Considering the possibility of spikes as a numpy array

geomagio/metadata/Flag.py

 from typing import Dict, Union, List
 from datetime import timedelta
 
+import numpy as np
 from obspy import UTCDateTime
 from pydantic import BaseModel, Field, validator
 from enum import Enum
@@ -35,7 +36,6 @@ class Flag(BaseModel):
             priority=1,
             data_valid=False,
             metadata= Disturbance{
-                "channels": "H,E,Z",
                 "description": "Spike in magnetic field strength",
                 "field_work": false,
                 "corrected": false,
@@ -47,19 +47,16 @@ class Flag(BaseModel):
         ```
     """
 
-    channels: Union[ChannelType, List[ChannelType]]
     description: str = Field(..., description="Description of the flag")
     field_work: bool = Field(..., description="Flag signaling field work")
-    corrected: bool = Field(
-        ...,
-        description="whether or not corrective action has been taken for spikes, noise, etc.",
-    )
+    corrected: int = Field(..., description="Corrected ID for processing stage")
+    # Should there be an attribute for data quality or is data_valid above enough?
 
 
 class DisturbanceType(str, Enum):
-    SPIKE = "SPIKE"
+    SPIKES = "SPIKES"
     OFFSET = "OFFSET"
-    NOISE = "NOISE"
+    DISTURBANCES = "DISTURBANCES"
 
 
 class Disturbance(Flag):
@@ -68,9 +65,9 @@ class Disturbance(Flag):
 
     Artificial disturbances consist of the following types:
 
-    SPIKE = Single data point that are outliers in the timeseries.
+    SPIKES = Single data points that are outliers in the timeseries.
     OFFSET = A relatively constant shift or deviation in the baseline magnetic field.
-    NOISE = A catch-all for any unwanted variations, may include multiple spikes, offsets and/or gaps.
+    DISTURBANCES = A catch-all for a continuous period of unwanted variations, may include multiple spikes, offsets and/or gaps.
 
     Attributes
     ----------
@@ -78,14 +75,17 @@ class Disturbance(Flag):
         The type of disturbance(s).
     source: str
         Source of the disturbance if known or suspected.
-    magnitude: float
-        Magnitude of the disturbance in nt if it is a singular spike or offset value.
+    amplitude: float
+       Amplitude of the disturbance in nt if it is an offset value or singular spike.
+    spikes: np.ndarray
+        NumPy array of timestamps as UTCDateTime. Can be a single spike or many spikes.
 
     """
 
     disturbance_type: DisturbanceType
     source: str = None
-    magnitude: float
+    amplitude: float
+    spikes: np.ndarray
 
 
 class Gap(Flag):
@@ -106,42 +106,72 @@ class Gap(Flag):
     handling: str = None
 
 
+class EventType(str, Enum):
+    GEOMAGNETIC_STORM = "GEOMAGNETIC_STORM"
+    EARTHQUAKE = "EARTHQUAKE"
+
+
 class Event(Flag):
     """
     This class is used to flag an event of interest such as a geomagnetic storm or earthquake.
 
     Attributes
     ----------
-    geomagnetic_storm: bool
-        Geomagnetic storm period.
+    event_type : EventType
+        The type of event.
+    scale : str
+        Geomagnetic storm scale denoted by G followed by a number 1 through 5.
+    Kp : int
+        Planetary K-index denoted by a number 1 through 10
+    url : str
+        A url related to the event. Could be NOAA SWPC or USGS Earthquakes page.
     """
 
-    geomagnetic_storm: bool = None  # if true, ignore spike detection filter
-    # hold intensity, K-index, more?
+    event_type: EventType
+    scale: str = None
+    Kp: int = None
+    url: str = None
 
 
 # More example usage:
-spike_data = {
-    "channels": ["F"],
-    "description": "Spike description",
-    "field_work": True,
-    "corrected": False,
-    "disturbance_type": DisturbanceType.SPIKE,
-    "source": "Lightning",
-    "magnitude": 5.0,
+timestamps_array = np.array(
+    [
+        UTCDateTime("2023-11-16T12:00:0"),
+        UTCDateTime("2023-11-16T12:01:10"),
+        UTCDateTime("2023-11-16T12:02:30"),
+    ]
+)
+
+spikes_data = {
+    "starttime": "2023-11-16 12:00:00",
+    "endtime": "2023-11-16 12:02:30",
+    "description": "Spikes description",
+    "field_work": False,
+    "corrected": 32265,
+    "disturbance_type": DisturbanceType.SPIKES,
+    "source": "processing",
+    "spikes": timestamps_array,
 }
 
 offset_data = {
-    "channels": ["X", "Y", "Z"],
     "description": "Offset description",
     "field_work": False,
-    "corrected": True,
+    "corrected": 47999,
     "disturbance_type": DisturbanceType.OFFSET,
     "source": "Bin change",
-    "magnitude": 10.0,
+    "amplitude": 10.0,
+}
+geomagnetic_storm_data = {
+    "description": "Geomagnetic storm",
+    "field_work": False,
+    "corrected": 36999,
+    "event_type": EventType.GEOMAGNETIC_STORM,
+    "scale": "G3",
+    "Kp": 7,
+    "url": "https://www.swpc.noaa.gov/products/planetary-k-index",
 }
 
-spike_instance = Disturbance(**spike_data)
+spike_instance = Disturbance(**spikes_data)
 offset_instance = Disturbance(**offset_data)
 
 print(spike_instance.dict())