feat(dap): added input_data and plot options

bec_lib/bec_lib/dap_plugin_objects.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import builtins
 import time
 import uuid
 from typing import TYPE_CHECKING
@@ -16,6 +17,13 @@ from bec_lib.scan_items import ScanItem
 if TYPE_CHECKING:
     from bec_lib.client import BECClient
 
+try:
+    import matplotlib.pyplot as plt
+
+    plt.ion()
+except ImportError:
+    plt = None
+
 
 class DAPPluginObjectBase:
     """
@@ -86,6 +94,8 @@ class DAPPluginObjectBase:
         return self._convert_result(response)
 
     def _convert_result(self, result: messages.BECMessage):
+        if not result.content["data"]:
+            return None
         if not callable(self._result_cls):
             return result.content["data"]
         # pylint: disable=not-callable
@@ -168,10 +178,14 @@ class LmfitService1DResult:
     Result of fitting 1D data using lmfit.
     """
 
-    def __init__(self, result: list[dict], model_name: str = None):
+    def __init__(self, result: list[dict], model_name: str = None, client: BECClient = None):
         self._data = result[0]
         self._report = result[1]
         self._model = model_name
+        if client:
+            self._client = client
+        else:
+            self._client = builtins.__dict__.get("bec")
         if "amplitude" in self.params:
             self.amplitude = self.params["amplitude"]
         if "center" in self.params:
@@ -240,6 +254,44 @@ class LmfitService1DResult:
         max_index = np.argmax(self._data["y"])
         return {"x": self._data["x"][max_index], "y": self._data["y"][max_index]}
 
+    @property
+    def input_data(self):
+        """
+        Get the input data used for the fit.
+
+        Returns:
+            dict: The input data used for the fit.
+        """
+        input_data = self._report.get("input")
+        scan_id = input_data.get("scan_id")
+        if not scan_id:
+            return None
+
+        scan_item = self._client.queue.scan_storage.find_scan_by_ID(scan_id)
+        if not scan_item:
+            return None
+
+        x = scan_item.data[input_data["device_x"]][input_data["signal_x"]].val
+        y = scan_item.data[input_data["device_y"]][input_data["signal_y"]].val
+
+        return {"x": x, "y": y}
+
+    def plot(self):
+        """
+        Plot the fit.
+        """
+        # move this to BECWidgets once it's available
+        if not plt:
+            raise ImportError(
+                "matplotlib is not installed. Cannot plot. Please install matplotlib using 'pip install matplotlib'."
+            )
+        input_data = self.input_data
+        plt.figure()
+        plt.plot(input_data["x"], input_data["y"], label="data", color="black", marker="o")
+        plt.plot(self._data["x"], self._data["y"], label=f"{self._model}", color="red")
+        plt.legend()
+        plt.show()
+
     def __str__(self) -> str:
         return f"{self._model} fit result: \n Params: {self.params} \n Min: {self.min} \n Max: {self.max}"