Implement IdentityAssigner for one FID object

nmrpy/data_objects.py

@@ -260,6 +260,7 @@ def __init__(self, *args, **kwargs):
         self.data = kwargs.get("data", [])
         self.peaks = None
         self.ranges = None
+        self.identities = None
         self._deconvoluted_peaks = None
         self._flags = {
             "ft": False,
@@ -350,6 +351,24 @@ def ranges(self, ranges):
                 raise AttributeError("ranges must be numbers")
         self._ranges = ranges
 
+    @property
+    def identities(self):
+        """
+        Assigned identities corresponding to the various ranges in :attr:`~nmrpy.data_objects.Fid.ranges`.
+        """
+        return self._identitites
+
+    @identities.setter
+    def identities(self, identities):
+        if identities is not None:
+            if not Fid._is_flat_iter(identities):
+                raise AttributeError("identitites must be a flat iterable")
+            if not all(isinstance(i, str) for i in identities):
+                raise AttributeError("identities must be strings")
+            self._identitites = numpy.array(identities)
+        else:
+            self._identities = identities
+
     @property
     def _bl_ppm(self):
         return self.__bl_ppm
@@ -1382,6 +1401,23 @@ def plot_deconv(self, **kwargs):
         setattr(self, plt.id, plt)
         pyplot.show()
 
+    def assign_identities(self):
+        """
+        Instantiate a identity-assignment GUI widget. Select a range from dropdown menu containing
+        :attr:`~nmrpy.data_objects.Fid.ranges`. Select a species from second dropdown menu
+        containing species defined in EnzymeML. When satisfied with assignment, press Assign button
+        to apply.
+        """
+
+        widget_title = "Assign identitiy for {}".format(self.id)
+        self._assigner_widget = IdentityAssigner(fid=self, title=widget_title)
+
+    def clear_identities(self):
+        """
+        Clear assigned identities stored in :attr:`~nmrpy.data_objects.Fid.identities`.
+        """
+        self.identities = None
+
 
 class FidArray(Base):
     """

nmrpy/plotting.py

@@ -11,7 +11,7 @@
 from matplotlib.widgets import Cursor
 from matplotlib.backend_bases import NavigationToolbar2, Event
 
-from ipywidgets import FloatText, Output, VBox
+from ipywidgets import FloatText, Output, VBox, Dropdown, Label, Button
 from IPython.display import display
 import asyncio
 
@@ -942,7 +942,7 @@ class Ssm:
             transform=trans,
             visible=False,
             animated=True,
-            **self.ssm.rectprops
+            **self.ssm.rectprops,
         )
         self.ax.add_patch(self.ssm.rect)
 
@@ -957,7 +957,7 @@ def makespan(self, left, width):
             transform=trans,
             visible=True,
             # animated=True,
-            **self.ssm.rectprops
+            **self.ssm.rectprops,
         )
         self.ax.add_patch(rect)
         return rect
@@ -1376,6 +1376,105 @@ class SpanDataSelector(DataSelector, SpanSelectorMixin, AssignMixin):
     pass
 
 
+class IdentityAssigner:
+    def __init__(self, fid, title):
+        self.fid = fid
+        self.title = title
+        self.selected_values = {}
+        if fid.data is [] or fid.data is None:
+            raise ValueError("data must exist.")
+        if fid.peaks is [] or fid.peaks is None:
+            raise RuntimeError(
+                f"`fid.peaks` are required but still empty. Please either assign them manually or using the `peakpicker` method."
+            )
+
+        # Create the label widget for the title
+        title_label = Label(value=title)
+
+        # Create the dropdown widget for the peaks
+        peak_dropdown = Dropdown(
+            options=[str(peak) for peak in fid.peaks],
+            description="Select a peak:",
+            layout={"width": "max-content"},
+            style={"description_width": "initial"},
+        )
+
+        # Create the dropdown widget for the species
+        species_dropdown = Dropdown(
+            options=[],
+            description="Select a species:",
+            layout={"width": "max-content"},
+            style={"description_width": "initial"},
+            disabled=True,
+        )
+
+        # Create the button to save selection to dict
+        save_button = Button(
+            description="Save selection", icon="file-arrow-down", disabled=True
+        )
+
+        # Create an output widget to display the selection
+        selection_output = Output()
+
+        # Define a method to handle the peak dropdown's change event
+        def on_peak_dropdown_change(event):
+            if event["type"] == "change" and event["name"] == "value":
+                selected_option = event["new"]
+                if selected_option != "":
+                    species_dropdown.options = [
+                        "3PG",
+                        "2PG",
+                        "Phosphate",
+                        "TEP",
+                        "PEP",
+                    ]
+                    species_dropdown.disabled = False
+                    save_button.disabled = False
+
+        # Attach the function to the dropdown's change event
+        peak_dropdown.observe(on_peak_dropdown_change)
+
+        # Define a method to handle the species dropdown's change event
+        def on_species_dropdown_change(event):
+            if event["type"] == "change" and event["name"] == "value":
+                selected_option = event["new"]
+                if selected_option != "":
+                    new_key = peak_dropdown.value
+                    self.selected_values[new_key] = selected_option
+
+        # Attach the function to the second dropdown's change event
+        species_dropdown.observe(on_species_dropdown_change)
+
+        # Define a function to handle the save button click event
+        def on_save_button_click(b):
+            with selection_output:
+                selection_output.clear_output(wait=True)
+                print("\nSaved selections:")
+                for key, value in self.selected_values.items():
+                    print(f"{key}: {value}")
+
+        # Attach the function to the save button's click event
+        save_button.on_click(on_save_button_click)
+
+        # Create a container for both the title and the dropdown
+        container = VBox(
+            [
+                title_label,
+                peak_dropdown,
+                species_dropdown,
+                save_button,
+                selection_output,
+            ]
+        )
+
+        # Display the container
+        display(container)
+
+
+class IdentityRangeAssigner:
+    ...
+
+
 class DataTraceSelector:
     """
     Interactive data-selection widget with traces and ranges. Traces are saved