use pandas instead of Dictreader to read the csv, and automatically i…

…nfer the datatypes

src/motile_plugin/import_export/load_tracks.py

@@ -1,36 +1,11 @@
-from csv import DictReader
-from typing import Any
-
 import networkx as nx
 import numpy as np
+import pandas as pd
 from motile_toolbox.candidate_graph import NodeAttr
 
 from motile_plugin.data_model import SolutionTracks
 
 
-def convert_value(value: Any):
-    """Converts the given value to float or int if possible, otherwise returns it as is"""
-
-    try:
-        # Try to convert to integer
-        int_value = int(value)
-        if str(int_value) == value:
-            return int_value
-    except ValueError:
-        pass
-
-    try:
-        # Try to convert to float
-        float_value = float(value)
-        if str(float_value) == value:
-            return float_value
-    except ValueError:
-        pass
-
-    # If conversion to int or float fails, return the original value
-    return value
-
-
 def tracks_from_csv(
     csvfile: str,
     selected_columns: dict,
@@ -56,49 +31,51 @@ def tracks_from_csv(
         Tracks: a tracks object
     """
     graph = nx.DiGraph()
-    with open(csvfile) as f:
-        reader = DictReader(f)
-        for row in reader:
-            _id = row[selected_columns["id"]]
-            if (
-                selected_columns.get("z") is not None
-                and selected_columns.get("z") != "Select Column"
-            ):
-                attrs = {
-                    "pos": [
-                        float(row[selected_columns["z"]]),
-                        float(row[selected_columns["y"]]),
-                        float(selected_columns["x"]),
-                    ],
-                    "time": int(row[selected_columns["t"]]),
-                }
-                ndims = 4
-                scale = [1, *scale]  # assumes 1 for time step
-            else:
-                attrs = {
-                    "pos": [
-                        float(row[selected_columns["y"]]),
-                        float(row[selected_columns["x"]]),
-                    ],
-                    "time": int(row[selected_columns["t"]]),
-                }
-                ndims = 3
-                scale = [1, scale[1], scale[2]]
-            if selected_columns["seg_id"] != "Select Column":
-                attrs["seg_id"] = int(row[selected_columns["seg_id"]])
-            for key in extra_columns:
-                if extra_columns[key] != "Select Column":
-                    attrs[key] = convert_value(
-                        row[extra_columns[key]]
-                    )  # try to convert strings to numerical values if possible
+    df = pd.read_csv(csvfile)
+
+    for _, row in df.iterrows():
+        _id = row["id"]
+
+        if (
+            selected_columns.get("z") is not None
+            and selected_columns.get("z") != "Select Column"
+        ):
+            attrs = {
+                "pos": [
+                    row[selected_columns["z"]],
+                    row[selected_columns["y"]],
+                    row[selected_columns["x"]],
+                ],
+                "time": row[selected_columns["t"]],
+            }
+            ndims = 4
+            scale = [1, *scale]  # assumes 1 for time step
+        else:
+            attrs = {
+                "pos": [
+                    row[selected_columns["y"]],
+                    row[selected_columns["x"]],
+                ],
+                "time": row[selected_columns["t"]],
+            }
+            ndims = 3
+            scale = [1, scale[1], scale[2]]
+
+        if selected_columns["seg_id"] != "Select Column":
+            attrs["seg_id"] = row[selected_columns["seg_id"]]
+
+        for key in extra_columns:
+            if extra_columns[key] != "Select Column":
+                attrs[key] = row[extra_columns[key]]
+
+        graph.add_node(_id, **attrs)
+        parent_id = row[selected_columns["parent_id"]]
+        if not pd.isna(parent_id):
+            parent_id = parent_id
+            if parent_id != -1:
+                assert parent_id in graph.nodes, f"{_id} {parent_id}"
+                graph.add_edge(parent_id, _id)
 
-            graph.add_node(_id, **attrs)
-            parent_id = row[selected_columns["parent_id"]].strip()
-            if parent_id != "":
-                parent_id = parent_id
-                if parent_id != -1:
-                    assert parent_id in graph.nodes, f"{_id} {parent_id}"
-                    graph.add_edge(parent_id, _id)
     if segmentation is not None:
         segmentation = np.expand_dims(
             segmentation, axis=1