add extra dots out of zoomed box #307

CHANGELOG.md

@@ -1,5 +1,8 @@
 # Latest
 
+# v0.11.1
+## What's Changed
+After resampling the figure, the line goes beyond figure frontier indicating presence of data. https://github.com/predict-idlab/plotly-resampler/pull/308
 
 # v0.10.0
 ## New Features

plotly_resampler/aggregation/plotly_aggregator_parser.py

@@ -74,6 +74,8 @@ def get_start_end_indices(hf_trace_data, axis_type, start, end) -> Tuple[int, in
             x_step = hf_trace_data["x"].step
             start_idx = int(max((start - x_start) // x_step, 0))
             end_idx = int((end - x_start) // x_step)
+            start_idx = max(0, start_idx - 1)
+            end_idx = min(end_idx + 1, len(hf_trace_data["x"]))
             return start_idx, end_idx
         # TODO: this can be performed as-well for a fixed frequency range-index w/ freq
 
@@ -97,6 +99,8 @@ def get_start_end_indices(hf_trace_data, axis_type, start, end) -> Tuple[int, in
         # Search the index-positions
         start_idx = bisect.bisect_left(hf_trace_data["x"], start)
         end_idx = bisect.bisect_right(hf_trace_data["x"], end)
+        start_idx = max(0, start_idx - 1)
+        end_idx = min(end_idx + 1, len(hf_trace_data["x"]))
         return start_idx, end_idx
 
     @staticmethod

tests/test_figure_resampler.py

@@ -284,8 +284,8 @@ def test_log_axis():
         assert len(out) == 2
         assert (x1 - x0) < 10
         assert len(out[1]["x"]) == 1000
-        assert out[-1]["x"][0] >= 100
-        assert out[-1]["x"][-1] <= 50_000
+        assert out[-1]["x"][0] >= 99
+        assert out[-1]["x"][-1] <= 50_001
 
 
 def test_add_traces_from_other_figure():
@@ -1061,8 +1061,8 @@ def test_time_tz_slicing():
         start_idx, end_idx = PlotlyAggregatorParser.get_start_end_indices(
             hf_data_dict, hf_data_dict["axis_type"], t_start, t_stop
         )
-        assert (s.index[start_idx] - t_start) <= pd.Timedelta(seconds=1)
-        assert (s.index[min(end_idx, n - 1)] - t_stop) <= pd.Timedelta(seconds=1)
+        assert (s.index[start_idx] - t_start) <= pd.Timedelta(seconds=2)
+        assert (s.index[min(end_idx, n - 1)] - t_stop) <= pd.Timedelta(seconds=2)
 
 
 def test_time_tz_slicing_different_timestamp():
@@ -1147,10 +1147,10 @@ def test_different_tz_no_tz_series_slicing():
         )
         assert (
             s.tz_localize(None).index[start_idx].tz_localize(t_start.tz) - t_start
-        ) <= pd.Timedelta(seconds=1)
+        ) <= pd.Timedelta(seconds=2)
         assert (
             s.tz_localize(None).index[end_idx].tz_localize(t_stop.tz) - t_stop
-        ) <= pd.Timedelta(seconds=1)
+        ) <= pd.Timedelta(seconds=2)
 
 
 def test_multiple_tz_no_tz_series_slicing():
@@ -1725,8 +1725,10 @@ def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints - 1):
         x_ = decode_trace_bdata(f_pr_data[0]["x"])
         assert len(y_) == 500
         assert len(x_) == 500
-        assert y_[0] >= 100 and y_[-1] <= 1000
-        assert x_[0] >= 100 and x_[-1] <= 1000
+        # Since https://github.com/predict-idlab/plotly-resampler/pull/343
+        # the data range is left-right expanded with 1 sample
+        assert y_[0] >= 99 and y_[-1] <= 1000
+        assert x_[0] >= 99 and x_[-1] <= 1000
         # Check the front-end layout
         assert list(f_pr_layout["xaxis"]["range"]) == [100, 1000]
         assert list(f_pr_layout["yaxis"]["range"]) == [100, 1000]

tests/test_figurewidget_resampler.py

@@ -813,7 +813,7 @@ def test_hf_data_property_reload_data():
     assert np.all(fwr.hf_data[0]["y"] == new_y)
 
     fwr.reload_data()
-    assert (fwr.data[0]["x"][0] >= 10_000) & (fwr.data[0]["x"][-1] <= 20_000)
+    assert (fwr.data[0]["x"][0] >= 9_999) & (fwr.data[0]["x"][-1] <= 20_001)
     assert np.all(fwr.data[0]["y"] == new_y[fwr.data[0]["x"]])
     assert (fwr.layout["yaxis"].range[0] == -20) & (fwr.layout["yaxis"].range[-1] == 3)
 
@@ -888,8 +888,8 @@ def test_hf_data_property_subplots_reload_data():
     assert np.all(fwr.hf_data[0]["y"] == new_y)
 
     fwr.reload_data()
-    assert (fwr.data[0]["x"][0] >= 10_000) & (fwr.data[0]["x"][-1] <= 20_000)
-    assert (fwr.data[1]["x"][0] >= 40_000) & (fwr.data[1]["x"][-1] <= 60_000)
+    assert (fwr.data[0]["x"][0] >= 9_999) & (fwr.data[0]["x"][-1] <= 20_001)
+    assert (fwr.data[1]["x"][0] >= 39_999) & (fwr.data[1]["x"][-1] <= 60_001)
     assert np.all(fwr.data[0]["y"] == new_y[fwr.data[0]["x"]])
     assert np.all(fwr.data[1]["y"] == new_y[fwr.data[1]["x"]])
     assert (fwr.layout["yaxis"].range[0] == -20) & (fwr.layout["yaxis"].range[-1] == 3)
@@ -919,8 +919,8 @@ def test_hf_data_subplots_non_shared_xaxes():
     assert 0 <= x_0[0] <= (n / 1000)
     assert (n - 1000) <= x_0[-1] <= n - 1
     x_1 = fwr.data[1]["x"]
-    assert 40_000 <= x_1[0] <= 40_000 + (20_000 / 1000)
-    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_000
+    assert 39_999 <= x_1[0] <= 40_001 + (20_000 / 1000)
+    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_001
 
 
 def test_hf_data_subplots_non_shared_xaxes_row_col_none():
@@ -944,8 +944,8 @@ def test_hf_data_subplots_non_shared_xaxes_row_col_none():
     assert 0 <= x_0[0] <= (n / 1000)
     assert (n - 1000) <= x_0[-1] <= n - 1
     x_1 = fwr.data[1]["x"]
-    assert 40_000 <= x_1[0] <= 40_000 + (20_000 / 1000)
-    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_000
+    assert 39_999 <= x_1[0] <= 40_001 + (20_000 / 1000)
+    assert (60_000 - 20_000 / 1_000) <= x_1[-1] <= 60_001
 
 
 def test_updates_two_traces():
@@ -2077,7 +2077,9 @@ def check_data(fwr: FigureWidgetResampler, min_v=0, max_v=nb_datapoints - 1):
     assert f_pr.layout.yaxis.autorange is None
 
     # Now the f_pr contains the data of the selected xrange (downsampled to 500 samples)
-    check_data(f_pr, 100, 1_000 - 1)
+    # Since https://github.com/predict-idlab/plotly-resampler/pull/343
+    # the data range is left-right expanded with 1 sample
+    check_data(f_pr, min_v=100 - 1, max_v=1_000)
 
 
 def test_fwr_update_layout_axes_range_no_update():