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feat(waveform): new Waveform widget based on NextGen PlotBase
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@ -1,8 +1,11 @@
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# pylint: disable = no-name-in-module,missing-class-docstring, missing-module-docstring
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from math import inf
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from unittest.mock import MagicMock, patch
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import fakeredis
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import pytest
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from bec_lib.bec_service import messages
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from bec_lib.endpoints import MessageEndpoints
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from bec_lib.redis_connector import RedisConnector
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from bec_widgets.tests.utils import DEVICES, DMMock, FakePositioner, Positioner
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@ -50,3 +53,150 @@ def mocked_client(bec_dispatcher):
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with patch("builtins.isinstance", new=isinstance_mock):
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yield client
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connector.shutdown() # TODO change to real BECClient
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##################################################
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# Client Fixture with DAP
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##################################################
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@pytest.fixture(scope="function")
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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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}
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}
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)
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yield msg
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@pytest.fixture(scope="function")
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def mocked_client_with_dap(mocked_client, 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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}
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client = mocked_client
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client.service_status = dap_services
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client.connector.set(
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topic=MessageEndpoints.dap_available_plugins("dap"), msg=dap_plugin_message
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)
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# Patch the client's DAP attribute so that the available models include "GaussianModel"
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patched_models = {"GaussianModel": {}, "LorentzModel": {}, "SineModel": {}}
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client.dap._available_dap_plugins = patched_models
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yield client
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