🤔 fix for [BUG] Error handling timezones #305

plotly_resampler/aggregation/plotly_aggregator_parser.py

@@ -38,8 +38,12 @@ def to_same_tz(
             return None
         elif reference_tz is not None:
             if ts.tz is not None:
-                assert ts.tz.__str__() == reference_tz.__str__()
-                return ts
+                # compare if these two have the same timezone / offset
+                print('to same tz', 'ts', ts.tz.__str__(), 'ref', reference_tz.__str__())
+                try:
+                    assert ts.tz.__str__() == reference_tz.__str__()
+                except AssertionError:
+                    assert ts.utcoffset() == reference_tz.utcoffset(ts.tz_convert(None))
             else:  # localize -> time remains the same
                 return ts.tz_localize(reference_tz)
         elif reference_tz is None and ts.tz is not None:
@@ -78,7 +82,8 @@ def get_start_end_indices(hf_trace_data, axis_type, start, end) -> Tuple[int, in
             # convert start & end to the same timezone
             if isinstance(hf_trace_data["x"], pd.DatetimeIndex):
                 tz = hf_trace_data["x"].tz
-                assert start.tz == end.tz
+                # print(start.tz.__str__(), end.tz.__str__())
+                assert start.tz.__str__() == end.tz.__str__()
                 start = PlotlyAggregatorParser.to_same_tz(start, tz)
                 end = PlotlyAggregatorParser.to_same_tz(end, tz)
 

tests/test_figure_resampler.py

@@ -758,6 +758,53 @@ def test_tz_xaxis_range():
     assert len(out[2]["x"]) == 2000
 
 
+def test_compare_tz_with_fixed_offset():
+    # related: https://github.com/predict-idlab/plotly-resampler/issues/305
+    fig = FigureResampler()
+
+    x = pd.date_range("2024-04-01T00:00:00", "2025-01-01T00:00:00", freq="H")
+    x = x.tz_localize("Asia/Taipei")
+    y = np.random.randn(len(x))
+
+    fig.add_trace(
+        go.Scattergl(x=x, y=y, name="demo", mode="lines+markers"),
+        max_n_samples=int(len(x) * 0.2),
+    )
+
+    relayout_data = {
+        "xaxis.range[0]": "2024-04-27T08:00:00+08:00",
+        "xaxis.range[1]": "2024-05-04T17:15:39.491031+08:00",
+    }
+
+    fig.construct_update_data_patch(relayout_data)
+
+
+def test_compare_tz_with_fixed_offset_2():
+    # related: https://github.com/predict-idlab/plotly-resampler/issues/305
+    fig = FigureResampler()
+
+    x = pd.date_range("2024-04-01T00:00:00", "2025-01-01T00:00:00", freq="H")
+    x = x.tz_localize("UTC")
+    x = x.tz_convert("Canada/Pacific")
+    y = np.random.randn(len(x))
+
+    fig.add_trace(
+        go.Scattergl(x=x, y=y, name="demo", mode="lines+markers"),
+        max_n_samples=int(len(x) * 0.2),
+    )
+
+    relayout_data = {
+        "xaxis.range[0]": pd.Timestamp("2024-03-01T00:00:00").tz_localize(
+            "Canada/Pacific"
+        ),
+        "xaxis.range[1]": pd.Timestamp("2024-03-31T00:00:00").tz_localize(
+            "Canada/Pacific"
+        ),
+    }
+
+    fig.construct_update_data_patch(relayout_data)
+
+
 def test_datetime_hf_x_no_index():
     df = pd.DataFrame(
         {"timestamp": pd.date_range("2020-01-01", "2020-01-02", freq="1s")}
@@ -1013,7 +1060,7 @@ def test_time_tz_slicing_different_timestamp():
     cs = [
         dr,
         dr.tz_localize(None).tz_localize("Europe/Amsterdam"),
-        dr.tz_convert("Europe/Brussels"),
+        dr.tz_convert("Europe/Lisbon"),
         dr.tz_convert("Australia/Perth"),
         dr.tz_convert("Australia/Canberra"),
     ]
@@ -1031,6 +1078,26 @@ def test_time_tz_slicing_different_timestamp():
                 hf_data_dict, hf_data_dict["axis_type"], t_start, t_stop
             )
 
+    # THESE have the same timezone offset -> no AssertionError should be raised
+    cs = [
+        dr.tz_localize(None).tz_localize("Europe/Amsterdam"),
+        dr.tz_convert("Europe/Brussels"),
+        dr.tz_convert("Europe/Oslo"),
+        dr.tz_convert("Europe/Paris"),
+        dr.tz_convert("Europe/Rome"),
+    ]
+
+    for i, s in enumerate(cs):
+        t_start, t_stop = sorted(s.iloc[np.random.randint(0, n, 2)].index)
+        t_start = t_start.tz_convert(cs[(i + 1) % len(cs)].index.tz)
+        t_stop = t_stop.tz_convert(cs[(i + 1) % len(cs)].index.tz)
+
+        hf_data_dict = construct_hf_data_dict(s.index, s.values)
+        start_idx, end_idx = PlotlyAggregatorParser.get_start_end_indices(
+            hf_data_dict, hf_data_dict["axis_type"], t_start, t_stop
+        )
+
+
 
 def test_different_tz_no_tz_series_slicing():
     n = 60 * 60 * 24 * 3