Feat/dependabot alerts

examples/dash_apps/04_minimal_cache_overview.py

@@ -103,7 +103,8 @@ def plot_graph(_):
 )
 
 
-# --- FigureResampler update callback ---
+# ------ FigureResampler update callback ------
+
 
 # The plotly-resampler callback to update the graph after a relayout event (= zoom/pan)
 # As we use the figure again as output, we need to set: allow_duplicate=True

examples/dash_apps/05_cache_overview_subplots.py

@@ -140,6 +140,7 @@ def plot_graph(_):
 
 # --- FigureResampler update callback ---
 
+
 # The plotly-resampler callback to update the graph after a relayout event (= zoom/pan)
 # As we use the figure again as output, we need to set: allow_duplicate=True
 @app.callback(

examples/dash_apps/11_sine_generator.py

@@ -182,6 +182,7 @@ def construct_display_graph(n, exp, n_added_graphs) -> FigureResampler:
 
 # --- FigureResampler update callback ---
 
+
 # The plotly-resampler callback to update the graph after a relayout event (= zoom/pan)
 # As we use the figure again as output, we need to set: allow_duplicate=True
 @app.callback(

examples/dash_apps/12_file_selector.py

@@ -109,6 +109,7 @@ def plot_graph(n_clicks, *folder_list):
 
 # --------- FigureResampler update callback ---------
 
+
 # The plotly-resampler callback to update the graph after a relayout event (= zoom/pan)
 # As we use the figure again as output, we need to set: allow_duplicate=True
 @app.callback(

examples/dash_apps/13_coarse_fine.py

@@ -10,6 +10,7 @@
 TODO: add an rectangle on the coarse graph
 
 """
+
 from __future__ import annotations
 
 __author__ = "Jonas Van Der Donckt"

plotly_resampler/aggregation/gap_handler_interface.py

@@ -96,8 +96,8 @@ def insert_fill_value_between_gaps(
         # Set the NaN values
         # We add the gap index offset (via the np.arange) to the indices to account for
         # the repeats (i.e., expanded y_agg array).
-        y_agg_exp_nan[
-            np.where(gap_mask)[0] + np.arange(gap_mask.sum())
-        ] = self.fill_value
+        y_agg_exp_nan[np.where(gap_mask)[0] + np.arange(gap_mask.sum())] = (
+            self.fill_value
+        )
 
         return y_agg_exp_nan, idx_exp_nan

plotly_resampler/figure_resampler/figure_resampler_interface.py

@@ -597,9 +597,7 @@ def _parse_get_trace_props(
         hf_y = (
             trace["y"]
             if hasattr(trace, "y") and hf_y is None
-            else hf_y.values
-            if isinstance(hf_y, (pd.Series, pd.Index))
-            else hf_y
+            else hf_y.values if isinstance(hf_y, (pd.Series, pd.Index)) else hf_y
         )
         # NOTE: the if will not be triggered for a categorical series its values
         if not hasattr(hf_y, "dtype"):
@@ -608,17 +606,21 @@ def _parse_get_trace_props(
         hf_text = (
             hf_text
             if hf_text is not None
-            else trace["text"]
-            if hasattr(trace, "text") and trace["text"] is not None
-            else None
+            else (
+                trace["text"]
+                if hasattr(trace, "text") and trace["text"] is not None
+                else None
+            )
         )
 
         hf_hovertext = (
             hf_hovertext
             if hf_hovertext is not None
-            else trace["hovertext"]
-            if hasattr(trace, "hovertext") and trace["hovertext"] is not None
-            else None
+            else (
+                trace["hovertext"]
+                if hasattr(trace, "hovertext") and trace["hovertext"] is not None
+                else None
+            )
         )
 
         hf_marker_size = (
@@ -1114,9 +1116,11 @@ def add_traces(
 
         # Convert each trace into a BaseTraceType object
         data = [
-            self._data_validator.validate_coerce(trace)[0]
-            if not isinstance(trace, BaseTraceType)
-            else trace
+            (
+                self._data_validator.validate_coerce(trace)[0]
+                if not isinstance(trace, BaseTraceType)
+                else trace
+            )
             for trace in data
         ]
 
@@ -1137,7 +1141,7 @@ def add_traces(
             limit_to_views = [limit_to_views] * len(data)
 
         zipped = zip(data, max_n_samples, downsamplers, gap_handlers, limit_to_views)
-        for (i, (trace, max_out, downsampler, gap_handler, limit_to_view)) in enumerate(
+        for i, (trace, max_out, downsampler, gap_handler, limit_to_view) in enumerate(
             zipped
         ):
             if (

pyproject.toml

@@ -48,7 +48,7 @@ numpy = [
 ]
 orjson = "^3.10.0"  # Faster json serialization (from 3.10 onwards f16 is supported)
 # Optional dependencies
-Flask-Cors = { version = "^3.0.10", optional = true }
+Flask-Cors = { version = "^4.0.2", optional = true }
 # Lock kaleido dependency until https://github.com/plotly/Kaleido/issues/156 is resolved
 kaleido = {version = "0.2.1", optional = true}
 tsdownsample = ">=0.1.3"
@@ -58,7 +58,7 @@ tsdownsample = ">=0.1.3"
 inline_persistent = ["kaleido", "Flask-Cors"]
 
 [tool.poetry.dev-dependencies]
-pytest = "^6.2.5"
+pytest = "^7.2.0"
 pytest-cov = "^3.0.0"
 selenium = "4.2.0"
 pytest-selenium = "^2.0.1"
@@ -72,7 +72,7 @@ ipywidgets = "^7.7.1" # needs to be v7 in order to support serialization
 memory-profiler = "^0.60.0"
 line-profiler = "^4.0"
 ruff = "^0.0.262"
-black = "^22.6.0"
+black = "^24.3.0"
 pytest-lazy-fixture = "^0.6.3"
 # yep = "^0.4"  # c code profiling
 mkdocs = "^1.5.3"

tests/conftest.py

@@ -1,6 +1,5 @@
 """Fixtures and helper functions for testing"""
 
-
 import os
 from typing import Union
 
@@ -71,6 +70,7 @@ def driver():
         driver = webdriver.Chrome(
             options=options,
             # executable_path="/home/jeroen/chromedriver",
+            # executable_path="/home/jonas/Documents/chromedriver-linux64/chromedriver",
             desired_capabilities=d,
         )
         # driver = webdriver.Firefox(executable_path='/home/jonas/git/gIDLaB/plotly-dynamic-resampling/geckodriver')

tests/utils.py

@@ -28,10 +28,12 @@ def construct_hf_data_dict(hf_x, hf_y, **kwargs):
     hf_data_dict = {
         "x": hf_x,
         "y": hf_y,
-        "axis_type": "date"
-        if isinstance(hf_x, pd.DatetimeIndex)
-        or pd.core.dtypes.common.is_datetime64_any_dtype(hf_x)
-        else "linear",
+        "axis_type": (
+            "date"
+            if isinstance(hf_x, pd.DatetimeIndex)
+            or pd.core.dtypes.common.is_datetime64_any_dtype(hf_x)
+            else "linear"
+        ),
         "downsampler": MinMaxLTTB(),
         "gap_handler": MedDiffGapHandler(),
         "max_n_samples": 1_000,