mirror of
https://github.com/ivan-usov-org/bec.git
synced 2025-04-22 02:20:02 +02:00
503 lines
26 KiB
Python
503 lines
26 KiB
Python
from math import inf
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from unittest import mock
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import pytest
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from bec_lib import MessageEndpoints, messages
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from bec_lib.dap_plugin_objects import DAPPluginObject, LmfitService1D
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from bec_lib.dap_plugins import DAPPlugins
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from bec_lib.device import DeviceBase
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from bec_lib.scan_items import ScanItem
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from bec_lib.scan_report import ScanReport
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@pytest.fixture
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def dap_plugin_message():
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msg = messages.AvailableResourceMessage(
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**{
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"resource": {
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"GaussianModel": {
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"class": "LmfitService1D",
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"user_friendly_name": "GaussianModel",
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"class_doc": "A model based on a Gaussian or normal distribution lineshape.\n\n The model has three Parameters: `amplitude`, `center`, and `sigma`.\n In addition, parameters `fwhm` and `height` are included as\n constraints to report full width at half maximum and maximum peak\n height, respectively.\n\n .. math::\n\n f(x; A, \\mu, \\sigma) = \\frac{A}{\\sigma\\sqrt{2\\pi}} e^{[{-{(x-\\mu)^2}/{{2\\sigma}^2}}]}\n\n where the parameter `amplitude` corresponds to :math:`A`, `center` to\n :math:`\\mu`, and `sigma` to :math:`\\sigma`. The full width at half\n maximum is :math:`2\\sigma\\sqrt{2\\ln{2}}`, approximately\n :math:`2.3548\\sigma`.\n\n For more information, see: https://en.wikipedia.org/wiki/Normal_distribution\n\n ",
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"run_doc": "A model based on a Gaussian or normal distribution lineshape.\n\n The model has three Parameters: `amplitude`, `center`, and `sigma`.\n In addition, parameters `fwhm` and `height` are included as\n constraints to report full width at half maximum and maximum peak\n height, respectively.\n\n .. math::\n\n f(x; A, \\mu, \\sigma) = \\frac{A}{\\sigma\\sqrt{2\\pi}} e^{[{-{(x-\\mu)^2}/{{2\\sigma}^2}}]}\n\n where the parameter `amplitude` corresponds to :math:`A`, `center` to\n :math:`\\mu`, and `sigma` to :math:`\\sigma`. The full width at half\n maximum is :math:`2\\sigma\\sqrt{2\\ln{2}}`, approximately\n :math:`2.3548\\sigma`.\n\n For more information, see: https://en.wikipedia.org/wiki/Normal_distribution\n\n \n Args:\n scan_item (ScanItem): Scan item or scan ID\n device_x (DeviceBase | str): Device name for x\n signal_x (DeviceBase | str): Signal name for x\n device_y (DeviceBase | str): Device name for y\n signal_y (DeviceBase | str): Signal name for y\n parameters (dict): Fit parameters\n ",
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"run_name": "fit",
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"signature": [
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{
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"name": "args",
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"kind": "VAR_POSITIONAL",
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"default": "_empty",
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"annotation": "_empty",
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},
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{
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"name": "scan_item",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "ScanItem | str",
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},
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{
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"name": "device_x",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "signal_x",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "device_y",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "signal_y",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "parameters",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "dict",
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},
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{
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"name": "kwargs",
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"kind": "VAR_KEYWORD",
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"default": "_empty",
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"annotation": "_empty",
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},
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],
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"auto_fit_supported": True,
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"params": {
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"amplitude": {
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"name": "amplitude",
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"value": 1.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"center": {
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"name": "center",
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"value": 0.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"sigma": {
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"name": "sigma",
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"value": 1.0,
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"vary": True,
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"min": 0,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"fwhm": {
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"name": "fwhm",
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"value": 2.35482,
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"vary": False,
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"min": -inf,
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"max": inf,
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"expr": "2.3548200*sigma",
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"brute_step": None,
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"user_data": None,
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},
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"height": {
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"name": "height",
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"value": 0.3989423,
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"vary": False,
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"min": -inf,
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"max": inf,
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"expr": "0.3989423*amplitude/max(1e-15, sigma)",
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"brute_step": None,
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"user_data": None,
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},
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},
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"class_args": [],
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"class_kwargs": {"model": "GaussianModel"},
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},
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"StepModel": {
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"class": "LmfitService1D",
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"user_friendly_name": "StepModel",
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"class_doc": "A model based on a Step function.\n\n The model has three Parameters: `amplitude` (:math:`A`), `center`\n (:math:`\\mu`), and `sigma` (:math:`\\sigma`).\n\n There are four choices for `form`:\n\n - `'linear'` (default)\n - `'atan'` or `'arctan'` for an arc-tangent function\n - `'erf'` for an error function\n - `'logistic'` for a logistic function (for more information, see:\n https://en.wikipedia.org/wiki/Logistic_function)\n\n The step function starts with a value 0 and ends with a value of\n :math:`A` rising to :math:`A/2` at :math:`\\mu`, with :math:`\\sigma`\n setting the characteristic width. The functional forms are defined as:\n\n .. math::\n :nowrap:\n\n \\begin{eqnarray*}\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'linear{}'}}) & = A \\min{[1, \\max{(0, \\alpha + 1/2)}]} \\\\\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'arctan{}'}}) & = A [1/2 + \\arctan{(\\alpha)}/{\\pi}] \\\\\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'erf{}'}}) & = A [1 + {\\operatorname{erf}}(\\alpha)]/2 \\\\\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'logistic{}'}})& = A \\left[1 - \\frac{1}{1 + e^{\\alpha}} \\right]\n \\end{eqnarray*}\n\n where :math:`\\alpha = (x - \\mu)/{\\sigma}`.\n\n ",
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"run_doc": "A model based on a Step function.\n\n The model has three Parameters: `amplitude` (:math:`A`), `center`\n (:math:`\\mu`), and `sigma` (:math:`\\sigma`).\n\n There are four choices for `form`:\n\n - `'linear'` (default)\n - `'atan'` or `'arctan'` for an arc-tangent function\n - `'erf'` for an error function\n - `'logistic'` for a logistic function (for more information, see:\n https://en.wikipedia.org/wiki/Logistic_function)\n\n The step function starts with a value 0 and ends with a value of\n :math:`A` rising to :math:`A/2` at :math:`\\mu`, with :math:`\\sigma`\n setting the characteristic width. The functional forms are defined as:\n\n .. math::\n :nowrap:\n\n \\begin{eqnarray*}\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'linear{}'}}) & = A \\min{[1, \\max{(0, \\alpha + 1/2)}]} \\\\\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'arctan{}'}}) & = A [1/2 + \\arctan{(\\alpha)}/{\\pi}] \\\\\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'erf{}'}}) & = A [1 + {\\operatorname{erf}}(\\alpha)]/2 \\\\\n & f(x; A, \\mu, \\sigma, {\\mathrm{form={}'logistic{}'}})& = A \\left[1 - \\frac{1}{1 + e^{\\alpha}} \\right]\n \\end{eqnarray*}\n\n where :math:`\\alpha = (x - \\mu)/{\\sigma}`.\n\n \n Args:\n scan_item (ScanItem): Scan item or scan ID\n device_x (DeviceBase | str): Device name for x\n signal_x (DeviceBase | str): Signal name for x\n device_y (DeviceBase | str): Device name for y\n signal_y (DeviceBase | str): Signal name for y\n parameters (dict): Fit parameters\n ",
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"run_name": "fit",
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"signature": [
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{
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"name": "args",
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"kind": "VAR_POSITIONAL",
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"default": "_empty",
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"annotation": "_empty",
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},
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{
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"name": "scan_item",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "ScanItem | str",
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},
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{
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"name": "device_x",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "signal_x",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "device_y",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "signal_y",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "parameters",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "dict",
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},
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{
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"name": "kwargs",
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"kind": "VAR_KEYWORD",
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"default": "_empty",
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"annotation": "_empty",
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},
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],
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"auto_fit_supported": True,
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"params": {
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"amplitude": {
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"name": "amplitude",
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"value": 1.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"center": {
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"name": "center",
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"value": 0.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"sigma": {
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"name": "sigma",
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"value": 1.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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},
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"class_args": [],
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"class_kwargs": {"model": "StepModel"},
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},
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"Ptychography": {
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"class": "PtychographyDAP",
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"user_friendly_name": "ptycho",
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"class_doc": "A model based on a Gaussian or normal distribution lineshape.\n\n The model has three Parameters: `amplitude`, `center`, and `sigma`.\n In addition, parameters `fwhm` and `height` are included as\n constraints to report full width at half maximum and maximum peak\n height, respectively.\n\n .. math::\n\n f(x; A, \\mu, \\sigma) = \\frac{A}{\\sigma\\sqrt{2\\pi}} e^{[{-{(x-\\mu)^2}/{{2\\sigma}^2}}]}\n\n where the parameter `amplitude` corresponds to :math:`A`, `center` to\n :math:`\\mu`, and `sigma` to :math:`\\sigma`. The full width at half\n maximum is :math:`2\\sigma\\sqrt{2\\ln{2}}`, approximately\n :math:`2.3548\\sigma`.\n\n For more information, see: https://en.wikipedia.org/wiki/Normal_distribution\n\n ",
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"run_doc": "A model based on a Gaussian or normal distribution lineshape.\n\n The model has three Parameters: `amplitude`, `center`, and `sigma`.\n In addition, parameters `fwhm` and `height` are included as\n constraints to report full width at half maximum and maximum peak\n height, respectively.\n\n .. math::\n\n f(x; A, \\mu, \\sigma) = \\frac{A}{\\sigma\\sqrt{2\\pi}} e^{[{-{(x-\\mu)^2}/{{2\\sigma}^2}}]}\n\n where the parameter `amplitude` corresponds to :math:`A`, `center` to\n :math:`\\mu`, and `sigma` to :math:`\\sigma`. The full width at half\n maximum is :math:`2\\sigma\\sqrt{2\\ln{2}}`, approximately\n :math:`2.3548\\sigma`.\n\n For more information, see: https://en.wikipedia.org/wiki/Normal_distribution\n\n \n Args:\n scan_item (ScanItem): Scan item or scan ID\n device_x (DeviceBase | str): Device name for x\n signal_x (DeviceBase | str): Signal name for x\n device_y (DeviceBase | str): Device name for y\n signal_y (DeviceBase | str): Signal name for y\n parameters (dict): Fit parameters\n ",
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"run_name": "fit",
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"signature": [
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{
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"name": "args",
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"kind": "VAR_POSITIONAL",
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"default": "_empty",
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"annotation": "_empty",
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},
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{
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"name": "scan_item",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "ScanItem | str",
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},
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{
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"name": "device_x",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "signal_x",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "device_y",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "signal_y",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "DeviceBase | str",
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},
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{
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"name": "parameters",
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"kind": "KEYWORD_ONLY",
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"default": None,
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"annotation": "dict",
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},
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{
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"name": "kwargs",
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"kind": "VAR_KEYWORD",
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"default": "_empty",
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"annotation": "_empty",
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},
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],
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"auto_fit_supported": True,
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"params": {
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"amplitude": {
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"name": "amplitude",
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"value": 1.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"center": {
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"name": "center",
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"value": 0.0,
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"vary": True,
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"min": -inf,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"sigma": {
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"name": "sigma",
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"value": 1.0,
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"vary": True,
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"min": 0,
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"max": inf,
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"expr": None,
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"brute_step": None,
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"user_data": None,
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},
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"fwhm": {
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"name": "fwhm",
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"value": 2.35482,
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"vary": False,
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"min": -inf,
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"max": inf,
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"expr": "2.3548200*sigma",
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"brute_step": None,
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"user_data": None,
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},
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"height": {
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"name": "height",
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"value": 0.3989423,
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"vary": False,
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"min": -inf,
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"max": inf,
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"expr": "0.3989423*amplitude/max(1e-15, sigma)",
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"brute_step": None,
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"user_data": None,
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},
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},
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"class_args": [],
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"class_kwargs": {"model": "GaussianModel"},
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},
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}
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}
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)
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yield msg
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@pytest.fixture
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def dap(dap_plugin_message):
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dap_services = {
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"BECClient": messages.StatusMessage(name="BECClient", status=1, info={}),
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"DAPServer/LmfitService1D": messages.StatusMessage(
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name="LmfitService1D", status=1, info={}
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),
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"DAPServer/PtychographyDAP": messages.StatusMessage(
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name="PtychographyDAP", status=1, info={}
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),
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}
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client = mock.MagicMock()
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client.service_status = dap_services
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client.connector.get.return_value = dap_plugin_message
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dap_plugins = DAPPlugins(client)
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yield dap_plugins
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def test_dap_plugins_construction(dap):
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assert hasattr(dap, "GaussianModel")
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assert hasattr(dap, "StepModel")
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assert hasattr(dap, "ptycho")
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# pylint: disable=no-member
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assert isinstance(dap.GaussianModel, LmfitService1D)
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assert isinstance(dap.StepModel, DAPPluginObject)
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assert isinstance(dap.ptycho, DAPPluginObject)
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def test_dap_plugin_fit(dap):
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with mock.patch.object(dap.GaussianModel, "_wait_for_dap_response") as mock_wait:
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dap.GaussianModel.fit()
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dap._parent.connector.set_and_publish.assert_called_once()
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mock_wait.assert_called_once()
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class ScanReportMock(ScanReport):
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def __init__(self, scan_id: str) -> None:
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super().__init__()
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self.request = mock.MagicMock()
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self.request.scan.scan_id = scan_id
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@pytest.mark.parametrize(
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"input",
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[
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"scan_id",
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ScanItem(
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mock.MagicMock(), queue_id="queue_id", scan_id="scan_id", scan_number=1, status="closed"
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),
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ScanReportMock("scan_id"),
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],
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)
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def test_dap_plugin_fit_input(dap, input):
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with mock.patch.object(dap.GaussianModel, "_wait_for_dap_response") as mock_wait:
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dap.GaussianModel.fit(input)
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request_id = dap._parent.connector.set_and_publish.call_args[0][1].metadata["RID"]
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dap._parent.connector.set_and_publish.assert_called_once_with(
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MessageEndpoints.dap_request(),
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messages.DAPRequestMessage(
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dap_cls="LmfitService1D",
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dap_type="on_demand",
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config={
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"args": ["scan_id"],
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"kwargs": {},
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"class_args": [],
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"class_kwargs": {"model": "GaussianModel"},
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},
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metadata={"RID": request_id},
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),
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)
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mock_wait.assert_called_once()
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def test_dap_auto_run(dap):
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with mock.patch.object(dap.GaussianModel, "_update_dap_config") as mock_update_dap_config:
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dap.GaussianModel.auto_run == False
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dap.GaussianModel.auto_run = True
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mock_update_dap_config.assert_called_once()
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dap.GaussianModel.auto_run = True
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def test_dap_wait_for_dap_response_waits_for_RID(dap):
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dap._parent.connector.get.return_value = messages.DAPResponseMessage(
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success=True, data=({}, None), metadata={"RID": "wrong_ID"}
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|
)
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with pytest.raises(TimeoutError):
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dap.GaussianModel._wait_for_dap_response(request_id="1234", timeout=0.1)
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|
|
|
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def test_dap_wait_for_dap_respnse_returns(dap):
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dap._parent.connector.get.return_value = messages.DAPResponseMessage(
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|
success=True, data=({}, None), metadata={"RID": "1234"}
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|
)
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|
val = dap.GaussianModel._wait_for_dap_response(request_id="1234", timeout=0.1)
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assert val == messages.DAPResponseMessage(
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|
success=True, data=({}, None), metadata={"RID": "1234"}
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|
)
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|
|
|
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|
def test_dap_select(dap):
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|
with mock.patch.object(dap.GaussianModel, "_update_dap_config") as mock_update_dap_config:
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|
obj = DeviceBase(name="samx", info={"device_info": {"hints": {"fields": ["samx"]}}})
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dap._parent.device_manager.devices.get.return_value = obj
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|
dap.GaussianModel.select("samx")
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|
mock_update_dap_config.assert_called_once()
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|
assert dap.GaussianModel._plugin_config["selected_device"] == ["samx", "samx"]
|
|
|
|
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|
def test_dap_select_raises_with_too_many_hints(dap):
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|
with pytest.raises(AttributeError):
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|
with mock.patch.object(dap.GaussianModel, "_update_dap_config") as mock_update_dap_config:
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|
obj = DeviceBase(
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|
name="samx", info={"device_info": {"hints": {"fields": ["samx", "samx2"]}}}
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|
)
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|
dap._parent.device_manager.devices.get.return_value = obj
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|
dap.GaussianModel.select("samx")
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|
mock_update_dap_config.assert_called_once()
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|
assert dap.GaussianModel._plugin_config["selected_device"] == ["samx", "samx"]
|
|
|
|
|
|
def test_dap_select_raises_on_device_without_hints(dap):
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|
with pytest.raises(AttributeError):
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|
dap._parent.device_manager.devices.get.return_value = DeviceBase(name="samx", info={})
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|
dap.GaussianModel.select("samx")
|
|
|
|
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|
def test_dap_select_raises_on_wrong_device(dap):
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|
dap._parent.device_manager.devices.get.return_value = None
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|
with pytest.raises(AttributeError):
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|
dap.GaussianModel.select("samx")
|
|
|
|
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|
def test_dap_get_data(dap):
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|
dap._parent.connector.get_last.return_value = messages.ProcessedDataMessage(
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|
data=[{"x": [1, 2, 3], "y": [4, 5, 6]}, {"fit_parameters": {"amplitude": 1}}]
|
|
)
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|
data = dap.GaussianModel.get_data()
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|
dap._parent.connector.get_last.assert_called_once_with(
|
|
MessageEndpoints.processed_data("GaussianModel")
|
|
)
|
|
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|
assert data.data == {"x": [1, 2, 3], "y": [4, 5, 6]}
|
|
assert data.amplitude == 1
|
|
|
|
|
|
def test_dap_update_dap_config_not_called_without_device(dap):
|
|
dap.GaussianModel._update_dap_config(request_id="1234")
|
|
dap._parent.connector.set_and_publish.assert_not_called()
|
|
|
|
|
|
def test_dap_update_dap_config(dap):
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|
dap.GaussianModel._plugin_config["selected_device"] = ["samx", "samx"]
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|
dap.GaussianModel._update_dap_config(request_id="1234")
|
|
dap._parent.connector.set_and_publish.assert_called_with(
|
|
MessageEndpoints.dap_request(),
|
|
messages.DAPRequestMessage(
|
|
dap_cls="LmfitService1D",
|
|
dap_type="continuous",
|
|
config=dap.GaussianModel._plugin_config,
|
|
metadata={"RID": "1234"},
|
|
),
|
|
)
|