q0B6b5fTfPM1HphDnNYvUFJkbNmPa3PFmJ+dJpN3Zq2Kirtp++4oP1xr5A2BOyqChQf0t4q/Wct/Z5k3PyexbEP4EzsWVadXkgQm9G+ZZMpwshZ1K4dP0uBiFiTgdQJJG0roNcLk7bfeeivEAPOIPvroI+nevXvIb+bMmaL2Sdq7d6+sWxc5qXb27NmyYMECOfbYY0Np3Ozo+fzsjKuHhhQNJBpKJ4Wp+MnqptKjTLgTKTORNE7ydRoHPSlO+TjNM5l4VmbKyC+WJpOVbRrU0+l1sc3EZQFPfZV8D6nLquJpdSZdc5RMvf4Yeez8Q+LW4/oO7GmKC8kigr4hsUUwvTQC23dFv2RqwTlzJm0oQeN7771XPv74Y7nqqquMlW49evSQBg0aGOcIh4wcOVIGDBgAp8BQateundx5552CvZMefPBB6dKli7Rv314wsduI5LI/a7fuEjRU6VILc2tUXrobfmgMYXjYlafCEQeCc6RLRHAN9PjIw648xNN7L3CeCYEOqA8Ebr2MVZus5wjNXBY9b0lPl6gbZVtxiNdbaFXO1IUbrLxDfj8tT3ylp/leCWUWx4E6mZnGTpKdUG7Mmh7Oo3oeGpFRzUolhuFT7IvwjjpRBhJ6UhFYtUL40ypVNDfClPiDvf/KzaNzAiO+sN+zzXkujJlLAikZSq1bt5avv/7aGIJ75513pHPnzsZ+SpUqVQrV6aijjhL0PMHD7/fLtGnTjGG0cePGybx58+SBBx4wjC30UCFOPovZSFFznNBIQ5JtDBNh9sXCjXGjF/nDT9mLX5oTio9Gt1zP7Fn928usV7wtWu+OoZtgJ2mIj1PHk1+mt8fJabmM5z4CL196hGHYoFfnzb4tElawWsViueSYOqF0yiiuXGr/aIeRpAwklbBB9fI5pzjff78SHKKkTeNqUX70IIF0Ehg/w509zfa/Joe1b9mypaDX6M033zQ2m6xRo0ZEyoEDB4q+oSRWr2GlG7YEgH/v3r3FHzSgIhLl6cnrM1ZJsj00fou3ORgueDCaDbBY+H5etS00F6fc6LE2RGLlYRUGPZAfBG6rOOn0e+X76LlB6cxf5XXtW/OMDSzt6mVxWVRS2+OqLda9ZLYJggH6SrfS4vDPtmJJ2B2MZvk/kfvDMgN6ZoxA3arWRokqMNk5R38+oa7KIuFjIHhPwXCD6In1+073T9ZdZ79wL1ayeTBdfhGoFDT83Vij+E9ZN2qdRZ122vRoJKPCuFlrZE6MPTXQw2SX78K124MGjrVRA+PHLh38Yw0f7dgVmeeKjTvly99j9zqh5wtGGvJOhyzfGG04wNiCcdI3gYnV6dAlHXn4fOEeOZUfmCm32XBBXePxnL3cfi8WpAcrCNyqHHV8dXrkhp3Kn8f8JdDl0MhtW5I1uBShOsFeJidGuYrv5Fg7OEzoJB7jFA6B849tgMq6Tmgoxbkkj32RvmEScwMZp2jHwZu1fX+sEv113PyQ9yvBRlNvtGeZGuBdu62mJ4eSWzrQyOt5WkaK4fnWj9npHYqhgqMgqx3LfT6fo7R4S1cRV24uM3qpMP9N+cU7rtuW/F4sr/3gPr56D2mgpChe9RmeIoGA1ksUSDPvN/o0T1G79CV/5NyD05cZc8o6gYbVA1kv00mBNJRMlLbuLJND7/yfdHn6Z6MHZ/Kv5RtrmqLl5HTbzui5QfGMpHiK7jFtXtjHQQ+OVc/XBm0363hlmsPXZ2EfKnOZyZx/MGetYeConhv03ux0aFjG2jB0ebAXLxl9VJpvLDbkVGE46sN2OIdAd9QDAredH/whiIO4ELjhR0mMALg99rn9ixdWciaWY3nsRjXC84vKfRL/C+MJQ20QuBPKgZFJIM8J0FDK4gVGT8Kwj/5Ia4nJLEXfpQ0nbtkVbXwlo+D42WuSSeapNAvWhPeyWmGzGg8V2hw0aNEowg2B22pvJhicCHt/7lpEiymlRfY/1Xc19ujds8oI5cDIUWIXzyqtW/30OsHtVj2t9OpyWHVjEnc8oySwrycIE7Ct8oFf50Mj54XCD+lwhCBtIIleJJ0pesOxog55IU9dalQqNupiFabHM7vTPedpzGVHSKxpBubyeU4CTgnYP32d5sB4cuXYX3JG4YsFsZeiWyn28fxwL9lvWuNvFdep34dzw4YSGmE0yPqD1mk+Xok38vMlsiGF4TC9nk56EuwaldOfmik74mw66fNFDA/qRUe4fzINw+qB0xeHdwPX/enODgEYOujtgTx0Tnh4CatCi/3R11ePD3c2tEQ51zrYawkGFepxaesDbNWqs194ODaRob0qFcLp9My7Hh5tTCL8xd7NcKCQQEwCNJRi4Jm/ZluM0OwHbdR2ZNZLT/Qt6tfV2/XkaXGv2x45KRyZwmDKV2Ppi982ygztUyKory6ouzIWy2IMz2VjN3X0BkAfXT8r94NTInfZ1+M88ql9mB4vV+6tWf5wcq7q6aZylcEDA0npFWu7jI4HVzN6nlT8Yxvup5JFHXsevX+UnxOPulWthyFPalLVMjkMTcuAPPVMdVJ/nmKJWy0aSjEQjfl+dYzQLAVpxTwzLXqPiS279sggbbK2Ft3W+f6c8FDPGzMzW0c00MpgsFNIfyNGfK8YV2Wm+V1W9UN9Vm+OXtWn4r4d5F9cFN0joMLVMdbqy68XbVLRLI8wlCwDTJ6x5lEtdfknR0Z+scRUm9ROcQ/ivoXAnVpu5an1BQ9bd0S/WJj3PoIhooyK8hxy/xf6oDcIArdZo4Nrh/fQM4clcl6xOPGmKRAcprQr49hGVeyCXO2/f2XrHrJklY7FKNk8CyFd4ndjnlPBZG5VxXXb7Bs4FSebx6/jTNrNpi6ZLisbPS2J1qHYNMRxzyRnH2eOZVCt3LJLxmR4TyibjshQ9WHMwSAIeQQdeqMePHX8H0YF8oLA7ThhMCLiIx0E7qCX4/8fzI38NJLjhDmK2OmQ6M0bq1QMN4oTBxwpAZt5RVW1eFVKi4x4MFwgdmlSrWaDahUcZdGoakVH8cyRYBS+pW24WarNyXO6LUGR6YUDeYIJJBDDiDLrwvPME1AltGlcRbzQy0VDSV0xDxxjdWt7QP2QimgE0RgqsTIknMzbCWWYJcfPqxL/9AhU0ydb41wXqxVperhyz1u9LbTiLlbvkoqf6hETzXF9cK0gev9HQGvAEYZ4ELiTKTeVtMmU54U0Pp99L2PTmmFjpGbl7GzaOLB9fUfYAqX2esfLoHKFcHN0YM3wMvFA0MiB0RMvPeLFixMr/PD9w1xjxYsVdnS9yrGCEw47qan13KqEM3JpAmzArF+3SkHD342qhu9MN2rnAp2OjzGO7gL1PKPC1n0rwdAoogdDV9zKWHD6Fqnnk2n3j8vsN32MVfaSGMv/repuldfPK8NG2j8mpnflpFV5yg/XCqLOccQ1hJgNI8RbYbF5KNIkKlbGM8pDuRC4E80z2fh/rAuvdkw2D6Rzeq0R110icnDtsOESSzefz95Q6nRI9YikgaDBjd4eCNx6j+1h+weM+UwqDOFwOzGYIgpJ4GRg+9Q3OywK9jr/94qWcUvVe1E6WvQuqgwuOsZ6rpaeXsX14rHL4ZH3hFvrQEPJdGW2aJs3Fvv94vPZ//BNSTN+mp6F/BlX07KA7bv2Wvrbec5alj+rrDDx266e38eZX6TSfb80OSNNpc/EEcNzMI70vMfPTm7OG+ZHIT+V14vfrlDOnB8fjzH/CQYbDDcI3LGU1TdzbV43eiKzMgZgEMAdKy+3hgVK/LaqdTgoerjRNrJNQCBoXMUylhAOfhC4VTZwW6WrWiE83OnUGFR52h2379L7X+1ihf1PtplojhgwvHDUBUZSIAZnPa7uRv0DQX66H93OCNjf1c7S512sVZvCb49onOJNlM07ABmqEBpBc6OqirIyil74brkKNo7Ngm+YhiPP/mzMwGqtbCHC8Jy5rFe1+Va45jAglMTaiXzKvMg5Rl/9Xr7tBYyPUPotkbuTZ+uhv3BN+leJxvvGm5mrfo56wxCAwK2HFYIbdUbdIWj8U6lzZc1QCgSNj1Tzgy6J9oa3qGc/0VztUYV8MyW9bXqtMlWeF/OloeTFq+ZBnb9dFN67yaz+6q3RfWXVK0R+LLRBDWeTSc15Z+P83h5NslGM58owG1LqTRvGD3qQ9ArtDr6Em787qIfD/ab2qRsY3TCg4E/JDIFAsPcBxggEbielIB7iQ9JhdMQrUy8P7ljxER5PLxUH8SCJ1uGUGMNodrpV1uZm2cUpTmFgA3VAXVA3c/4IKzFNgjfHsTq/4ZTwnLXXLm9uFcXSr4rFHDbo9enAI2Vi/8OlUml25txZKhfDk4ZSDDjZCSqMUlZuiuwNSLTW2yyWUyeaR6biu3UCYqbqm2y+PZ+fLTCS7NKv2LwzFNS4RvmcGPRKKc8xcb5ZFytvlYfTo14utldA3jDMIHA7zYfxygkcVa+ysTqv/Mz6LxpMNOgQuK1j5d4XQ192WpzZvJZdkFzcynq+kW0CLeDPbew359SiZcUJ4+qQWuFtICqWOLfibuvcOCs6prsQGkomohVLwmPWpiCepkBg4q/hvZuSyabHETVDkzuTSZ/JNAfVKm/UM1lGvuQNA2Tx+vDwtqrXy9NXKqdxrFExc2+WMHRg8OgCPxSMI/zNvWEI00XFQ8+W8l9rGhpU/upYMTi0o9yBAnvOVMvg9VRMkzkGgtcEhhkkYHFN4IcwXQLBNMmUdXxj+yE2lR+MkMiyytujc45M3shSeafz2OyAsKFkNY9KbAo7ukF8BjZJc+pNQ8mEf3uavn1myrbgT1dvDY6tJEBhumnysovm1EfVwvn7VFTSgvOAAdLn1Z8t670zxg7mlglMnuj5MXlFnFoZNyoCDB4YSDDklJ86+nzOrvCFL8w2tnBAOSqtflRDj/Czi4OwfJTLjqvjmmoFggaRMkbgTlQxpIFBk2g6PX4gaGyFdYjfDCO+Sg93IFiHVHVoVX+/uL18qkx1RJkoW51bHREHYhXmVb/4V8irNaPeniWARsusfDb2DjKX6eQcPV2B4EPPSVzGiU1glLa67M2Zqw2jY9n68HBc7NQic1ZsM4b2YPAoWbu1fNNYGCZW91W8PK3CF2RgcrdVOVn0y3hRhzjcXiDjimShgAo2Q1E7UUX17QAAEABJREFUyhJ7WcyCqkYRVV3a22co55I/NJRMF+L5qb+ZfJI7HXFe+MOVyeXAVOkk0LxOJWlYzfo7UKmU0+c498wdSKUebkj78yqL1WXOOnMM9a97e17QUEqtMdpisY15zcqRCwsm/7peYu0cj14pZajBQDOUC/7ZUZbYFhnBJPzvQQL6flBO1A8Ee4fCvUvlQ21O0iUTR78f1b5eXQ6r4Sgr9BJB11iRncSJld6tYTSUTFemjs0W/P3a5K7r+IIja5u05GmiBDCOfmPHRokmixu/9n4lUY3zfy48LG66eBFOObiafZRgyJUnFIaBduXrvwRrm/x/DPXpjUO8nPZYbH9v3l9nz5698uaP9vtFrdlsvXDh28Ub4xXPcI8SgIEAYyeeIeHG6u2nbZGQjH6BYI866g4JBI2+WHkEgnFf/5PzVXKx8spmGA0lE+1L2ljPyk9wDzHx++1fhe/qal2GSZXQqdUuxaFAOhwR6Hl0bTmqvrPPC/R0OHGyUfXShMf4HSkbjHRNu/Dy2+Bp1P/mB4TrEmsVTlTCPPeAYZRKFS95ea6j5GtjfAeyzxjrPIL2laO88yVSINhoovGEwJ0v9TLXo3rFIkfPgQrF/tCClER5ID44QuA262B1nkvjDTpCV+igdIMb/urcS0e/l5TNhq71q1e0LEbfVdcygsmzUfUKyifqePyBVaP8EvHodrizrtJE8nR73P1SHEdPZFfgs1rUDOHAjxs/+Fh7hQSff6H4cOxIcS7C8DOayv5VIod7kK8umFcAvSA1K5XIJcfkrsdT18uN7o9+Tm3FJeqE4TQcIa/NWCVOr7Gaz4RerUc+XYLklDwjMNDhd/AyWe3x/VpkMvuovGHw4NkDgTsqQp550FAyXVBseHVpm4Ym38RP/Q5XyjjJ+cSmkYZV20bRnz9APicGDTA07HAnK4mmf6NPdrpRm9XJ3hL8KsE3RDM/fWm3CjuxSfnwWIqLtVR2EUc8fPAQstt7paLZOotIHf8E1/ml3s1CEQe2rxdy55tj5OepGyjJ9lSt32Y9DJdvjAu5Pp0dzvGxeoaki5vPZz+CYVdG01rRnQJ4LkD0NDjHswjPpLB/8q4alYpCidsdWP4MDXm41EFDyeLClGGbYJN/FQe7p5qSWJ5e16Ge1Az2APz15OQ/wHhUgypyZvNwr4cq6Oj64b0tlJ/5ONbB+DB+FPhxmNOazx88+yCpX62C0Z3sJL45fSLnPl/4QdA+xreRrPL8V7CHBj9yCOr2Zt8WEdHaBQ1M3aNaoNioE+IiDcIqW+wo2+d463lCTnsbkK+V6MNqB1k8zKzS6EvPrcKVH66Tqlc9bXL76c0i76dRPQ9VSTx/3LRzr6Sy4u39Ocn3SGG1JiZ26+W3bVzF0VCN58EXaAXwzMBvDL81HUHjGKMMerxU3SgX5UOPWHn9yWYhCtIhj1hpnYYhL+gCgRvpfL7ws7xOnJ5zxHeD0FByeBVOO7yWOJ0L8p8LDzWMIdwcvUy7sZ67b/5Lp4OrOyw5Oprdm8n5R+9vPIBRLqSltimYysVpg6rixzpWrVAcKzgtYfjBoi4wLlWGiU7KrlMlcrWbmYH5Gqly9KPPF/5xK3/MOYA7UBL5MypN4pMA+r21n2aUdzo0fJ88fM7BKM5S4s1ju+XUhobxFygJv83pGfl80fXTw49raN2LqccpJPeUeRscVfeO96NX0aZqSDsq2BSJp7knUKnU+reXK81qVCp/fuOZgGcsBG7ogyPOIXDDr5Al8glfyCS0ulttOmlnnGjJEnJaDe/YZaAaZBUeMDXMyt/JcB/qgZu/a5zu4kCwQUU8JTBYVDnqeFDtcNct4iv/TB+rBcIPHOgFHXG0K/fAON+J0xsu5GNVF/ihHF3gZ1WmVZe2VTzl98IlzQSGoMpbz9fJNUU+u+LMFO52eGSPEdKgHFWmXj78apjmhGHVINJQSIAE3EHA7vcLf3domD9a0FCyuJYnHRa9XXygxB/srUkNFxqgQNAAsSjS8OoV7BEyHDH+PH3RYUE9iqRhcMgrRjQj6JgYvQCXHpvY5F/oDf1hSBiZJ/mnXtXytxgnyetVLTHqao6rdIE+cCMcx2R1gyGKvCDIB/klIkiDtEoqx3hzfO3yIwTxMGFblVElanmuChGj/ogPCfuKbDdNGD+3pfU3psAEaaGjnj6eO95k8njp44U3qBZ7snq89AwngVwSwO8JvysI3Fa6wB+/P6sw+nmLQGotv7fq6ljbmpVK48a95sS6cePEioAfEX5kepyj6sWfY6Tin9XCumFU4TheYmEMqd6YRloviz7sg3SxROkN3eGOFdcqzGkPCdImEhfxoc8tnRrBaQgeUk70rGoxedvIIMk/0APlonxzFth3CX5Ha1sVVNV6yBBmJzDoVFiFosif7qH7B1RQ6Phmn+aGoRXySMGhNqdLNgszC6zS+0f3Jslm5+l0RX6fp/Wn8t4loJ5NeD7B7d2aZFfzyKdtdst2bWkVY/T6KKVb1A3P2VB+VscybUkUlghbxVF+LeqG98ZRfjgGgr1ZOEICxeXDThU1P/hbid6wqvBii4c08scPBxJwUHeVl36MVzcV95DaYWMQBlpdF0zma1IzPISo9EzHESx1AwEbrcEPeVcPlBg9S4kwP3T/sJ76RGzkh3yRF3qscA5RRjHciQry03VPNL1VfD2/04+Ib+hb5UE/EvASAfyO8LuEwO0l3TOlKziABwTuTJWTznxpKCVAExcVFxdyhMVEaZWVPudF+Tk5lhRbX46Da4UbyLrB4SjkpXS5up19z5aKozdQSAtRYagL3PDLhvRvE16GHohj7C3ZsFOcGmDZ0D3VMmpWLk4pi8ql0avxzBmqHiv4+3zp67lo07gKsrSUQHF0OXd3O1BOMm1rEQga4bjfIHCXWqSzLICeJEAC2SbA8jQC1i2zFqEQnebJ3ANPrBdzCAOGyFO9rD9b0bB6/GE8MK4dbETVhFrkBz8lPp9POaOOPR3Ma0KjhMYJAndUJln0qBGspyoOm/gt37RLncpDZx8UcmfTkUkmyBvcIXBnul4oA2VB4E5XeecdFfkZHeSv8j68TriXUPlVrlAkV8fZXVwffsQ9X2paLQg/iMozX45bdu7Ol6qwHiRQEARoKDm4zFbDBGiE0FhA4NbnHRRphk130/40DoqLiNKmURXDSEM5EJSlR/BrZen+bnGjoYPeSpQxiPNAnB6lZOqwU9sDC4aYVR6ZKNeqHK/7BbQeIPRm6fVZuzVs4MIf1xPXGm7IkfUqi3l4EP66lBSFHz+4Vju1YWoVDzro+Sp/Lx/nrtyWfvWZIwmQQMYIhJ9UGSvCexlj/oiudaXS+JgaaT1Hh2gTayvYDKchf31Yaa/FxzgR54ZTYu8SjoYEjRQEbqRxi6CBc5tOYJPK3B2kL1Sx20vpkNqBKCRFfl9MAx8JcG/gvoUEYhjNgaDBdprNZ3swzw15pSKnantVpZIP05IACeQngfgWQH7WO2atDq4TnlTttLH3az07xf7wUBke8mgIIHDHLNgi0Cs7l+qqg1mi9R3358jdsvX8EnG3DPZkqPgVHc6B0Q1WlTbDR09mf3OnxpZ6X9a6fIdy3N+47hC4LSMn6VmjovX8rkDQwDr9iPAeUcO6HRgq4byWkcOFoYAEHOpeTiBJ3KgOb8u4+TACCZBAdgjQUEoT50DwrRcNBATuRLPtoa0CQnrkA4E70bxyER96Ql8I3InqYGzAqRmbz118uLEqLNG8mmor2Hy+sMGq64M8uzWroXvR7YAAtpTA9YXoQ6jmnc8dZBU3CsrAdVIRzzfNkVL+OLbQFlYEgoYT/CCnadf4rq7WRh7i2cl/r2hp9IrZhSfr/5cUPl+UbJlMRwIkkDwBGkoW7PBh3In9D5dPBx5Z/qC0iJNurz/ZfDcs3eW4Kb+Aybg8TBuybKB9hywRnc152qU9p0XqvQ12edM/cQK4bvoQmLmXT1/NZx5u043eZprRtH/lkpAiR9a3385j9+69ohvYoUQZcpzVnFsjZAgtsyWBjBCgoZQRrIln6rfp/Ug8J++mCGi9AZjjksmaHF4nel5NJstj3vEJHKp9EsccO6AZ1XpvFvwx0Rw9UJDalUuNnki461cr/2Cz2Y3zMZcdESri19VbZY/FHMFS69G+ULpEHW/1bWHoBp0TTcv4JJBuAszPOQEaSs5ZpT0mHph4aEPgTnsBHssQDMACAncm1Uf+KAcCdybLYt7OCJyThjlFzkoS2U/bjb17cIiuRiDaKlKr8vTerVMPqR5VRJOaFaL80OuF+U16ABcR6DToJgHvEKCh5J1rRU1JIK8JYLUpDFdINo3X84+qI/3ahjdChYFjp8N+Fcp3xtcvhFVvVEDrHUXci1vVSXEYH7lQSIAEckGAhlIuqLNMEiCBnBLQh+/gbl43etNMKwWdfGPRKt1Frfa38qYfCZCABwjQUPLARaKK3iNAjb1FIKDNgYLbTvt6VUujghpWq2jMPUIvlBLkAVHnB1iki8qIHiRAAq4kQEPJlZeFSpEACbiFgG7woPfpYlPvUP8TwsN2btGZepAACaSPAA0lgyX/kAAJkIAzAu2aVI2IeGCN6MncERF4QgIk4GkCNJQ8ffmoPAmQQLYJ6Lu/Y+I3epyyrQPLI4G4BBghbQRoKKUNJTMiARIoBAIwjNTcI7gLoc6sIwkUMgEaSoV89Vl3EiABtxCgHiRAAi4lQEPJpReGapEACZAACZAACeSeQMqG0ieffCI9e/aUDh06yIABA2TRokVxa5VMmriZMgIJZJMAyyIBEiABEigIAikZSlOmTJEuXbpI8+bNZciQIbJkyRJp3769rF+/3hZeMmlsM2MACZAACZAACZAACWSQQEqGEoyj8847T+6++2457bTT5PXXX5fNmzfLqFGjbFVOJo1tZs4DGJMESIAESIAESIAEEiaQtKG0fft2mTp1qpxxxhmhQitWrChdu3aVSZMmhfx0RzJp9PR0kwAJkAAJkAAJgAAlWwSSNpQwzLZ7926pX79+hK44/+OPPyL81EkyaZB2x44dsnHjxpBs2LAB3qFzPSxd7k2bNsm6desoZMB7gPcA7wHeA7wHMnwPoM1NV/ttlQ+Mhr179+KQsCRtKO3atcsorEKFyF1pA4GAqDAjgvZH+SeSBsmHDx8u1apVC0njxo3hLY0aNQr56eGpumvXri3HHnustGrVSlq2bEkhA94DvAdSvgf4LOGzlPeA9T2AthZtLtreVNtvq/SwFWA0wBjDMVFJ2lCqVauWUdaaNWuMo/qzevVqUWHKTx2VfyJpkPb2228X9CIpQU/P/PnzjUnjyi/eUa3GwzFe3HwPBwNwxTHf6+qkfuBAHhuM3xhZlHPAfUMWYRbkQRa4B6zEC78TLDCDnhjxwrM+UUnaUNp///2lYcOG8s0330SUOW3aNKM3JsJz30kyaZAUPVBVq1YVJdWrV5eDDjrI6E1Sfk6OyMtJvEKIQ4fMwTYAABAASURBVBbh+wnXO5pHZDjiFIqQRfjak0WYBe5/8gjzIItIFnv27JHPPvtMnn32WXnsscdkxIgRWRWUOXr0aGPutPnaoJcJ9orfn5zJk1wqaBGU/v37G1B+//334JnIm2++KTNnzpR+/foZ5/iDYTPsswQ3xEkaxKOQAAmQAAmQAAm4n0DNmjVl7NixMnv2bGNKzHHHHSdt2rTJqqDMevXqyYwZMwQGU7LDbFa0UzKU/v73vxur3A4//HCjh+dPf/qTjBw5Utq2bRsqC0bUrFmzQudO0oQiJ+BgVBIgARIgARIggewTOPHEEwUTpc8//3yB+8gjj8z6nD6UiY2vsWXRli1b5Ouvv04biJQMpZKSEnnxxRcFq9zeeecdWblypVxzzTURysEwQk+T8nSSRsVN5xHDd3fddZfgmM58vZgXGJBF+MqRB1mECYRdvC/CLOAiD1AolyyxKC/M5X/B4vjjj5eDDz5YKlWqlHNtq1SpIk2aNJFffvklbbqkZCgpLerUqSOw5ipXrizmf1ih1qJFC7O3xEoTFTkNHriYQ4cOpaEUZEkWQQjaf/IIwyALsggTiHTx3gjzIItIFj6fT2CghH1Ffv31V3nyySd1r6y5oQt6ldJVYFoMpXQpw3xIgARIwLMEqDgJFDAB80TpefPmGXOFcoHE5/MJhgLTVTYNpXSRZD4kQAIkQAIkQAIZITBlyhR5+umno/J+/vnn5aOPPoryT6cHDaV00mReXiJAXUmABEiABDxCAFN4HnnkEXn99ddDGo8bN07+/e9/G1N/Qp4ZcHjSUMLmUTfffLOcdNJJcuaZZxrbEsRjEy8NuunefvttufTSS+WUU06RK664QqZPnx4vW1eEv/LKK9KjRw85+eST5Y477hAnyyLjpQGve+65R7p16ybdu3eXO++8U1atWuWK+sZS4ueff5a+fftK+/btpXfv3vLdd9/Fim6EJZIG+4Zh4iK2uTASu/gPPv1z7733SseOHeW0006T//znP3G7o+OlwWeLsAzXLG+99ZaLSZSrhrdOrMrBypi//OUvsnz58vKAGH+dpMHnEu6++27p3LmznHvuufLBBx/EyNEdQag7GIAFmKCe8TSLl+af//ynmO8LnHfq1Cle1jkNx/4/o0aNMlZwQ9d//etfsnPnzpg6OUmDxU1YzITf3qmnnip//etfZeHCheL2f3hm4tmJZ2jfvn0Fz8d4OqP9jBcH4du2bRO0s9hj6d1335WysjJ4hwSbSb/88svy1FNPGVsNvPfee/Lxxx8b4diH8f7775fBgwcLNo/EIrLbbrtNHnroIalbt64RJ1N//JnKOO357ssQD+ouXbrI559/Ltixu2vXrnLJJZcIut/2RYk6OEmDG/qll14yDA489GrUqGFsc/DJJ59E5ecmD2yyhUb77LPPFhiPuLHOOOOMmA2ikzTIAw+DW265RQYOHCiTJ082DFM0pG6qv64LfjhYmgq9hw4dKtjbA8Yj9vbS4+nuRNKgQYQhvX37dpk7d66ejSvd0PW5554zHtCXXXaZ4KEyZMiQmLrGS4MHIh6kV155pTzxxBMhwUtLzIxzHIjfBV4mWrdubRj9WBEDIyHWhE8nadauXWs8JyZNmiQ33HCDwPjAFik//PBDjmtsXzzqjLpjDglegPDpiNNPP13ef/9920RO0lx++eWh+0HdG8uWLRM0cLYZuyAAz340vn369JHrrrvOeKGAO5Zq8dKgzYHRNWXKFOO+wAsseMP4gDEQK+9chuFZid8yPiUyNPgMRT3wTIVhYqcX0iCeXbjyR71hNGJDSuzw/fDDDxsvFqpNwVc98GKOFfQrVqwwrgV0+PTTT1UWxkv7WWedZYShXbrggguMl/lQhAw5PGcoYauB77//XvAGiwcfrPRBgwYZViYe4lacnKTBAwPx0FCgR+nBBx80emhgVFjl6QY/fDsPy/zxIx8wYIDgBnrttdeM3VHt3mqdpsGDH0zwlgwjDA8+vFn8+OOPbqi6pQ4PPPCA8VAePXq08XaIa3f00UcL3nQtEwQ9E0lz9dVXCzZP1fcJC2bhyv/oDcX9jBcI7CuCPc7uu+8+wRsZegutlE4kDfZOQ2+BErc3hviNoCFHg4WHMdigEcdD24oF/JykQRwYzvi9oXcbL3Hjx4+XI444Alm4Up555hlBQwQGYIE6oAcBRzuFnaQ58MADI3qU8KxZunSpwKi2yzfX/vicFnok0GjjZQK9a6jrmDFjjM2TrfRzkgaf2MLmi3i+wAjFcxQrwHDPoVda5eu24z/+8Q/jGj766KPGMxTPD7xwoh52uiKNz+ezCw75P/7448bKOBhCuNcmTJhgbCmELYYQCb16TZo0MTauRDh6nmCgI0yXYcOGGVsSwfCK9+Knp0vF7TlDCQ043oCwA6eqOAyExYsX23YROkljtf8D/PBjV+W47Yi3VrzRovdH6XbYYYcJBHVWfvrRaZqKFSvqyYweJWwDj70yIgJcdII646Gkr77AvQF/OzUR5iQNHp5Y7oofqV1ebvJHvfCAa9euXUgtsMDbG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", 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REa5eMcwhsHcH6jXDcInrTFcB+Wp5g0aZI8++yzgv7BU6PhQ7lO6hdMR8DDL9Af/7c4jzCfD78b6N969eq5AT919BABGlQe6kyjTYHBg4tOTvkx9wIXIzxpFZwO82FgUGFODIZusHQCnrjBXSKGhZAWebEOEDwVuOjjKTgYMPgxRJn6U2X4YcSEWFykMKcD86kwYRZDChjeQVmQpk2bqsfekQ/DifjRhwGGJ5BwEUYaXW6++WaJZmjhcXHM74IxCAMKj4pjkiuWKsBFEWVg4jp0DJ/QDCMP4frkdKSFwNOBcEzuxrEuZupCGSgTBhD2IWCMYVG0DxPrMZkej4wjLhbBvBlMRIe+aC8m8KJvMeQD4yqWMmJNAwMYQ0cwPPDAA55WhMcRF20M06I/w8uCLvAe4pxas2aNWhsJeobPCYqFK8qGkYA+xlOMOLewThTOUXi5EJ8Tz2jnAPJB4PnC+Ycny3DO4oYC/QXBOYo0kGjlxHsuoaxgwROHqAs3LXp4tLoQj7TB5xL+52AMYpgXS3XgIROsRLf5GwAAEABJREFUD4dzCumDxUn94vP51BPJulGMJ4hhGGOhXtxk6XrnxEJPwy0JWEGABpUVFF1eBi4AsXij4AUJnweFpuORa3ifcOcPDxW8AY0bN0ZUQODNwl0kvCG4kMFbgYsjysTwgp4QC2vixxAXKBhd3bt3FzzCrcfrWxhyWBcKk+cxzwsufly4cZetp8EWw4rwQmE/kuCCh0nw0Btp4Z3BD7CeFsNR0DH4AoQ45EN48EUM4TD8EA79cBwsyGOkLpSBMnH3jX1d4PmBoYAhD8TDo6DHxbLF04MwhLGcADhCt/B2xlJOLGnAFAYz+h76om/hdcSk9Gj54ZGC5wceLQw/4xyKlDY3rnoe9BUWf4UXBt7J8HM0Gs9o54BeLs49GP5gCGMUfR/p3I5WDvRH/0E/vUxsczqXEK8LDDLkxw2KHhatLsQjbfi5hEVUwQas4d3BjYnPF3nOkVP6BW2BwIuN3y/cwMGbCaMP4brkxEJPwy0JWEGABpUVFB1VBpUhARIgARIgARKwmwANKruJsz4SyEMCWAsMHohoog+P5KEKLJoESIAEYiPgsVQ0qDzWoWxO/iYAgwlzX6IJJqM7mRCGBjHHJ3zOmpN1zg+6sV/yQy+zjWYJ0KAyS5D5ScBBBDCPJJp3CuF4as5B6mZThfNdsiExE2BZXvaLZShZkIcJ0KDycOeyaSRAAiRAAiRAAvYQoEFlD2fW4kUCbBMJkAAJkAAJnCGQ7w0qrAaMRSKxkvLBgweFQgY8B3gO8BzgOeDVcwDXOlzzcO07YwdYtsHbDazghnIsU8rGgpxsUNmCAa8YwXo8eK8Y1o6hpAoZkAHPAZ4DPAe8eQ7gWodrHq59Vl5kYQRVrVLMkusH3sOI8qzUz46y8r1BhdesAHStLk9LnfuHeFMe0NpFkTpkQAZePgfs/P2ym6OdbUNdcbfPPb+xtbo+jUue6Nc+dWDB14kTJ2TnH5myYVkV2bO2qmFB/p07dwrKs0AtW4vI9waVz3d6NeAChYsIhQx4DvAc4DnAc8Dr5wCsDJ/v9LUP+1ZK0WIiZsVKfewsK98bVHbCZl0kQALGCDAXCZAACTidAA0qp/cQ9SMBEiABEiABlxDIEr+YFZc0NZuaNKiyIcmPAWwzCZAACZAACZgnkKWZU2bFvBaJKYEGVWK4s1YSIAESIAES8ByBTL9fzEqOUBwcSYPKwZ1D1UiABEiABEjATQSyOOTnpu6iriRAAiRAAnlEgMWSgCkCMKgyNaPKqCC/KQUSmJkeqgTCZ9UkQAIkQAIkQALeIECDyhv9yFa4hQD1JAESIAEPE4CHyay4FQ8NKrf2HPUmARIgARIgAYcRMDshHfkd1qSY1fGaQRVzw5mQBEiABEiABEjAWgJZWnFmRSvClR8aVK7sNipNAiRAAiTgbgLe1N7oZPTgfG4lQ4PKrT1HvUmABEiABEjAYQQy/SJmxWFNilkdGlQxo2JCEiABNxGgriRAAiRgJwEaVHbSZl0kQAIkQAIk4GECZudPIb9b8XjGoDp8+LCsXbtWTp486da+cJneVJcESIAESIAEQglkiU8yTQjyh5boniPXG1THjx+XHj16SNmyZeWmm26SqlWryqRJk9zTA9SUBEiABEiABDxCIMsvYlYsR2FTga43qO677z6ZO3eu/PLLL7J69WpZs2aNHDhwIH58Pi2LTVJ82ymxU0quOi5elVIrj4mdYjdHO9uGuuxun531oX12ip1tU3X9qv2f2yV2/6bY1a4z9RTfqv1G2yja1cczHzPeKT2vW2G42qBatWqVTJkyRYYPHy7VqlVTfVC0aFHp1auX2ucXCZAACZBAnhNgBSQQIKAbRWa2gcJctuNqg+rzzz+X5ORkad26tezatUvWr18vmZmZLusCqksCJEACJEAC3iCQ5fdpQ37mxK0kXG1Qbdu2TcqVKyfdu3eXiy66SFq0aCFlypTJcQ4V5lwdPHhQgsWtnUe98wkBNpMESIAESMDxBFxtUBUoUEA2bdokJUqUkO3bt8vmzZtlyJAhcu+996o5VZHoZ2RkSGpqakAqV64cKRnDSIAESIAESIAE4iSQaeIJPz1vnFU6JrmrDarzzjtPgezdu7ckJZ1uygMPPKCGAb/99lsVF/6Vnp6uJq1j4jpky5Yt4Ul4TAIkQAIkQAIkYIBApiSJWTFQrSOynLZCHKFK/Eq0atVKZfrjjz/UFl8YysOwXsmSJXGYTTDnKiUlRYIlWyIGkAAJkAAJkIDjCDhfIb/JOVTI7/xWRtbQ1QbVP/7xD8GyCZBPP/1UvvrqK7n99tslLS1NbrjhhsgtZigJkAAJkAAJkECeENCH7cxs80QxGwp1tUEFPq+99pp07txZXnzxRXn22WflwgsvlO+++06KFy+OaAoJkAAJxEyACUmABMwRyPRrQ34mxZwGicvteoOqYMGC8thjj8m8efOUvPTSS+pJv8QhZc0kQAIkQAIkQAL5jYDrDar81mHubi+1JwESIAES8DIBvIsvS5LEuPhci4cGlWu7joqTAAmQAAmQgLMImJk7ped1RIsMKEGDygA0ZiEBEiABEiABEshOgHOosjNhCAmQAAmQAAnkFQGW61ECp4f8fNqQn3GJB82+fftk2rRpMmLECPnkk0/k1KlTOWbfvXu3PPfcc9kEi4TnmDGGSHqoYoDEJCRAAiRAAiRAArkTwNypTEkyvLgn8udey+kUkyZNkoYNGyqDCgbRE088oZ70D16b8nTKv7/x3t8BAwbInj175NixYwHJysr6O5HBPRpUBsExGwk4mgCVIwESIAGPE6hZs6b8/PPP8sEHH8jw4cNl+fLlcuLECRk8eHCuLe/bt2+Il6pq1aq55sktAQ2q3AgxngRIgARIgARIICYCds6hatKkSciak0WKFJEGDRrI+vXrc9V1ypQpaphw9uzZkpmZmWv6WBLQoIqFUvY0DCEBEiABEiABEggjgCE7s4Ii8Rq5YMEr5RCek2AY78svvxQYWjmlS01NlR9//FHWrl0rDzzwgBo23LlzZ05ZYoqjQRUTJiYiARIgARIgATcSsFfnTL9PzAo0rly5ssDw0SUjIwPBUeXkyZNy1113qYW9e/fuHTVd+fLlZfXq1QIP1ZgxY2TVqlUCY+3RRx+NmifWCBpUOim/tmOTFPorU+yUwnuOiFel0N6jYqfYzbGQ1nd2it3ts7M+O88T1GVn22yv68/DUthO0f4P7Gyjnb/PqEu7+njmY2ZCup4XMLZs2SIHDhwISHp6OoIjCobsOnTooAyluXPnhgwDhmcoVaqUlCtXLhBcrFgx9fq6r7/+OhBmdIcGlVFyzEcCJEACIkIIJEACfxPI8ieJWUFpKSkpEizJyckIzia6MbV48WKBUZSWlpYtTW4BSUlJcujQodyS5RpPgypXRExAAiRAAiRAAiTgNAIwpjp16iSLFi1SxlS1atWyqYhlErDuFJZVQORPP/0kyId9yNGjR9XwX4sWLXBoSmhQmcLHzHlPgDWQAAmQAAm4hYA+bGdmG2tbn3rqKZk6daq0atVKbWE4QSZMmBAoYseOHYJ1pzZs2KDCFi5cqCahP/TQQ/LYY49J3bp1BWtQjRw5UsWb+aJBZYYe85IACZAACZAACQQIYHlMM5PSkT9QWC47WCKhf//+UqFChcACnVisE5PM9ayYL4U0VapUUUE9evSQjz76SOrVqyeYoI71q7CWVfXq1VW8ma8kM5mZlwRIgARIgARIgAR0AlmSJGZFLyu37R133BGyOCe8UxB4n/S8MKgQVjVo4U7sd+/eXfr06SM33XSTFCxYUE9uaksPlSl8zEwCJEACJGATAVbjAgJ2LuzpNBw0qJzWI9SHBEiABEiABFxKIEt8mofKnLi06Zpvzq2aU28SIAFrCbA0EiABEiABwwTooTKMjhlJgARIgARIgASCCXDIL5gG9/OKAMslARIgARIgAU8TMLNcgp7XrYDooXJrz1FvEiABEiABEsgTAsYLzfL7xKwYrz2xOWlQJZY/aycBEiABEiABzxDAkgm6p8nIFvndCoMGlVt7jnqTAAm4lgAVJwGvEjD7Hj/kdysbGlRu7TnqTQIkQAIkQAIk4BgCNKgc0xVUxDoCLIkESIAESCARBDLFJ2YlEXpbUScNKisosgwSIAESIAESIAHBkJ1ZcStGQwaVWxtLvUmABEiABEiABPKOQKZWdKYpL5VWgEs/NKhc2nFUmwRIgARIIFcCTGAzAbPeKeS3WWXLqqNBZRlKFpQQAn6/iJ1idyN9PhE7xcsshX+WEbDznERdlinOgvKaAFdKz2vCLJ8ESMCbBNgqEiABEggi4BefZJkQ5A8qzlW79FC5qruoLAmQAAmQAAmQgBMJ0KByYq/8rRP3SIAESIAESMA1BDjk55quiq7oyZMnpW3bttKkSRPZsWNH9ISMIQESIAESIAESsJjA6eLMvscP+U+X5L5vz3io+vXrJ2vXrpUlS5bI8ePH3dcT1JgESIAESIAEXE7AyPv7wvO4FYEnDKrZs2fLzJkzZejQoW7tB+pNAiRAAjkSYCQJuIEAPExmxQ3tjKSj6w0qDO/dd999MmXKFClatGikNjKMBEiABEiABEjABgJZkiRmxQY186QKVxtUWVlZ0rFjR+nRo4dceumlMQHCcODBgwclWGLKyEQeJ8DmkQAJkAAJkIBxAq42qIYMGSInTpyQ9PT0mAlkZGRIampqQCpXrhxzXiYkARIgARIgARKITiDT7xOzEr10Z8fYZlDlBYZJkybJzp07pWnTpurpPniqUM+tt94qL7/8MnazCYyvAwcOiC5btmzJloYBJEACJEACJEAC8RMwO38K+eOv1Rk5XG1QTZs2TSZPniwjRoxQ0rNnT0X16aefVksoqIOwr+TkZElJSQkR4R8JkAAJkAAJnCbAbxME/P4kyTIhyG+i+oRmdbVBVb9+feWZwtpTkNq1ayuYDRo0kLS0NLXPLxIgARIgARIgAXsIZIo25GdS7NHU+lpcbVBZj4MlkgAJ5DkBVkACJOBZAll+0TxUPhPiXjSeMqgaNmwo3333nZQvX969PULNSYAESIAESIAEXEfAUwYV5kZh6A/zpFzXE9YpzJJIgARIgARIICEEzMyf0vMmRHELKvWUQWUBDxZBAiRAAiRAAiRgkECWaMN9MUvktAarTng2GlQJ7wIqQAIkQAIkQALeIGB2DSrkdysJGlRu7TnqTQIkQAK5EGA0CdhNQB+2M7O1W2er6qNBpZP0aTt2iVaVpz9+v4hd4tM6zU6xq116PXafKF5mqTO1a2t339lZn10M9XrsbBvq0n7CxE5BnR4RNeTn14byjIo2XOhWFDSo3Npz1NsBBKgCCZAACZAACZwmQIPqNAd+kwAJkAAJkAAJmCTg1zxMyktlcIv8JlVIWHZHG1QJo8KKSYAESIAESIAE4iaQZXSoLyhf3JU6JAMNKod0BNUgARIgARJwLQEqfoZAlon3+KLK8uUAABAASURBVOl5zxTlug0NKtd1GRUmARIgARIgAWcSoIfKmf1CrUiABEjgNAF+kwAJuIKAmflTel5XNDSCkvRQRYDCIBIgARIgARIgARKIhwANqnhoeTctW0YCJEACJEACpglwyM80QhZAAiRAAiRAAiSQ3wnkvUHlXML0UDm3b6gZCZAACZAACbiKgJ0GVWZmprzzzjvStm1badKkiXTo0EGWLFmSK6/du3dLz5495dJLL5VrrrlG3nvvvVzzxJKABlUslJiGBEiABPIJATaTBMwQsNOg6tu3r8yZM0cZUiNHjpTKlSvL5ZdfLl9//bVE+ztx4oRcddVVsnLlShk2bJjcdtttcvfdd8vEiROjZYk5nAZVzKiYkARIgARIgARIICcCfi1Sf1rPyBb5tSJi+jz//PMyefJkZRQ1btxYhg4dKldeeaWMHTs2av73339fVq9eLdOmTVOG1YMPPiiPPPKIDBw4UPz+eGqXbH80qLIhYQAJ5CUBlk0CJEAC3iVgp4cqOTk5G8iCBQtKVlZWtnA9AN6rRo0aSenSpfUgadOmjWzZskV+//33QJiRHRpURqgxDwmQAAmQAAmQQJ4ROHjwoATL8ePHc61rwYIF8uWXX0q7du2ipoXhVL58+ZB4/RhxIRFxHnjOoIqz/UxOAiRAAiRAAiRgEQGrPFSYD5Wamiq6ZGRk5KghvEswpDp37ix33HFH1LSnTp2SwoULh8Trnq6TJ0+GhMd7QIMqXmJMTwIkQAIkQALmCXiyBKsMKniLDhw4ILqkp6dH5bV+/Xo1H6pFixYyYcKEqOkQUapUKdmzZw92A6IfBw8DBiLj2KFBFQcsJiUBEiABEiABEohOwCqDKiUlRYJF9yKF17xhwwZp2bKlXHbZZWoJhQIFCoQnCTm++OKLZdmyZYIlF/SIRYsWyVlnnSW1a9fWgwxtaVAZwsZMJEACjidABUmABGwn4Pf7xKzEqvSmTZuUMYU1qLAeVSRjas2aNVK3bl1ZunSpKrZTp05y5MgReemll9TxH3/8ISNGjBCEw6hSgQa/aFAZBMdsORDw+UTsErH5z6526fX4/aL9OtknduLU2+jVrZ0svV6X3f8H2k+Y2Cke6r8sDZxZiRVH//79BUbVzz//LPXr11eGE4wnzKXSyzh69Khac+rw4cMqqFKlSmrJhFdeeUXOPfdctXZVnTp15OWXX1bxZr5oUJmhl7/zsvUkQAIkQAIkkDACWHfql19+UQYSVjvXBQt26krVqlVLkAarouthWCZh27ZtsnDhQtm6davMmDFDihUrpkcb3tKgMoyOGUmABEiABEiABIIJWDWHKrjMaPvwNsEjFS7Vq1cPZClSpIjUrVtXihYtGgjDDtarqlGjhpQpUwaHlggNKkswshASIAESIAESIAG/jXOonEabBpXTeoT6kAAJkIDLCFBdEtAJ2Omh0ut0ypYGlVN6gnqQAAmQAAmQgMsJ0EPl8g6k+iTgbQJsHQmQAAm4gwAMKjNeKuR3R0uza0kPVXYmDCEBEiABEiABEiCBuAjQoBKRuIgxMQmQAAmQAAmQQEQCfi3Ur30ZFi2/Wz+uN6iysrLkf//7n0ydOlXmz58fspy8WzuFepMACZAACZBABAKODzK7qCfyO76RURR0tUGF9+/Uq1dPHn74Yfn444+la9euguPNmzdHaS6DSYAESIAESIAE8oqAn8sm5BXavC93+vTpsmTJEuWhWrVqlVrt9JFHHsn7ilkDCZCA9wiwRSRAAqYImJmQruc1pUACM7vaQ9W0aVOpWbNmAF/hwoWlVatW6r09gUDukAAJkAAJkAAJ2ELAb2b+1Jm8tiiaB5W42qAK54H5VHPnzlUvSQyP04+PHz8uBw8eDBE9jts8J8AKSIAESIAEXEBgxYoVcvPNN0taWpqULl06RFq2bOmCFtivoqcMqgEDBsiaNWvk2WefjUoyIyNDUlNTA1K5cuWoaRlBAiRAAiRAAvmNQGZmpuAFwtjiujpq1CgJln79+kVF4vfMHKqoTYwa4RmD6pVXXhEI5lTVqlUraoPT09PlwIEDAdmyZUvUtIwgARIgARIggfxGYOPGjXLs2DGZMWOG3HvvvdK+ffsQue6666IioUEVFY07IkaMGCH9+/eXDz/8UK699toclU5OTpaUlJQQEf6RAAmQAAnYSoCVOZcAhvhwrSxQoEDcSuoTy81s467UIRlc76F69dVXBV4neKZat27tEKxUgwRIgARIgATcSQDTYurXry8TJ06MuwH+MxPLzWzjrtQhGVxtUH3wwQeCJRJuuukm2bZtm0yYMEHJpEmTHIKXapBAogiwXhIgARIwRuD333+X9evXyz333CPVq1eXZs2ahQjWfIxW8mlDyid+w3OpopXs/HBXG1RYJgHju8WLF5fFixcHZOnSpc4nTw1JgARIgARIwIEEihUrJu3atZOBAwdKp06d1HJEWJJIl8aNGztQ68Sr5GqD6pZbblEeKd0zpW/xNEJeo3VV+X4LfLAsQwQMXNXxVJYE8oiAzyfiZRF7/zRnjtgmMTStXLlyMmjQoKjSvXv3qKX4tYaYlaiFOzzC1QaVw9lSPRIgARIgARJINAHT9fv92k15jKUgpVmJsSrHJaNB5bguoUIkQAIkQAIkkFgCJ0+eFDxBf8EFF0iRIkXknHPOkauvvlrwDl3J4c9PD1UOdBhFAiRAAiQQnQBjSMCDBB566CF5/vnn1Vyqt99+W63zWKZMGWnRooV8++230Vts1j2F/NFLd3QMPVSO7h4qRwIkQAIkQAL2Eti/f7/gafn58+fL4MGD5c477xQ82ffee+9Jnz59ZOTIkdEVMumhUpPJopfu6BgaVI7uHionIoRAAiRAAiRgI4Ht27dLpUqVJNJbR7BKOt8wErkzaFBF5sJQEiABEiABEsiXBCpWrKjWdvz111+ztX/WrFlSpUqVbOF6gF8bsjMrellu2yaJ2zSmviRAAiRAAiRAAnlGACulY43HK664Qvr16yeYQ/X666+r+VTDhw+XRx99NGrdfpNDfsgftXCHR9BD5fAOonokQAIkQAKnCfDbPgJ4rRvmT82dO1cwQX3QoEFy9OhRWbhwoTRt2jS6IppBpeZBmdlGL93RMTSoHN09VI4ESIAESIAE7CeAFyM/+OCDsnz5cvnrr79k586d8umnn8oll1ySozJ+DvnlyIeRJEAC+YIAG0kCJEACJgloBpWYFZMqJCo7PVSJIs96SYAESIAESMAhBNasWSP169dXSyTo+ziOJFhGIZrafm2oz6xEK9vp4TSobOwhVkUCJEACJEACTiRQsmRJ6dixo7Rp00b0fRxHEqRxYhsSrZNhg+q7776Tp556KmbBo5aJbizrJwESIAESIAESyE4Aq6D/+9//ls6dO0uZMmUE+9EEabKXEBRiZsgvqBi37Ro2qJYsWSJvvfWWLFiwIFfB6qqfffaZ29hQXxIgARIgARLIlwR+++03CXaEDB06VBo1aqQMrVOnTkVlYna4D/mjFu7wCMMGFdrVrl07mTdvXq7Ss2dPJKeQAAmQAAmAAIUEHE6gU6dOUrZsWaUllkoYMmSIXHvttfLJJ5/ImDFjVHjELzPeKT1vxIKdH2jYoLrmmmukffv2MbUwnrQxFchEJEACJEACJEACeUJg165daqV0fYmE6dOnS/fu3eW5556TF198UWbPnp1DvT4tzqxoRbjwY9igqlOnjjRp0iSmJseTNqYCmYgEcibAWBIgARIgAYMEjhw5EpLzq6++EjhGEHjOOefI4cOHsRtZdC+TmW3kkh0fatigcnzLqCAJkAAJkAAJkEDcBCpXriyYJ4WV0vGqmfXr18uVV16pylm2bFmui3uqhPnwy5hBFQHU77//riar3XbbbXLDDTeEyPjx4yPkyMdBZix3I3m9jNqnuZbtFLtZ2tk21GV3++ysz6/981BE3MjAzvMEdWmniunFKeMow6eltU3QvlykYMGC6v19U6dOlZEjR8obb7whycnJcuzYMXnttddyfJefJdxy0c+p0ZYYVFu3bpVLL71UFi9eLOXLl5caNWqESOnSpZ3afupFAiRAAiTgYQJsmjECGOJbtWqVbNy4UfAAGkopUqSIYNFPeLBwHFH82k2uWYlYsPMDLTGovvjiC7n44ovV8gmjR4+WESNGhMitt97qfBLUkARIgARIgARIQBEwvmyCeSeoUsCFX5YYVIUKFZJKlSq5sPlUmQRIwBwB5iYBEvAiAS6bEH+vWmJQtW7dWubPny+//vpr/BowBwmQAAmQAAmQgGMImFo2wexwH/I7hkR8ilhiUJUqVUq6desmF110kTRs2FBatGgRIqNGjYpPK6YOEOAOCZAACZAACdhJwNSyCXYqeqYuv98vK1euVPO9zgRF3WBi/Y8//ig/hsmhQ4ei5ok1whKDCp6pAQMGSMuWLdVaFc2aNZNgqVatWqz6MB0JkAAJkAAJkEACCWDSuYFlE5TGVjytqAqK4QvG0UsvvSTnn3++ejAO7x7MLRtWJGjQoIF07NhRunTpEpDVq1fnljXXeEsMqm+//VZatWolc+fOlWHDhqnVVLGiqi7XX399roowAQmQAAmQAAmQQOIJuGXZBAxN7ty5U71zEK/FiYccHqYL9lLpq8LHU0Z4WksMKrzvp0yZMuFl85gESIAESMAqAiyHBGwk4IZlE6pUqSLwUP3jH/+ImwwMMXir4ImLO3OUDJYYVFhBFV6q5cuXR6mGwSRAAiRAAiRAAp4n4NdaaFa0IvL6c9VVVwmkWLFi0rdvX7UyvNk6LTGo4Do7ceKEGsOsXbu2escf3vOny4gRI8zqyfwkkNcEWD4JkAAJ5FsCWLCzfv36cuedd6rFO7EfTZAmKiizxhTya4UfPHhQguX48eNaqPkPDKhp06bJnj17ZPPmzfLll1/KmDFjZMiQIaYLt8Sgqlq1qvTo0UMGDRokd911V8hrZ/Aamjp16phWlAWQAAmQAAmQAAnkDYGSJUuqidpt2rQRfR8TtyMJ0uSNFn+XionxqampoktGRsbfkSb20tLS1MrvPp9PlXL55ZdL9+7dZfLkyerYzJclBlWjRo3kqaeeiioYixUzWjIvCZAACZAACZBAnhHAPGg8Jde5c2fR93EcSZAmqiLwMJkVrfAtW7bIgQMHApKenq6F5s2nYsWKgvrMlm6JQWVWCeYnARIgARIgAacQoB4mCGBhTrOiVZ+SkiLBkpycrIXG/zl69Khac0pfZyrS0OHXX38ttWrVir/wsByWGVSYLQ9L9rbbbss25Dd+/Piwaq073L59u3LflShRQs4991zp2bOnAKB1NbAkEiABEiABEshfBDB/6YUXXpAOHTrILbfcEiKPPvpoVBh2rkMFJX755RdlMOneLCyFsGLFCkQpwdwwrDv1/fffq+Mnn3xSHn/8cZk9e7Z89dVXcv/998unn36qlntSCUx8WWJQbd26VU1IX7x4sZQvX14FAXLNAAAQAElEQVRq1KgRIqVLlzahYvSsWVlZynjbv3+/Aop1sGbNmiUPPvhg9EyMIQESSDABVk8CJOBkAlh5HOs6vf766wLPUKVKlSRY4LyIqr/Z4T7kj1p49ogHHnhAunTpIvv27ZPdu3er/YcffjiQ8KyzzlJvccFkdAQOHTpUsMzC6NGj5ZlnnkGQ/PTTT8qWUAcmviwxqPCU38UXXywLFiwQKDlixAgZESS33nqrCRWjZ0W9WKoBnZ6Wlib169cXTFzD5DKsMRE9J2NIgARIgARIgAQiEdi0aZNg5XAYGm+++abg9XHB0q9fv0jZEhK2aNEi5VCBZ0oXDOHpytSsWVPF6wt3FipUSE1CnzlzpnzzzTcybtw4serBOUsMKigI61VvgF3bhQsXKqsZHjG9zquvvlrguYK3TA/Lb1u2lwRIgARIgASMEsA1vUSJElK8eHGjReTLfJYYVK1bt5b58+cL3ulnJ8UdO3YIVmkPrrNUqVJSoEABieahwoQ0jA0HS3B+7pMACVhHICnJJxdenCYtWtdTWxwL/0iABBxNAE+9nXfeeYL1muJVFIsRxDGPSrKljbdCB6W3xKCCEdOtWzc1TtmwYUNp0aJFiMBVaHebMQYcqU4MCerrWmCLtS4ipcvTMHXGaTXYttUqwpobXhQNo6c/fr+InWIhzMuvukDe/ORReXHCPZKecbva4hjhFlbj3KLs/n+zk4TdbbPzfwB1aT+ZYqOYfSgurvwxnieYa9S+fXupW7euelcv3terC9ZtilpMXMpokCOlj1q4syMsMajgmRowYIC0bNlSsOZUs2bNJFiqVauWJxQwMQ6T0IIL37t3r2RmZqon/oLD9X2sZaE/DYCtFWtP6GVzSwIkcJoAjKanXmwvK/Yfk1tHL5TaT89RWxwjHPGnU/I7TwmwcBIwQADXUDzdh6UE8HoWzD8Kltq1a0cvVbsHFLMSvXRHx1hiUOE9frBc8ZTdsGHD1OOHzz33XGB7/fXX5wmEpk2bqsW4NmzYECgfj0H6fD71+ptAYNAOnlgIXtsC+0HR3CUBEjBJAMN63R7/p3y5epfcP3mZLN+yX46cyFRbHCP8vsdaC9KZrIrZSYAE8oAAlkGCw2Hp0qXy6quvCrxVwdKrV6/otZo1ppA/eumOjrHEoMI8JqysandL8VhnvXr11GtvMGcK601gxXa4KStUqGC3Oqwv/xFgiyMQqNugipSvUELGfL1OjVYGJ8Foymvz1kmFiucI0gXHcZ8ESMAZBHBNx4T0s88+O26Fss2J0gykeMPirtQhGSwxqK688kqBlwpLGNjZLkw+x6OPSUlJkpaWptbCwlAjHoO0Uw/WRQIk8DeBkmVOPxm0ZtdffwcG7a3ZeTpcTxcUxV0SIAEHEDjnnHMEk9InTZrkAG3co4IlBhXWgzpx4oQyaDC22qRJEzXkpm+xJpUhJDFkQqdjldNjx44Jntx74403RF/AK4bsTEICJGAxgb27TxtMNc89bViFF1+z3OlwPV14PI9JgAQSSwBDfpiPjAUzsSwRHBXB0rVr1+gKah4pzqGKjifXmKpVq6pht0GDBsldd92lVhy94YYbAlurFs3KVREmIAESSDiBFcs3yY7t+6VHy+ri84Wqg+MHW1SX7dv2CdKFxvKIBNxLwEuawylx++23y8CBA6Vjx47ZnvJr3Lhx9ObSoIrOJpaYRo0aCeYuRRM8+RdLOUxDAiTgfgJZWX4Z//JsubrWuTKu08XS8LwSUrRwAbXFMcInvDJHkM79rWULSMB7BMqVKydwkESTnJZNiHe+VKT0biVqeMgPy9IvW7YspnbHkzamApmIBEjA0QQWfvWrPNfnPalboohM73G5rHy2tdrWSU1W4YiPrwFMTQIkYCeB3377TWbNmhWoEk/5wXny73//W06dOhUIz7YTaV2peMOyFeqOAMMG1Zw5cwTvzIulmfGkjaU8piEBEnA+ARhN99w4XPrc96ZkpE9T23tvGiEId7721JAE8jeBTp06Bd5Egte8DRkyRPBk/SeffCJjxoyJDodDftHZ5BTz0UcfCV47k5uMHTs2p2IY5wACVIEE8oIAhvV+XrZR5s35RbDFcV7UwzJJgASsI7Br1y7Ztm2bYDFPlDp9+nTBMB/Wl3zxxRdl9uzZCI4okYbw4g2LWLALAg17qC6++GJp166dWpYeS9PnJG3atBGstuoCHlSRBEiABEiABPI1gSNHjoS0Hwtm63OhsaTC4cOHQ+JtPnBsdYYNqiuuuEJeeumlmOWmm25yLAQqRgIkQAIkQAIkcJoA3nGLeVKDBw+W4cOHy/r16wXrTSIWc6d1zxWOs4lfCzErWhFu/Bg2qNzYWOpMAiRAAiSQCwFG53sCBQsWlLffflumTp0qI0eOFKzviNe2Yb3H1157TR599NHojDRjKt4hvuD0ag2r6KU7OoYGlaO7h8qRAAmQAAmQgP0EMMS3atUq2bhxo5reAw2KFCkieMUbPFg4jiiaQaWMIjPbiAU7P5AGlfP7iBp6iwBbQwIkQALeJWDGkNLzupSOJQbVyZMnXdp8qk0CJEACJEACJBBOYMWKFXLzzTer9+SWLl1agqVly5bhyQPHwcN3RvcDhblsxxKDCmtSYJL6xIkTJeGz/13WAVSXBEiABEiABJxEIDMzU/B0PrYDBgyQUaNGhUi/fv2cpK5jdLHEoMITfBdddJE8/vjjgiXr7733Xlm0aJHwjwRIgARIgARIIDIBp4Zi3hQmoM+YMUNwPW/fvr0Ey3XXXedU1ROqlyUGVdWqVZX1umPHDhk/frxs2bJF4LG64IIL5IUXXpCdO3cmtJExVa6P3dqxjUkhFyfyaxC9Ki7ulphUt7PfYlLIwkQ+n4idIh7+s/M8QV0eRommGR0aM5QPFeYiGN7DU30FChTIJWWEaO3nn5PSI3CJNwgd0F6zZD/77DNZt26d8lb17dtX8ERA27Zt5Ycffoi3SKYnARIgAYMEmI0ESMAIgdTUVKlfv75gGk+8+Q0ZeZoRFpwv3jqdkt4SD1VwY+AqHDhwoDRv3ly+//575S589913pVChQtK0aVNZsGBBcHLukwAJkAAJkAAJOIjA77//rhbzvOeee6R69erSrFmzEOnatWvO2moGkmEvVc4lOzrWEoPq6NGj8s4778jVV18t1apVE7wM+amnnhIMAU6YMEFuv/12ee+996Rnz54yc+ZMRwOhcrETYEoSIAESIAHvEShWrJhaewrOkU6dOkmrVq1CpHHjxtEbbcaY0vNGL93RMZYYVGPHjpVevXpJnTp15KeffpIlS5ZIt27dBJ0S3HosFIZ3/gWHcZ8ESIAESIAESMA5BPBw2aBBg2RQFOnevbtzlI1NE1tSWWJQwQO1bds2efXVV6VevXpRFceTAR07dowazwgSIAESIAESIAH3EgieC2V0362tt8SgqlixohQpUsStDKg3CZAACZCAGQLM60kCP/74o4wYMUKeeOIJ+fe//x2Q4cOHR2+vPmxnZhu9dEfHWGJQObqFVI4ESIAESIAESCAuAv/5z3/k8ssvl0mTJsnrr7+u1pbEFi9L/u2336KWZdQrFZwvauEOj6BB5fAOonokICKEQAIkQAK2EcDr5NLT0+XLL79USyfgYTMs1r1+/XrBcgoNGjSIrosZz5SeN3rpjo6hQeXo7qFyJEACJEACJGAvgY0bNwompjdp0kQKFiwoWDUdGpQtW1ZNVP/vf/+Lw8iiG0VmtpFLdnwoDSp0EYUESIAESIAESEAROHz4cOApfRhWW7dulRMnTqi4woULy19//aX2+RVKgAZVKA8ekQAJkAAJkIBjCditGF5DU6tWLcEin2+99ZY8/vjjAs9VND2C50IZ3Y9WttPDaVA5vYeoHwmQAAmQAAnYSKBKlSoyZMiQQI1YuPuPP/5Qw30XXXSRYMHPQGT4jpmhPj1veJkuOaZB5ZKOopokQAJ2EGAdJEAC55xzjlx//fUBEDVr1pTPPvtMMLdq8uTJgnf9BSLDd3SjyMw2vEyXHNOgcklHUU0SIAESIAESsIPAzz//LJdccomhqowO8wXnM1SxAzLRoHJAJ+QnFdhWEiABEiABZxMoU6aM7Nixw5iSZjxTel5jNSc8Fw2qhHcBFSABEiABEiAB5xAoX768YK7UqFGjxO+HleMc3WzUJO6qaFDFjYwZSIAESIAESMC7BH7//XfZvHmzPPzww1KpUiW57LLLpFmzZgHp2rWrRPsLHrozuh+tbKeH06Byeg9RPxIgARLwIgG2yXEEVq1apSajFytWTNq1a6ee5uvWrZtcd9110qpVq4A0btw4uu5waJmV6KU7Osb1BtXu3btl3Lhx0rdvX8G7h/BopyHiPi2XXaJVZesHLls7xaeB9KrYyRF12XqiaJXZ2W9adbZ+wNNOsbVxWmVe7juteZ7+mDVA4s0fBSYW79y+fbtaJX3QoEFqmYRBEbbdu3ePUoIWHK8ukdJrxbjx42qDasaMGdKoUSNZtmyZlCxZUj7//HOpXr26LFmyxI19QZ1JwEoCLIsESIAEbCeg3U6LWbFdaYsqdLVBVadOHVm5cqWMHTtWeag+/vhjad68ufTr188iPCyGBEiABEiABPIPgUOHDsm8efNylO+//z46kEgep3jDopceMWbp0qUyYMAAwQKkERNECJwzZ44888wzMnLkSNm2bVuEFPEHudqgqlGjhpx99tkhrYaRtXPnzpCwPDlgoSRAAiRAAiTgMQLr1q2Tli1b5ij33XefI1qNKT5YL+vBBx+U999/Xz788MOY9IL+d999txw4cECNbNWuXVt++OGHmPLmlMjVBlV4w/BCR0CFlyo8Tj8+fvy4HDx4MET0OG5JgARIgARIwGsE4mnPBRdcIL/99luO8sknn0Qt0uiTfcH5ohYeFlGoUCF5/fXXBR4zGEVh0REPv/32W3njjTdk5syZ8sorr6gtDEg80RgxQxyBjjKojh49Kj179sxRAC9S+7KysgQWZ2ZmpgwePDhSEhWWkZGhls3H0vmQypUrq3B+kQAJkAAJkEB+J1C4cGHB6E9OkuN1M97hvUjpY+wEvCIHHqoYk6tk06dPFxiNl156qTrGV5cuXWTRokWya9cuHBoWRxlUBQoUkFq1auUokToSxtS9994rCxculC+++EKwyms0Iunp6crNB1cfZMuWLdGSMpwESIAEYiDAJCRAAiEEIhlJsYadKSh8JAmjS2eiTG3Wrl2rDMbgQmA84hhx2BoVRxlUsIxz81C1adMmpK1YxRXrZMyePVu++uorwUscQxKEHSQnJ0tKSkqICP9IgARIgARIIJ8TwPpTDRo0MEUheOjO6D4UgPMEo0i6YHQJ4WYFU4NgAwSXgzpwjDhsjYqjDKp4GwFj6v7775dZs2bJ119/rdx48ZbB9M4nQA1JgARIgATyngA8NRMnTjRXUayeqJzSaRpg9AijSLpgdEkLNv2B0Ygygwvav3+/OkSc0gRc2QAAEABJREFU2jH45WqDCu8ZmjBhglx44YUyevTowNyrxx9/3CAOZiMBEiABEiABEjBKwKhXKjgf6oYXKVgwuoRws4L5U2vWrAkpBsc+ny/XEa6QTBEOkkQihLokCIt6YnX0G2+8MWTeVW7Dfi5pHtUkARIgARIgARIwQWDHjh2CtSnXr1+vSsErdfAU4zfffKOOMdI1fvx4adGiRY7zr1XiXL5c7aHC+4QizbnCMGAu7WY0CZAACZCA2whQX+cTyGkoL9a4OFo5cOBAZTDhPYQrVqxQ+8FP+uPJvWHDhsnmzZtVqU2aNJHHHntMbrnlFsHDbFdeeaUsX75cvbpOJTDx5WqDykS7mZUESIAESIAESMBiAsFDd0b341EJE8pLlCgh99xzj3Tp0kWwjzC9jAoVKggmtFerVk0PkpdfflnNva5Xr54yqlavXi1YFDyQwOAODSqD4JiNBDxIgE0iARIgAXMEYvVC5ZQuDg3gbcKQXrD06tUrUELZsmWV1+q8884LhGHnsssuk969eysjrGTJkggyLTSoTCNkASRAAiRAAiRAAopAToZSrHGqIPd90aCys89YFwmQAAmQAAl4mIDRYb7gfG7FQ4PKrT1HvUmABEiABEggjwiw2PgJ0KCKnxlzkAAJkAAJkAAJRCIQ67BeTukileuCMBpULugkqkgCJOA1AmwPCXiTgM/vF7PiVjI0qBLRc36tUjvF5xOxU8TGP+2fV+wUG5vGqiwmYOf/AOqyWH1HFYf22SmOarz1yvi1K7Ftol0OrG9BUIlWXNuCinPTrtaNblKXupJAbASYigRIgARIwH4CwZPLje7br7U1NdKgsoYjSyEBEiABEiABEqCHKt5zgOlJgARIgARIgARIgAR0AvRQ6SS4JQESIAES8B4BtshWAkaH+YLz2aqwhZXRoLIQJosiARIgARIggXxNgEN++br72XgSIAHjBJiTBEiABAIEgj1NRvcDhblshx4ql3UY1SUBEiABEiABxxKgh8qxXUPFSIAESIAESIAEXETAqGcK+VzUzGyq0kOVDQkDSIAESIAESIAE4iWQ39PToMrvZwDbTwIkQAIkQAJWEbDizRVW6WJzOTSobAbO6kiABEjAGAHmIgHnE8CwnVlxfisja0iDKjIXhpIACZAACZAACcRLgJPS4yXG9CTgPQJsEQmQAAmQgDkCviwRsyIu/aOHyqUdR7VJgARIgARIwHEE6KGyo0tYBwmQAAmQAAmQAAl4kwA9VN7sV7aKBEiABEjAKAHmM0zA7IR05DdceYIz0qBKcAewehIgARIgARLwDAEum+CZrmRDSIAEnE+AGpIACXiUADxMZsWtaOih0nvOiol0sZbh0yq1U6y4Y3BqGRpKWz8+rePsFFsbp1VmZz9r1dn6sbNtiajLTph2t8/O/znUZSdLrS6zBkhc+bX68vQT63Uwp3R5qmDeFU6DKu/YJq5k1kwCJEACJEACCSAQl3GnGVWR0idAbUuqpEFlCUYWQgIkQAIkQAIkEC8BL6WnQeWl3mRbSIAESIAESCCRBKwYDk6k/ibqpkFlAh6zkgAJkICzCVA7ErCXQKQhvHjD7NXYuto8ZVBt2bJFVqxYISdPnrSOEEsiARIgARIgARKIjYBfS2ZWtCLc+PGMQbV27VqpXbu21KtXT7Zt2+bGvqDOLiRAlUmABEiABP4mEK83KlL6v0tz154nDKrjx49L+/bt5e6773YXfWpLAiRAAiRAAl4ikKW5p8yKS3k43KCKjWqfPn2kfv360q5du9gyMBUJkAAJkAAJkID1BDR7SsyK9VrZUqLrDaqPP/5YZs2aJa+++qotwFgJCZAACZAACWQjwIB8T8BRBlVWVpaaVI6J5dFk8+bNgU7bunWrdOvWTaZMmSLFihULhOe0g+HBgwcPSrDklJ5xJEACJEACJEACsRHwackizYuKOUzL79aPowyqw4cPq7lQmA8VTYYNGxZgDWPquuuuU8YUDLANGzaoOExQ37Fjh9oP/8rIyJDU1NSAVK5cOTwJj0mABJxHgBqRAAm4gQDXoXJGLxUvXjxXD9Xo0aMDyvp8Pvnhhx8CRtizzz6r4h5++GGZOHGi2g//Sk9PlwMHDgQESy2Ep+ExCZAACZAACZBA/ARi9kT5RaKlFZf+OcpDFS9DzJ2CZ0oX3YiaO3euPPnkkxGLS05OlpSUlBAR/omQAQmQAAmQAAmYJaAZSpyUbhYi85MACZAACZAACeRrAj5tyM+s5AbQqfGu9lCFQy1atKjUqVNHChcuHB7FYxIgARIgARIgARLIMwKeMqguvfRSNQerQoUKeQaMBZMACZCAtwmwdSRggkCWltesaEW48eMpg8qNHUCdSYAESIAESMArBMwO9yG/W1nQoHJrz1Fv1xKg4iRAAiTgWQI2T0rftm2bdOnSRS644AK57LLLZMKECTmixbJKaWlpkhYmixcvzjFfLJE0qGKh5PY0Pp+IV0U8/ufVfkO7PN51tv/P2ckT/Wen2Nk21mWOgF+zqMxKjBocO3ZMrrrqKvnjjz9k0qRJ0qNHD8GySa+99lrUEk6cOCGbNm2SadOmybx58wJSv379qHlijfCgQRVr05mOBEiABEiABEjASgLR1paKJzxWfaZOnSobN26Ud999Vxo1aiSdOnWSxx57TAYPHix480pO5WBR77QgL1WRIkVySh5THA2qmDAxEQmQAAmQAAlYTIDFmSIwT/MwwZAqUaJEoJzWrVsL3pSCob1AYISdf/7zn1K3bl1p27atLFq0KEKK+INoUMXPjDlIgARIgARIgAQiETA73If8WrnB79vFPt7DqwWHfPA+33LlyoWE6ceYWxUScebA5/NJhw4dZPjw4fL2229L9erVpVmzZjJ79uwzKYxvaFAZZ8ecJEACziZA7UiABGwm4MsSMSui/WFILjU1VXTJyMjQQkM/GNYrWLBgSGChQoXUcWZmptqGf2Hy+pQpU6RFixbSsGFDeeGFF+T222+Xp59+Ojxp3Mc0qOJGxgwkQAIkQAIkQ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Load the font from CDN so widget icons render. + - https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css extra_javascript: - assets/javascripts/extra.js @@ -179,6 +181,7 @@ plugins: - url: https://scipp.github.io/scitiff/objects.inv - url: https://scipp.github.io/plopp/objects.inv - search + - markdown-exec # Determines additional directories to watch when running mkdocs serve watch: @@ -204,7 +207,13 @@ nav: - The Measurement class: tutorials/measurement.ipynb - API Reference: - API Reference: api-reference/index.md - - Measurement: api-reference/measurement/measurement.md + - measurement: api-reference/measurement/measurement.md - regions_of_interest: - regions_of_interest: api-reference/regions_of_interest/index.md - RectangleROI: api-reference/regions_of_interest/rectangle_roi.md + - sample_model: + - SampleModel: api-reference/sample_model/index.md + - Lattice: api-reference/sample_model/lattice.md + - AtomSite: api-reference/sample_model/atom_site.md + - Atoms: api-reference/sample_model/atoms.md + - 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name: watchdog - version: 6.0.0 - sha256: a175f755fc2279e0b7312c0035d52e27211a5bc39719dd529625b1930917345b - requires_dist: - - pyyaml>=3.10 ; extra == 'watchmedo' - requires_python: '>=3.9' -- pypi: https://files.pythonhosted.org/packages/fe/d7/a71b12b625c44cfcb95d41397752edccc0a731903081609cffd6720f671d/asteval-1.0.9-py3-none-any.whl - name: asteval - version: 1.0.9 - sha256: 60513ea5d608d27bdb0cf3785dc993a6c3adeaee45f1bded0953a09e904e461f - requires_dist: - - build ; extra == 'dev' - - twine ; extra == 'dev' - - sphinx ; extra == 'doc' - - pytest ; extra == 'test' - - pytest-cov ; extra == 'test' - - coverage ; extra == 'test' - - asteval[dev,doc,test] ; extra == 'all' - requires_python: '>=3.10' + requires_python: '>=3.11' diff --git a/pixi.toml b/pixi.toml index 9c262e1..d86e8ac 100644 --- a/pixi.toml +++ b/pixi.toml @@ -3,14 +3,13 @@ ########### [workspace] - # Supported platforms for the lock file (pixi.lock) platforms = [ 'win-64', # Set minimum supported version for glibc to be 2.35 to ensure packages # like `crysfml` that only have wheels for glibc 2.35+ # (manylinux_2_35_x86_64) are used. - #libc = { family = 'glibc', version = '2.35' } + # libc = { family = 'glibc', version = '2.35' } { platform = 'linux-64', glibc = '2.35' }, # Set minimum supported version for macOS to be 14.0 to ensure packages # like `scipp` that only have wheels for macOS 14.0+ (macosx_14_0_arm64) @@ -36,19 +35,19 @@ PYTHONIOENCODING = 'utf-8' # Set specific Python versions to be used in CI testing. [feature.py-min.dependencies] -python = '3.11.*' +python = '3.12.*' [feature.py-max.dependencies] -python = '3.13.*' +python = '3.14.*' # Development dependencies for local development and testing with # editable installations. [feature.dev.dependencies] -nodejs = '*' # Required for Prettier (non-Python formatting) -jupyterlab = '*' # Jupyter notebooks -ipython = '*' # Interactive Python shell -pixi-kernel = '*' # Pixi Jupyter kernel +nodejs = '*' # Required for Prettier (non-Python formatting) +jupyterlab = '*' # Jupyter notebooks +ipython = '*' # Interactive Python shell +pixi-kernel = '*' # Pixi Jupyter kernel [feature.dev.pypi-dependencies] pip = '*' @@ -58,9 +57,9 @@ easyimaging = { path = '.', editable = true, extras = ['dev'] } # of editable installations. [feature.user.dependencies] -jupyterlab = '*' # Jupyter notebooks -ipython = '*' # Interactive Python shell -pixi-kernel = '*' # Pixi Jupyter kernel +jupyterlab = '*' # Jupyter notebooks +ipython = '*' # Interactive Python shell +pixi-kernel = '*' # Pixi Jupyter kernel [feature.user.pypi-dependencies] pip = '*' @@ -71,13 +70,12 @@ easyimaging = '*' ############## [environments] - # The `default` feature is always included in all environments. # Additional features can be specified per environment. # Specific environments for CI testing with different Python versions. -py-311-env = { features = ['py-min', 'dev'] } -py-313-env = { features = ['py-max', 'dev'] } +py-312-env = { features = ['py-min', 'dev'] } +py-314-env = { features = ['py-max', 'dev'] } # The `default` environment is developer-oriented for local development # and testing with editable installation of the current package. @@ -92,7 +90,6 @@ user = { features = ['py-max', 'user'] } ####### [tasks] - ################## # 🧪 Testing Tasks ################## @@ -177,9 +174,9 @@ notebook-tweak = 'python tools/tweak_notebooks.py docs/docs/tutorials/' notebook-exec = 'python -m pytest --nbmake docs/docs/tutorials/ --nbmake-timeout=1200 --overwrite --color=yes -n auto -v' notebook-prepare = { depends-on = [ - #'notebook-convert', + # 'notebook-convert', 'notebook-strip', - #'notebook-tweak', + # 'notebook-tweak', ] } ######################## @@ -271,7 +268,7 @@ clean-pycache = "find . -type d -name '__pycache__' -prune -exec rm -rf '{}' +" post-install = { depends-on = [ 'npm-config', 'prettier-install', - #'pre-commit-setup', + # 'pre-commit-setup', ] } ########################## diff --git a/prettierrc.toml b/prettierrc.toml index 2e02224..7ddeb9e 100644 --- a/prettierrc.toml +++ b/prettierrc.toml @@ -1,22 +1,25 @@ plugins = [ - "prettier-plugin-toml", # use the TOML plugin + 'prettier-plugin-toml', # use the TOML plugin ] -endOfLine = 'lf' # change line endings to LF -proseWrap = 'always' # change wrapping in Markdown files -semi = false # remove semicolons -singleQuote = true # use single quotes instead of double quotes -tabWidth = 2 # change tab width to 2 spaces -useTabs = false # use spaces instead of tabs +endOfLine = 'lf' # change line endings to LF +proseWrap = 'always' # change wrapping in Markdown files +semi = false # remove semicolons +singleQuote = true # use single quotes instead of double quotes +tabWidth = 2 # change tab width to 2 spaces +trailingCommentAlignment = true # align trailing comments +useTabs = false # use spaces instead of tabs -printWidth = 88 # wrap lines at 88 characters +printWidth = 88 # wrap lines at 88 characters [[overrides]] -files = ["*.md"] +files = ['*.md'] + [overrides.options] -printWidth = 73 # wrap Markdown files at 73 characters +printWidth = 73 # wrap Markdown files at 73 characters [[overrides]] -files = ["*.yml", "*.yaml"] +files = ['*.yml', '*.yaml'] + [overrides.options] -printWidth = 88 # wrap YAML files at 88 characters +printWidth = 88 # wrap YAML files at 88 characters diff --git a/pyproject.toml b/pyproject.toml index 3b2f999..15c29fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ [project] name = 'easyimaging' -dynamic = ['version'] # Use versioningit to manage the version +dynamic = ['version'] # Use versioningit to manage the version description = 'Neutron imaging data analysis' authors = [{ name = 'EasyScience contributors' }] readme = 'README.md' @@ -17,55 +17,58 @@ classifiers = [ 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.11', 'Programming Language :: Python :: 3.12', 'Programming Language :: Python :: 3.13', + 'Programming Language :: Python :: 3.14', 'Development Status :: 3 - Alpha', ] -requires-python = '>=3.11, <3.14' +requires-python = '>=3.12, <3.15' dependencies = [ - 'easyscience>=2.2.0', # The base library of the EasyScience framework - 'scitiff', # The data-format loader/saver for neutron tiffs - 'essimaging', # The ESS imaging reduction module - 'plopp', # The plotting library of scipp - 'ncrystal[composer]', # The backend calculator - 'pooch', # Data downloader + # 'easyscience>2.2.0', # The base library of the EasyScience framework + 'easyscience@git+https://github.com/easyscience/easyscience.git@develop', + 'scitiff', # The data-format loader/saver for neutron tiffs + 'essimaging', # The ESS imaging reduction module + 'plopp', # The plotting library of scipp + 'ncrystal[composer]', # The backend calculator + 'pooch', # Data downloader + 'pandas', # For pretty tables ] [project.optional-dependencies] dev = [ - 'GitPython', # Interact with Git repositories - 'build', # Building the package - 'pre-commit', # Pre-commit hooks - 'jinja2', # Templating - 'nbmake', # Building notebooks - 'nbstripout', # Strip output from notebooks - 'nbqa', # Linting and formatting notebooks - 'pytest', # Testing - 'pytest-cov', # Test coverage - 'pytest-xdist', # Enable parallel testing - 'ruff', # Linting and formatting code - 'radon', # Code complexity and maintainability - 'validate-pyproject[all]', # Validate pyproject.toml - 'versioningit', # Automatic versioning from git tags - 'jupytext', # Jupyter notebook text format support - 'jupyterquiz', # Quizzes in Jupyter notebooks - 'pydoclint', # Docstring linter - 'docstring-parser-fork!=0.0.15', # Parser used by pydoclint. 0.0.15 splits NumPy-style defaults -> DOC105 failures - 'format-docstring', # Docstring formatter - 'docstripy', # Convert docstrings to other formats - 'interrogate', # Docstring coverage checker - 'copier', # Template management - 'mike', # MkDocs: Versioned documentation support - 'mkdocs', # Static site generator - 'mkdocs-material', # Documentation framework on top of MkDocs - 'mkdocs-autorefs', # MkDocs: Auto-references support - 'mkdocs-jupyter', # MkDocs: Jupyter notebook support - 'mkdocs-plugin-inline-svg', # MkDocs: Inline SVG support - 'mkdocs-markdownextradata-plugin', # MkDocs: Markdown extra data support, such as global variables - 'mkdocstrings-python', # MkDocs: Python docstring support - 'pyyaml', # YAML parser - 'spdx-headers', # SPDX license header validation + 'GitPython', # Interact with Git repositories + 'build', # Building the package + 'pre-commit', # Pre-commit hooks + 'jinja2', # Templating + 'nbmake', # Building notebooks + 'nbstripout', # Strip output from notebooks + 'nbqa', # Linting and formatting notebooks + 'pytest', # Testing + 'pytest-cov', # Test coverage + 'pytest-xdist', # Enable parallel testing + 'ruff', # Linting and formatting code + 'radon', # Code complexity and maintainability + 'validate-pyproject[all]', # Validate pyproject.toml + 'versioningit', # Automatic versioning from git tags + 'jupytext', # Jupyter notebook text format support + 'jupyterquiz', # Quizzes in Jupyter notebooks + 'pydoclint', # Docstring linter + 'docstring-parser-fork!=0.0.15', # Parser used by pydoclint. 0.0.15 splits NumPy-style defaults -> DOC105 failures + 'format-docstring', # Docstring formatter + 'docstripy', # Convert docstrings to other formats + 'interrogate', # Docstring coverage checker + 'copier', # Template management + 'mike', # MkDocs: Versioned documentation support + 'mkdocs', # Static site generator + 'mkdocs-material', # Documentation framework on top of MkDocs + 'mkdocs-autorefs', # MkDocs: Auto-references support + 'mkdocs-jupyter', # MkDocs: Jupyter notebook support + 'mkdocs-plugin-inline-svg', # MkDocs: Inline SVG support + 'mkdocs-markdownextradata-plugin', # MkDocs: Markdown extra data support, such as global variables + 'mkdocstrings-python', # MkDocs: Python docstring support + 'markdown-exec[ansi]', # Execute Markdown code blocks + 'pyyaml', # YAML parser + 'spdx-headers', # SPDX license header validation ] [project.urls] @@ -97,7 +100,7 @@ packages = ['src/easyimaging'] allow-direct-references = true [tool.hatch.version] -source = 'versioningit' # Use versioningit to manage the version +source = 'versioningit' # Use versioningit to manage the version ################################ # Configuration for versioningit @@ -113,9 +116,9 @@ source = 'versioningit' # Use versioningit to manage the version # pixi.lock update without any changes to the source code. [tool.versioningit.format] -distance = '{base_version}+dev{distance}' # example: 1.2.3.post4+dev3 -dirty = '{base_version}+dirty{distance}' # example: 0.5.8+dirty3 -distance-dirty = '{base_version}+devdirty{distance}' # example: 0.5.8+devdirty3 +distance = '{base_version}+dev{distance}' # example: 1.2.3.post4+dev3 +dirty = '{base_version}+dirty{distance}' # example: 0.5.8+dirty3 +distance-dirty = '{base_version}+devdirty{distance}' # example: 0.5.8+devdirty3 # Configure how versioningit detects versions from Git # - 'match' ensures it only considers tags starting with 'v' @@ -133,9 +136,9 @@ default-tag = 'v999.0.0' # https://interrogate.readthedocs.io/en/latest/ [tool.interrogate] -fail-under = 0 # Minimum docstring coverage percentage to pass +fail-under = 0 # Minimum docstring coverage percentage to pass verbose = 1 -#exclude = ['src/**/__init__.py'] +# exclude = ['src/**/__init__.py'] ####################################### # Configuration for coverage/pytest-cov @@ -145,13 +148,13 @@ verbose = 1 # https://coverage.readthedocs.io/en/latest/ [tool.coverage.run] -branch = true # Measure branch coverage as well -source = ['src'] # Limit coverage to the source code directory +branch = true # Measure branch coverage as well +source = ['src'] # Limit coverage to the source code directory [tool.coverage.report] -show_missing = true # Show missing lines -skip_covered = false # Skip files with 100% coverage in the report -fail_under = 0 # Minimum coverage percentage to pass +show_missing = true # Show missing lines +skip_covered = false # Skip files with 100% coverage in the report +fail_under = 0 # Minimum coverage percentage to pass ########################## # Configuration for pytest @@ -175,115 +178,117 @@ testpaths = ['tests'] [tool.ruff] exclude = ['ProofOfConcept*', 'tmp'] indent-width = 4 -line-length = 127 # See also `max-line-length` in [tool.ruff.lint.pycodestyle] -preview = true # Enable new rules that are not yet stable, like DOC -builtins = ['display'] # Clutch-fix/patch to https://github.com/nbQA-dev/nbQA/issues/882 +line-length = 127 # See also `max-line-length` in [tool.ruff.lint.pycodestyle] +preview = true # Enable new rules that are not yet stable, like DOC +builtins = [ + 'display' +] # Clutch-fix/patch to https://github.com/nbQA-dev/nbQA/issues/882 # Formatting options for Ruff [tool.ruff.format] -docstring-code-format = true # Whether to format code snippets in docstrings -docstring-code-line-length = 127 # Line length for code snippets in docstrings -indent-style = 'space' # PEP 8 recommends using spaces over tabs -quote-style = 'single' # But double quotes in docstrings (PEP 8, PEP 257) +docstring-code-format = true # Whether to format code snippets in docstrings +docstring-code-line-length = 127 # Line length for code snippets in docstrings +indent-style = 'space' # PEP 8 recommends using spaces over tabs +quote-style = 'single' # But double quotes in docstrings (PEP 8, PEP 257) # Linting rules to use with Ruff [tool.ruff.lint] select = [ # Various rules - #'C90', # https://docs.astral.sh/ruff/rules/#mccabe-c90 - #'D', # https://docs.astral.sh/ruff/rules/#pydocstyle-d - 'F', # https://docs.astral.sh/ruff/rules/#pyflakes-f - #'FLY', # https://docs.astral.sh/ruff/rules/#flynt-fly - #'FURB', # https://docs.astral.sh/ruff/rules/#refurb-furb - 'I', # https://docs.astral.sh/ruff/rules/#isort-i - #'N', # https://docs.astral.sh/ruff/rules/#pep8-naming-n - #'NPY', # https://docs.astral.sh/ruff/rules/#numpy-specific-rules-npy - #'PGH', # https://docs.astral.sh/ruff/rules/#pygrep-hooks-pgh - #'PERF', # https://docs.astral.sh/ruff/rules/#perflint-perf - #'RUF', # https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf - #'TRY', # https://docs.astral.sh/ruff/rules/#tryceratops-try - #'UP', # https://docs.astral.sh/ruff/rules/#pyupgrade-up + # 'C90', # https://docs.astral.sh/ruff/rules/#mccabe-c90 + # 'D', # https://docs.astral.sh/ruff/rules/#pydocstyle-d + 'F', # https://docs.astral.sh/ruff/rules/#pyflakes-f + # 'FLY', # https://docs.astral.sh/ruff/rules/#flynt-fly + # 'FURB', # https://docs.astral.sh/ruff/rules/#refurb-furb + 'I', # https://docs.astral.sh/ruff/rules/#isort-i + # 'N', # https://docs.astral.sh/ruff/rules/#pep8-naming-n + # 'NPY', # https://docs.astral.sh/ruff/rules/#numpy-specific-rules-npy + # 'PGH', # https://docs.astral.sh/ruff/rules/#pygrep-hooks-pgh + # 'PERF', # https://docs.astral.sh/ruff/rules/#perflint-perf + # 'RUF', # https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf + # 'TRY', # https://docs.astral.sh/ruff/rules/#tryceratops-try + # 'UP', # https://docs.astral.sh/ruff/rules/#pyupgrade-up # pycodestyle (E, W) rules - 'E', # https://docs.astral.sh/ruff/rules/#error-e - 'W', # https://docs.astral.sh/ruff/rules/#warning-w + 'E', # https://docs.astral.sh/ruff/rules/#error-e + 'W', # https://docs.astral.sh/ruff/rules/#warning-w # Pylint (PL) rules - #'PLC', # https://docs.astral.sh/ruff/rules/#convention-plc - #'PLE', # https://docs.astral.sh/ruff/rules/#error-ple - #'PLR', # https://docs.astral.sh/ruff/rules/#refactor-plr - #'PLW', # https://docs.astral.sh/ruff/rules/#warning-plw + # 'PLC', # https://docs.astral.sh/ruff/rules/#convention-plc + # 'PLE', # https://docs.astral.sh/ruff/rules/#error-ple + # 'PLR', # https://docs.astral.sh/ruff/rules/#refactor-plr + # 'PLW', # https://docs.astral.sh/ruff/rules/#warning-plw # flake8 rules - #'A', # https://docs.astral.sh/ruff/rules/#flake8-builtins-a - #'ANN', # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann - #'ARG', # https://docs.astral.sh/ruff/rules/#flake8-unused-arguments-arg - #'ASYNC', # https://docs.astral.sh/ruff/rules/#flake8-async-async - #'B', # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b - #'BLE', # https://docs.astral.sh/ruff/rules/#flake8-blind-except-ble - #'C4', # https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4 - #'COM', # https://docs.astral.sh/ruff/rules/#flake8-commas-com - #'DTZ', # https://docs.astral.sh/ruff/rules/#flake8-datetimez-dtz - #'EM', # https://docs.astral.sh/ruff/rules/#flake8-errmsg-em - #'FA', # https://docs.astral.sh/ruff/rules/#flake8-future-annotations-fa - #'FBT', # https://docs.astral.sh/ruff/rules/#flake8-boolean-trap-fbt - #'FIX', # https://docs.astral.sh/ruff/rules/#flake8-fixme-fix - #'G', # https://docs.astral.sh/ruff/rules/#flake8-logging-format-g - #'ICN', # https://docs.astral.sh/ruff/rules/#flake8-import-conventions-icn - #'INP', # https://docs.astral.sh/ruff/rules/#flake8-no-pep420-inp - #'ISC', # https://docs.astral.sh/ruff/rules/#flake8-implicit-str-concat-isc - #'LOG', # https://docs.astral.sh/ruff/rules/#flake8-logging-log - #'PIE', # https://docs.astral.sh/ruff/rules/#flake8-pie-pie - #'PT', # https://docs.astral.sh/ruff/rules/#flake8-pytest-style-pt - #'PTH', # https://docs.astral.sh/ruff/rules/#flake8-use-pathlib-pth - #'PYI', # https://docs.astral.sh/ruff/rules/#flake8-pyi-pyi - #'RET', # https://docs.astral.sh/ruff/rules/#flake8-return-ret - #'RSE', # https://docs.astral.sh/ruff/rules/#flake8-raise-rse - 'S', # https://docs.astral.sh/ruff/rules/#flake8-bandit-s - #'SIM', # https://docs.astral.sh/ruff/rules/#flake8-simplify-sim - #'SLF', # https://docs.astral.sh/ruff/rules/#flake8-self-slf - #'SLOT', # https://docs.astral.sh/ruff/rules/#flake8-slots-slot - #'T20', # https://docs.astral.sh/ruff/rules/#flake8-print-t20 - #'TC', # https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc - #'TD', # https://docs.astral.sh/ruff/rules/#flake8-todos-td - #'TID', # https://docs.astral.sh/ruff/rules/#flake8-tidy-imports-tid + # 'A', # https://docs.astral.sh/ruff/rules/#flake8-builtins-a + # 'ANN', # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann + # 'ARG', # https://docs.astral.sh/ruff/rules/#flake8-unused-arguments-arg + # 'ASYNC', # https://docs.astral.sh/ruff/rules/#flake8-async-async + # 'B', # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b + # 'BLE', # https://docs.astral.sh/ruff/rules/#flake8-blind-except-ble + # 'C4', # https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4 + # 'COM', # https://docs.astral.sh/ruff/rules/#flake8-commas-com + # 'DTZ', # https://docs.astral.sh/ruff/rules/#flake8-datetimez-dtz + # 'EM', # https://docs.astral.sh/ruff/rules/#flake8-errmsg-em + # 'FA', # https://docs.astral.sh/ruff/rules/#flake8-future-annotations-fa + # 'FBT', # https://docs.astral.sh/ruff/rules/#flake8-boolean-trap-fbt + # 'FIX', # https://docs.astral.sh/ruff/rules/#flake8-fixme-fix + # 'G', # https://docs.astral.sh/ruff/rules/#flake8-logging-format-g + # 'ICN', # https://docs.astral.sh/ruff/rules/#flake8-import-conventions-icn + # 'INP', # https://docs.astral.sh/ruff/rules/#flake8-no-pep420-inp + # 'ISC', # https://docs.astral.sh/ruff/rules/#flake8-implicit-str-concat-isc + # 'LOG', # https://docs.astral.sh/ruff/rules/#flake8-logging-log + # 'PIE', # https://docs.astral.sh/ruff/rules/#flake8-pie-pie + # 'PT', # https://docs.astral.sh/ruff/rules/#flake8-pytest-style-pt + # 'PTH', # https://docs.astral.sh/ruff/rules/#flake8-use-pathlib-pth + # 'PYI', # https://docs.astral.sh/ruff/rules/#flake8-pyi-pyi + # 'RET', # https://docs.astral.sh/ruff/rules/#flake8-return-ret + # 'RSE', # https://docs.astral.sh/ruff/rules/#flake8-raise-rse + 'S', # https://docs.astral.sh/ruff/rules/#flake8-bandit-s + # 'SIM', # https://docs.astral.sh/ruff/rules/#flake8-simplify-sim + # 'SLF', # https://docs.astral.sh/ruff/rules/#flake8-self-slf + # 'SLOT', # https://docs.astral.sh/ruff/rules/#flake8-slots-slot + # 'T20', # https://docs.astral.sh/ruff/rules/#flake8-print-t20 + # 'TC', # https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc + # 'TD', # https://docs.astral.sh/ruff/rules/#flake8-todos-td + # 'TID', # https://docs.astral.sh/ruff/rules/#flake8-tidy-imports-tid ] # Exceptions to the linting rules # Ignore specific rules globally ignore = [ - 'COM812', # https://docs.astral.sh/ruff/rules/missing-trailing-comma/ + 'COM812', # https://docs.astral.sh/ruff/rules/missing-trailing-comma/ # The following is replaced by 'D'/[tool.ruff.lint.pydocstyle] and [tool.pydoclint] - 'DOC', # https://docs.astral.sh/ruff/rules/#pydoclint-doc + 'DOC', # https://docs.astral.sh/ruff/rules/#pydoclint-doc # Disable, as [tool.format_docstring] split one-line docstrings into the canonical multi-line layout - 'D200', # https://docs.astral.sh/ruff/rules/unnecessary-multiline-docstring/ + 'D200', # https://docs.astral.sh/ruff/rules/unnecessary-multiline-docstring/ ] # Ignore specific rules in certain files or directories [tool.ruff.lint.per-file-ignores] '*/__init__.py' = [ - 'F401', # re-exports are intentional in __init__.py + 'F401', # re-exports are intentional in __init__.py ] 'tests/**' = [ - 'ANN', # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann - 'D', # https://docs.astral.sh/ruff/rules/#pydocstyle-d - 'DOC', # https://docs.astral.sh/ruff/rules/#pydoclint-doc - 'INP001', # https://docs.astral.sh/ruff/rules/implicit-namespace-package/ - 'S101', # https://docs.astral.sh/ruff/rules/assert/ + 'ANN', # https://docs.astral.sh/ruff/rules/#flake8-annotations-ann + 'D', # https://docs.astral.sh/ruff/rules/#pydocstyle-d + 'DOC', # https://docs.astral.sh/ruff/rules/#pydoclint-doc + 'INP001', # https://docs.astral.sh/ruff/rules/implicit-namespace-package/ + 'S101', # https://docs.astral.sh/ruff/rules/assert/ ] 'docs/**' = [ - 'INP001', # https://docs.astral.sh/ruff/rules/implicit-namespace-package/ - 'T201', # https://docs.astral.sh/ruff/rules/print/ + 'INP001', # https://docs.astral.sh/ruff/rules/implicit-namespace-package/ + 'T201', # https://docs.astral.sh/ruff/rules/print/ ] 'docs/docs/tutorials/**' = [ - 'E402', # https://docs.astral.sh/ruff/rules/module-import-not-at-top-of-file/ + 'E402', # https://docs.astral.sh/ruff/rules/module-import-not-at-top-of-file/ ] # Specific options for certain rules [tool.ruff.lint.flake8-tidy-imports] # Disallow all relative imports -#ban-relative-imports = 'all' +# ban-relative-imports = 'all' [tool.ruff.lint.isort] # Forces all from imports to appear on their own line @@ -298,7 +303,7 @@ max-complexity = 10 # https://peps.python.org/pep-0008/#maximum-line-length # Use 127 characters as the project-wide maximum for regular code lines. # Use 127 characters for docstrings. -max-line-length = 127 # See also `line-length` in [tool.ruff] +max-line-length = 127 # See also `line-length` in [tool.ruff] max-doc-length = 127 [tool.ruff.lint.pydocstyle] @@ -325,7 +330,7 @@ max-positional-args = 6 # the parameter declarations in the code (in function's signature). [tool.pydoclint] -exclude = '\.' # Temporarily disable pydoclint until we are ready +exclude = '\.' # Temporarily disable pydoclint until we are ready style = 'numpy' check-style-mismatch = true check-arg-defaults = true @@ -339,7 +344,7 @@ allow-init-docstring = true # https://github.com/jsh9/format-docstring [tool.format_docstring] -exclude = '\.' # Temporarily disable format-docstring until we are ready +exclude = '\.' # Temporarily disable format-docstring until we are ready docstring_style = 'numpy' line_length = 127 fix_rst_backticks = true diff --git a/src/easyimaging/measurement/measurement.py b/src/easyimaging/measurement/measurement.py index 8ee91a3..f6f24d3 100644 --- a/src/easyimaging/measurement/measurement.py +++ b/src/easyimaging/measurement/measurement.py @@ -3,14 +3,17 @@ from __future__ import annotations +import numbers import warnings from functools import partial from pathlib import Path +from typing import Any import ess.imaging as essimaging import numpy as np import plopp as pp import scipp as sc +from easyscience import global_object from easyscience.base_classes import EasyList from easyscience.base_classes import NewBase from scipp import DimensionError @@ -23,8 +26,6 @@ from ..utils import _is_notebook from ..utils import _to_edges -Numeric = int | float - class Measurement(NewBase): """Object responsible for managing and inspecting the **normalized** measurement data of a time-of-flight neutron imaging @@ -352,10 +353,6 @@ def from_tiff_stack( def save_scitiff(self, filename: str | Path) -> None: """Save the `Measurement` data to a **SciTiff** file with the time-of-flight values and other provided metadata. - Note that [SciTiff](https://scipp.github.io/scitiff/) does not support ``float64`` precision numbers, so if the - [`Measurement`][..] contains data of type ``float64``, it will be downcast to ``float32`` when saving, causing a small - loss of precision. - Parameters ---------- filename : str | Path @@ -368,19 +365,20 @@ def save_scitiff(self, filename: str | Path) -> None: RuntimeError If the file cannot be written to the specified ``filename`` path. - Warns + Warnings ----- - UserWarning - If the [Measurement][..] object's data is of type ``float64``; It will be downcast to ``float32``. + [SciTiff](https://scipp.github.io/scitiff/) does not support ``float64`` precision numbers, so if the + [`Measurement`][..] contains data of type ``float64``, it will be downcast to ``float32`` when saving, causing a small + loss of precision. """ if not isinstance(filename, (str, Path)): raise TypeError('filename must be a string or Path object.') try: nan_mask = self._data_array.masks.pop('non_finite') if str(self._data_array.dtype) == 'float64': - warnings.warn( + global_object.log.warning( 'The data array is of type float64, which is not directly supported by the SciTIFF format. ' - 'It will be downcast to float32 when saving, which may result in loss of precision. ' + 'It will be downcast to float32 when saving, which may result in loss of precision.' ) save_scitiff(self._data_array.astype('float32'), filename) else: @@ -444,6 +442,49 @@ def _data_array(self, value: sc.DataArray) -> None: """ raise AttributeError('Cannot set _data_array, it is a read-only property.') + @property + def time_of_flights(self) -> sc.Variable: + """The time-of-flight values of the measurement. + + Parameters + ---------- + value : sc.Variable | np.ndarray + The new time-of-flight values to set.
      + If a numpy array is provided, the unit is assumed to be seconds. + + Returns + ------- + sc.Variable + A copy of the time-of-flight values as a [`sc.Variable`][scipp.Variable]. + + Raises + ------ + ValueError + If ``value`` is not 1-dimensional with the same length as the number of frames.
      + If any element in ``value`` is negative. + TypeError + If ``value`` is not a [`sc.Variable`][scipp.Variable] or [`np.ndarray`][numpy.ndarray]. + UnitError + If ``value`` is a [`sc.Variable`][scipp.Variable] and does not have a unit of time. + """ + return self._data_array.coords['tof'].copy() + + @time_of_flights.setter + def time_of_flights(self, value: sc.Variable | np.ndarray) -> None: + # Setters have no docstrings. They should be written in the getter docstring instead. + value = self._validate_provided_coord( + data_array=self._data_array, + coord=value, + coord_name='time_of_flights', + dim='t', + length_context='frames in the measurement', + expected_dim_string='time', + expected_unit='s', + ) + if any(value.to(unit='s') < sc.scalar(0, unit='s')): + raise ValueError('time_of_flight values must be non-negative.') + self._data_array.coords['tof'] = value + @property def x_positions(self) -> sc.Variable: """The x-coordinates of the physical positions of the pixels. @@ -607,55 +648,12 @@ def delete_physical_coord_positions(self) -> None: else: raise ValueError('Cannot delete physical coordinate positions because they are not set.') - @property - def time_of_flights(self) -> sc.Variable: - """The time-of-flight values of the measurement. - - Parameters - ---------- - value : sc.Variable | np.ndarray - The new time-of-flight values to set.
      - If a numpy array is provided, the unit is assumed to be seconds. - - Returns - ------- - sc.Variable - A copy of the time-of-flight values as a [`sc.Variable`][scipp.Variable]. - - Raises - ------ - ValueError - If ``value`` is not 1-dimensional with the same length as the number of frames.
      - If any element in ``value`` is negative. - TypeError - If ``value`` is not a [`sc.Variable`][scipp.Variable] or [`np.ndarray`][numpy.ndarray]. - UnitError - If ``value`` is a [`sc.Variable`][scipp.Variable] and does not have a unit of time. - """ - return self._data_array.coords['tof'].copy() - - @time_of_flights.setter - def time_of_flights(self, value: sc.Variable | np.ndarray) -> None: - # Setters have no docstrings. They should be written in the getter docstring instead. - value = self._validate_provided_coord( - data_array=self._data_array, - coord=value, - coord_name='time_of_flights', - dim='t', - length_context='frames in the measurement', - expected_dim_string='time', - expected_unit='s', - ) - if any(value.to(unit='s') < sc.scalar(0, unit='s')): - raise ValueError('time_of_flight values must be non-negative.') - self._data_array.coords['tof'] = value - @property def regions_of_interest(self) -> EasyList[RectangleROI]: """The list of regions of interest (ROIs) defined for this measurement.
      This attribute is **read-only**. The returned list is an [EasyList][easyscience.base_classes.EasyList] of - [regions of interest][...regions_of_interest.RectangleROI] objects. + [regions of interest][.....regions_of_interest.RectangleROI] objects. To modify the list or set new ROIs, interact directly with the returned list. Example @@ -687,7 +685,74 @@ def regions_of_interest(self) -> EasyList[RectangleROI]: # 'Please simply add or remove ROIs directly from the list.' # ) - def rebin(self, dimensions: dict[str, Numeric]) -> None: + @property + def unique_name(self) -> str: + """ + The unique identifier of the measurement object. + + Parameters + ---------- + value : str + The new unique name for the [measurement][..] object. + + Returns + ------- + str + The current unique name of the [measurement][..]. + + Raises + ------ + TypeError + If ``value`` is not a string. + ValueError + If the new unique name is already in use. + + Notes + ----- + Setting a new unique name does not free up the old name. + """ + return super().unique_name + + @unique_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def unique_name(self, value: str): + super(Measurement, type(self)).unique_name.fset(self, value) + + @property + def display_name(self) -> str: + """ + The pretty display name of the measurement object. + + This name is used when the object is displayed in user interfaces and reports. + + Parameters + ---------- + value : str | None + The new display name for the [measurement][..] object. + + Returns + ------- + str + The current display name of the [measurement][..]. + + Raises + ------ + TypeError + If ``value`` is not a string or ``None``. + + Notes + ----- + If it has not been set, the [unique name][..unique_name] of the [measurement][..] object + will be used as its display name. + """ + return super().display_name + + @display_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def display_name(self, value: str): + super(Measurement, type(self)).display_name.fset(self, value) + + def rebin(self, dimensions: dict[str, numbers.Real]) -> None: """Rebin the measurement image stack. This operation increases the signal-to-noise ratio of the [Measurement][..] data, at the cost of a reduced resolution, @@ -747,7 +812,7 @@ def rebin(self, dimensions: dict[str, Numeric]) -> None: """ if not isinstance(dimensions, dict): raise TypeError('dimensions must be a dictionary mapping dimension names to rebin factors.') - if all(isinstance(value, Numeric) and value == 1 for value in dimensions.values()): # Reverts to original data + if all(isinstance(value, numbers.Real) and value == 1 for value in dimensions.values()): # Reverts to original data # self.revert_rebin() # If we want secondary rebins to work on the original data return sizes = dimensions.copy() @@ -778,20 +843,12 @@ def rebin(self, dimensions: dict[str, Numeric]) -> None: def revert_rebin(self) -> None: """Revert any rebinning applied to the measurement data, restoring it to its original resolution. - This method undos the rebinning performed by the [rebin][..rebin] method, if it has been applied. - - Warns - ------ - UserWarning - If no rebinning has been applied and the measurement data is already in its original state. + This method undoes the rebinning performed by the [rebin][..rebin] method, if it has been applied. """ if hasattr(self, '_rebinned_data_array'): del self._rebinned_data_array else: - warnings.warn( - 'No rebinning to revert. The data array is already in its original state.', - UserWarning, - ) + global_object.log.warning('No rebinning to revert. The data array is already in its original state.') # ------------------------------------------------------------------------------------------------------------------------- # ----------------------------------------- Plotting methods -------------------------------------------------------------- @@ -1428,3 +1485,62 @@ def __repr__(self): display names of all associated regions of interest. """ return f'{self.display_name} with shape {self._data_array.shape} and regions of interest: {[roi.display_name for roi in self.regions_of_interest]}' # noqa: E501 + + def to_dict(self, skip: list[str] | None = None) -> dict[str, Any]: + """Convert the measurement object to a dictionary representation. + + Used for saving the measurement to file. + + Parameters + ---------- + skip : List[str] | None + A list of attribute names to exclude from the output dictionary. + + Returns + ------- + Dict[str, Any] + A dictionary representation of the [Measurement][..] object. + + Notes + ----- + A [unique_name][..unique_name] that was generated automatically is not + written to the dictionary; only an explicitly supplied one is, so that a + deserialized object gets a fresh name instead of clashing with an existing one. + Likewise [display_name][..display_name] is omitted when it has not been set. + """ + return super().to_dict(skip=skip) + + @classmethod + def from_dict(cls, obj_dict: dict[str, Any]) -> Measurement: + """Recreate the measurement object from its dictionary representation created with the [to_dict][to_dict] method. + + Parameters + ---------- + obj_dict : dict + A dictionary representation of the [Measurement][..] object. + + Returns + ------- + Measurement + An instance of the [Measurement][..] object recreated from the dictionary. + + Raises + ------ + ValueError + If the dictionary does not represent a serialized [Measurement][..] object. + """ + return super().from_dict(obj_dict) + + def __copy__(self) -> Measurement: + """Create a deep copy of the measurement object. + + Notes + ----- + This uses the [to_dict][to_dict] and [from_dict][from_dict] methods to create the deep copy. + + Returns + ------- + Measurement + A deep copy of the measurement object. + """ + return super().__copy__() diff --git a/src/easyimaging/regions_of_interest/rectangle_roi.py b/src/easyimaging/regions_of_interest/rectangle_roi.py index 06f81c3..fccb087 100644 --- a/src/easyimaging/regions_of_interest/rectangle_roi.py +++ b/src/easyimaging/regions_of_interest/rectangle_roi.py @@ -3,11 +3,8 @@ from __future__ import annotations +from collections.abc import Sequence from typing import Any -from typing import Dict -from typing import List -from typing import Sequence -from typing import Tuple import scipp as sc from easyscience.base_classes import NewBase @@ -17,11 +14,11 @@ class RectangleROI(NewBase): """Object representing a rectangular region of interest (ROI) in an image. - This object is most simply created by the [`roi_creator`][...Measurement.roi_creator] method in the - [Measurement][...Measurement] class. Alternatively it can be created manually as shown in the example below. + This object is most simply created by the [`roi_creator`][....Measurement.roi_creator] method in the + [Measurement][....Measurement] class. Alternatively it can be created manually as shown in the example below. Physical coordinate ranges are used by default, if they are provided **and** can be used in the - associated [Measurement][...Measurement] class. + associated [Measurement][....Measurement] class. Otherwise, the obligatory pixel coordinate ranges are used as a fallback. Example @@ -171,22 +168,22 @@ def delete_physical_coord_range(self) -> None: else: raise ValueError('Cannot delete physical coordinate ranges because they are not set.') - def pixel_slice(self) -> Tuple[slice, slice]: + def pixel_slice(self) -> tuple[slice, slice]: """Get a pixel index slice object corresponding to the ROI. Returns ------- - tuple + tuple[slice, slice] Two (x,y) slice objects representing the pixel ranges of the ROI. """ return slice(self._x_pixel_start, self._x_pixel_end), slice(self._y_pixel_start, self._y_pixel_end) - def slice(self) -> Tuple[slice, slice]: + def slice(self) -> tuple[slice, slice]: """Get a physical coordinate slice object corresponding to the ROI. Returns ------- - tuple + tuple[slice, slice] Two (x,y) slice objects representing the physical coordinate ranges of the ROI. Raises @@ -466,6 +463,73 @@ def y_end(self, value: sc.Variable): self._check_scalar(value, 'y_end') self._y_end = value + @property + def unique_name(self) -> str: + """ + The unique identifier of the rectangular ROI object. + + Parameters + ---------- + value : str + The new unique name for the [RectangleROI][..] object. + + Returns + ------- + str + The current unique name of the [rectangular ROI][..]. + + Raises + ------ + TypeError + If ``value`` is not a string. + ValueError + If the new unique name is already in use. + + Notes + ----- + Setting a new unique name does not free up the old name. + """ + return super().unique_name + + @unique_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def unique_name(self, value: str): + super(RectangleROI, type(self)).unique_name.fset(self, value) + + @property + def display_name(self) -> str: + """ + The pretty display name of the rectangular ROI object. + + This name is used when the object is displayed in user interfaces and reports. + + Parameters + ---------- + value : str | None + The new display name for the [RectangleROI][..] object. + + Returns + ------- + str + The current display name of the [rectangular ROI][..]. + + Raises + ------ + TypeError + If ``value`` is not a string or ``None``. + + Notes + ----- + If it has not been set, the [unique name][..unique_name] of the [RectangleROI][..] object + will be used as its display name. + """ + return super().display_name + + @display_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def display_name(self, value: str): + super(RectangleROI, type(self)).display_name.fset(self, value) + def _check_input_sequence(self, value: Sequence, name: str, typename: str, expected_type: any) -> None: """Validate that ``value`` is a two-element sequence of the expected type. @@ -569,7 +633,7 @@ def _single_coord_setter_check(self, value: sc.Variable, name: str) -> None: 'Please use the set_physical_coord_range method.' ) - def to_dict(self, skip: List[str] | None = None) -> Dict[str, Any]: + def to_dict(self, skip: list[str] | None = None) -> dict[str, Any]: """Convert the ROI instance to a dictionary representation. Used for saving the ROI to file. @@ -579,14 +643,20 @@ def to_dict(self, skip: List[str] | None = None) -> Dict[str, Any]: skip : list[str] | None A list of attribute names to exclude from the output dictionary.
      The ``'x_pixel_range'``, ``'y_pixel_range'``, ``'x_range'``, and - ``'y_range'`` keys are always handled explicitly and appended - regardless of this parameter. + ``'y_range'`` keys cannot be excluded. Returns ------- dict A dictionary containing the serialised ROI data, including pixel ranges and, if available, physical coordinate ranges encoded as scipp scalar dicts. + + Notes + ----- + A [unique_name][..unique_name] that was generated automatically is not + written to the dictionary; only an explicitly supplied one is, so that a + deserialized object gets a fresh name instead of clashing with an existing one. + Likewise [display_name][..display_name] is omitted when it has not been set. """ if skip is None: skip = [] @@ -629,19 +699,23 @@ def to_dict(self, skip: List[str] | None = None) -> Dict[str, Any]: return out_dict @classmethod - def from_dict(cls, input_dict: Dict[str, Any]) -> RectangleROI: - """Create a [`RectangleROI`][..] instance from a dictionary representation. + def from_dict(cls, input_dict: dict[str, Any]) -> RectangleROI: + """Recreate the RectangleROI object from its dictionary representation created with the [to_dict][to_dict] method. Parameters ---------- input_dict : dict - A dictionary in a format as produced by [`to_dict`][..to_dict], containing at minimum - ``'x_pixel_range'`` and ``'y_pixel_range'`` keys. + A dictionary representation of the [RectangleROI][..] object. Returns ------- RectangleROI - A new [`RectangleROI`][..] instance initialised with the data from the dictionary. + An instance of the [RectangleROI][..] object recreated from the dictionary. + + Raises + ------ + ValueError + If the dictionary does not represent a serialized [RectangleROI][..] object. """ temp_dict = input_dict.copy() if 'x_range' in input_dict and 'y_range' in input_dict: @@ -666,3 +740,17 @@ def __repr__(self) -> str: repr_str += f', x_range=({self._x_start}, {self._x_end}), y_range=({self._y_start}, {self._y_end})' repr_str += ')' return repr_str + + def __copy__(self) -> RectangleROI: + """Create a deep copy of the RectangleROI object. + + Notes + ----- + This uses the [to_dict][to_dict] and [from_dict][from_dict] methods to create the deep copy. + + Returns + ------- + RectangleROI + A deep copy of the RectangleROI object. + """ + return super().__copy__() diff --git a/src/easyimaging/sample_model/__init__.py b/src/easyimaging/sample_model/__init__.py new file mode 100644 index 0000000..4030596 --- /dev/null +++ b/src/easyimaging/sample_model/__init__.py @@ -0,0 +1,6 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +from .atom_site import AtomSite +from .atoms import Atoms +from .lattice import Lattice diff --git a/src/easyimaging/sample_model/atom_site.py b/src/easyimaging/sample_model/atom_site.py new file mode 100644 index 0000000..499d1da --- /dev/null +++ b/src/easyimaging/sample_model/atom_site.py @@ -0,0 +1,455 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import numbers +from typing import Any + +from easyscience import global_object +from easyscience.base_classes import ModelBase +from easyscience.variable import Parameter + +from .atoms import Atoms + +DEBYE_INFINITESIMAL = 1e-9 + + +class AtomSite(ModelBase): + """An [`AtomSite`][.] object represents a specific position in a crystal structure and its occupying atom, defined by its + [atomic species][...atoms.Atom], vibrational properties, and fractional coordinates $(a, b, c)$ within the host lattice's + unit cell. + + Fractional coordinates $(a, b, c)$ are wrapped around the unit-cell to the half-open range [0, 1). + + An [`AtomSite`][.] is most commonly created as part of a [`Lattice`][...lattice.Lattice], either directly via its + constructor, or indirectly through one of the crystal-structure convenience functions in + [`easyimaging.sample_model.crystals`][...crystals]. + + Example + ------- + Creating an [`AtomSite`][.] instance manually: + ```python + from easyimaging.sample_model import AtomSite, Atoms + + site = AtomSite( + atom=Atoms.Fe, + fract_a=0.0, + fract_b=0.0, + fract_c=0.0, + debye_temperature=470.0, + ) + ``` + """ + + def __init__( + self, + atom: Atoms | str, + fract_a: numbers.Real, + fract_b: numbers.Real, + fract_c: numbers.Real, + debye_temperature: numbers.Real | None = None, + unique_name: str | None = None, + display_name: str | None = None, + ): + """ + Initialize an AtomSite instance. + + Parameters + ---------- + atom : Atoms | str + The atomic species that occupies the site. Can be either an [`Atoms`][....atoms.Atoms] object or its name as a + string. + fract_a : int | float + The fractional a-coordinate of the site. + fract_b : int | float + The fractional b-coordinate of the site. + fract_c : int | float + The fractional c-coordinate of the site. + debye_temperature : float | int | None + The Debye temperature of the atom site in Kelvin. If None, a default value of 300 K is used. + unique_name : str | None + A unique identifier for the [`AtomSite`][..]. Defaults to ``'AtomSite'``, prepended with the atomic species label, + and appended with a unique integer. + display_name : str | None + A prettily formatted name for the [`AtomSite`][..]. Defaults to [`unique_name`][..unique_name] if not provided. + + Raises + ------ + TypeError + If `atom` is not an [`Atoms`][....atoms.Atoms] enum member or a string.
      + If any of the fractional coordinates or the debye_temperature are not numbers. + ValueError + If any of the fractional coordinates are not within the range [-1.0, 1.0]. + If the debye_temperature is not positive. + KeyError + If the provided atom string does not correspond to a valid [`Atoms`][....atoms.Atoms] enum member. + """ + + atom = self._validate_atom(atom) + self._atom = atom + + wrapped_fract_a = self._validate_fract_value(fract_a, 'a') + wrapped_fract_b = self._validate_fract_value(fract_b, 'b') + wrapped_fract_c = self._validate_fract_value(fract_c, 'c') + if debye_temperature is not None: + self._validate_debye_temperature(debye_temperature) + + if unique_name is None: + unique_name = global_object.generate_unique_name(f'{atom.name} AtomSite') + super().__init__(unique_name=unique_name, display_name=display_name) + self._default_unique_name = True # This gets set to False by the super init + else: + super().__init__(unique_name=unique_name, display_name=display_name) + + if debye_temperature is None: + global_object.log.warning( + f"Debye temperature not provided for AtomSite '{self.unique_name}'. Setting to default value of 300 K." + ) + debye_temperature = 300.0 + + self._fract_a = self._generate_fract_parameter(wrapped_fract_a, 'a') + self._fract_b = self._generate_fract_parameter(wrapped_fract_b, 'b') + self._fract_c = self._generate_fract_parameter(wrapped_fract_c, 'c') + + debye_name = global_object.generate_unique_name(f'{self.unique_name}_debye_temperature') + self._debye_temperature = Parameter( + name='debye_temperature', + value=debye_temperature, + unit='K', + min=DEBYE_INFINITESIMAL, + fixed=True, + unique_name=debye_name, + ) + + @property + def atom(self) -> Atoms: + """The atomic species occupying the site. + + Parameters + ---------- + value : Atoms | str + An [`Atoms`][....atoms.Atoms] enum member, or the name of one as a string. + + Returns + ------- + Atoms + The atomic species occupying the site. + + Raises + ------ + TypeError + If ``value`` is neither an [`Atoms`][....atoms.Atoms] enum member nor a string. + KeyError + If ``value`` is a string that does not correspond to a valid [`Atoms`][....atoms.Atoms] member. + """ + return self._atom + + @atom.setter + def atom(self, value: Atoms | str): + # Setters have no docstrings. They should be written in the getter docstring instead. + value = self._validate_atom(value) + self._atom = value + if self._default_unique_name: + self.unique_name = global_object.generate_unique_name(f'{value.name} AtomSite') + self._default_unique_name = True + # Change _default_unique_name when Parameter uses NewBase + self.fract_a.unique_name = global_object.generate_unique_name(f'{self.unique_name}_fract_a') + self.fract_b.unique_name = global_object.generate_unique_name(f'{self.unique_name}_fract_b') + self.fract_c.unique_name = global_object.generate_unique_name(f'{self.unique_name}_fract_c') + self.debye_temperature.unique_name = global_object.generate_unique_name(f'{self.unique_name}_debye_temperature') + + @property + def fract_a(self) -> Parameter: + """The fractional x-coordinate of the site. + + The value is wrapped around the unit-cell to the half-open range [0, 1). + + Parameters + ---------- + value : int | float + A number between -1.0 and 1.0. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] holding the fractional x-coordinate of the site. + + Raises + ------ + TypeError + If ``value`` is not a number. + ValueError + If ``value`` is not between -1.0 and 1.0. + """ + return self._fract_a + + @fract_a.setter + def fract_a(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + wrapped_value = self._validate_fract_value(value, 'a') + self._fract_a.value = wrapped_value + + @property + def fract_b(self) -> Parameter: + """The fractional y-coordinate of the site. + + The value is wrapped around the unit-cell to the half-open range [0, 1). + + Parameters + ---------- + value : int | float + A number between -1.0 and 1.0. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] holding the fractional y-coordinate of the site. + + Raises + ------ + TypeError + If ``value`` is not a number. + ValueError + If ``value`` is not between -1.0 and 1.0. + """ + return self._fract_b + + @fract_b.setter + def fract_b(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + wrapped_value = self._validate_fract_value(value, 'b') + self._fract_b.value = wrapped_value + + @property + def fract_c(self) -> Parameter: + """The fractional z-coordinate of the site. + + The value is wrapped around the unit-cell to the half-open range [0, 1). + + Parameters + ---------- + value : int | float + A number between -1.0 and 1.0. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] holding the fractional z-coordinate of the site. + + Raises + ------ + TypeError + If ``value`` is not a number. + ValueError + If ``value`` is not between -1.0 and 1.0. + """ + return self._fract_c + + @fract_c.setter + def fract_c(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + wrapped_value = self._validate_fract_value(value, 'c') + self._fract_c.value = wrapped_value + + @property + def debye_temperature(self) -> Parameter: + """The Debye temperature of the atom site. + + Parameters + ---------- + value : int | float + A positive number, in Kelvin. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] holding the Debye temperature of the atom site, in Kelvin. + + Raises + ------ + TypeError + If ``value`` is not a number. + ValueError + If ``value`` is not positive. + """ + return self._debye_temperature + + @debye_temperature.setter + def debye_temperature(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_debye_temperature(value) + self._debye_temperature.value = value + + @property + def unique_name(self) -> str: + """ + The unique identifier of the AtomSite object. + + Parameters + ---------- + value : str + The new unique name for the [AtomSite][..] object. + + Returns + ------- + str + The current unique name of the [AtomSite][..]. + + Raises + ------ + TypeError + If ``value`` is not a string. + ValueError + If the new unique name is already in use. + + Notes + ----- + Setting a new unique name does not free up the old name. + """ + return super().unique_name + + @unique_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def unique_name(self, value: str): + super(AtomSite, type(self)).unique_name.fset(self, value) + + @property + def display_name(self) -> str: + """ + The pretty display name of the AtomSite object. + + This name is used when the object is displayed in user interfaces and reports. + + Parameters + ---------- + value : str | None + The new display name for the [AtomSite][..] object. + + Returns + ------- + str + The current display name of the [AtomSite][..]. + + Raises + ------ + TypeError + If ``value`` is not a string or ``None``. + + Notes + ----- + If it has not been set, the [unique name][..unique_name] of the [AtomSite][..] object + will be used as its display name. + """ + return super().display_name + + @display_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def display_name(self, value: str): + super(AtomSite, type(self)).display_name.fset(self, value) + + def _validate_fract_value(self, value: numbers.Real, axis: str): + if not isinstance(value, numbers.Real) or isinstance(value, bool): + raise TypeError(f'"fract_{axis}" must be a numeric value') + if not (-1.0 <= value <= 1.0): + raise ValueError(f'"fract_{axis}" must be between -1.0 and 1.0') + wrapped_value = value % 1.0 + return 0.0 if wrapped_value == 1.0 else wrapped_value # a tiny negative value rounds up to 1.0 + + def _validate_debye_temperature(self, value: numbers.Real): + if not isinstance(value, numbers.Real): + raise TypeError('"debye_temperature" must be a numeric value') + if value <= DEBYE_INFINITESIMAL: + raise ValueError('"debye_temperature" must be positive') + + def _validate_atom(self, value): + if isinstance(value, str): + try: + value = Atoms[value] + except KeyError: + raise KeyError(f'"atom" must be a valid Atoms enum or a valid Atoms name string. Got: {value}') + elif not isinstance(value, Atoms): + raise TypeError(f'"atom" must be a valid Atoms enum or a valid Atoms name string. Got: {value}') + return value + + def _generate_fract_parameter(self, fract_value: numbers.Real, axis: str) -> Parameter: + + unique_name = global_object.generate_unique_name(f'{self.unique_name}_fract_{axis}') + + # Change _default_unique_name when Parameter uses NewBase + return Parameter(name=f'fract_{axis}', value=fract_value, min=0.0, max=1.0, fixed=True, unique_name=unique_name) + + def __repr__(self): + """Return a string representation of the AtomSite. + + Returns + ------- + str + A human-readable summary including the atomic species and fractional coordinates. + """ + return ( + f"AtomSite(atomic_species='{self.atom.name}', fract_a={self.fract_a.value}," + f' fract_b={self.fract_b.value}, fract_c={self.fract_c.value})' + ) + + def to_dict(self, skip: list[str] | None = None) -> dict[str, Any]: + """Convert the AtomSite object to a dictionary representation. + + Used for saving the [AtomSite][..] to file. + + Parameters + ---------- + skip : List[str] | None + A list of attribute names to exclude from the output dictionary. + + Returns + ------- + Dict[str, Any] + A dictionary representation of the [AtomSite][..] object. + + Notes + ----- + A [unique_name][..unique_name] that was generated automatically is not + written to the dictionary; only an explicitly supplied one is, so that a + deserialized object gets a fresh name instead of clashing with an existing one. + Likewise [display_name][..display_name] is omitted when it has not been set. + """ + temp_dict = super().to_dict(skip=skip) + if 'atom' not in (skip or []): + temp_dict['atom'] = self.atom.name + return temp_dict + + @classmethod + def from_dict(cls, obj_dict: dict[str, Any]) -> AtomSite: + """Recreate the AtomSite object from its dictionary representation created with the [to_dict][to_dict] method. + + Parameters + ---------- + obj_dict : dict + A dictionary representation of the [AtomSite][..] object. + + Returns + ------- + AtomSite + An instance of the [AtomSite][..] object recreated from the dictionary. + + Raises + ------ + ValueError + If the dictionary does not represent a serialized [AtomSite][..] object. + """ + return super().from_dict(obj_dict) + + def __copy__(self) -> AtomSite: + """Create a deep copy of the AtomSite object. + + Notes + ----- + This uses the [to_dict][to_dict] and [from_dict][from_dict] methods to create the deep copy. + + Returns + ------- + AtomSite + A deep copy of the AtomSite object. + """ + return super().__copy__() diff --git a/src/easyimaging/sample_model/atoms.py b/src/easyimaging/sample_model/atoms.py new file mode 100644 index 0000000..fb85199 --- /dev/null +++ b/src/easyimaging/sample_model/atoms.py @@ -0,0 +1,400 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import dataclass +from enum import Enum + + +@dataclass(frozen=True) +class Atom: + """A dataclass representing a known atomic species, identified by its element symbol and an optional isotope + mass number. + + This is the base class of the [`Atoms`][..Atoms] enumeration, where each member is an [`Atom`][.] instance. + + Attributes + ---------- + element : str + The chemical element symbol, e.g. ``'Mg'``. + isotope : int | None + The isotope mass number, e.g. ``56`` for iron-56. ``None`` indicates the naturally occurring + (isotopically unspecified) abundance of the element. + enum_id : int + A unique numeric identifier for the atomic species. + """ + + element: str + isotope: int | None + enum_id: int + + +class Atoms(Atom, Enum): + """An enumeration of known atomic species, each member being an [`Atom`][.] instance with its ``element``, + ``isotope``, and ``enum_id`` attributes. + + Members are named after their element symbol for the naturally occurring abundance, e.g. ``Atoms.Fe``, and + after their element symbol followed by the isotope mass number for a specific isotope, e.g. ``Atoms.Fe56``. + + Example + ------- + ```python + from easyimaging.sample_model import Atoms + + natural_iron = Atoms.Fe + iron_56 = Atoms.Fe56 + iron_56_by_name = Atoms['Fe56'] + + print(natural_iron.element, natural_iron.isotope) # 'Fe' None + print(iron_56.element, iron_56.isotope) # 'Fe' 56 + print(iron_56 in Atoms) # True + ``` + """ + + H = 'H', None, 11 + H1 = 'H', 1, 12 + H2 = 'H', 2, 13 + H3 = 'H', 3, 14 + He = 'He', None, 21 + He3 = 'He', 3, 22 + He4 = 'He', 4, 23 + Li = 'Li', None, 31 + Li6 = 'Li', 6, 32 + Li7 = 'Li', 7, 33 + Be = 'Be', None, 41 + Be9 = 'Be', 9, 42 + B = 'B', None, 51 + B10 = 'B', 10, 52 + B11 = 'B', 11, 53 + C = 'C', None, 61 + C12 = 'C', 12, 62 + C13 = 'C', 13, 63 + N = 'N', None, 71 + N14 = 'N', 14, 72 + N15 = 'N', 15, 73 + O = 'O', None, 81 # noqa: E741 + O16 = 'O', 16, 82 + O17 = 'O', 17, 83 + O18 = 'O', 18, 84 + F = 'F', None, 91 + F19 = 'F', 19, 92 + Ne = 'Ne', None, 101 + Ne20 = 'Ne', 20, 102 + Ne21 = 'Ne', 21, 103 + Ne22 = 'Ne', 22, 104 + Na = 'Na', None, 111 + Na23 = 'Na', 23, 112 + Mg = 'Mg', None, 121 + Mg24 = 'Mg', 24, 122 + Mg25 = 'Mg', 25, 123 + Mg26 = 'Mg', 26, 124 + Al = 'Al', None, 131 + Al27 = 'Al', 27, 132 + Si = 'Si', None, 141 + Si28 = 'Si', 28, 142 + Si29 = 'Si', 29, 143 + Si30 = 'Si', 30, 144 + P = 'P', None, 151 + P31 = 'P', 31, 152 + S = 'S', None, 161 + S32 = 'S', 32, 162 + S33 = 'S', 33, 163 + S34 = 'S', 34, 164 + S36 = 'S', 36, 165 + Cl = 'Cl', None, 171 + Cl35 = 'Cl', 35, 172 + Cl37 = 'Cl', 37, 173 + Ar = 'Ar', None, 181 + Ar36 = 'Ar', 36, 182 + Ar38 = 'Ar', 38, 183 + Ar40 = 'Ar', 40, 184 + K = 'K', None, 191 + K39 = 'K', 39, 192 + K40 = 'K', 40, 193 + K41 = 'K', 41, 194 + Ca = 'Ca', None, 201 + Ca40 = 'Ca', 40, 202 + Ca42 = 'Ca', 42, 203 + Ca43 = 'Ca', 43, 204 + Ca44 = 'Ca', 44, 205 + Ca46 = 'Ca', 46, 206 + Ca48 = 'Ca', 48, 207 + Sc = 'Sc', None, 211 + Sc45 = 'Sc', 45, 212 + Ti = 'Ti', None, 221 + Ti46 = 'Ti', 46, 222 + Ti47 = 'Ti', 47, 223 + Ti48 = 'Ti', 48, 224 + Ti49 = 'Ti', 49, 225 + Ti50 = 'Ti', 50, 226 + V = 'V', None, 231 + V50 = 'V', 50, 232 + V51 = 'V', 51, 233 + Cr = 'Cr', None, 241 + Cr50 = 'Cr', 50, 242 + Cr52 = 'Cr', 52, 243 + Cr53 = 'Cr', 53, 244 + Cr54 = 'Cr', 54, 245 + Mn = 'Mn', None, 251 + Mn55 = 'Mn', 55, 252 + Fe = 'Fe', None, 261 + Fe54 = 'Fe', 54, 262 + Fe56 = 'Fe', 56, 263 + Fe57 = 'Fe', 57, 264 + Fe58 = 'Fe', 58, 265 + Co = 'Co', None, 271 + Co59 = 'Co', 59, 272 + Ni = 'Ni', None, 281 + Ni58 = 'Ni', 58, 282 + Ni60 = 'Ni', 60, 283 + Ni61 = 'Ni', 61, 284 + Ni62 = 'Ni', 62, 285 + Ni64 = 'Ni', 64, 286 + Cu = 'Cu', None, 291 + Cu63 = 'Cu', 63, 292 + Cu65 = 'Cu', 65, 293 + Zn = 'Zn', None, 301 + Zn64 = 'Zn', 64, 302 + Zn66 = 'Zn', 66, 303 + Zn67 = 'Zn', 67, 304 + Zn68 = 'Zn', 68, 305 + Zn70 = 'Zn', 70, 306 + Ga = 'Ga', None, 311 + Ga69 = 'Ga', 69, 312 + Ga71 = 'Ga', 71, 313 + Ge = 'Ge', None, 321 + Ge70 = 'Ge', 70, 322 + Ge72 = 'Ge', 72, 323 + Ge73 = 'Ge', 73, 324 + Ge74 = 'Ge', 74, 325 + Ge76 = 'Ge', 76, 326 + As = 'As', None, 331 + As75 = 'As', 75, 332 + Se = 'Se', None, 341 + Se74 = 'Se', 74, 342 + Se76 = 'Se', 76, 343 + Se77 = 'Se', 77, 344 + Se78 = 'Se', 78, 345 + Se80 = 'Se', 80, 346 + Se82 = 'Se', 82, 347 + Br = 'Br', None, 351 + Br79 = 'Br', 79, 352 + Br81 = 'Br', 81, 353 + Kr = 'Kr', None, 361 + Kr86 = 'Kr', 86, 362 + Rb = 'Rb', None, 371 + Rb85 = 'Rb', 85, 372 + Rb87 = 'Rb', 87, 373 + Sr = 'Sr', None, 381 + Sr84 = 'Sr', 84, 382 + Sr86 = 'Sr', 86, 383 + Sr87 = 'Sr', 87, 384 + Sr88 = 'Sr', 88, 385 + Y = 'Y', None, 391 + Y89 = 'Y', 89, 392 + Zr = 'Zr', None, 401 + Zr90 = 'Zr', 90, 402 + Zr91 = 'Zr', 91, 403 + Zr92 = 'Zr', 92, 404 + Zr94 = 'Zr', 94, 405 + Zr96 = 'Zr', 96, 406 + Nb = 'Nb', None, 411 + Nb93 = 'Nb', 93, 412 + Mo = 'Mo', None, 421 + Mo92 = 'Mo', 92, 422 + Mo94 = 'Mo', 94, 423 + Mo95 = 'Mo', 95, 424 + Mo96 = 'Mo', 96, 425 + Mo97 = 'Mo', 97, 426 + Mo98 = 'Mo', 98, 427 + Mo100 = 'Mo', 100, 428 + Tc = 'Tc', None, 431 + Ru = 'Ru', None, 441 + Rh = 'Rh', None, 451 + Rh103 = 'Rh', 103, 452 + Pd = 'Pd', None, 461 + Pd102 = 'Pd', 102, 462 + Pd104 = 'Pd', 104, 463 + Pd105 = 'Pd', 105, 464 + Pd106 = 'Pd', 106, 465 + Pd108 = 'Pd', 108, 466 + Pd110 = 'Pd', 110, 467 + Ag = 'Ag', None, 471 + Ag107 = 'Ag', 107, 472 + Ag109 = 'Ag', 109, 473 + Cd = 'Cd', None, 481 + Cd106 = 'Cd', 106, 482 + Cd108 = 'Cd', 108, 483 + Cd110 = 'Cd', 110, 484 + Cd111 = 'Cd', 111, 485 + Cd112 = 'Cd', 112, 486 + Cd113 = 'Cd', 113, 487 + Cd114 = 'Cd', 114, 488 + Cd116 = 'Cd', 116, 489 + In = 'In', None, 491 + In113 = 'In', 113, 492 + In115 = 'In', 115, 493 + Sn = 'Sn', None, 501 + Sn112 = 'Sn', 112, 502 + Sn114 = 'Sn', 114, 503 + Sn115 = 'Sn', 115, 504 + Sn116 = 'Sn', 116, 505 + Sn117 = 'Sn', 117, 506 + Sn118 = 'Sn', 118, 507 + Sn119 = 'Sn', 119, 508 + Sn120 = 'Sn', 120, 509 + Sn122 = 'Sn', 122, 510 + Sn124 = 'Sn', 124, 511 + Sb = 'Sb', None, 521 + Sb121 = 'Sb', 121, 522 + Sb123 = 'Sb', 123, 523 + Te = 'Te', None, 531 + Te120 = 'Te', 120, 532 + Te122 = 'Te', 122, 533 + Te123 = 'Te', 123, 534 + Te124 = 'Te', 124, 535 + Te125 = 'Te', 125, 536 + Te126 = 'Te', 126, 537 + Te128 = 'Te', 128, 538 + Te130 = 'Te', 130, 539 + I = 'I', None, 541 # noqa: E741 + I127 = 'I', 127, 542 + Xe = 'Xe', None, 551 + Cs = 'Cs', None, 561 + Cs133 = 'Cs', 133, 562 + Ba = 'Ba', None, 571 + Ba130 = 'Ba', 130, 572 + Ba132 = 'Ba', 132, 573 + Ba134 = 'Ba', 134, 574 + Ba135 = 'Ba', 135, 575 + Ba136 = 'Ba', 136, 576 + Ba137 = 'Ba', 137, 577 + Ba138 = 'Ba', 138, 578 + La = 'La', None, 581 + La138 = 'La', 138, 582 + La139 = 'La', 139, 583 + Ce = 'Ce', None, 591 + Ce136 = 'Ce', 136, 592 + Ce138 = 'Ce', 138, 593 + Ce140 = 'Ce', 140, 594 + Ce142 = 'Ce', 142, 595 + Pr = 'Pr', None, 601 + Pr141 = 'Pr', 141, 602 + Nd = 'Nd', None, 611 + Nd142 = 'Nd', 142, 612 + Nd143 = 'Nd', 143, 613 + Nd144 = 'Nd', 144, 614 + Nd145 = 'Nd', 145, 615 + Nd146 = 'Nd', 146, 616 + Nd148 = 'Nd', 148, 617 + Nd150 = 'Nd', 150, 618 + Pm = 'Pm', None, 621 + Sm = 'Sm', None, 631 + Sm144 = 'Sm', 144, 632 + Sm147 = 'Sm', 147, 633 + Sm148 = 'Sm', 148, 634 + Sm149 = 'Sm', 149, 635 + Sm150 = 'Sm', 150, 636 + Sm152 = 'Sm', 152, 637 + Sm154 = 'Sm', 154, 638 + Eu = 'Eu', None, 641 + Eu151 = 'Eu', 151, 642 + Eu153 = 'Eu', 153, 643 + Gd = 'Gd', None, 651 + Gd152 = 'Gd', 152, 652 + Gd154 = 'Gd', 154, 653 + Gd155 = 'Gd', 155, 654 + Gd156 = 'Gd', 156, 655 + Gd157 = 'Gd', 157, 656 + Gd158 = 'Gd', 158, 657 + Gd160 = 'Gd', 160, 658 + Tb = 'Tb', None, 661 + Tb159 = 'Tb', 159, 662 + Dy = 'Dy', None, 671 + Dy156 = 'Dy', 156, 672 + Dy158 = 'Dy', 158, 673 + Dy160 = 'Dy', 160, 674 + Dy161 = 'Dy', 161, 675 + Dy162 = 'Dy', 162, 676 + Dy163 = 'Dy', 163, 677 + Dy164 = 'Dy', 164, 678 + Ho = 'Ho', None, 679 + Ho165 = 'Ho', 165, 680 + Er = 'Er', None, 681 + Er162 = 'Er', 162, 682 + Er164 = 'Er', 164, 683 + Er166 = 'Er', 166, 684 + Er167 = 'Er', 167, 685 + Er168 = 'Er', 168, 686 + Er170 = 'Er', 170, 687 + Tm = 'Tm', None, 691 + Tm169 = 'Tm', 169, 692 + Yb = 'Yb', None, 701 + Yb168 = 'Yb', 168, 702 + Yb170 = 'Yb', 170, 703 + Yb171 = 'Yb', 171, 704 + Yb172 = 'Yb', 172, 705 + Yb173 = 'Yb', 173, 706 + Yb174 = 'Yb', 174, 707 + Yb176 = 'Yb', 176, 708 + Lu = 'Lu', None, 711 + Lu175 = 'Lu', 175, 712 + Lu176 = 'Lu', 176, 713 + Hf = 'Hf', None, 721 + Hf174 = 'Hf', 174, 722 + Hf176 = 'Hf', 176, 723 + Hf177 = 'Hf', 177, 724 + Hf178 = 'Hf', 178, 725 + Hf179 = 'Hf', 179, 726 + Hf180 = 'Hf', 180, 727 + Ta = 'Ta', None, 731 + Ta180 = 'Ta', 180, 732 + Ta181 = 'Ta', 181, 733 + W = 'W', None, 741 + W180 = 'W', 180, 742 + W182 = 'W', 182, 743 + W183 = 'W', 183, 744 + W184 = 'W', 184, 745 + W186 = 'W', 186, 746 + Re = 'Re', None, 751 + Re185 = 'Re', 185, 752 + Re187 = 'Re', 187, 753 + Os = 'Os', None, 761 + Os184 = 'Os', 184, 762 + Os186 = 'Os', 186, 763 + Os187 = 'Os', 187, 764 + Os188 = 'Os', 188, 765 + Os189 = 'Os', 189, 766 + Os190 = 'Os', 190, 767 + Os192 = 'Os', 192, 768 + Ir = 'Ir', None, 771 + Pt = 'Pt', None, 781 + Pt190 = 'Pt', 190, 782 + Pt192 = 'Pt', 192, 783 + Pt194 = 'Pt', 194, 784 + Pt195 = 'Pt', 195, 785 + Pt196 = 'Pt', 196, 786 + Pt198 = 'Pt', 198, 787 + Au = 'Au', None, 791 + Au197 = 'Au', 197, 792 + Hg = 'Hg', None, 801 + Hg196 = 'Hg', 196, 802 + Hg199 = 'Hg', 199, 803 + Tl = 'Tl', None, 811 + Tl203 = 'Tl', 203, 812 + Tl205 = 'Tl', 205, 813 + Pb = 'Pb', None, 821 + Pb204 = 'Pb', 204, 822 + Pb206 = 'Pb', 206, 823 + Pb207 = 'Pb', 207, 824 + Pb208 = 'Pb', 208, 825 + Bi = 'Bi', None, 831 + Bi209 = 'Bi', 209, 832 + Ra = 'Ra', None, 841 + Th = 'Th', None, 851 + Th232 = 'Th', 232, 852 + Pa = 'Pa', None, 861 + U = 'U', None, 871 + U233 = 'U', 233, 872 + U234 = 'U', 234, 873 + U235 = 'U', 235, 874 + U238 = 'U', 238, 875 diff --git a/src/easyimaging/sample_model/crystals.py b/src/easyimaging/sample_model/crystals.py new file mode 100644 index 0000000..20fa5e6 --- /dev/null +++ b/src/easyimaging/sample_model/crystals.py @@ -0,0 +1,340 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +""" +This module contains convenience functions to easily create [`Lattice`][easyimaging.sample_model.Lattice] objects and occupy +them with [`Atoms`][easyimaging.sample_model.atoms.Atoms] to create common crystallographic lattices.""" + +import numbers + +from easyscience import global_object + +from .atom_site import AtomSite +from .atoms import Atoms +from .lattice import Lattice + +_BCC = ((0.0, 0.0, 0.0), (0.5, 0.5, 0.5)) +_FCC = ((0.0, 0.0, 0.0), (0.5, 0.5, 0.0), (0.5, 0.0, 0.5), (0.0, 0.5, 0.5)) +_HCP = ((0.0, 0.0, 0.0), (2 / 3, 1 / 3, 0.5)) + + +def body_centered_cubic( + length_a: numbers.Real, + atom: Atoms | str, + debye_temperature: numbers.Real | None = None, + unique_name: str | None = None, + display_name: str | None = None, +) -> Lattice: + """ + Create a body-centered cubic (BCC) lattice. + + This function creates a [`Lattice`][...Lattice] object with lattice constants $a=b=c$ and lattice angles + $\\alpha=\\beta=\\gamma=90^\\circ$. The [`Lattice`][...Lattice] object is then populated with two atoms at the fractional + coordinates: $(0,0,0)$ and $(\\frac{1}{2},\\frac{1}{2},\\frac{1}{2})$, corresponding to a BCC lattice. + + Parameters + ---------- + length_a : float | int + The length of the cubic lattice vectors in Å. + atom : Atoms | str + The atomic species of the lattice, either an [`Atoms`][...atoms.Atoms] object or its name as a string. + debye_temperature : float | int | None + The Debye temperature of the atoms in Kelvin.
      If None, a default value of 300 K is used. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'BCC Lattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to the [`unique_name`][...Lattice.unique_name] if not + provided. + Returns + ------- + Lattice + A [`Lattice`][..] object representing the BCC lattice. + + Raises + ------ + ValueError + If ``length_a`` is not positive. + TypeError + If `atom` is not an [`Atoms`][...atoms.Atoms] enum member or a string. + KeyError + If the provided atom string does not correspond to a valid [`Atoms`][...atoms.Atoms] enum member. + """ + if unique_name is None: + name = atom if isinstance(atom, str) else getattr(atom, 'name', 'Unknown') + unique_name = global_object.generate_unique_name(f'{name} BCC Lattice') + if debye_temperature is None: + global_object.log.warning(f"Debye temperature not provided for '{unique_name}'. Setting to default value of 300 K.") + debye_temperature = 300.0 + lattice = Lattice.cubic( + length_a=length_a, + atom_sites=[AtomSite(atom, *xyz, debye_temperature=debye_temperature) for xyz in _BCC], + unique_name=unique_name, + display_name=display_name, + ) + return lattice + + +def face_centered_cubic( + length_a: numbers.Real, + atom: Atoms | str, + debye_temperature: numbers.Real | None = None, + unique_name: str | None = None, + display_name: str | None = None, +) -> Lattice: + """ + Create a face-centered cubic (FCC) lattice. + + This function creates a [`Lattice`][...Lattice] object with lattice constants $a=b=c$ and lattice angles + $\\alpha=\\beta=\\gamma=90^\\circ$. The [`Lattice`][...Lattice] object is then populated with four atoms at the fractional + coordinates: $(0,0,0)$, $(\\frac{1}{2},\\frac{1}{2},0)$, $(\\frac{1}{2},0,\\frac{1}{2})$, and + $(0,\\frac{1}{2},\\frac{1}{2})$, corresponding to an FCC lattice. + + Parameters + ---------- + length_a : float | int + The length of the cubic lattice vectors in Å. + atom : Atoms | str + The atomic species of the lattice, either an [`Atoms`][...atoms.Atoms] object or its name as a string. + debye_temperature : float | int | None + The Debye temperature of the atoms in Kelvin.
      If None, a default value of 300 K is used. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'FCC Lattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to the [`unique_name`][...Lattice.unique_name] if not + provided. + Returns + ------- + Lattice + A [`Lattice`][..] object representing the FCC lattice. + + Raises + ------ + ValueError + If ``length_a`` is not positive. + TypeError + If `atom` is not an [`Atoms`][...atoms.Atoms] enum member or a string. + KeyError + If the provided atom string does not correspond to a valid [`Atoms`][...atoms.Atoms] enum member. + """ + if unique_name is None: + name = atom if isinstance(atom, str) else getattr(atom, 'name', 'Unknown') + unique_name = global_object.generate_unique_name(f'{name} FCC Lattice') + if debye_temperature is None: + global_object.log.warning(f"Debye temperature not provided for '{unique_name}'. Setting to default value of 300 K.") + debye_temperature = 300.0 + lattice = Lattice.cubic( + length_a=length_a, + atom_sites=[AtomSite(atom, *xyz, debye_temperature=debye_temperature) for xyz in _FCC], + unique_name=unique_name, + display_name=display_name, + ) + return lattice + + +def diamond_cubic( + length_a: numbers.Real, + atom: Atoms | str, + debye_temperature: numbers.Real | None = None, + unique_name: str | None = None, + display_name: str | None = None, +) -> Lattice: + """ + Create a diamond cubic lattice. + + This function creates a [`Lattice`][...Lattice] object with lattice constants $a=b=c$ and lattice angles + $\\alpha=\\beta=\\gamma=90^\\circ$. The diamond lattice corresponds to two interpenetrating FCC lattices, offset along the + body diagonal by one-quarter of the cube's length. The [`Lattice`][...Lattice] object is therefore populated with atoms at + the fractional coordinates: + + 1. $(0,0,0)$, $(\\frac{1}{2},\\frac{1}{2},0)$, $(\\frac{1}{2},0,\\frac{1}{2})$ and $(0,\\frac{1}{2},\\frac{1}{2})$. + 2. $(\\frac{1}{4},\\frac{1}{4},\\frac{1}{4})$, $(\\frac{3}{4},\\frac{3}{4},\\frac{1}{4})$, $(\\frac{3}{4},\\frac{1}{4}, + \\frac{3}{4})$ and $(\\frac{1}{4},\\frac{3}{4},\\frac{3}{4})$. + + Parameters + ---------- + length_a : float | int + The length of the cubic lattice vectors in Å. + atom : Atoms | str + The atomic species of the lattice, either an [`Atoms`][...atoms.Atoms] object or its name as a string. + debye_temperature : float | int | None + The Debye temperature of the atoms in Kelvin.
      If None, a default value of 300 K is used. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'Diamond Cubic Lattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to the [`unique_name`][...Lattice.unique_name] if not + provided. + Returns + ------- + Lattice + A [`Lattice`][..] object representing the diamond cubic lattice. + + Raises + ------ + ValueError + If ``length_a`` is not positive. + TypeError + If `atom` is not an [`Atoms`][...atoms.Atoms] enum member or a string. + KeyError + If the provided atom string does not correspond to a valid [`Atoms`][...atoms.Atoms] enum member. + """ + if unique_name is None: + name = atom if isinstance(atom, str) else getattr(atom, 'name', 'Unknown') + unique_name = global_object.generate_unique_name(f'{name} Diamond Cubic Lattice') + if debye_temperature is None: + global_object.log.warning(f"Debye temperature not provided for '{unique_name}'. Setting to default value of 300 K.") + debye_temperature = 300.0 + sites = [] + for a, b, c in _FCC: + sites.append(AtomSite(atom=atom, fract_a=a, fract_b=b, fract_c=c, debye_temperature=debye_temperature)) + sites.append( + AtomSite(atom=atom, fract_a=a + 0.25, fract_b=b + 0.25, fract_c=c + 0.25, debye_temperature=debye_temperature) + ) + lattice = Lattice.cubic( + length_a=length_a, + atom_sites=sites, + unique_name=unique_name, + display_name=display_name, + ) + return lattice + + +def zincblende( + length_a: numbers.Real, + atom1: Atoms | str, + atom2: Atoms | str, + debye_temperature1: numbers.Real | None = None, + debye_temperature2: numbers.Real | None = None, + unique_name: str | None = None, + display_name: str | None = None, +) -> Lattice: + """ + Create a zincblende lattice. + + This function creates a [`Lattice`][...Lattice] object with lattice constants $a=b=c$ and lattice angles + $\\alpha=\\beta=\\gamma=90^\\circ$. The zincblende lattice corresponds to a [`diamond_cubic`][..diamond_cubic] lattice with + two different atomic species for the two FCC sublattices and as such is populated with atoms at the same fractional + coordinates: + + 1. $(0,0,0)$, $(\\frac{1}{2},\\frac{1}{2},0)$, $(\\frac{1}{2},0,\\frac{1}{2})$ and $(0,\\frac{1}{2},\\frac{1}{2})$. + 2. $(\\frac{1}{4},\\frac{1}{4},\\frac{1}{4})$, $(\\frac{3}{4},\\frac{3}{4},\\frac{1}{4})$, $(\\frac{3}{4},\\frac{1}{4}, + \\frac{3}{4})$ and $(\\frac{1}{4},\\frac{3}{4},\\frac{3}{4})$. + + Parameters + ---------- + length_a : float | int + The length of the cubic lattice vectors in Å. + atom1 : Atoms | str + The atomic species of the first sublattice, either an [`Atoms`][...atoms.Atoms] object or its name as a string. + atom2 : Atoms | str + The atomic species of the second sublattice, either an [`Atoms`][...atoms.Atoms] object or its name as a string. + debye_temperature1 : float | int | None + The Debye temperature of the atoms in the first sublattice in Kelvin.
      If None, a default value of 300 K is used. + debye_temperature2 : float | int | None + The Debye temperature of the atoms in the second sublattice in Kelvin.
      If None, a default value of 300 K is used. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'Zincblende Lattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to the [`unique_name`][...Lattice.unique_name] if not + provided. + Returns + ------- + Lattice + A [`Lattice`][..] object representing the zincblende lattice. + + Raises + ------ + ValueError + If ``length_a`` is not positive. + TypeError + If `atom1` or `atom2` is not an [`Atoms`][...atoms.Atoms] enum member or a string. + KeyError + If the provided `atom1` or `atom2` strings does not correspond to a valid [`Atoms`][...atoms.Atoms] enum member. + """ + if unique_name is None: + name1 = atom1 if isinstance(atom1, str) else getattr(atom1, 'name', 'Unknown') + name2 = atom2 if isinstance(atom2, str) else getattr(atom2, 'name', 'Unknown') + unique_name = global_object.generate_unique_name(f'{name1}{name2} Zincblende Lattice') + if debye_temperature1 is None: + global_object.log.warning( + f"Debye temperature not provided for '{unique_name}' (atom1). Setting to default value of 300 K." + ) + debye_temperature1 = 300.0 + if debye_temperature2 is None: + global_object.log.warning( + f"Debye temperature not provided for '{unique_name}' (atom2). Setting to default value of 300 K." + ) + debye_temperature2 = 300.0 + sites = [] + for a, b, c in _FCC: + sites.append(AtomSite(atom=atom1, fract_a=a, fract_b=b, fract_c=c, debye_temperature=debye_temperature1)) + sites.append( + AtomSite(atom=atom2, fract_a=a + 0.25, fract_b=b + 0.25, fract_c=c + 0.25, debye_temperature=debye_temperature2) + ) + lattice = Lattice.cubic( + length_a=length_a, + atom_sites=sites, + unique_name=unique_name, + display_name=display_name, + ) + return lattice + + +def hexagonal_close_packed( + length_a: numbers.Real, + length_c: numbers.Real, + atom: Atoms | str, + debye_temperature: numbers.Real | None = None, + unique_name: str | None = None, + display_name: str | None = None, +) -> Lattice: + """ + Create a hexagonal close-packed (HCP) lattice. + + This function creates a [`Lattice`][...Lattice] object with lattice constants $a=b\\neq c$ and lattice angles + $\\alpha=\\beta=90^\\circ$, $\\gamma=120^\\circ$. The [`Lattice`][...Lattice] object is then populated with two + atoms at the fractional coordinates $(0,0,0)$ and $(\\frac{2}{3},\\frac{1}{3},\\frac{1}{2})$, corresponding to the two + atoms in the primitive HCP unit cell. + + Parameters + ---------- + length_a : float | int + The length of the a-axis lattice vector in Å. + length_c : float | int + The length of the c-axis lattice vector in Å. + atom : Atoms | str + The atomic species of the lattice, either an [`Atoms`][...atoms.Atoms] object or its name as a string. + debye_temperature : float | int | None + The Debye temperature of the atoms in Kelvin.
      If None, a default value of 300 K is used. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'HCP Lattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to the [`unique_name`][...Lattice.unique_name] if not + provided. + Returns + ------- + Lattice + A [`Lattice`][..] object representing the HCP lattice. + + Raises + ------ + ValueError + If ``length_a`` is not positive. + TypeError + If `atom` is not an [`Atoms`][...atoms.Atoms] enum member or a string. + KeyError + If the provided atom string does not correspond to a valid [`Atoms`][...atoms.Atoms] enum member. + """ + if unique_name is None: + name = atom if isinstance(atom, str) else getattr(atom, 'name', 'Unknown') + unique_name = global_object.generate_unique_name(f'{name} HCP Lattice') + if debye_temperature is None: + global_object.log.warning(f"Debye temperature not provided for '{unique_name}'. Setting to default value of 300 K.") + debye_temperature = 300.0 + lattice = Lattice.hexagonal( + length_a=length_a, + length_c=length_c, + atom_sites=[AtomSite(atom, *xyz, debye_temperature=debye_temperature) for xyz in _HCP], + unique_name=unique_name, + display_name=display_name, + ) + return lattice diff --git a/src/easyimaging/sample_model/lattice.py b/src/easyimaging/sample_model/lattice.py new file mode 100644 index 0000000..0723fa9 --- /dev/null +++ b/src/easyimaging/sample_model/lattice.py @@ -0,0 +1,736 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +from __future__ import annotations + +import numbers +from collections.abc import Sequence +from typing import TYPE_CHECKING +from typing import Any + +from easyscience import Parameter +from easyscience import global_object +from easyscience.base_classes import EasyList +from easyscience.base_classes import ModelBase + +from .atom_site import AtomSite + +if TYPE_CHECKING: + from easyscience.variable.descriptor_base import DescriptorBase + +INFINITESIMAL = 1e-3 + + +class Lattice(ModelBase): + """A Lattice represents the periodic arrangement of atoms in a crystal structure, defined by its lattice + parameter: (``length_a``, ``length_b``, ``length_c``), lattice-vector angles: (``alpha``, ``beta``, ``gamma``) + and a sequence of [`AtomSite`][...atom_site.AtomSite] objects, with their corresponding [atomic species][...atoms.Atoms], + occupying it. + + A [`Lattice`][.] object can currently be created in 3 ways of descending specificity: + + - Directly via the constructor by supplying all lattice parameters and atomic sites. + - Using the [`cubic`][.cubic] or [`hexagonal`][.hexagonal] convenience methods for the corresponding lattice symmetries. + Atomic sites still needs to be supplied manually. + - With one of the crystal-structure convenience functions in the [`crystals`][...crystals] module, which + additionally populates the [`AtomSite`][...atom_site.AtomSite] objects for common crystal structures. + + Example + ------- + Creating a hexagonal close-packed (HCP) magnesium [Lattice][.]: + + **1. Directly via the constructor** + + ```python + from easyimaging.sample_model import AtomSite, Atoms, Lattice + + lattice = Lattice( + length_a=3.21, + length_b=3.21, + length_c=5.21, + alpha=90.0, + beta=90.0, + gamma=120.0, + atom_sites=[ + AtomSite(atom=Atoms.Mg, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=400.0), + AtomSite(atom=Atoms.Mg, fract_a=2 / 3, fract_b=1 / 3, fract_c=0.5, debye_temperature=400.0), + ], + ) + ``` + + **2. Using the [`hexagonal`][.hexagonal] convenience constructor** + + ```python + from easyimaging.sample_model import AtomSite, Atoms, Lattice + + lattice = Lattice.hexagonal( + length_a=3.21, + length_c=5.21, + atom_sites=[ + AtomSite(atom=Atoms.Mg, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=400.0), + AtomSite(atom=Atoms.Mg, fract_a=2 / 3, fract_b=1 / 3, fract_c=0.5, debye_temperature=400.0), + ], + ) + ``` + + **3. Using the [`hexagonal_close_packed`][...crystals.hexagonal_close_packed] constructor from the + [`crystals`][...crystals] module** + + ```python + from easyimaging.sample_model import Atoms + from easyimaging.sample_model.crystals import hexagonal_close_packed + + lattice = hexagonal_close_packed( + length_a=3.21, + length_c=5.21, + atom=Atoms.Mg, + debye_temperature=400.0, + ) + ``` + """ + + def __init__( + self, + length_a: numbers.Real, + length_b: numbers.Real, + length_c: numbers.Real, + alpha: numbers.Real = 90.0, + beta: numbers.Real = 90.0, + gamma: numbers.Real = 90.0, + atom_sites: Sequence[AtomSite] | None = None, + unique_name: str | None = None, + display_name: str | None = None, + ): + """ + Initialize a Lattice instance. + + Parameters + ---------- + length_a : float | int + The a-component of the lattice parameter in Å. + length_b : float | int + The b-component of the lattice parameter in Å. + length_c : float | int + The c-component of the lattice parameter in Å. + alpha : float | int + The angle between the $\\vec{b}$ and $\\vec{c}$ lattice vectors (in degrees). + beta : float | int + The angle between the $\\vec{a}$ and $\\vec{c}$ lattice vectors (in degrees). + gamma : float | int + The angle between the $\\vec{a}$ and $\\vec{b}$ lattice vectors (in degrees). + atom_sites : Sequence[AtomSite] | None + A sequence of [`AtomSite`][..] objects to insert into the lattice. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'Lattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to [`unique_name`][..unique_name] if not provided. + + Raises + ------ + ValueError + If any of ``length_a``, ``length_b``, or ``length_c`` is not positive and non-zero.
      + If any of ``alpha``, ``beta``, or ``gamma`` is not between 0 and 180 degrees. + TypeError + If any of ``length_a``, ``length_b``, or ``length_c`` is not a numeric value.
      + If any of ``alpha``, ``beta``, or ``gamma`` is not a numeric value.
      + If ``atom_sites`` is provided and is not a sequence of [`AtomSite`][....atom_site.AtomSite] objects. + """ + for length, axis in ((length_a, 'a'), (length_b, 'b'), (length_c, 'c')): + self._validate_length(length, axis) + for angle, name in ((alpha, 'alpha'), (beta, 'beta'), (gamma, 'gamma')): + self._validate_angle(angle, name) + + if atom_sites is not None: + if not isinstance(atom_sites, Sequence) or not all(isinstance(site, AtomSite) for site in atom_sites): + raise TypeError('atom_sites must be a sequence of AtomSite objects.') + + super().__init__(unique_name=unique_name, display_name=display_name) + + self._atom_sites = EasyList( + *atom_sites if atom_sites else [], + protected_types=AtomSite, + unique_name=global_object.generate_unique_name(f'{self.unique_name}_atom_sites'), + ) + self._atom_sites._default_unique_name = True # This gets set to False by the super init + self._length_a = self._create_length_parameter(length_a, 'a') + self._length_b = self._create_length_parameter(length_b, 'b') + self._length_c = self._create_length_parameter(length_c, 'c') + self._alpha = self._create_angle_parameter(alpha, 'alpha') + self._beta = self._create_angle_parameter(beta, 'beta') + self._gamma = self._create_angle_parameter(gamma, 'gamma') + + @classmethod + def cubic( + cls, + length_a: numbers.Real, + atom_sites: Sequence[AtomSite] | None = None, + unique_name: str | None = None, + display_name: str | None = None, + ) -> Lattice: + """ + Create a cubic lattice with $a=b=c$ and $\\alpha=\\beta=\\gamma=90^\\circ$. + + Parameters + ---------- + length_a : float | int + The lattice constant of the cubic lattice in Å. + atom_sites : Sequence[AtomSite] | None + A sequence of [`AtomSite`][..] objects to insert into the lattice. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'CubicLattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to [`unique_name`][..unique_name] if not provided. + + Returns + ------- + Lattice + A new instance of a cubic [`Lattice`][..]. + + Raises + ------ + ValueError + If ``length_a`` is not positive. + TypeError + If ``atom_sites`` is provided and is not a sequence of [`AtomSite`][....atom_site.AtomSite] objects. + """ + default_unique_name = False + if unique_name is None: + unique_name = global_object.generate_unique_name('CubicLattice') + default_unique_name = True + lattice = cls( + length_a=length_a, + length_b=length_a, + length_c=length_a, + alpha=90.0, + beta=90.0, + gamma=90.0, + atom_sites=atom_sites, + unique_name=unique_name, + display_name=display_name, + ) + lattice._default_unique_name = default_unique_name + lattice.length_b.make_dependent_on('length_a', {'length_a': lattice.length_a}) + lattice.length_c.make_dependent_on('length_a', {'length_a': lattice.length_a}) + # Lock the angles by making them dependent on a hidden parameter + lattice._angle_lock = Parameter( + name='angle_lock', + value=90, + unit='deg', + min=INFINITESIMAL, + max=180.0 - INFINITESIMAL, + fixed=True, + ) + lattice.alpha.make_dependent_on('angle_lock', {'angle_lock': lattice._angle_lock}) + lattice.beta.make_dependent_on('angle_lock', {'angle_lock': lattice._angle_lock}) + lattice.gamma.make_dependent_on('angle_lock', {'angle_lock': lattice._angle_lock}) + return lattice + + @classmethod + def hexagonal( + cls, + length_a: numbers.Real, + length_c: numbers.Real, + atom_sites: Sequence[AtomSite] | None = None, + unique_name: str | None = None, + display_name: str | None = None, + ) -> Lattice: + """ + Create a hexagonal lattice with $a=b\\neq c$ and $\\alpha=\\beta=90^\\circ$, $\\gamma=120^\\circ$. + + Parameters + ---------- + length_a : float | int + The lattice constant of the a and b lattice vectors in angstrom. + length_c : float | int + The lattice constant of the c lattice vector in angstrom. + atom_sites : Sequence[AtomSite] | None + A sequence of [`AtomSite`][..] objects to insert into the lattice. + unique_name : str | None + A unique identifier for the [`Lattice`][..]. Defaults to ``'HexagonalLattice'`` appended by a unique integer. + display_name : str | None + A prettily formatted name for the [`Lattice`][..]. Defaults to [`unique_name`][..unique_name] if not provided. + + Returns + ------- + Lattice + A new instance of a hexagonal [`Lattice`][..]. + + Raises + ------ + ValueError + If ``length_a`` or ``length_c`` is not positive. + TypeError + If ``atom_sites`` is provided and is not a sequence of [`AtomSite`][....atom_site.AtomSite] objects. + """ + default_unique_name = False + if unique_name is None: + unique_name = global_object.generate_unique_name('HexagonalLattice') + default_unique_name = True + lattice = cls( + length_a=length_a, + length_b=length_a, + length_c=length_c, + alpha=90.0, + beta=90.0, + gamma=120.0, + atom_sites=atom_sites, + unique_name=unique_name, + display_name=display_name, + ) + lattice._default_unique_name = default_unique_name + lattice.length_b.make_dependent_on('length_a', {'length_a': lattice.length_a}) + # Lock the angles by making them dependent on a hidden parameter + lattice._angle_lock = Parameter( + name='angle_lock', + value=90, + unit='deg', + min=INFINITESIMAL, + max=180.0 - INFINITESIMAL, + fixed=True, + ) + lattice.alpha.make_dependent_on('angle_lock', {'angle_lock': lattice._angle_lock}) + lattice.beta.make_dependent_on('angle_lock', {'angle_lock': lattice._angle_lock}) + lattice.gamma.make_dependent_on('4*angle_lock/3', {'angle_lock': lattice._angle_lock}) + return lattice + + @property + def length_a(self) -> Parameter: + """The a-component of the lattice parameter. + + Parameters + ---------- + value : int | float + A positive non-zero number for the length, in Å. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] representing the length of the lattice vector $\\vec{a}$. + + Raises + ------ + ValueError + If ``value`` is not positive and non-zero. + TypeError + If ``value`` is not a number. + """ + return self._length_a + + @length_a.setter + def length_a(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_length(value, 'a') + self._length_a.value = value + + @property + def length_b(self) -> Parameter: + """The b-component of the lattice parameter. + + Parameters + ---------- + value : int | float + A positive non-zero number, in Å. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] representing the length of the lattice vector $\\vec{b}$. + + Raises + ------ + ValueError + If ``value`` is not positive and non-zero. + TypeError + If ``value`` is not a number. + """ + return self._length_b + + @length_b.setter + def length_b(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_length(value, 'b') + self._length_b.value = value + + @property + def length_c(self) -> Parameter: + """The c-component of the lattice parameter. + + Parameters + ---------- + value : int | float + A positive non-zero number, in Å. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] representing the length of the lattice vector $\\vec{c}$. + + Raises + ------ + ValueError + If ``value`` is not positive and non-zero. + TypeError + If ``value`` is not a number. + """ + return self._length_c + + @length_c.setter + def length_c(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_length(value, 'c') + self._length_c.value = value + + @property + def alpha(self) -> Parameter: + """The angle between the $\\vec{b}$ and $\\vec{c}$ lattice vectors. + + Parameters + ---------- + value : int | float + A number between 0 and 180 degrees. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] representing the angle between the $\\vec{b}$ and $\\vec{c}$ + lattice vectors. + + Raises + ------ + ValueError + If ``value`` is not between 0 and 180 degrees. + TypeError + If ``value`` is not a number. + """ + return self._alpha + + @alpha.setter + def alpha(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_angle(value, 'alpha') + self._alpha.value = value + + @property + def beta(self) -> Parameter: + """The angle between the $\\vec{a}$ and $\\vec{c}$ lattice vectors. + + Parameters + ---------- + value : int | float + A number between 0 and 180 degrees. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] representing the angle between the $\\vec{a}$ and $\\vec{c}$ + lattice vectors. + + Raises + ------ + ValueError + If ``value`` is not between 0 and 180 degrees. + TypeError + If ``value`` is not a number. + """ + return self._beta + + @beta.setter + def beta(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_angle(value, 'beta') + self._beta.value = value + + @property + def gamma(self) -> Parameter: + """The angle between the $\\vec{a}$ and $\\vec{b}$ lattice vectors. + + Parameters + ---------- + value : int | float + A number between 0 and 180 degrees. + + Returns + ------- + Parameter + The [`Parameter`][easyscience.variable.Parameter] representing the angle between the $\\vec{a}$ and $\\vec{b}$ + lattice vectors. + + Raises + ------ + ValueError + If ``value`` is not between 0 and 180 degrees. + TypeError + If ``value`` is not a number. + """ + return self._gamma + + @gamma.setter + def gamma(self, value: numbers.Real): + # Setters have no docstrings. They should be written in the getter docstring instead. + self._validate_angle(value, 'gamma') + self._gamma.value = value + + @property + def atom_sites(self) -> EasyList: + """The list of atomic sites in the lattice.
      This attribute is **read-only**. + + The returned list is an [EasyList][easyscience.base_classes.EasyList] of + [`AtomSite`][....atom_site.AtomSite] objects. + To add or remove atomic sites to the lattice, interact directly with the returned list. + + Example + ------- + ```python + lattice.atom_sites.append(new_site) # Add a new atomic site to the lattice + lattice.atom_sites.remove('existing_site') # Remove an existing atomic site + ``` + + Returns + ------- + EasyList[AtomSite] + A list of the atomic sites contained in the lattice. + """ + return self._atom_sites + + @property + def unique_name(self) -> str: + """ + The unique identifier of the lattice object. + + Parameters + ---------- + value : str + The new unique name for the [lattice][..] object. + + Returns + ------- + str + The current unique name of the [lattice][..]. + + Raises + ------ + TypeError + If ``value`` is not a string. + ValueError + If the new unique name is already in use. + + Notes + ----- + Setting a new unique name does not free up the old name. + """ + return super().unique_name + + @unique_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def unique_name(self, value: str): + super(Lattice, type(self)).unique_name.fset(self, value) + + @property + def display_name(self) -> str: + """ + The pretty display name of the lattice object. + + This name is used when the object is displayed in user interfaces and reports. + + Parameters + ---------- + value : str | None + The new display name for the [lattice][..] object. + + Returns + ------- + str + The current display name of the [lattice][..]. + + Raises + ------ + TypeError + If ``value`` is not a string or ``None``. + + Notes + ----- + If it has not been set, the [unique name][..unique_name] of the [lattice][..] object + will be used as its display name. + """ + return super().display_name + + @display_name.setter + # Setters have no docstrings. They should be written in the getter docstring instead. + def display_name(self, value: str): + super(Lattice, type(self)).display_name.fset(self, value) + + def get_all_variables(self) -> list[DescriptorBase]: + """ + Get all ``Descriptors`` and ``Parameters`` in the lattice object. + + + Notes + ----- + Fittable [`Parameters`][easyscience.Parameter] is a subset of + [`Descriptors`][easyscience.variable.descriptor_base.DescriptorBase] and are hence also included in the list. + + + Returns + ------- + list[DescriptorBase] + A list of all [`Descriptors`][easyscience.variable.descriptor_base.DescriptorBase] contained in the [`Lattice`][..] + object. + """ + return super().get_all_variables() + + def get_all_parameters(self) -> list[Parameter]: + """ + Get **all** ``Parameters`` in the lattice object. + + Returns + ------- + list[Parameter] + A list of all [`Parameters`][easyscience.Parameter] contained in the [`Lattice`][..] object. + """ + return super().get_all_parameters() + + def get_fittable_parameters(self) -> list[Parameter]: + """ + Get all fittable ``Parameters`` in the lattice object. + + In comparison to [`get_all_parameters`][..get_all_parameters], this method excludes dependent + [`Parameters`][easyscience.Parameter]. + + Returns + ------- + list[Parameter] + A list of all fittable [`Parameters`][easyscience.Parameter] contained in the [`Lattice`][..] object. + """ + return super().get_fittable_parameters() + + def get_free_parameters(self) -> list[Parameter]: + """ + Get all **free** ``Parameters`` in the lattice object. + + In comparison to [`get_fittable_parameters`][..get_fittable_parameters], this method also excludes currently fixed + [`Parameters`][easyscience.Parameter]. + + Returns + ------- + list[Parameter] + A list of all free [`Parameters`][easyscience.Parameter] contained in the [`Lattice`][..] object. + """ + return super().get_free_parameters() + + def get_fit_parameters(self) -> list[Parameter]: + """ + This is an alias for [`get_free_parameters`][..get_free_parameters]. + + Returns + ------- + list[Parameter] + A list of all free [`Parameters`][easyscience.Parameter] contained in the [`Lattice`][..] object. + """ + return super().get_fit_parameters() + + def _validate_length(self, value: numbers.Real, axis: str): + if not isinstance(value, numbers.Real): + raise TypeError(f'Lattice length_{axis} must be a numeric value. Got {type(value).__name__}') + if not (value > INFINITESIMAL): + raise ValueError(f'Lattice length_{axis} must be positive and non-zero.') + + def _validate_angle(self, value: numbers.Real, name: str): + if not isinstance(value, numbers.Real): + raise TypeError(f'Lattice angle {name} must be a numeric value. Got {type(value).__name__}') + if not (INFINITESIMAL < value < 180 - INFINITESIMAL): + raise ValueError(f'Lattice angle {name} must be between 0 and 180 degrees.') + + def _create_length_parameter(self, length_value: numbers.Real, axis: str) -> Parameter: + unique_name = global_object.generate_unique_name(f'{self.unique_name}_length_{axis}') + parameter = Parameter( + name=f'length_{axis}', value=length_value, unit='angstrom', min=INFINITESIMAL, fixed=True, unique_name=unique_name + ) + parameter._default_unique_name = True # This gets set to False by the super init + return parameter + + def _create_angle_parameter(self, angle_value: numbers.Real, name: str) -> Parameter: + unique_name = global_object.generate_unique_name(f'{self.unique_name}_angle_{name}') + parameter = Parameter( + name=f'angle_{name}', + value=angle_value, + unit='deg', + min=INFINITESIMAL, + max=180.0 - INFINITESIMAL, + fixed=True, + unique_name=unique_name, + ) + parameter._default_unique_name = True # This gets set to False by the super init + return parameter + + def to_dict(self, skip: list[str] | None = None) -> dict[str, Any]: + """Convert the lattice object to a dictionary representation. + + Used for saving the [Lattice][..] to file. + + Parameters + ---------- + skip : list[str] | None + A list of attribute names to exclude from the output dictionary. + + Returns + ------- + dict[str, Any] + A dictionary representation of the [Lattice][..] object. + + Notes + ----- + A [unique_name][..unique_name] that was generated automatically is not + written to the dictionary; only an explicitly supplied one is, so that a + deserialized object gets a fresh name instead of clashing with an existing one. + Likewise [display_name][..display_name] is omitted when it has not been set. + """ + out_dict = super().to_dict(skip=skip) + angle_lock = getattr(self, '_angle_lock', None) + if angle_lock is not None: + out_dict['angle_lock'] = angle_lock.as_dict(skip=skip) + return out_dict + + @classmethod + def from_dict(cls, obj_dict: dict) -> Lattice: + """Recreate the lattice object from its dictionary representation created with the [to_dict][to_dict] method. + + Parameters + ---------- + obj_dict : dict + A dictionary representation of the [Lattice][..] object. + + Returns + ------- + Lattice + An instance of the [Lattice][..] object recreated from the dictionary. + + Raises + ------ + TypeError + If the input is not a dictionary. + ValueError + If the dictionary does not represent a serialized [Lattice][..] object. + """ + + if not isinstance(obj_dict, dict): + raise TypeError(f'Expected a dictionary. Got {type(obj_dict).__name__}') + temp_obj_dict = dict(obj_dict) # Don't modify the original dictionary + angle_lock_dict = temp_obj_dict.pop('angle_lock', None) + instance = super().from_dict(temp_obj_dict) + if angle_lock_dict is not None: + instance._angle_lock = Parameter.from_dict(angle_lock_dict) + return instance + + def __copy__(self) -> Lattice: + """Create a deep copy of the lattice object. + + Notes + ----- + This uses the [to_dict][to_dict] and [from_dict][from_dict] methods to create the deep copy. + + Returns + ------- + Lattice + A deep copy of the lattice object. + """ + return super().__copy__() diff --git a/src/easyimaging/utils/__init__.py b/src/easyimaging/utils/__init__.py index 7f816d2..ad10ec4 100644 --- a/src/easyimaging/utils/__init__.py +++ b/src/easyimaging/utils/__init__.py @@ -3,5 +3,3 @@ from .jupyter_notebook_utilities import _is_notebook from .scipp_utilities import _to_edges - -__all__ = [_to_edges, _is_notebook] diff --git a/tests/unit/measurement/test_measurement.py b/tests/unit/measurement/test_measurement.py index 86dd608..31b3bc2 100644 --- a/tests/unit/measurement/test_measurement.py +++ b/tests/unit/measurement/test_measurement.py @@ -1,7 +1,7 @@ # SPDX-FileCopyrightText: 2026 EasyScience contributors # SPDX-License-Identifier: BSD-3-Clause -import warnings +import logging from copy import copy from pathlib import Path from typing import MutableSequence @@ -542,37 +542,37 @@ def test_from_tiff_stack_invalid_y_positions(self, coord, error, expected_messag y_positions=coord, ) - def test_save_scitiff_downcast(self, valid_data_array, tmp_path): + def test_save_scitiff_downcast(self, valid_data_array, tmp_path, caplog): # When measurement = Measurement(data_array=valid_data_array) - # Then Expect - with pytest.warns( - UserWarning, - match='The data array is of type float64, which is not directly supported by the SciTIFF format. ' - 'It will be downcast to float32 when saving, which may result in loss of precision.', - ): + # Then + with caplog.at_level(logging.WARNING): measurement.save_scitiff(tmp_path / 'test.tiff') + # Expect + assert any('It will be downcast to float32 when saving' in record.message for record in caplog.records) assert 'non_finite' in measurement._data_array.masks - def test_save_scitiff(self, valid_data_array, tmp_path): + def test_save_scitiff(self, valid_data_array, tmp_path, caplog): # When data_array = valid_data_array.astype('float32') measurement = Measurement(data_array=data_array) - # Then Expect - with warnings.catch_warnings(): - warnings.simplefilter('error') # Ensure no warnings are raised for valid data type + # Then + with caplog.at_level(logging.WARNING): measurement.save_scitiff(tmp_path / 'test.tiff') + # Expect + assert not any('downcast' in record.message for record in caplog.records) # No warning for a supported data type assert 'non_finite' in measurement._data_array.masks - def test_save_scitiff_path_object(self, valid_data_array, tmp_path): + def test_save_scitiff_path_object(self, valid_data_array, tmp_path, caplog): # When data_array = valid_data_array.astype('float32') measurement = Measurement(data_array=data_array) path = Path(tmp_path) / 'test.tiff' - # Then Expect - with warnings.catch_warnings(): - warnings.simplefilter('error') # Ensure no warnings are raised for valid data type + # Then + with caplog.at_level(logging.WARNING): measurement.save_scitiff(path) + # Expect + assert not any('downcast' in record.message for record in caplog.records) # No warning for a supported data type assert 'non_finite' in measurement._data_array.masks def test_save_scitiff_invalid_filename_type(self, valid_data_array): @@ -1045,15 +1045,14 @@ def test_revert_rebin(self, valid_data_array): del measurement._data_array.masks['non_finite'] assert sc.identical(measurement._data_array, valid_data_array) - def test_revert_rebin_no_rebin(self, valid_data_array): + def test_revert_rebin_no_rebin(self, valid_data_array, caplog): # When measurement = Measurement(data_array=valid_data_array) - # Then Expect - with pytest.warns( - UserWarning, - match='No rebinning to revert. The data array is already in its original state.', - ): + # Then + with caplog.at_level(logging.WARNING): measurement.revert_rebin() + # Expect + assert any('No rebinning to revert' in record.message for record in caplog.records) # Without making image comparisons, this is the best we can do to test the plot function @pytest.mark.parametrize( diff --git a/tests/unit/sample_model/test_atom_site.py b/tests/unit/sample_model/test_atom_site.py new file mode 100644 index 0000000..3ea47ec --- /dev/null +++ b/tests/unit/sample_model/test_atom_site.py @@ -0,0 +1,301 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +import logging + +import numpy as np +import pytest +from easyscience import global_object + +from easyimaging.sample_model import Atoms +from easyimaging.sample_model import AtomSite +from easyimaging.sample_model.atom_site import DEBYE_INFINITESIMAL +from easyimaging.sample_model.atoms import Atom + + +class TestAtomSite: + @pytest.fixture + def atom_site(self): + return AtomSite( + atom=Atoms.Fe, + fract_a=0.1, + fract_b=0.2, + fract_c=0.3, + debye_temperature=250.0, + unique_name=global_object.generate_unique_name('test_atom_site'), + display_name='Test Atom Site', + ) + + def test_init_valid(self, atom_site): + # When Then + site = atom_site + # Expect + assert site.atom is Atoms.Fe + assert site.fract_a.value == 0.1 + assert site.fract_b.value == 0.2 + assert site.fract_c.value == 0.3 + assert site.debye_temperature.value == 250.0 + assert site.unique_name.startswith('test_atom_site') + assert site.display_name == 'Test Atom Site' + + def test_init_parameter_defaults(self, atom_site): + # When + site = atom_site + # Then Expect + assert site.fract_a.min == 0.0 + assert site.fract_a.max == 1.0 + assert site.fract_a.unit == 'dimensionless' + assert site.fract_b.min == 0.0 + assert site.fract_b.max == 1.0 + assert site.fract_b.unit == 'dimensionless' + assert site.fract_c.min == 0.0 + assert site.fract_c.max == 1.0 + assert site.fract_c.unit == 'dimensionless' + assert site.debye_temperature.min == DEBYE_INFINITESIMAL + assert site.debye_temperature.max == np.inf + assert site.debye_temperature.unit == 'K' + + def test_init_default_unique_name(self): + # When + site = AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + # Then + unique_name = site.unique_name + # Expect + assert site.unique_name.startswith('Fe AtomSite') + assert site.display_name == site.unique_name + assert site.debye_temperature.unique_name.startswith(unique_name + '_debye_temperature') + assert site.fract_a.unique_name.startswith(unique_name + '_fract_a') + assert site.fract_b.unique_name.startswith(unique_name + '_fract_b') + assert site.fract_c.unique_name.startswith(unique_name + '_fract_c') + + def test_init_missing_debye_temperature_defaults_and_warns(self, caplog): + # When + with caplog.at_level(logging.WARNING): + site = AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0) + # Then Expect + assert site.debye_temperature.value == 300.0 + assert any('Debye temperature not provided' in record.message for record in caplog.records) + + def test_init_atom_from_string(self): + # When + site = AtomSite(atom='Fe', fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + # Then Expect + assert site.atom is Atoms.Fe + assert site.unique_name.startswith('Fe AtomSite') + + def test_init_accepts_numpy_scalars(self): + # When + site = AtomSite( + atom=Atoms.Fe, + fract_a=np.float32(0.5), + fract_b=np.int64(0), + fract_c=np.float64(-0.25), + debye_temperature=np.int64(470), + ) + # Then Expect + assert (site.fract_a.value, site.fract_b.value, site.fract_c.value) == (0.5, 0.0, 0.75) + assert site.debye_temperature.value == 470.0 + + @pytest.mark.parametrize('invalid_atom', ['not_an_atom', 'fe', ''], ids=['unknown', 'wrong_case', 'empty']) + def test_init_invalid_atom_string(self, invalid_atom): + # When Then Expect + with pytest.raises(KeyError, match='"atom" must be a valid Atoms enum or a valid Atoms name string'): + AtomSite(atom=invalid_atom, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + + @pytest.mark.parametrize('invalid_atom', [26, None, 1.5, Atom('Fe', None, 261)], ids=['int', 'none', 'float', 'bare_atom']) + def test_init_invalid_atom_type(self, invalid_atom): + # When Then Expect + with pytest.raises(TypeError, match='"atom" must be a valid Atoms enum or a valid Atoms name string'): + AtomSite(atom=invalid_atom, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + + @pytest.mark.parametrize('axis', ['a', 'b', 'c'], ids=['a', 'b', 'c']) + @pytest.mark.parametrize( + 'invalid_value, error, message', + [ + (1.5, ValueError, r'must be between -1\.0 and 1\.0'), + (-1.5, ValueError, r'must be between -1\.0 and 1\.0'), + (float('nan'), ValueError, r'must be between -1\.0 and 1\.0'), + ('0.5', TypeError, 'must be a numeric value'), + (True, TypeError, 'must be a numeric value'), + ], + ids=['above_range', 'below_range', 'nan', 'wrong_type', 'bool'], + ) + def test_init_invalid_fract_value(self, axis, invalid_value, error, message): + # When + kwargs = {'atom': Atoms.Fe, 'fract_a': 0.0, 'fract_b': 0.0, 'fract_c': 0.0, 'debye_temperature': 1.0} + kwargs[f'fract_{axis}'] = invalid_value + # Then Expect + with pytest.raises(error, match=message): + AtomSite(**kwargs) + + @pytest.mark.parametrize('axis', ['a', 'b', 'c'], ids=['a', 'b', 'c']) + @pytest.mark.parametrize('value, expected', [(1.0, 0.0), (-0.25, 0.75), (-1.0, 0.0)], ids=['one', 'negative', 'minus_one']) + def test_init_wraps_fract_value(self, axis, value, expected): + # When + kwargs = {'atom': Atoms.Fe, 'fract_a': 0.0, 'fract_b': 0.0, 'fract_c': 0.0, 'debye_temperature': 1.0} + kwargs[f'fract_{axis}'] = value + # Then + site = AtomSite(**kwargs) + # Expect + assert getattr(site, f'fract_{axis}').value == expected + + @pytest.mark.parametrize( + 'invalid_value, error, message', + [ + (-1.0, ValueError, 'must be positive'), + (0.0, ValueError, 'must be positive'), + ('cold', TypeError, 'must be a numeric value'), + (True, TypeError, 'value=True must be a number'), + ], + ids=['negative', 'zero', 'wrong_type', 'bool'], + ) + def test_init_invalid_debye_temperature(self, invalid_value, error, message): + # When Then Expect + with pytest.raises(error, match=message): + AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=invalid_value) + + def test_atom_setter_valid(self, atom_site): + # When + site = atom_site + # Then + site.atom = Atoms.Co + # Expect + assert site.atom is Atoms.Co + + def test_atom_setter_valid_string(self, atom_site): + # When + site = atom_site + # Then + site.atom = 'Co' + # Expect + assert site.atom is Atoms.Co + + @pytest.mark.parametrize('new_atom', [Atoms.Co, 'Co'], ids=['enum', 'string']) + def test_atom_setter_regenerates_default_unique_name(self, new_atom): + # When + site = AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + # Then + site.atom = new_atom + # Expect + assert site.unique_name.startswith('Co AtomSite') + + def test_atom_setter_regenerates_default_unique_name_on_every_change(self): + # When + site = AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + # Then + site.atom = Atoms.Co + site.atom = Atoms.Ni + # Expect + unique_name = site.unique_name + assert unique_name.startswith('Ni AtomSite') + assert site.fract_a.unique_name.startswith(unique_name + '_fract_a') + assert site.fract_b.unique_name.startswith(unique_name + '_fract_b') + assert site.fract_c.unique_name.startswith(unique_name + '_fract_c') + assert site.debye_temperature.unique_name.startswith(unique_name + '_debye_temperature') + assert 'unique_name' not in site.to_dict() + + def test_atom_setter_registers_regenerated_unique_names(self): + # When + site = AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + site.atom = Atoms.Co + # Then + other_site = AtomSite(atom=Atoms.Co, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + # Expect + assert global_object.map.get_item_by_key(site.unique_name) is site + assert other_site.unique_name != site.unique_name + assert other_site.fract_a.unique_name != site.fract_a.unique_name + assert other_site.debye_temperature.unique_name != site.debye_temperature.unique_name + + def test_atom_setter_keeps_explicit_unique_name(self, atom_site): + # When + site = atom_site + # Then + site.atom = Atoms.Co + # Expect + assert site.unique_name.startswith('test_atom_site') + + @pytest.mark.parametrize( + 'invalid_atom, error', + [ + ('not_an_atom', KeyError), + (26, TypeError), + (None, TypeError), + (Atom('Co', None, 271), TypeError), + ], + ids=['invalid_string', 'int', 'none', 'bare_atom'], + ) + def test_atom_setter_invalid_leaves_atom_unchanged(self, atom_site, invalid_atom, error): + # When + site = atom_site + original_unique_name = site.unique_name + # Then Expect + with pytest.raises(error, match='"atom" must be a valid Atoms enum or a valid Atoms name string'): + site.atom = invalid_atom + assert site.atom is Atoms.Fe + assert site.unique_name == original_unique_name + + @pytest.mark.parametrize('attribute', ['fract_a', 'fract_b', 'fract_c']) + @pytest.mark.parametrize('value', [0.75, np.float32(0.75)], ids=['float', 'float32']) + def test_fract_setter_valid(self, atom_site, attribute, value): + # When + site = atom_site + # Then + setattr(site, attribute, value) + # Expect + assert getattr(site, attribute).value == 0.75 + + @pytest.mark.parametrize('attribute', ['fract_a', 'fract_b', 'fract_c']) + @pytest.mark.parametrize('value, expected', [(1.0, 0.0), (-0.25, 0.75), (-1.0, 0.0)], ids=['one', 'negative', 'minus_one']) + def test_fract_setter_wraps_value(self, atom_site, attribute, value, expected): + # When + site = atom_site + # Then + setattr(site, attribute, value) + # Expect + assert getattr(site, attribute).value == expected + + @pytest.mark.parametrize('attribute', ['fract_a', 'fract_b', 'fract_c']) + def test_fract_setter_wraps_tiny_negative_value_below_one(self, atom_site, attribute): + # When + site = atom_site + # Then + setattr(site, attribute, -1e-17) # 1.0 - 1e-17 rounds to 1.0 in floating point + # Expect + assert 0.0 <= getattr(site, attribute).value < 1.0 + + @pytest.mark.parametrize('attribute', ['fract_a', 'fract_b', 'fract_c']) + def test_fract_setter_invalid_leaves_value_unchanged(self, atom_site, attribute): + # When + site = atom_site + original_value = getattr(site, attribute).value + # Then Expect + with pytest.raises(ValueError, match=r'must be between -1\.0 and 1\.0'): + setattr(site, attribute, 2.0) + assert getattr(site, attribute).value == original_value + + @pytest.mark.parametrize('value', [400.0, np.float32(400.0), np.int64(400)], ids=['float', 'float32', 'int64']) + def test_debye_temperature_setter_valid(self, atom_site, value): + # When + site = atom_site + # Then + site.debye_temperature = value + # Expect + assert site.debye_temperature.value == 400.0 + + @pytest.mark.parametrize('invalid_value', [-10.0, 0.0], ids=['negative', 'zero']) + def test_debye_temperature_setter_invalid_leaves_value_unchanged(self, atom_site, invalid_value): + # When + site = atom_site + # Then Expect + with pytest.raises(ValueError, match='must be positive'): + site.debye_temperature = invalid_value + assert site.debye_temperature.value == 250.0 + + @pytest.mark.parametrize('atom', [Atoms.Fe, Atoms.Fe56], ids=['natural', 'isotope']) + def test_to_dict_stores_atom_name(self, atom): + # When + site = AtomSite(atom=atom, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0) + # Then + dict_repr = site.to_dict() + # Expect + assert dict_repr['atom'] == atom.name diff --git a/tests/unit/sample_model/test_atoms.py b/tests/unit/sample_model/test_atoms.py new file mode 100644 index 0000000..92d4c50 --- /dev/null +++ b/tests/unit/sample_model/test_atoms.py @@ -0,0 +1,90 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +from dataclasses import FrozenInstanceError + +import pytest + +from easyimaging.sample_model.atoms import Atom +from easyimaging.sample_model.atoms import Atoms + + +class TestAtom: + def test_init_stores_fields(self): + # When + atom = Atom(element='Fe', isotope=56, enum_id=263) + # Then Expect + assert atom.element == 'Fe' + assert atom.isotope == 56 + assert atom.enum_id == 263 + + def test_init_natural_abundance_isotope_is_none(self): + # When + atom = Atom(element='Fe', isotope=None, enum_id=261) + # Then Expect + assert atom.isotope is None + + def test_is_immutable(self): + # When + atom = Atom(element='Fe', isotope=56, enum_id=263) + # Then Expect + with pytest.raises(FrozenInstanceError): + atom.element = 'Cu' + assert atom.element == 'Fe' + + +class TestAtoms: + @pytest.mark.parametrize( + 'member, element, isotope, enum_id', + [ + (Atoms.H, 'H', None, 11), + (Atoms.H2, 'H', 2, 13), + (Atoms.Fe, 'Fe', None, 261), + (Atoms.Fe56, 'Fe', 56, 263), + (Atoms.U238, 'U', 238, 875), + ], + ids=['natural_hydrogen', 'deuterium', 'natural_iron', 'iron_56', 'uranium_238'], + ) + def test_member_attributes(self, member, element, isotope, enum_id): + # When Then Expect + assert member.element == element + assert member.isotope == isotope + assert member.enum_id == enum_id + + def test_membership_valid_member(self): + # When Then Expect + assert Atoms.Fe in Atoms + + @pytest.mark.parametrize('value', ['Fe', None, 42], ids=['string', 'none', 'integer']) + def test_membership_invalid_value(self, value): + # When Then Expect + assert value not in Atoms + + def test_lookup_by_member_name(self): + # When + member = Atoms['Fe56'] + # Then Expect + assert member is Atoms.Fe56 + + def test_members_are_hashable(self): + # When + debye_temperatures = {Atoms.Fe: 470.0, Atoms.Cu: 343.0} + species = {Atoms.Fe, Atoms.Fe56, Atoms.Fe} + # Then Expect + assert debye_temperatures[Atoms.Fe] == 470.0 + assert species == {Atoms.Fe, Atoms.Fe56} + + @pytest.mark.parametrize('field, value', [('element', 'Cu'), ('isotope', 63)], ids=['element', 'isotope']) + def test_members_are_immutable(self, field, value): + # When + original_value = getattr(Atoms.Fe, field) + # Then Expect + with pytest.raises(FrozenInstanceError): + setattr(Atoms.Fe, field, value) + assert getattr(Atoms.Fe, field) == original_value + + def test_enum_ids_are_unique_across_all_members(self): + # When + enum_ids = [member.enum_id for member in Atoms] + # Then Expect + assert len(enum_ids) == len(set(enum_ids)) diff --git a/tests/unit/sample_model/test_crystals.py b/tests/unit/sample_model/test_crystals.py new file mode 100644 index 0000000..cac5582 --- /dev/null +++ b/tests/unit/sample_model/test_crystals.py @@ -0,0 +1,382 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +import pytest + +from easyimaging.sample_model.atoms import Atoms +from easyimaging.sample_model.crystals import body_centered_cubic +from easyimaging.sample_model.crystals import diamond_cubic +from easyimaging.sample_model.crystals import face_centered_cubic +from easyimaging.sample_model.crystals import hexagonal_close_packed +from easyimaging.sample_model.crystals import zincblende + + +def _fract_coords(lattice): + return [(site.fract_a.value, site.fract_b.value, site.fract_c.value) for site in lattice.atom_sites] + + +class TestBodyCenteredCubic: + def test_valid_lattice_and_sites(self): + # When + lattice = body_centered_cubic(length_a=2.87, atom=Atoms.Fe) + # Then Expect + assert lattice.length_a.value == 2.87 + assert lattice.length_b.value == 2.87 + assert lattice.length_c.value == 2.87 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 90.0 + assert len(lattice.atom_sites) == 2 + assert all(site.atom is Atoms.Fe for site in lattice.atom_sites) + assert _fract_coords(lattice) == [(0.0, 0.0, 0.0), (0.5, 0.5, 0.5)] + + def test_default_debye_temperature(self): + # When + lattice = body_centered_cubic(length_a=2.87, atom=Atoms.Fe) + # Then Expect + assert all(site.debye_temperature.value == 300.0 for site in lattice.atom_sites) + + def test_explicit_debye_temperature(self): + # When + lattice = body_centered_cubic(length_a=2.87, atom=Atoms.Fe, debye_temperature=470.0) + # Then Expect + assert all(site.debye_temperature.value == 470.0 for site in lattice.atom_sites) + + def test_default_naming(self): + # When + lattice = body_centered_cubic(length_a=2.87, atom=Atoms.Fe) + # Then Expect + assert lattice.unique_name.startswith('Fe BCC Lattice') + + def test_default_naming_with_atom_as_string(self): + # When + lattice = body_centered_cubic(length_a=2.87, atom='Fe') + # Then Expect + assert lattice.unique_name.startswith('Fe BCC Lattice') + + def test_explicit_naming(self): + # When + lattice = body_centered_cubic(length_a=2.87, atom=Atoms.Fe, unique_name='my_bcc', display_name='My BCC') + # Then Expect + assert lattice.unique_name == 'my_bcc' + assert lattice.display_name == 'My BCC' + + def test_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_a must be positive and non-zero.'): + body_centered_cubic(length_a=0.0, atom=Atoms.Fe) + + def test_invalid_length_type(self): + # When Then Expect + with pytest.raises(TypeError, match='Lattice length_a must be a numeric value'): + body_centered_cubic(length_a='2.87', atom=Atoms.Fe) + + def test_invalid_atom(self): + # When Then Expect + with pytest.raises(TypeError, match='"atom" must be a valid Atoms enum or a valid Atoms name string.'): + body_centered_cubic(length_a=2.87, atom=255) + + +class TestFaceCenteredCubic: + def test_valid_lattice_and_sites(self): + # When + lattice = face_centered_cubic(length_a=3.6, atom=Atoms.Cu) + # Then Expect + assert lattice.length_a.value == 3.6 + assert lattice.length_b.value == 3.6 + assert lattice.length_c.value == 3.6 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 90.0 + assert len(lattice.atom_sites) == 4 + assert all(site.atom is Atoms.Cu for site in lattice.atom_sites) + assert _fract_coords(lattice) == [ + (0.0, 0.0, 0.0), + (0.5, 0.5, 0.0), + (0.5, 0.0, 0.5), + (0.0, 0.5, 0.5), + ] + + def test_default_debye_temperature(self): + # When + lattice = face_centered_cubic(length_a=3.6, atom=Atoms.Cu) + # Then Expect + assert all(site.debye_temperature.value == 300.0 for site in lattice.atom_sites) + + def test_explicit_debye_temperature(self): + # When + lattice = face_centered_cubic(length_a=3.6, atom=Atoms.Cu, debye_temperature=470.0) + # Then Expect + assert all(site.debye_temperature.value == 470.0 for site in lattice.atom_sites) + + def test_default_naming(self): + # When + lattice = face_centered_cubic(length_a=3.6, atom=Atoms.Cu) + # Then Expect + assert lattice.unique_name.startswith('Cu FCC Lattice') + + def test_default_naming_with_atom_as_string(self): + # When + lattice = face_centered_cubic(length_a=3.6, atom='Cu') + # Then Expect + assert lattice.unique_name.startswith('Cu FCC Lattice') + + def test_explicit_naming(self): + # When + lattice = face_centered_cubic(length_a=3.6, atom=Atoms.Cu, unique_name='my_fcc') + # Then Expect + assert lattice.unique_name == 'my_fcc' + + def test_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_a must be positive and non-zero.'): + face_centered_cubic(length_a=-1.0, atom=Atoms.Cu) + + def test_invalid_length_type(self): + # When Then Expect + with pytest.raises(TypeError, match='Lattice length_a must be a numeric value'): + face_centered_cubic(length_a='3.6', atom=Atoms.Cu) + + def test_invalid_atom(self): + # When Then Expect + with pytest.raises(TypeError, match='"atom" must be a valid Atoms enum or a valid Atoms name string.'): + face_centered_cubic(length_a=3.6, atom=127) + + +class TestDiamondCubic: + def test_valid_lattice_and_sites(self): + # When + lattice = diamond_cubic(length_a=5.43, atom=Atoms.Si) + # Then Expect + assert lattice.length_a.value == 5.43 + assert lattice.length_b.value == 5.43 + assert lattice.length_c.value == 5.43 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 90.0 + assert len(lattice.atom_sites) == 8 + assert all(site.atom is Atoms.Si for site in lattice.atom_sites) + assert _fract_coords(lattice) == [ + (0.0, 0.0, 0.0), + (0.25, 0.25, 0.25), + (0.5, 0.5, 0.0), + (0.75, 0.75, 0.25), + (0.5, 0.0, 0.5), + (0.75, 0.25, 0.75), + (0.0, 0.5, 0.5), + (0.25, 0.75, 0.75), + ] + + def test_default_debye_temperature(self): + # When + lattice = diamond_cubic(length_a=5.43, atom=Atoms.Si) + # Then Expect + assert all(site.debye_temperature.value == 300.0 for site in lattice.atom_sites) + + def test_explicit_debye_temperature(self): + # When + lattice = diamond_cubic(length_a=5.43, atom=Atoms.Si, debye_temperature=470.0) + # Then Expect + assert all(site.debye_temperature.value == 470.0 for site in lattice.atom_sites) + + def test_default_naming(self): + # When + lattice = diamond_cubic(length_a=5.43, atom=Atoms.Si) + # Then Expect + assert lattice.unique_name.startswith('Si Diamond Cubic Lattice') + + def test_default_naming_with_atom_as_string(self): + # When + lattice = diamond_cubic(length_a=5.43, atom='Si') + # Then Expect + assert lattice.unique_name.startswith('Si Diamond Cubic Lattice') + + def test_explicit_naming(self): + # When + lattice = diamond_cubic(length_a=5.43, atom=Atoms.Si, unique_name='my_diamond_cubic') + # Then Expect + assert lattice.unique_name == 'my_diamond_cubic' + + def test_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_a must be positive and non-zero.'): + diamond_cubic(length_a=0.0, atom=Atoms.Si) + + def test_invalid_length_type(self): + # When Then Expect + with pytest.raises(TypeError, match='Lattice length_a must be a numeric value'): + diamond_cubic(length_a='5.43', atom=Atoms.Si) + + def test_invalid_atom(self): + # When Then Expect + with pytest.raises(TypeError, match='"atom" must be a valid Atoms enum or a valid Atoms name string.'): + diamond_cubic(length_a=5.43, atom=64) + + +class TestZincblende: + def test_valid_lattice_and_sites(self): + # When + lattice = zincblende(length_a=5.65, atom1=Atoms.Ga, atom2=Atoms.As) + # Then Expect + assert lattice.length_a.value == 5.65 + assert lattice.length_b.value == 5.65 + assert lattice.length_c.value == 5.65 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 90.0 + assert len(lattice.atom_sites) == 8 + assert [site.atom for site in lattice.atom_sites] == [ + Atoms.Ga, + Atoms.As, + Atoms.Ga, + Atoms.As, + Atoms.Ga, + Atoms.As, + Atoms.Ga, + Atoms.As, + ] + assert _fract_coords(lattice) == [ + (0.0, 0.0, 0.0), + (0.25, 0.25, 0.25), + (0.5, 0.5, 0.0), + (0.75, 0.75, 0.25), + (0.5, 0.0, 0.5), + (0.75, 0.25, 0.75), + (0.0, 0.5, 0.5), + (0.25, 0.75, 0.75), + ] + + def test_default_debye_temperature(self): + # When + lattice = zincblende(length_a=5.65, atom1=Atoms.Ga, atom2=Atoms.As) + # Then Expect + assert all(site.debye_temperature.value == 300.0 for site in lattice.atom_sites) + + @pytest.mark.parametrize( + 'debye_temperature1, debye_temperature2', + [(240.0, None), (None, 300.0), (240.0, 280.0)], + ids=['first_debye_temperature', 'second_debye_temperature', 'both_debye_temperatures'], + ) + def test_explicit_debye_temperatures_per_sublattice(self, debye_temperature1, debye_temperature2): + # When + lattice = zincblende( + length_a=5.65, + atom1=Atoms.Ga, + atom2=Atoms.As, + debye_temperature1=debye_temperature1, + debye_temperature2=debye_temperature2, + ) + # Then + expected_values = [] + for dt in [debye_temperature1, debye_temperature2] * 4: + expected_values.append(dt if dt is not None else 300.0) + # Expect + assert [site.debye_temperature.value for site in lattice.atom_sites] == expected_values + + def test_default_naming(self): + # When + lattice = zincblende(length_a=5.65, atom1=Atoms.Ga, atom2=Atoms.As) + # Then Expect + assert lattice.unique_name.startswith('GaAs Zincblende Lattice') + + @pytest.mark.parametrize( + 'atom1, atom2', + [('Ga', Atoms.As), (Atoms.Ga, 'As'), ('Ga', 'As')], + ids=['atom1_as_string', 'atom2_as_string', 'both_as_strings'], + ) + def test_default_naming_with_atom_as_string(self, atom1, atom2): + # When + lattice = zincblende(length_a=5.65, atom1=atom1, atom2=atom2) + # Then Expect + assert lattice.unique_name.startswith('GaAs Zincblende Lattice') + + def test_explicit_naming(self): + # When + lattice = zincblende(length_a=5.65, atom1=Atoms.Ga, atom2=Atoms.As, unique_name='my_zincblende') + # Then Expect + assert lattice.unique_name == 'my_zincblende' + + def test_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_a must be positive and non-zero.'): + zincblende(length_a=0.0, atom1=Atoms.Ga, atom2=Atoms.As) + + def test_invalid_length_type(self): + # When Then Expect + with pytest.raises(TypeError, match='Lattice length_a must be a numeric value'): + zincblende(length_a='5.65', atom1=Atoms.Ga, atom2=Atoms.As) + + @pytest.mark.parametrize( + 'atom1, atom2', + [(255, Atoms.As), (Atoms.Ga, 127)], + ids=['invalid_atom1', 'invalid_atom2'], + ) + def test_invalid_atom(self, atom1, atom2): + # When Then Expect + with pytest.raises(TypeError, match='"atom" must be a valid Atoms enum or a valid Atoms name string.'): + zincblende(length_a=5.65, atom1=atom1, atom2=atom2) + + +class TestHexagonalClosePacked: + def test_valid_lattice_and_sites(self): + # When + lattice = hexagonal_close_packed(length_a=2.95, length_c=4.68, atom=Atoms.Mg) + # Then Expect + assert lattice.length_a.value == 2.95 + assert lattice.length_b.value == 2.95 + assert lattice.length_c.value == 4.68 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 120.0 + assert len(lattice.atom_sites) == 2 + assert all(site.atom is Atoms.Mg for site in lattice.atom_sites) + assert _fract_coords(lattice) == [(0.0, 0.0, 0.0), (pytest.approx(2 / 3), pytest.approx(1 / 3), 0.5)] + + def test_default_debye_temperature(self): + # When + lattice = hexagonal_close_packed(length_a=2.87, length_c=4.68, atom=Atoms.Fe) + # Then Expect + assert all(site.debye_temperature.value == 300.0 for site in lattice.atom_sites) + + def test_explicit_debye_temperature(self): + # When + lattice = hexagonal_close_packed(length_a=2.87, length_c=4.68, atom=Atoms.Fe, debye_temperature=470.0) + # Then Expect + assert all(site.debye_temperature.value == 470.0 for site in lattice.atom_sites) + + def test_default_naming(self): + # When + lattice = hexagonal_close_packed(length_a=2.95, length_c=4.68, atom=Atoms.Mg) + # Then Expect + assert lattice.unique_name.startswith('Mg HCP Lattice') + + def test_default_naming_with_atom_as_string(self): + # When + lattice = hexagonal_close_packed(length_a=2.95, length_c=4.68, atom='Mg') + # Then Expect + assert lattice.unique_name.startswith('Mg HCP Lattice') + + def test_explicit_naming(self): + # When + lattice = hexagonal_close_packed(length_a=2.95, length_c=4.68, atom=Atoms.Mg, unique_name='my_hcp') + # Then Expect + assert lattice.unique_name == 'my_hcp' + + def test_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_a must be positive and non-zero.'): + hexagonal_close_packed(length_a=0.0, length_c=4.68, atom=Atoms.Mg) + + @pytest.mark.parametrize('axis', ['a', 'c']) + def test_invalid_length_type(self, axis): + # When + kwargs = dict(length_a=2.95, length_c=4.68, atom=Atoms.Mg) + kwargs[f'length_{axis}'] = '1.0' + # Then Expect + with pytest.raises(TypeError, match=f'Lattice length_{axis} must be a numeric value'): + hexagonal_close_packed(**kwargs) + + def test_invalid_atom(self): + # When Then Expect + with pytest.raises(TypeError, match='"atom" must be a valid Atoms enum or a valid Atoms name string.'): + hexagonal_close_packed(length_a=2.95, length_c=4.68, atom=13) diff --git a/tests/unit/sample_model/test_lattice.py b/tests/unit/sample_model/test_lattice.py new file mode 100644 index 0000000..bf1a398 --- /dev/null +++ b/tests/unit/sample_model/test_lattice.py @@ -0,0 +1,433 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +import numpy as np +import pytest +from easyscience import global_object + +from easyimaging.sample_model.atom_site import AtomSite +from easyimaging.sample_model.atoms import Atoms +from easyimaging.sample_model.lattice import INFINITESIMAL +from easyimaging.sample_model.lattice import Lattice + + +class TestLattice: + @pytest.fixture + def atom_sites(self): + return [ + AtomSite(atom=Atoms.Fe, fract_a=0.0, fract_b=0.0, fract_c=0.0, debye_temperature=1.0), + AtomSite(atom=Atoms.Fe, fract_a=0.5, fract_b=0.5, fract_c=0.5, debye_temperature=1.0), + ] + + @pytest.fixture + def lattice(self, atom_sites): + return Lattice( + length_a=1.0, + length_b=2.0, + length_c=3.0, + alpha=80.0, + beta=85.0, + gamma=95.0, + atom_sites=atom_sites, + unique_name='test_lattice', + display_name='Test Lattice', + ) + + def test_init_valid(self, lattice, atom_sites): + # When Then Expect + assert lattice.length_a.value == 1.0 + assert lattice.length_b.value == 2.0 + assert lattice.length_c.value == 3.0 + assert lattice.alpha.value == 80.0 + assert lattice.beta.value == 85.0 + assert lattice.gamma.value == 95.0 + assert lattice.unique_name == 'test_lattice' + assert lattice.display_name == 'Test Lattice' + assert len(lattice.atom_sites) == 2 + assert lattice.atom_sites[0] is atom_sites[0] + assert lattice.atom_sites[1] is atom_sites[1] + + def test_init_parameter_defaults(self): + # When + lattice = Lattice(length_a=1.0, length_b=2.0, length_c=3.0) + # Then Expect + assert lattice.length_a.min == 1e-3 + assert lattice.length_b.min == 1e-3 + assert lattice.length_c.min == 1e-3 + assert lattice.length_a.unit == 'Å' + assert lattice.length_b.unit == 'Å' + assert lattice.length_c.unit == 'Å' + assert lattice.alpha.min == INFINITESIMAL + assert lattice.alpha.max == 180 - INFINITESIMAL + assert lattice.alpha.unit == 'deg' + assert lattice.beta.min == INFINITESIMAL + assert lattice.beta.max == 180 - INFINITESIMAL + assert lattice.beta.unit == 'deg' + assert lattice.gamma.min == INFINITESIMAL + assert lattice.gamma.max == 180 - INFINITESIMAL + assert lattice.gamma.unit == 'deg' + + def test_init_default_angles(self): + # When Then + lattice = Lattice(length_a=1.0, length_b=2.0, length_c=3.0) + # Expect + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 90.0 + + def test_init_default_unique_name(self, atom_sites): + # When + lattice = Lattice(length_a=1.0, length_b=1.0, length_c=1.0, atom_sites=atom_sites) + # Then + unique_name = lattice.unique_name + # Expect + assert lattice.unique_name.startswith('Lattice') + assert lattice.display_name == lattice.unique_name + assert lattice.alpha.unique_name.startswith(unique_name + '_angle_alpha') + assert lattice.beta.unique_name.startswith(unique_name + '_angle_beta') + assert lattice.gamma.unique_name.startswith(unique_name + '_angle_gamma') + assert lattice.length_a.unique_name.startswith(unique_name + '_length_a') + assert lattice.length_b.unique_name.startswith(unique_name + '_length_b') + assert lattice.length_c.unique_name.startswith(unique_name + '_length_c') + + def test_init_no_atom_sites_is_empty(self): + # When Then + lattice = Lattice(length_a=1.0, length_b=1.0, length_c=1.0) + # Expect + assert len(lattice.atom_sites) == 0 + + @pytest.mark.parametrize( + 'length_a, length_b, length_c, invalid_axis', + [(0.0, 1.0, 1.0, 'a'), (1.0, 0.0, 1.0, 'b'), (1.0, 1.0, -1.0, 'c')], + ids=['a', 'b', 'c'], + ) + def test_init_invalid_lengths(self, length_a, length_b, length_c, invalid_axis): + # When Then Expect + with pytest.raises(ValueError, match=f'Lattice length_{invalid_axis} must be positive and non-zero.'): + Lattice(length_a=length_a, length_b=length_b, length_c=length_c) + + @pytest.mark.parametrize('axis', ['a', 'b', 'c']) + @pytest.mark.parametrize('invalid_value', ['1.0', None, [1.0]], ids=['string', 'none', 'list']) + def test_init_invalid_length_type(self, axis, invalid_value): + # When + kwargs = dict(length_a=1.0, length_b=1.0, length_c=1.0) + kwargs[f'length_{axis}'] = invalid_value + # Then Expect + with pytest.raises(TypeError, match=f'Lattice length_{axis} must be a numeric value'): + Lattice(**kwargs) + + @pytest.mark.parametrize( + 'alpha, beta, gamma, invalid_name', + [(0.0, 90.0, 90.0, 'alpha'), (180.0, 90.0, 90.0, 'alpha'), (90.0, -10.0, 90.0, 'beta'), (90.0, 90.0, 200.0, 'gamma')], + ids=['alpha_low', 'alpha_high', 'beta', 'gamma'], + ) + def test_init_invalid_angles(self, alpha, beta, gamma, invalid_name): + # When Then Expect + with pytest.raises(ValueError, match=f'Lattice angle {invalid_name} must be between 0 and 180 degrees.'): + Lattice(length_a=1.0, length_b=1.0, length_c=1.0, alpha=alpha, beta=beta, gamma=gamma) + + @pytest.mark.parametrize('name', ['alpha', 'beta', 'gamma']) + @pytest.mark.parametrize('invalid_value', ['90.0', None, [90.0]], ids=['string', 'none', 'list']) + def test_init_invalid_angle_type(self, name, invalid_value): + # When + kwargs = dict(length_a=1.0, length_b=1.0, length_c=1.0, alpha=90.0, beta=90.0, gamma=90.0) + kwargs[name] = invalid_value + # Then Expect + with pytest.raises(TypeError, match=f'Lattice angle {name} must be a numeric value'): + Lattice(**kwargs) + + @pytest.mark.parametrize('atom_sites', ['not_a_list', ['not_an_atom_site']], ids=['not_a_list', 'not_an_atom_site']) + def test_init_invalid_atom_sites_type(self, atom_sites): + # When Then Expect + with pytest.raises(TypeError, match='atom_sites must be a sequence of AtomSite objects.'): + Lattice(length_a=1.0, length_b=1.0, length_c=1.0, atom_sites=atom_sites) + + def test_init_accepts_atom_sites_tuple(self, atom_sites): + # When Then + lattice = Lattice(length_a=1.0, length_b=1.0, length_c=1.0, atom_sites=tuple(atom_sites)) + # Expect + assert len(lattice.atom_sites) == 2 + assert all(new_site is site for new_site, site in zip(lattice.atom_sites, atom_sites)) + + def test_init_accepts_atom_sites_of_other_lattice(self, atom_sites): + # When + other_lattice = Lattice(length_a=1.0, length_b=1.0, length_c=1.0, atom_sites=atom_sites) + # Then + lattice = Lattice(length_a=2.0, length_b=2.0, length_c=2.0, atom_sites=other_lattice.atom_sites) + # Expect + assert len(lattice.atom_sites) == 2 + assert all(new_site is site for new_site, site in zip(lattice.atom_sites, atom_sites)) + assert lattice.atom_sites is not other_lattice.atom_sites + + def test_init_accepts_numpy_scalars(self): + # When + lattice = Lattice( + length_a=np.float32(2.5), + length_b=np.int64(3), + length_c=np.float64(4.0), + alpha=np.float32(80.0), + beta=np.int64(90), + gamma=np.float64(100.0), + ) + # Then Expect + assert (lattice.length_a.value, lattice.length_b.value, lattice.length_c.value) == (2.5, 3.0, 4.0) + assert (lattice.alpha.value, lattice.beta.value, lattice.gamma.value) == (80.0, 90.0, 100.0) + + def test_cubic_valid(self): + # When + lattice = Lattice.cubic(length_a=3.0) + # Then Expect + assert lattice.length_a.value == 3.0 + assert lattice.length_b.value == 3.0 + assert lattice.length_c.value == 3.0 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 90.0 + assert lattice.unique_name.startswith('CubicLattice') + + def test_cubic_length_b_and_c_track_length_a(self): + # When + lattice = Lattice.cubic(length_a=3.0) + # Then + lattice.length_a = 5.0 + # Expect + assert lattice.length_b.value == 5.0 + assert lattice.length_c.value == 5.0 + + def test_cubic_with_atom_sites(self, atom_sites): + # When + lattice = Lattice.cubic(length_a=3.0, atom_sites=atom_sites) + # Then Expect + assert len(lattice.atom_sites) == 2 + + def test_cubic_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_a must be positive and non-zero.'): + Lattice.cubic(length_a=0.0) + + def test_cubic_invalid_length_type(self): + # When Then Expect + with pytest.raises(TypeError, match='Lattice length_a must be a numeric value'): + Lattice.cubic(length_a='3.0') + + def test_hexagonal_valid(self): + # When + lattice = Lattice.hexagonal(length_a=2.0, length_c=4.0) + # Then Expect + assert lattice.length_a.value == 2.0 + assert lattice.length_b.value == 2.0 + assert lattice.length_c.value == 4.0 + assert lattice.alpha.value == 90.0 + assert lattice.beta.value == 90.0 + assert lattice.gamma.value == 120.0 + assert lattice.unique_name.startswith('HexagonalLattice') + + def test_hexagonal_length_b_tracks_length_a_only(self): + # When + lattice = Lattice.hexagonal(length_a=2.0, length_c=4.0) + # Then + lattice.length_a = 6.0 + lattice.length_c = 5.0 + # Expect + assert lattice.length_b.value == 6.0 + assert lattice.length_c.value == 5.0 + + def test_hexagonal_invalid_length_value(self): + # When Then Expect + with pytest.raises(ValueError, match='Lattice length_c must be positive and non-zero.'): + Lattice.hexagonal(length_a=1.0, length_c=0.0) + + @pytest.mark.parametrize('axis', ['a', 'c']) + def test_hexagonal_invalid_length_type(self, axis): + # When + kwargs = dict(length_a=1.0, length_c=1.0) + kwargs[f'length_{axis}'] = '1.0' + # Then Expect + with pytest.raises(TypeError, match=f'Lattice length_{axis} must be a numeric value'): + Lattice.hexagonal(**kwargs) + + @pytest.mark.parametrize('attribute', ['length_a', 'length_b', 'length_c']) + @pytest.mark.parametrize('value', [9.0, np.float32(9.0), np.int64(9)], ids=['float', 'float32', 'int64']) + def test_length_setter_valid(self, lattice, attribute, value): + # When Then + setattr(lattice, attribute, value) + # Expect + assert getattr(lattice, attribute).value == 9.0 + + @pytest.mark.parametrize('invalid_value', [0.0, -1.0]) + @pytest.mark.parametrize('attribute', ['length_a', 'length_b', 'length_c']) + def test_length_setter_invalid(self, lattice, attribute, invalid_value): + # When + axis = attribute[-1] + # Then Expect + with pytest.raises(ValueError, match=f'Lattice length_{axis} must be positive and non-zero.'): + setattr(lattice, attribute, invalid_value) + + @pytest.mark.parametrize('invalid_value', ['9.0', None, [9.0]], ids=['string', 'none', 'list']) + @pytest.mark.parametrize('attribute', ['length_a', 'length_b', 'length_c']) + def test_length_setter_invalid_type(self, lattice, attribute, invalid_value): + # When + axis = attribute[-1] + # Then Expect + with pytest.raises(TypeError, match=f'Lattice length_{axis} must be a numeric value'): + setattr(lattice, attribute, invalid_value) + + @pytest.mark.parametrize('attribute', ['alpha', 'beta', 'gamma']) + @pytest.mark.parametrize('value', [100.0, np.float32(100.0), np.int64(100)], ids=['float', 'float32', 'int64']) + def test_angle_setter_valid(self, lattice, attribute, value): + # When Then + setattr(lattice, attribute, value) + # Expect + assert getattr(lattice, attribute).value == 100.0 + + @pytest.mark.parametrize('attribute', ['alpha', 'beta', 'gamma']) + @pytest.mark.parametrize('invalid_value', [0.0, 180.0, 250.0]) + def test_angle_setter_invalid(self, lattice, attribute, invalid_value): + # When Then Expect + with pytest.raises(ValueError, match=f'Lattice angle {attribute} must be between 0 and 180 degrees.'): + setattr(lattice, attribute, invalid_value) + + @pytest.mark.parametrize('attribute', ['alpha', 'beta', 'gamma']) + @pytest.mark.parametrize('invalid_value', ['100.0', None, [100.0]], ids=['string', 'none', 'list']) + def test_angle_setter_invalid_type(self, lattice, attribute, invalid_value): + # When Then Expect + with pytest.raises(TypeError, match=f'Lattice angle {attribute} must be a numeric value'): + setattr(lattice, attribute, invalid_value) + + def test_from_dict_roundtrip(self, lattice): + # When + dict_repr = lattice.to_dict() + # Then + global_object.map._clear() + new_instance = Lattice.from_dict(dict_repr) + new_dict = new_instance.to_dict() + # Expect + assert new_dict == dict_repr + + def test_cubic_fittable_parameters_only_include_length_a(self): + # When + lattice = Lattice.cubic(length_a=3.0) + # Then + fittable_parameters = lattice.get_fittable_parameters() + # Expect + assert len(fittable_parameters) == 1 + assert fittable_parameters[0] is lattice.length_a + + def test_hexagonal_fittable_parameters_only_include_length_a_and_c(self): + # When + lattice = Lattice.hexagonal(length_a=2.0, length_c=4.0) + # Then + fittable_parameters = lattice.get_fittable_parameters() + # Expect + assert len(fittable_parameters) == 2 + assert any(parameter is lattice.length_a for parameter in fittable_parameters) + assert any(parameter is lattice.length_c for parameter in fittable_parameters) + + @pytest.mark.parametrize( + 'constructor_kwargs', [{'length_a': 3.0}, {'length_a': 2.0, 'length_c': 4.0}], ids=['cubic', 'hexagonal'] + ) + @pytest.mark.parametrize('attribute', ['alpha', 'beta', 'gamma']) + def test_symmetry_constructors_lock_angles(self, constructor_kwargs, attribute): + # When + constructor = Lattice.hexagonal if 'length_c' in constructor_kwargs else Lattice.cubic + lattice = constructor(**constructor_kwargs) + original_value = getattr(lattice, attribute).value + # Then Expect + assert not getattr(lattice, attribute).independent + with pytest.raises(AttributeError, match='This is a dependent parameter'): + setattr(lattice, attribute, 100.0) + assert getattr(lattice, attribute).value == original_value + + def test_to_dict_without_symmetry_constructor_has_no_angle_lock(self, lattice): + # When Then + dict_repr = lattice.to_dict() + # Expect + assert 'angle_lock' not in dict_repr + assert dict_repr['unique_name'] == 'test_lattice' + assert dict_repr['display_name'] == 'Test Lattice' + assert dict_repr['length_a']['value'] == 1.0 + assert dict_repr['gamma']['value'] == 95.0 + assert len(dict_repr['atom_sites']['data']) == 2 + + @pytest.mark.parametrize( + 'constructor_kwargs', [{'length_a': 3.0}, {'length_a': 2.0, 'length_c': 4.0}], ids=['cubic', 'hexagonal'] + ) + def test_to_dict_with_symmetry_constructor_has_angle_lock(self, constructor_kwargs): + # When + constructor = Lattice.hexagonal if 'length_c' in constructor_kwargs else Lattice.cubic + lattice = constructor(**constructor_kwargs) + # Then + dict_repr = lattice.to_dict() + # Expect + assert dict_repr['angle_lock']['value'] == 90.0 + + def test_to_dict_skip_parameter(self, lattice): + # When Then + dict_repr = lattice.to_dict(skip=['unique_name', 'alpha']) + # Expect + assert 'unique_name' not in dict_repr + assert 'alpha' not in dict_repr + assert 'beta' in dict_repr + + def test_from_dict(self, lattice): + # When + dict_repr = lattice.to_dict() + global_object.map._clear() + # Then + new_lattice = Lattice.from_dict(dict_repr) + # Expect + assert new_lattice.unique_name == 'test_lattice' + assert new_lattice.display_name == 'Test Lattice' + assert new_lattice.length_a.value == 1.0 + assert new_lattice.length_b.value == 2.0 + assert new_lattice.length_c.value == 3.0 + assert new_lattice.alpha.value == 80.0 + assert new_lattice.beta.value == 85.0 + assert new_lattice.gamma.value == 95.0 + assert len(new_lattice.get_fittable_parameters()) == 6 + 4 * 2 + assert len(new_lattice.atom_sites) == 2 + assert all(isinstance(site, AtomSite) for site in new_lattice.atom_sites) + assert [site.fract_a.value for site in new_lattice.atom_sites] == [0.0, 0.5] + + # def test_from_dict_cubic_keeps_lengths_and_angles_locked(self, atom_sites): + # # When + # dict_repr = Lattice.cubic(length_a=3.0, atom_sites=atom_sites).to_dict() + # global_object.map._clear() + # # Then + # new_lattice = Lattice.from_dict(dict_repr) + # resolve_all_parameter_dependencies(new_lattice) + # new_lattice.length_a = 5.0 + # # Expect + # assert new_lattice.length_b.value == 5.0 + # assert new_lattice.length_c.value == 5.0 + # assert new_lattice.alpha.value == 90.0 + # assert new_lattice.beta.value == 90.0 + # assert new_lattice.gamma.value == 90.0 + # assert len(new_lattice.atom_sites) == 2 + # lattice_fittable = [p for p in new_lattice.get_fittable_parameters() if p.name.startswith(('length', 'angle'))] + # assert len(lattice_fittable) == 1 + # assert lattice_fittable[0] is new_lattice.length_a + + # def test_from_dict_hexagonal_keeps_lengths_and_angles_locked(self): + # # When + # dict_repr = Lattice.hexagonal(length_a=2.0, length_c=4.0).to_dict() + # global_object.map._clear() + # # Then + # new_lattice = Lattice.from_dict(dict_repr) + # resolve_all_parameter_dependencies(new_lattice) + # new_lattice.length_a = 6.0 + # # Expect + # assert new_lattice.length_b.value == 6.0 + # assert new_lattice.length_c.value == 4.0 + # assert new_lattice.alpha.value == 90.0 + # assert new_lattice.beta.value == 90.0 + # assert new_lattice.gamma.value == 120.0 + # fittable_parameters = new_lattice.get_fittable_parameters() + # assert len(fittable_parameters) == 2 + # assert any(parameter is new_lattice.length_a for parameter in fittable_parameters) + # assert any(parameter is new_lattice.length_c for parameter in fittable_parameters) + + def test_from_dict_invalid_type(self): + # When Then Expect + with pytest.raises(TypeError, match='Expected a dictionary. Got str'): + Lattice.from_dict('not_a_dict')