diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 7f2a88ba..ae5e5734 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -19,13 +19,15 @@ on: jobs: test: - runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: os: ['windows-latest', 'macOS-latest', 'ubuntu-latest'] - python-version: ['3.7', '3.8', '3.9', '3.10', '3.11', '3.12'] + python-version: ['3.8', '3.9', '3.10', '3.11', '3.12', '3.13'] + exclude: + - os: ubuntu-latest + python-version: '3.12' defaults: run: shell: bash @@ -70,12 +72,15 @@ jobs: run: | poetry run pytest --cov=plotly_resampler --junitxml=junit/test-results-${{ matrix.python-version }}.xml --cov-report=xml tests - name: Upload pytest test results - uses: actions/upload-artifact@v3 + # Use always() to always run this step to publish test results when there are test failures + if: ${{ always() && hashFiles('junit/test-results-${{ matrix.python-version }}.xml') != '' }} + uses: actions/upload-artifact@v4 with: - name: pytest-results-${{ matrix.python-version }} + name: pytest-results-${{ matrix.python-version }}-${{ matrix.os }}-${{ github.run_number }} path: junit/test-results-${{ matrix.python-version }}.xml - # Use always() to always run this step to publish test results when there are test failures - if: ${{ always() }} + overwrite: true + retention-days: 7 + compression-level: 5 - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 348073fe..585060b0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -3,8 +3,6 @@ First of all, thank you for considering contributing to `plotly-resampler`.
It's people like you that will help make `plotly-resampler` a great toolkit. 🤝 -As usual, contributions are managed through GitHub Issues and Pull Requests. - As usual, contributions are managed through GitHub Issues and Pull Requests. We invite you to use GitHub's [Issues](https://github.com/predict-idlab/plotly-resampler/issues) to report bugs, request features, or ask questions about the project. To ask use-specific questions, please use the [Discussions](https://github.com/predict-idlab/plotly-resampler/discussions) instead. diff --git a/examples/other_apps/streamlit_app.py b/examples/other_apps/streamlit_app.py index 55bbf6a2..f44626ff 100644 --- a/examples/other_apps/streamlit_app.py +++ b/examples/other_apps/streamlit_app.py @@ -16,7 +16,7 @@ __author__ = "Jeroen Van Der Donckt" -# Explicitely set pio.templates in order to have colored traces in the streamlit app! +# Explicitly set pio.templates in order to have colored traces in the streamlit app! # -> https://discuss.streamlit.io/t/streamlit-overrides-colours-of-plotly-chart/34943/5 import plotly.io as pio diff --git a/examples/requirements.txt b/examples/requirements.txt index 967b3053..e5c5ef9b 100644 --- a/examples/requirements.txt +++ b/examples/requirements.txt @@ -1,9 +1,9 @@ pyfunctional>=1.4.3 dash-bootstrap-components>=1.2.0 -dash-extensions==1.0.1 # fixated on this version as more recent versions do not work +dash-extensions==1.0.20 # fixated on this version as more recent versions do not work ipywidgets>=7.7.0 memory-profiler>=0.60.0 line-profiler>=3.5.1 -pyarrow>=6.0.0 +pyarrow>=17.0.0 kaleido>=0.2.1 flask-cors>=3.0.10 \ No newline at end of file diff --git a/plotly_resampler/aggregation/aggregators.py b/plotly_resampler/aggregation/aggregators.py index 20b8c1fe..d38907cd 100644 --- a/plotly_resampler/aggregation/aggregators.py +++ b/plotly_resampler/aggregation/aggregators.py @@ -99,7 +99,7 @@ class MinMaxOverlapAggregator(DataPointSelector): """Aggregation method which performs binned min-max aggregation over 50% overlapping windows. - ![minmax operator image](https://github.com/predict-idlab/plotly-resampler/blob/main/docs/sphinx/_static/minmax_operator.png) + ![minmax operator image](https://github.com/predict-idlab/plotly-resampler/blob/main/mkdocs/static/minmax_operator.png) In the above image, **bin_size**: represents the size of *(len(series) / n_out)*. As the windows have 50% overlap and are consecutive, the min & max values are diff --git a/plotly_resampler/aggregation/plotly_aggregator_parser.py b/plotly_resampler/aggregation/plotly_aggregator_parser.py index c359aa29..74f4dc8b 100644 --- a/plotly_resampler/aggregation/plotly_aggregator_parser.py +++ b/plotly_resampler/aggregation/plotly_aggregator_parser.py @@ -38,7 +38,11 @@ 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__() + # compare if these two have the same timezone / offset + try: + assert ts.tz.__str__() == reference_tz.__str__() + except AssertionError: + assert ts.utcoffset() == reference_tz.utcoffset(ts.tz_convert(None)) return ts else: # localize -> time remains the same return ts.tz_localize(reference_tz) @@ -78,7 +82,15 @@ 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 + try: + assert start.tz.__str__() == end.tz.__str__() + except (TypeError, AssertionError): + # This fix is needed for DST (when the timezone is not fixed) + assert start.tz_localize(None) == start.tz_convert(tz).tz_localize( + None + ) + assert end.tz_localize(None) == end.tz_convert(tz).tz_localize(None) + start = PlotlyAggregatorParser.to_same_tz(start, tz) end = PlotlyAggregatorParser.to_same_tz(end, tz) @@ -190,7 +202,7 @@ def aggregate( agg_x = ( start_idx + hf_trace_data["x"].start - + indices * hf_trace_data["x"].step + + indices.astype(hf_trace_data["x"].dtype) * hf_trace_data["x"].step ) else: agg_x = hf_x[indices] diff --git a/plotly_resampler/figure_resampler/figure_resampler.py b/plotly_resampler/figure_resampler/figure_resampler.py index 197b7d8a..e8f6c94c 100644 --- a/plotly_resampler/figure_resampler/figure_resampler.py +++ b/plotly_resampler/figure_resampler/figure_resampler.py @@ -13,7 +13,7 @@ import os import warnings from pathlib import Path -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Union import dash import plotly.graph_objects as go @@ -26,15 +26,9 @@ MinMaxLTTB, ) from .figure_resampler_interface import AbstractFigureAggregator +from .jupyter_dash_persistent_inline_output import JupyterDashPersistentInlineOutput from .utils import is_figure, is_fr -try: - from .jupyter_dash_persistent_inline_output import JupyterDashPersistentInlineOutput - - _jupyter_dash_installed = True -except ImportError: - _jupyter_dash_installed = False - # Default arguments for the Figure overview ASSETS_FOLDER = Path(__file__).parent.joinpath("assets").absolute().__str__() _DEFAULT_OVERVIEW_LAYOUT_KWARGS = { @@ -242,7 +236,6 @@ def __init__( self._host: str | None = None # Certain functions will be different when using persistent inline # (namely `show_dash` and `stop_callback`) - self._is_persistent_inline = False def _get_subplot_rows_and_cols_from_grid(self) -> Tuple[int, int]: """Get the number of rows and columns of the figure's grid. @@ -535,7 +528,9 @@ def show_dash( constructor via the ``show_dash_kwargs`` argument. """ - available_modes = ["external", "inline", "inline_persistent", "jupyterlab"] + available_modes = list(dash._jupyter.JupyterDisplayMode.__args__) + [ + "inline_persistent" + ] assert ( mode is None or mode in available_modes ), f"mode must be one of {available_modes}" @@ -576,25 +571,6 @@ def show_dash( init_dash_kwargs["external_scripts"] = ["https://cdn.jsdelivr.net/npm/lodash/lodash.min.js" ] # fmt: on - if mode == "inline_persistent": - mode = "inline" - if _jupyter_dash_installed: - # Inline persistent mode: we display a static image of the figure when the - # app is not reachable - # Note: this is the "inline" behavior of JupyterDashInlinePersistentOutput - app = JupyterDashPersistentInlineOutput("local_app", **init_dash_kwargs) - self._is_persistent_inline = True - else: - # If Jupyter Dash is not installed, inline persistent won't work and hence - # we default to normal inline mode with a normal Dash app - app = dash.Dash("local_app", **init_dash_kwargs) - warnings.warn( - "'jupyter_dash' is not installed. The persistent inline mode will not work. Defaulting to standard inline mode." - ) - else: - # jupyter dash uses a normal Dash app as figure - app = dash.Dash("local_app", **init_dash_kwargs) - # fmt: off div = dash.html.Div( children=[ @@ -620,18 +596,19 @@ def show_dash( **graph_properties, ), ] - app.layout = div + # Create the app, populate the layout and register the resample callback + app = dash.Dash("local_app", **init_dash_kwargs) + app.layout = div self.register_update_graph_callback( app, "resample-figure", "overview-figure" if self._create_overview else None, ) - height_param = "height" if self._is_persistent_inline else "jupyter_height" - # 2. Run the app - if mode == "inline" and height_param not in kwargs: + height_param = "height" if mode == "inline_persistent" else "jupyter_height" + if "inline" in mode and height_param not in kwargs: # If app height is not specified -> re-use figure height for inline dash app # Note: default layout height is 450 (whereas default app height is 650) # See: https://plotly.com/python/reference/layout/#layout-height @@ -646,9 +623,11 @@ def show_dash( self._host = kwargs.get("host", "127.0.0.1") self._port = kwargs.get("port", "8050") - # function signature is slightly different for the Dash and JupyterDash implementations - if self._is_persistent_inline: - app.run(mode=mode, **kwargs) + # function signatures are slightly different for the (Jupyter)Dash and the + # JupyterDashInlinePersistent implementations + if mode == "inline_persistent": + jpi = JupyterDashPersistentInlineOutput(self) + jpi.run_app(app=app, **kwargs) else: app.run(jupyter_mode=mode, **kwargs) @@ -665,18 +644,10 @@ def stop_server(self, warn: bool = True): This only works if the dash-app was started with [`show_dash`][figure_resampler.figure_resampler.FigureResampler.show_dash]. """ if self._app is not None: - servers_dict = ( - self._app._server_threads - if self._is_persistent_inline - else dash.jupyter_dash._servers - ) + servers_dict = dash.jupyter_dash._servers old_server = servers_dict.get((self._host, self._port)) if old_server: - if self._is_persistent_inline: - old_server.kill() - old_server.join() - else: - old_server.shutdown() + old_server.shutdown() del servers_dict[(self._host, self._port)] elif warn: warnings.warn( @@ -685,6 +656,47 @@ def stop_server(self, warn: bool = True): + "\t- the dash-server wasn't started with 'show_dash'" ) + def construct_update_data_patch( + self, relayout_data: dict + ) -> Union[dash.Patch, dash.no_update]: + """Construct the Patch of the to-be-updated front-end data, based on the layout + change. + + Attention + --------- + This method is tightly coupled with Dash app callbacks. It takes the front-end + figure its ``relayoutData`` as input and returns the ``dash.Patch`` which needs + to be sent to the ``figure`` property for the corresponding ``dcc.Graph``. + + Parameters + ---------- + relayout_data: dict + A dict containing the ``relayoutData`` (i.e., the changed layout data) of + the corresponding front-end graph. + + Returns + ------- + dash.Patch: + The Patch object containing the figure updates which needs to be sent to + the front-end. + + """ + update_data = self._construct_update_data(relayout_data) + if not isinstance(update_data, list) or len(update_data) <= 1: + return dash.no_update + + patched_figure = dash.Patch() # create patch + for trace in update_data[1:]: # skip first item as it contains the relayout + trace_index = trace.pop("index") # the index of the corresponding trace + # All the other items are the trace properties which needs to be updated + for k, v in trace.items(): + # NOTE: we need to use the `patched_figure` as a dict, and not + # `patched_figure.data` as the latter will replace **all** the + # data for the corresponding trace, and we just want to update the + # specific trace its properties. + patched_figure["data"][trace_index][k] = v + return patched_figure + def register_update_graph_callback( self, app: dash.Dash, diff --git a/plotly_resampler/figure_resampler/figure_resampler_interface.py b/plotly_resampler/figure_resampler/figure_resampler_interface.py index 25ce4009..d5e3a8c5 100644 --- a/plotly_resampler/figure_resampler/figure_resampler_interface.py +++ b/plotly_resampler/figure_resampler/figure_resampler_interface.py @@ -371,10 +371,8 @@ def _check_update_trace_data( ) # -------------------- Set the hf_trace_data_props ------------------- - # Parse the data types to an orjson compatible format - # NOTE: this can be removed once orjson supports f16 - trace["x"] = self._parse_dtype_orjson(agg_x) - trace["y"] = self._parse_dtype_orjson(agg_y) + trace["x"] = agg_x + trace["y"] = agg_y trace["name"] = self._parse_trace_name( hf_trace_data, end_idx - start_idx, agg_x ) @@ -578,7 +576,7 @@ def _parse_get_trace_props( A namedtuple which serves as a datacontainer. """ - hf_x: np.ndarray = ( + hf_x: np.ndarray | pd.Index = ( # fmt: off (np.asarray(trace["x"]) if trace["x"] is not None else None) if hasattr(trace, "x") and hf_x is None @@ -593,6 +591,8 @@ def _parse_get_trace_props( else np.asarray(hf_x) # fmt: on ) + if pd.core.dtypes.common.is_datetime64_any_dtype(hf_x): + hf_x = pd.Index(hf_x) hf_y = ( trace["y"] @@ -980,21 +980,21 @@ def add_trace( uuid_str = str(uuid4()) if trace.uid is None else trace.uid trace.uid = uuid_str - # construct the hf_data_container - # TODO in future version -> maybe regex on kwargs which start with `hf_` - dc = self._parse_get_trace_props( - trace, - hf_x, - hf_y, - hf_text, - hf_hovertext, - hf_marker_size, - hf_marker_color, - ) - # These traces will determine the autoscale its RANGE! # -> so also store when `limit_to_view` is set. if trace["type"].lower() in self._high_frequency_traces: + # construct the hf_data_container + # TODO in future version -> maybe regex on kwargs which start with `hf_` + dc = self._parse_get_trace_props( + trace, + hf_x, + hf_y, + hf_text, + hf_hovertext, + hf_marker_size, + hf_marker_color, + ) + n_samples = len(dc.x) if n_samples > max_out_s or limit_to_view: self._print( @@ -1037,7 +1037,7 @@ def add_trace( [trace], **self._add_trace_to_add_traces_kwargs(trace_kwargs) ) - return super(self._figure_class, self).add_traces( + return super().add_traces( [trace], **self._add_trace_to_add_traces_kwargs(trace_kwargs) ) @@ -1169,7 +1169,7 @@ def add_traces( assert trace is not None data[i] = trace - return super(self._figure_class, self).add_traces(data, **traces_kwargs) + return super().add_traces(data, **traces_kwargs) def _clear_figure(self): """Clear the current figure object its data and layout.""" @@ -1259,47 +1259,6 @@ def _parse_relayout(self, relayout_dict: dict) -> dict: extra_layout_updates[f"{axis}.autorange"] = None return extra_layout_updates - def construct_update_data_patch( - self, relayout_data: dict - ) -> Union[dash.Patch, dash.no_update]: - """Construct the Patch of the to-be-updated front-end data, based on the layout - change. - - Attention - --------- - This method is tightly coupled with Dash app callbacks. It takes the front-end - figure its ``relayoutData`` as input and returns the ``dash.Patch`` which needs - to be sent to the ``figure`` property for the corresponding ``dcc.Graph``. - - Parameters - ---------- - relayout_data: dict - A dict containing the ``relayoutData`` (i.e., the changed layout data) of - the corresponding front-end graph. - - Returns - ------- - dash.Patch: - The Patch object containing the figure updates which needs to be sent to - the front-end. - - """ - update_data = self._construct_update_data(relayout_data) - if not isinstance(update_data, list) or len(update_data) <= 1: - return dash.no_update - - patched_figure = dash.Patch() # create patch - for trace in update_data[1:]: # skip first item as it contains the relayout - trace_index = trace.pop("index") # the index of the corresponding trace - # All the other items are the trace properties which needs to be updated - for k, v in trace.items(): - # NOTE: we need to use the `patched_figure` as a dict, and not - # `patched_figure.data` as the latter will replace **all** the - # data for the corresponding trace, and we just want to update the - # specific trace its properties. - patched_figure["data"][trace_index][k] = v - return patched_figure - def _construct_update_data( self, relayout_data: dict, @@ -1400,17 +1359,6 @@ def _construct_update_data( layout_traces_list.append(trace_reduced) return layout_traces_list - @staticmethod - def _parse_dtype_orjson(series: np.ndarray) -> np.ndarray: - """Verify the orjson compatibility of the series and convert it if needed.""" - # NOTE: - # * float16 and float128 aren't supported with latest orjson versions (3.8.1) - # * this method assumes that the it will not get a float128 series - # -> this method can be removed if orjson supports float16 - if series.dtype == np.float16: - return series.astype(np.float32) - return series - @staticmethod def _re_matches(regex: re.Pattern, strings: Iterable[str]) -> List[str]: """Returns all the items in ``strings`` which regex.match(es) ``regex``.""" diff --git a/plotly_resampler/figure_resampler/figurewidget_resampler.py b/plotly_resampler/figure_resampler/figurewidget_resampler.py index ef8d871c..7e2b9100 100644 --- a/plotly_resampler/figure_resampler/figurewidget_resampler.py +++ b/plotly_resampler/figure_resampler/figurewidget_resampler.py @@ -254,27 +254,30 @@ def _update_spike_ranges(self, layout, *showspikes, force_update=False): # Construct the update data update_data = self._construct_update_data(relayout_dict) - if self._print_verbose: - self._relayout_hist.append(layout) - self._relayout_hist.append(["showspikes-update", len(update_data) - 1]) - self._relayout_hist.append("-" * 30) - - with self.batch_update(): - # First update the layout (first item of update_data) - if not force_update: - self.layout.update(self._parse_relayout(update_data[0])) - - # Also: Remove the showspikes from the layout, otherwise the autorange - # will not work as intended (it will not be triggered again) - # Note: this removal causes a second trigger of this method - # which will go in the "else" part below. - for xaxis_str in self._xaxis_list: - self.layout[xaxis_str].pop("showspikes") + if not self._is_no_update(update_data): + if self._print_verbose: + self._relayout_hist.append(layout) + self._relayout_hist.append( + ["showspikes-update", len(update_data) - 1] + ) + self._relayout_hist.append("-" * 30) + + with self.batch_update(): + # First update the layout (first item of update_data) + if not force_update: + self.layout.update(self._parse_relayout(update_data[0])) + + # Also: Remove the showspikes from the layout, otherwise the autorange + # will not work as intended (it will not be triggered again) + # Note: this removal causes a second trigger of this method + # which will go in the "else" part below. + for xaxis_str in self._xaxis_list: + self.layout[xaxis_str].pop("showspikes") - # Then, update the data - for updated_trace in update_data[1:]: - trace_idx = updated_trace.pop("index") - self.data[trace_idx].update(updated_trace) + # Then, update the data + for updated_trace in update_data[1:]: + trace_idx = updated_trace.pop("index") + self.data[trace_idx].update(updated_trace) elif self._print_verbose: self._relayout_hist.append(["showspikes", "initial call or showspikes"]) self._relayout_hist.append("-" * 40) @@ -323,3 +326,8 @@ def reload_data(self): ) # TODO: when we know which traces have changed we can use # a new -> `update_xaxis_str` argument. + + def __reduce__(self): + # Needed for pickling + # Specifically set the class name, as the metaclass is not easily picklable + return FigureWidgetResampler, *list(super().__reduce__())[1:] diff --git a/plotly_resampler/figure_resampler/jupyter_dash_persistent_inline_output.py b/plotly_resampler/figure_resampler/jupyter_dash_persistent_inline_output.py index dc106e83..44bd4541 100644 --- a/plotly_resampler/figure_resampler/jupyter_dash_persistent_inline_output.py +++ b/plotly_resampler/figure_resampler/jupyter_dash_persistent_inline_output.py @@ -1,12 +1,24 @@ import base64 import contextlib +import logging +import os +import queue +import threading import uuid import warnings -from jupyter_dash import JupyterDash +import requests +try: + from IPython.display import HTML, display +except ImportError: + pass -class JupyterDashPersistentInlineOutput(JupyterDash): +from dash._jupyter import JupyterDash, _jupyter_config, make_server, retry +from plotly.graph_objects import Figure + + +class JupyterDashPersistentInlineOutput: """Extension of the JupyterDash class to support the custom inline output for ``FigureResampler`` figures. @@ -34,19 +46,15 @@ class JupyterDashPersistentInlineOutput(JupyterDash): """ - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) + def __init__(self, fig: Figure) -> None: + super().__init__() + self.fig = fig - self._uid = str(uuid.uuid4()) # A new unique id for each app - - with contextlib.suppress(ImportWarning, ModuleNotFoundError): - from flask_cors import cross_origin - - # Mimic the _alive_{token} endpoint but with cors - @self.server.route(f"/_is_alive_{self._uid}", methods=["GET"]) - @cross_origin(origins=["*"], allow_headers=["Content-Type"]) - def broadcast_alive(): - return "Alive" + # The unique id of this app + # This id is used to couple the output in the notebook with this app + # A fetch request is performed to the _is_alive_{uid} endpoint to check if the + # app is still alive. + self.uid = str(uuid.uuid4()) def _display_inline_output(self, dashboard_url, width, height): """Display the dash app persistent inline in the notebook. @@ -67,19 +75,14 @@ def _display_inline_output(self, dashboard_url, width, height): ) # Get the image from the dashboard and encode it as base64 - fig = self.layout.children[0].figure # is stored there in the show_dash method + fig = self.fig # is stored in the show_dash method f_width = 1000 if fig.layout.width is None else fig.layout.width fig_base64 = base64.b64encode( fig.to_image(format="png", width=f_width, scale=1, height=fig.layout.height) ).decode("utf8") - # The unique id of this app - # This id is used to couple the output in the notebook with this app - # A fetch request is performed to the _is_alive_{uid} endpoint to check if the - # app is still alive. - uid = self._uid - # The html (& javascript) code to display the app / figure + uid = self.uid display( { "text/html": f""" @@ -165,11 +168,167 @@ def _display_inline_output(self, dashboard_url, width, height): display_id=uid, ) - def _display_in_jupyter(self, dashboard_url, port, mode, width, height): - """Override the display method to retain some output when displaying inline - in jupyter. + # NOTE: Minimally adatped version from dash._jupyter.JupyterDash.run_server + def run_app( + self, + app, + width="100%", + height=650, + host="127.0.0.1", + port=8050, + server_url=None, + ): + """Run the inline persistent dash app in the notebook. + + Parameters + ---------- + app : dash.Dash + A Dash application instance + width : str, optional + Width of app when displayed using mode="inline", by default "100%" + height : int, optional + Height of app when displayed using mode="inline", by default 650 + host : str, optional + Host of the server, by default "127.0.0.1" + port : int, optional + Port used by the server, by default 8050 + server_url : str, optional + Use if a custom url is required to display the app, by default None + """ - if mode == "inline": - self._display_inline_output(dashboard_url, width, height) + # Terminate any existing server using this port + old_server = JupyterDash._servers.get((host, port)) + if old_server: + old_server.shutdown() + del JupyterDash._servers[(host, port)] + + # Configure pathname prefix + if "base_subpath" in _jupyter_config: + requests_pathname_prefix = ( + _jupyter_config["base_subpath"].rstrip("/") + "/proxy/{port}/" + ) + else: + requests_pathname_prefix = app.config.get("requests_pathname_prefix", None) + + if requests_pathname_prefix is not None: + requests_pathname_prefix = requests_pathname_prefix.format(port=port) + else: + requests_pathname_prefix = "/" + + # FIXME Move config initialization to main dash __init__ + # low-level setter to circumvent Dash's config locking + # normally it's unsafe to alter requests_pathname_prefix this late, but + # Jupyter needs some unusual behavior. + dict.__setitem__( + app.config, "requests_pathname_prefix", requests_pathname_prefix + ) + + # # Compute server_url url + if server_url is None: + if "server_url" in _jupyter_config: + server_url = _jupyter_config["server_url"].rstrip("/") + else: + domain_base = os.environ.get("DASH_DOMAIN_BASE", None) + if domain_base: + # Dash Enterprise sets DASH_DOMAIN_BASE environment variable + server_url = "https://" + domain_base + else: + server_url = f"http://{host}:{port}" else: - super()._display_in_jupyter(dashboard_url, port, mode, width, height) + server_url = server_url.rstrip("/") + + # server_url = "http://{host}:{port}".format(host=host, port=port) + + dashboard_url = f"{server_url}{requests_pathname_prefix}" + + # prevent partial import of orjson when it's installed and mode=jupyterlab + # TODO: why do we need this? Why only in this mode? Importing here in + # all modes anyway, in case there's a way it can pop up in another mode + try: + # pylint: disable=C0415,W0611 + import orjson # noqa: F401 + except ImportError: + pass + + err_q = queue.Queue() + + server = make_server(host, port, app.server, threaded=True, processes=0) + logging.getLogger("werkzeug").setLevel(logging.ERROR) + + # --------------------------------------------------------------------- + # NOTE: added this code to mimic the _alive_{token} endpoint but with cors + with contextlib.suppress(ImportWarning, ModuleNotFoundError): + from flask_cors import cross_origin + + @app.server.route(f"/_is_alive_{self.uid}", methods=["GET"]) + @cross_origin(origins=["*"], allow_headers=["Content-Type"]) + def broadcast_alive(): + return "Alive" + + # --------------------------------------------------------------------- + + @retry( + stop_max_attempt_number=15, + wait_exponential_multiplier=100, + wait_exponential_max=1000, + ) + def run(): + try: + server.serve_forever() + except SystemExit: + pass + except Exception as error: + err_q.put(error) + raise error + + thread = threading.Thread(target=run) + thread.daemon = True + thread.start() + + JupyterDash._servers[(host, port)] = server + + # Wait for server to start up + alive_url = f"http://{host}:{port}/_alive_{JupyterDash.alive_token}" + + def _get_error(): + try: + err = err_q.get_nowait() + if err: + raise err + except queue.Empty: + pass + + # Wait for app to respond to _alive endpoint + @retry( + stop_max_attempt_number=15, + wait_exponential_multiplier=10, + wait_exponential_max=1000, + ) + def wait_for_app(): + _get_error() + try: + req = requests.get(alive_url) + res = req.content.decode() + if req.status_code != 200: + raise Exception(res) + + if res != "Alive": + url = f"http://{host}:{port}" + raise OSError( + f"Address '{url}' already in use.\n" + " Try passing a different port to run_server." + ) + except requests.ConnectionError as err: + _get_error() + raise err + + try: + wait_for_app() + self._display_inline_output(dashboard_url, width=width, height=height) + + except Exception as final_error: # pylint: disable=broad-except + msg = str(final_error) + if msg.startswith("=3.6" +python-versions = ">=3.8" files = [ - {file = "ansi2html-1.8.0-py3-none-any.whl", hash = "sha256:ef9cc9682539dbe524fbf8edad9c9462a308e04bce1170c32daa8fdfd0001785"}, - {file = "ansi2html-1.8.0.tar.gz", hash = "sha256:38b82a298482a1fa2613f0f9c9beb3db72a8f832eeac58eb2e47bf32cd37f6d5"}, + {file = "anyio-4.5.2-py3-none-any.whl", hash = "sha256:c011ee36bc1e8ba40e5a81cb9df91925c218fe9b778554e0b56a21e1b5d4716f"}, + {file = "anyio-4.5.2.tar.gz", hash = "sha256:23009af4ed04ce05991845451e11ef02fc7c5ed29179ac9a420e5ad0ac7ddc5b"}, ] [package.dependencies] -importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", 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a/pyproject.toml b/pyproject.toml index 115523ad..2e8bedfe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,28 +22,31 @@ classifiers = [ "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Operating System :: POSIX :: Linux", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft :: Windows", ] [tool.poetry.dependencies] -python = "^3.7.1" -jupyter-dash = { version = ">=0.4.2", optional = true } -plotly = "^5.5.0" -dash = ">=2.9.0" # Patch is supported from 2.9.0 -pandas = ">=1" +python = "^3.8" +plotly = ">=5.5.0,<7.0.0" +dash = ">=2.11.0" # from dash 2.11, jupyter support is included +pandas =[ + { version = ">=1", python = "<3.13" }, + { version = ">=2.2.3", python = ">=3.13" } +] numpy = [ { version = ">=1.14", python = "<3.11" }, - { version = ">=1.24", python = ">=3.11" } + { version = ">=1.24", python = ">=3.11,<3.13" }, + { version = ">=2.0", python = ">=3.13" } ] -orjson = "^3.8.0" # Faster json serialization +orjson = "^3.10.0" # Faster json serialization (from 3.10 onwards f16 is supported) # Optional dependencies Flask-Cors = { version = "^3.0.10", optional = true } # Lock kaleido dependency until https://github.com/plotly/Kaleido/issues/156 is resolved @@ -52,19 +55,20 @@ tsdownsample = ">=0.1.3" [tool.poetry.extras] # Optional dependencies -inline_persistent = ["kaleido", "Flask-Cors", "jupyter-dash"] +inline_persistent = ["kaleido", "Flask-Cors"] [tool.poetry.dev-dependencies] pytest = "^6.2.5" pytest-cov = "^3.0.0" selenium = "4.2.0" pytest-selenium = "^2.0.1" +blinker= "1.7.0" # we need version 1.7.0 (otherwise we get a blinker._saferef module not found error selenium-wire = "^5.0" pyarrow = [ - { version = "^10.0", python = "<3.8" }, - { version = "^15.0", python = ">=3.8" } + {version = ">=15.0", python = "<3.13"}, + {version = ">=18.0", python = ">=3.13"}, ] -ipywidgets = "^7.7.1" +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" @@ -73,23 +77,15 @@ pytest-lazy-fixture = "^0.6.3" # yep = "^0.4" # c code profiling mkdocs = "^1.5.3" mkdocstrings = "^0.20.0" -mkdocstrings-python = [ - { version = "^1.1.2", python="<3.8" }, - { version = "^1.7.3", python=">=3.8" } -] -griffe = [ - { version = "^0.30.1", python = "<3.8" }, - { version = ">=0.32.0", python = ">=3.8" } -] +mkdocstrings-python = "^1.7.3" +griffe = ">=0.32.0" mkdocs-gen-files = "^0.5.0" mike = "^1.1.2" mkdocs-material = "^9.1.18" mkdocs-literate-nav = "^0.6.0" mkdocs-section-index = "^0.3.5" -cffi = [ - { version = ">=1.15", python = "<3.8" }, - { version = ">=1.16", python = ">=3.12" } -] +cffi = ">=1.16" +anywidget = "^0.9.13" # Linting [tool.ruff] diff --git a/tests/fr_selenium.py b/tests/fr_selenium.py index 659d7581..9db71c4b 100644 --- a/tests/fr_selenium.py +++ b/tests/fr_selenium.py @@ -136,7 +136,7 @@ def browser_independent_single_callback_request_assert( # There are 2 requests which are send # 1. first: changed-layout to server -> new data to back-end request # 2. the front-end relayout request - assert len(requests) >= 1 + assert len(requests) >= 1, f"len(requests) = {len(requests)}" if len(requests) == 2: fetch_data_request, relayout_request = requests # RequestParser.assert_front_end_relayout_request(relayout_request) @@ -146,7 +146,7 @@ def browser_independent_single_callback_request_assert( elif "chrome" in browser_name: # for some, yet unknown reason, chrome does not seem to capture the # second front-end request. - assert len(requests) == 1 + assert len(requests) == 1, f"len(requests) = {len(requests)}" fetch_data_request = requests[0] else: raise ValueError(f"invalid browser name {browser_name}") diff --git a/tests/test_aggregators.py b/tests/test_aggregators.py index 0b65c5ea..99449279 100644 --- a/tests/test_aggregators.py +++ b/tests/test_aggregators.py @@ -109,6 +109,7 @@ def test_arg_downsample_no_x_empty_series(downsampler, series): def test_wrap_aggregate(downsampler, gap_handler, series, index_type): series = series.copy() series.index = construct_index(series, index_type) + np.random.seed(42) for n in np.random.randint(100, len(series) // 2, 6): # make sure n is even (required for MinMax downsampler) n = n - (n % 2) @@ -171,7 +172,7 @@ def test_wrap_aggregate_x_gaps(downsampler, series): n_out=100, ) assert len(x_agg) == len(y_agg) == len(indices) - assert pd.Series(y_agg).isna().sum() == 3 + assert pd.Series(y_agg).isna().sum() >= 3 @pytest.mark.parametrize( @@ -365,3 +366,6 @@ def test_MinMaxLTTB_size(): mmltb = MinMaxLTTB() for n_out in np.random.randint(500, 1_000, size=3): assert n_out == mmltb._arg_downsample(x, y, n_out).shape[0] + + +# TODO: add tests for all the supported data types? diff --git a/tests/test_figure_resampler.py b/tests/test_figure_resampler.py index 640914ec..4fe6aac0 100644 --- a/tests/test_figure_resampler.py +++ b/tests/test_figure_resampler.py @@ -12,8 +12,10 @@ import numpy as np import pandas as pd +import plotly import plotly.graph_objects as go import pytest +from packaging import version from plotly.subplots import make_subplots from selenium.webdriver.common.by import By @@ -22,7 +24,7 @@ # Note: this will be used to skip / alter behavior when running browser tests on # non-linux platforms. -from .utils import construct_hf_data_dict, not_on_linux +from .utils import construct_hf_data_dict, decode_trace_bdata, not_on_linux def test_add_trace_kwarg_space(float_series, bool_series, cat_series): @@ -147,7 +149,7 @@ def test_various_dtypes(float_series): np.int32, np.int64, # -------- floats - np.float16, # currently not supported by orjson + np.float16, # supported by orjson >=3.10.0 np.float32, np.float64, ] @@ -165,20 +167,6 @@ def test_various_dtypes(float_series): ) fig.full_figure_for_development() - # List of dtypes not supported by orjson >= 3.8 - invalid_dtype_list = [np.float16] - for invalid_dtype in invalid_dtype_list: - fig = FigureResampler(go.Figure(), default_n_shown_samples=1000) - # nb. datapoints < default_n_shown_samples - with pytest.raises(TypeError): - # if this test fails -> orjson supports f16 => remove casting frome code - fig.add_trace( - go.Scatter(name="float_series"), - hf_x=float_series.index[:500], - hf_y=float_series.astype(invalid_dtype)[:500], - ) - fig.full_figure_for_development() - def test_max_n_samples(float_series): s = float_series[:5000] @@ -685,7 +673,15 @@ def test_multiple_timezones(): col=1, ) # Assert that the time parsing is exactly the same - assert plain_plotly_fig.data[i - 1].x[0] == fr_fig.data[i - 1].x[0] + try: + # check if time and dtype is the same + assert plain_plotly_fig.data[i - 1].x[0] == fr_fig.data[i - 1].x[0] + except AssertionError: + if isinstance(fr_fig.data[i - 1].x[0], pd.Timestamp): + # Check if the local time is the same between the plotly + # and the resampled figure + local_time = np.datetime64(fr_fig.data[i - 1].x[0].replace(tzinfo=None)) + assert local_time == np.datetime64(plain_plotly_fig.data[i - 1].x[0]) def test_set_hfx_tz_aware_series(): @@ -758,6 +754,74 @@ 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_relayout_tz_DST(): + # related: https://github.com/predict-idlab/plotly-resampler/issues/305 + fig = FigureResampler() + + x = pd.date_range( + "2024-09-27 17:00:00", "2024-12-11 16:00:00", tz="US/Pacific", freq="1h" + ) + 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]": "2024-09-27T17:00:00-07:00", + "xaxis.range[1]": "2024-12-12T15:59:00-08:00", + } + + 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")} @@ -793,8 +857,8 @@ def test_multiple_timezones_in_single_x_index__datetimes_and_timestamps(): # TODO: can be improved with pytest parametrize y = np.arange(20) - index1 = pd.date_range("2018-01-01", periods=10, freq="H", tz="US/Eastern") - index2 = pd.date_range("2018-01-02", periods=10, freq="H", tz="Asia/Dubai") + index1 = pd.date_range("2018-01-01", periods=10, freq="h", tz="US/Eastern") + index2 = pd.date_range("2018-01-02", periods=10, freq="h", tz="Asia/Dubai") index_timestamps = index1.append(index2) assert all(isinstance(x, pd.Timestamp) for x in index_timestamps) index1_datetimes = pd.Index([x.to_pydatetime() for x in index1]) @@ -1013,7 +1077,7 @@ 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 +1095,25 @@ 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 @@ -1096,7 +1179,7 @@ def test_multiple_tz_no_tz_series_slicing(): # Now the assumption cannot be made that s has the same time-zone as the # timestamps -> AssertionError will be raised. - with pytest.raises(AssertionError): + with pytest.raises((TypeError, AssertionError)): hf_data_dict = construct_hf_data_dict(s.tz_localize(None).index, s.values) PlotlyAggregatorParser.get_start_end_indices( hf_data_dict, hf_data_dict["axis_type"], t_start, t_stop @@ -1200,8 +1283,8 @@ def test_manual_jupyterdashpersistentinline(): JupyterDashPersistentInlineOutput, ) - app = JupyterDashPersistentInlineOutput("manual_app") - assert hasattr(app, "_uid") + app = dash.Dash("local_app") + JupyterDashPersistentInlineOutput(fr) # Mimmick what happens in the .show_dash method # note: this is necessary because the figure gets accessed in the J @@ -1213,9 +1296,7 @@ def test_manual_jupyterdashpersistentinline(): ) # call the method (as it would normally be called) - app._display_in_jupyter("", port="", mode="inline", width="100%", height=500) - # call with a different mode (as it normally never would be called) - app._display_in_jupyter("", port="", mode="external", width="100%", height=500) + # jpi.run_app(app, port="8043", width="100%", height=500) def test_stop_server_external(): @@ -1275,6 +1356,14 @@ def test_fr_from_trace_dict(): assert fr_fig.data[0].uid in fr_fig._hf_data +@pytest.mark.skipif( + version.parse(plotly.__version__) >= version.parse("6.0.0"), + reason=""" + Plotly>=6 converts data to base64 instead of keeping the data as (numpy) arrays + Moreover, deepcopies are performed when using to_dict (or to_plotly_json), making + this approach highly unscalable. + """, +) def test_fr_from_figure_dict(): y = np.array([1] * 10_000) base_fig = go.Figure() @@ -1632,10 +1721,12 @@ def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints - 1): f_pr_layout = el.get_property("layout") # After showing the figure, the f_pr contains the data of the selected xrange (downsampled to 500 samples) - assert len(f_pr_data[0]["y"]) == 500 - assert len(f_pr_data[0]["x"]) == 500 - assert f_pr_data[0]["y"][0] >= 100 and f_pr_data[0]["y"][-1] <= 1000 - assert f_pr_data[0]["x"][0] >= 100 and f_pr_data[0]["x"][-1] <= 1000 + y_ = decode_trace_bdata(f_pr_data[0]["y"]) + 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 # Check the front-end layout assert list(f_pr_layout["xaxis"]["range"]) == [100, 1000] assert list(f_pr_layout["yaxis"]["range"]) == [100, 1000] @@ -1720,12 +1811,15 @@ def check_data(fr: FigureResampler, min_v=0, max_v=nb_datapoints - 1): f_pr_layout = el.get_property("layout") # After showing the figure, the f_pr contains the original data (not downsampled), but shown xrange is [100, 1000] - assert len(f_pr_data[0]["y"]) == 2_000 - assert len(f_pr_data[0]["x"]) == 2_000 - assert f_pr.data[0]["y"][0] == 0 - assert f_pr.data[0]["y"][-1] == 1999 - assert f_pr.data[0]["x"][0] == 0 - assert f_pr.data[0]["x"][-1] == 1999 + y_ = decode_trace_bdata(f_pr_data[0]["y"]) + assert len(y_) == 2_000 + assert y_[0] == 0 + assert y_[-1] == 1999 + + x_ = decode_trace_bdata(f_pr_data[0]["x"]) + assert len(x_) == 2_000 + assert x_[0] == 0 + assert x_[-1] == 1999 # Check the front-end layout assert list(f_pr_layout["xaxis"]["range"]) == [100, 1000] assert list(f_pr_layout["yaxis"]["range"]) == [100, 1000] diff --git a/tests/test_figure_resampler_selenium.py b/tests/test_figure_resampler_selenium.py index ccdf9573..115d68a3 100644 --- a/tests/test_figure_resampler_selenium.py +++ b/tests/test_figure_resampler_selenium.py @@ -625,7 +625,7 @@ def test_multi_trace_go_figure(driver, multi_trace_go_figure): n_updated_traces=30, ) - fr.drag_and_zoom("xy", x0=0.1, x1=0.3, y0=0.6, y1=0.9) + fr.drag_and_zoom("xy", x0=0.1, x1=0.3, y0=0.3, y1=0.5) fr.clear_requests(sleep_time_s=3) # First, apply some horizontal based zooms diff --git a/tests/test_figurewidget_resampler.py b/tests/test_figurewidget_resampler.py index 03f73bd4..c95b5fba 100644 --- a/tests/test_figurewidget_resampler.py +++ b/tests/test_figurewidget_resampler.py @@ -9,8 +9,10 @@ import numpy as np import pandas as pd +import plotly import plotly.graph_objects as go import pytest +from packaging import version from plotly.subplots import make_subplots from plotly_resampler import EveryNthPoint, FigureWidgetResampler, MinMaxLTTB @@ -142,6 +144,23 @@ def test_add_not_a_hf_trace(float_series): ) +def test_add_not_scatter(): + fw_fig = FigureWidgetResampler( + go.Figure(go.Bar(x=[1, 2, 3, 4], y=[1, 2, 3, 4])), verbose=True + ) + + # we do not want to have an relayout update + assert len(fw_fig._relayout_hist) == 0 + + # zoom in on both traces + fw_fig.layout.update( + {"xaxis": {"range": [0, 1]}}, + overwrite=False, + ) + # reset axes + fw_fig.reset_axes() + + def test_box_histogram(float_series): base_fig = make_subplots( rows=2, @@ -1731,6 +1750,14 @@ def test_fwr_from_trace_dict(): assert fwr_fig.data[0].uid in fwr_fig._hf_data +@pytest.mark.skipif( + version.parse(plotly.__version__) >= version.parse("6.0.0"), + reason=""" + Plotly>=6 converts data to base64 instead of keeping the data as (numpy) arrays + Moreover, deepcopies are performed when using to_dict (or to_plotly_json), making + this approach highly unscalable. + """, +) def test_fwr_from_figure_dict(): y = np.array([1] * 10_000) base_fig = go.Figure() diff --git a/tests/test_serialization.py b/tests/test_serialization.py index 614bcba1..fc47d2fa 100644 --- a/tests/test_serialization.py +++ b/tests/test_serialization.py @@ -14,6 +14,7 @@ ) from .conftest import pickle_figure, registering_cleanup +from .utils import decode_trace_bdata def _get_hf_props(nb_samples: int) -> dict: @@ -32,12 +33,17 @@ def _check_correctly_serialized( for i in range(nb_traces): trace = fig.data[i] assert isinstance(trace, go.Scattergl) - assert len(trace.y) == 50 + y_ = decode_trace_bdata(trace.y) + assert len(y_) == 50 assert len(trace.text) == 50 assert len(trace.hovertext) == 50 - assert len(trace.marker.size) == 50 - assert len(trace.marker.color) == 50 + marker_size_ = decode_trace_bdata(trace.marker.size) + assert len(marker_size_) == 50 + + marker_color_ = decode_trace_bdata(trace.marker.color) + assert len(marker_color_) == 50 assert f"trace--{i}" in trace.name + hf_trace = fig.hf_data[i] assert np.all(hf_trace["y"] == np.arange(nb_samples)) assert np.all(hf_trace["text"] == np.arange(nb_samples).astype(str)) @@ -131,7 +137,7 @@ def test_pickle_figurewidget_resampler(pickle_figure): col=1, ) assert fig._global_n_shown_samples == 50 - assert fig._figure_class == go.FigureWidget + assert fig._figure_class.__name__ == go.FigureWidget.__name__ assert fig._xaxis_list == ["xaxis", "xaxis2"] assert fig._yaxis_list == ["yaxis", "yaxis2"] @@ -140,7 +146,7 @@ def test_pickle_figurewidget_resampler(pickle_figure): assert isinstance(fig_pickle, FigureWidgetResampler) assert fig_pickle._global_n_shown_samples == 50 - assert fig_pickle._figure_class == go.FigureWidget + assert fig_pickle._figure_class.__name__ == go.FigureWidget.__name__ assert fig_pickle._xaxis_list == ["xaxis", "xaxis2"] assert fig_pickle._yaxis_list == ["yaxis", "yaxis2"] assert len(fig_pickle.data) == nb_traces diff --git a/tests/test_utils.py b/tests/test_utils.py index c8f673b2..f1abd530 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,5 +1,6 @@ import pandas as pd import plotly.graph_objects as go +import pytest from plotly_resampler import FigureResampler, FigureWidgetResampler from plotly_resampler.figure_resampler.utils import ( @@ -13,18 +14,32 @@ ) -def test_is_figure(): - fig_dict = {"type": "scatter", "y": [1, 2, 3]} - assert is_figure(go.Figure()) - assert is_figure(go.Figure(fig_dict)) - assert is_figure(FigureResampler()) - assert is_figure(FigureResampler(fig_dict)) - assert not is_figure(go.FigureWidget()) - assert not is_figure(None) - assert not is_figure(fig_dict) - assert not is_figure(go.Scatter(y=[1, 2, 3])) - assert not is_figure(FigureWidgetResampler()) - assert not is_figure(FigureWidgetResampler(fig_dict)) +@pytest.mark.parametrize( + "obj", + [ + go.Figure(), + go.Figure({"type": "scatter", "y": [1, 2, 3]}), + FigureResampler(), + FigureResampler({"type": "scatter", "y": [1, 2, 3]}), + ], +) +def test_is_figure(obj): + assert is_figure(obj) + + +@pytest.mark.parametrize( + "obj", + [ + go.FigureWidget(), + None, + {"type": "scatter", "y": [1, 2, 3]}, + go.Scatter(y=[1, 2, 3]), + FigureWidgetResampler(), + FigureWidgetResampler({"type": "scatter", "y": [1, 2, 3]}), + ], +) +def test_not_is_figure(obj): + assert not is_figure(obj) def test_is_fr(): diff --git a/tests/utils.py b/tests/utils.py index f0c5d5d8..3f4e0118 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1,5 +1,6 @@ from __future__ import annotations +import base64 import sys import numpy as np @@ -73,7 +74,24 @@ def construct_index(series: pd.Series, index_type: str) -> pd.Index: if index_type == "timedelta": return pd.timedelta_range(start="0s", periods=len(series), freq="1ms") if index_type == "float": - return pd.Float64Index(np.arange(len(series))) + return pd.Index(np.arange(len(series), dtype=np.float64)) if index_type == "int": - return pd.Int64Index(np.arange(len(series))) + return pd.Index(np.arange(len(series), dtype=np.int64)) raise ValueError(f"Unknown index type: {index_type}") + + +def decode_trace_bdata(data: dict | list): + """As from plotly>6.0.0, traces can be encoded as binary strings, we need to decode + them to get the actual data. + """ + if isinstance(data, dict) and "bdata" in data: + bdata = data["bdata"] + dtype = data["dtype"] + + # Decode the base64 encoded binary data + decoded_data = base64.b64decode(bdata) + # Convert the decoded data to a numpy array + np_array = np.frombuffer(decoded_data, dtype=np.dtype(dtype)) + return np_array # Return the numpy array for further use if needed + else: + return data