diff --git a/examples/README.md b/examples/README.md index 3bd8e857..325bf0d4 100644 --- a/examples/README.md +++ b/examples/README.md @@ -17,6 +17,7 @@ The [basic example notebook](basic_example.ipynb) covers most use-cases in which Additionally, this notebook also shows some more advanced functionalities, such as: * Retaining (a static) plotly-resampler figure in your notebook +* How to utilize an x-axis overview (i.e., a rangeslider) to navigate through your time series * Showing how to style the marker color and size of plotly-resampler figures * Adjusting trace data of plotly-resampler figures at runtime * How to add (shaded) confidence bounds to your time series @@ -43,6 +44,8 @@ The [dash_apps](dash_apps/) folder contains example dash apps in which `plotly-r | [global variable](dash_apps/01_minimal_global.py) | *bad practice*: minimal example in which a global `FigureResampler` variable is used | | [server side caching](dash_apps/02_minimal_cache.py) | *good practice*: minimal example in which we perform server side caching of the `FigureResampler` variable | | [runtime graph construction](dash_apps/03_minimal_cache_dynamic.py) | minimal example where graphs are constructed based on user interactions at runtime. [Pattern matching callbacks](https://dash.plotly.com/pattern-matching-callbacks) are used construct these plotly-resampler graphs dynamically. Again, server side caching is performed. | +| [xaxis overview (rangeslider)](dash_apps/04_minimal_cache_overview.py) | minimal example where a linked xaxis overview is shown below the `FigureResampler` figure. This xaxis rangeslider utilizes [clientside callbacks](https://dash.plotly.com/clientside-callbacks) to realize this behavior. | +| [xaxis overview (subplots)](dash_apps/05_cache_overview_subplots.py) | example where a linked xaxis overview is shown below the `FigureResampler` figure (with subplots). | | **advanced apps** | | | [dynamic sine generator](dash_apps/11_sine_generator.py) | exponential sine generator which uses [pattern matching callbacks](https://dash.plotly.com/pattern-matching-callbacks) to remove and construct plotly-resampler graphs dynamically | | [file visualization](dash_apps/12_file_selector.py) | load and visualize multiple `.parquet` files with plotly-resampler | diff --git a/examples/basic_example.ipynb b/examples/basic_example.ipynb index 9cb3c5f2..dc94e13e 100644 --- a/examples/basic_example.ipynb +++ b/examples/basic_example.ipynb @@ -31,6 +31,7 @@ "sys.path.append(\"..\")\n", "from plotly_resampler import FigureResampler, FigureWidgetResampler, EveryNthPoint\n", "from plotly_resampler.aggregation import NoGapHandler, MedDiffGapHandler\n", + "from plotly_resampler.aggregation import MinMaxLTTB\n", "\n", "\n", "USE_PNG = True # Set to false to use dynamic plots" @@ -229,116 +230,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "08dc25ff", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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This row index starts at 0.\n", + "> * This is functionality not extensively validated yet, so please report any issues you encounter!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "113180cb", + "metadata": {}, + "outputs": [], + "source": [ + "# Data that will be used for the plotly-resampler figures\n", + "x = np.arange(1_000_000)\n", + "noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000\n", + "x_time = pd.date_range(\"2020-01-01\", freq=\"1s\", periods=len(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c022be3f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig: FigureResampler = FigureResampler(\n", + " make_subplots(rows=2, cols=2, shared_xaxes=\"columns\", horizontal_spacing=0.03),\n", + " default_downsampler=MinMaxLTTB(parallel=True),\n", + " # Enable the overview axis\n", + " xaxis_overview=True,\n", + " # Specify the subplot rows that will be used for the overview axis of each column\n", + " overview_row_idxs=[1, 0],\n", + " # Additonal kwargs for the overview axis\n", + " xaxis_overview_kwargs={\"height\": 100},\n", + ")\n", + "\n", + "# Figure construction logic\n", + "# fmt: off\n", + "log = noisy_sin * 0.9999995**x\n", + "exp = noisy_sin * 1.000002**x\n", + "fig.add_trace(go.Scattergl(name=\"log\", legend='legend1'), hf_x=x, hf_y=log)\n", + "fig.add_trace(go.Scattergl(name=\"exp\", legend='legend1'), hf_x=x, hf_y=exp)\n", + "\n", + "fig.add_trace(go.Scattergl(name=\"-log\", legend='legend2'), hf_x=x, hf_y=-exp, row=1, col=2)\n", + "\n", + "fig.add_trace(go.Scattergl(name=\"log\", legend='legend3'), hf_x=x, hf_y=-log, row=2, col=1)\n", + "fig.add_trace(go.Scattergl(name=\"3-exp\", legend='legend3'), hf_x=x, hf_y=3 - exp, row=2, col=1)\n", + "\n", + "fig.add_trace(go.Scattergl(name=\"log\", legend='legend4'), hf_x=x, hf_y=log**2, row=2, col=2)\n", + "\n", + "# fmt: on\n", + "fig.update_layout(\n", + " # NOTE: we can specify how each legend is positioned\n", + " # (i.e., above the corresponding subplot)\n", + " legend1=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02),\n", + " legend2=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, x=0.52),\n", + " legend3=dict(orientation=\"h\", y=0.51, x=0),\n", + " legend4=dict(orientation=\"h\", y=0.51, x=0.52),\n", + ")\n", + "fig.update_layout(margin=dict(b=15), template=\"plotly_white\")\n", + "fig.show_dash(mode=\"inline\", port=8004)\n" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -1019,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "499ac9f3", "metadata": {}, "outputs": [ @@ -1030,28 +1038,22 @@ " 'default_n_samples': True,\n", " 'name': 'noisy_sine',\n", " 'axis_type': 'linear',\n", - " 'downsampler': ,\n", + " 'downsampler': ,\n", " 'default_downsampler': True,\n", - " 'gap_handler': ,\n", + " 'gap_handler': ,\n", " 'default_gap_handler': True,\n", " 'x': RangeIndex(start=0, stop=2000000, step=1),\n", - " 'y': array([0.00000000e+00, 6.17457477e-06, 1.22029567e-05, ...,\n", - " 1.53657590e+01, 1.50289450e+01, 1.54797793e+01]),\n", + " 'y': array([0.00000000e+00, 6.29341095e-06, 1.17529597e-05, ...,\n", + " 1.52508808e+01, 1.54912212e+01, 1.53639634e+01]),\n", " 'text': None,\n", - " 'hovertext': None}]" + " 'hovertext': None,\n", + " 'marker_size': None,\n", + " 'marker_color': None}]" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dash is running on http://127.0.0.1:8050/\n", - "\n" - ] - }, { "data": { "text/html": [ @@ -1067,7 +1069,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1093,12 +1095,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "94127fae", "metadata": {}, "outputs": [], "source": [ - "fig.hf_data[0][\"y\"] = -10 * noisy_sine\n", + "fig.hf_data[0][\"y\"] = 10 * noisy_sine\n", "# make sure to interact win the figure to see the change" ] }, @@ -1524,7 +1526,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 4, "id": "d02edc70", "metadata": {}, "outputs": [ @@ -1651,7 +1653,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "id": "7384c081-a733-41e5-a80c-99b7b31d0520", "metadata": {}, "outputs": [], @@ -1668,83 +1670,70 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, + "id": "03201872", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2021-09-29 10:04:44+02:00', '2021-09-29 10:05:14+02:00',\n", + " '2021-09-29 10:05:44+02:00', '2021-09-29 10:06:14+02:00',\n", + " '2021-09-29 10:06:44+02:00', '2021-09-29 10:07:14+02:00',\n", + " '2021-09-29 10:07:44+02:00', '2021-09-29 10:08:14+02:00',\n", + " '2021-09-29 10:08:44+02:00', '2021-09-29 10:09:14+02:00',\n", + " ...\n", + " '2021-11-18 19:20:34+01:00', '2021-11-18 19:21:00+01:00',\n", + " '2021-11-18 19:21:04+01:00', '2021-11-18 19:21:34+01:00',\n", + " '2021-11-18 19:22:00+01:00', '2021-11-18 19:22:04+01:00',\n", + " '2021-11-18 19:22:34+01:00', '2021-11-18 19:23:00+01:00',\n", + " '2021-11-18 19:23:04+01:00', '2021-11-18 19:23:34+01:00'],\n", + " dtype='datetime64[ns, Europe/Brussels]', name='timestamp', length=194046, freq=None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_gusb.index" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "c1f1d9a2-63a1-484e-a346-ae6b04c997b6", "metadata": { "tags": [] }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonas/git/github/plotly-resampler/plotly_resampler/figure_resampler/figure_resampler.py:477: UserWarning:\n", + "\n", + "'jupyter_dash' is not installed. 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -2521,7 +2510,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.12" }, "toc-autonumbering": true, "vscode": { diff --git a/examples/dash_apps/04_minimal_cache_overview.py b/examples/dash_apps/04_minimal_cache_overview.py new file mode 100644 index 00000000..99239690 --- /dev/null +++ b/examples/dash_apps/04_minimal_cache_overview.py @@ -0,0 +1,122 @@ +"""Minimal dash app example. + +Click on a button, and see a plotly-resampler graph of two sinusoids. +In addition, another graph is shown, which is an overview of the main graph. +This other graph is bidirectionally linked to the main graph; when you select a region +in the overview graph, the main graph will zoom in on that region and vice versa. + +This example uses the dash-extensions its ServersideOutput functionality to cache +the FigureResampler per user/session on the server side. This way, no global figure +variable is used and shows the best practice of using plotly-resampler within dash-apps. + +""" + +import numpy as np +import plotly.graph_objects as go +import dash +from dash import Input, Output, State, callback_context, dcc, html, no_update +from dash_extensions.enrich import DashProxy, Serverside, ServersideOutputTransform +from trace_updater import TraceUpdater + +# The overview figure requires clientside callbacks, whose JavaScript code is located +# in the assets folder. We need to tell dash where to find this folder. +from plotly_resampler import FigureResampler, ASSETS_FOLDER + +# -------------------------------- Data and constants --------------------------------- +# Data that will be used for the plotly-resampler figures +x = np.arange(2_000_000) +noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000 + +# The ids of the components used in the app (we put them here to avoid typos) +GRAPH_ID = "graph-id" +OVERVIEW_GRAPH_ID = "overview-graph" +STORE_ID = "store" +TRACEUPDATER_ID = "traceupdater" + + +# --------------------------------------Globals --------------------------------------- +# Remark how the assests folder is passed to the Dash(proxy) application +app = DashProxy( + __name__, transforms=[ServersideOutputTransform()], assets_folder=ASSETS_FOLDER +) + +app.layout = html.Div( + [ + html.H1("plotly-resampler + dash-extensions", style={"textAlign": "center"}), + html.Button("plot chart", id="plot-button", n_clicks=0), + html.Hr(), + # The graph and its needed components to serialize and update efficiently + # Note: we also add a dcc.Store component, which will be used to link the + # server side cached FigureResampler object + dcc.Graph(id=GRAPH_ID), + dcc.Graph(id=OVERVIEW_GRAPH_ID), + dcc.Loading(dcc.Store(id=STORE_ID)), + TraceUpdater(id=TRACEUPDATER_ID, gdID=GRAPH_ID), + ] +) + + +# ------------------------------------ DASH logic ------------------------------------- +# --- construct and store the FigureResampler on the serverside --- +@app.callback( + [ + Output(GRAPH_ID, "figure"), + Output(OVERVIEW_GRAPH_ID, "figure"), + Output(STORE_ID, "data"), + ], + Input("plot-button", "n_clicks"), + prevent_initial_call=True, +) +def plot_graph(_): + global app + ctx = callback_context + if len(ctx.triggered) and "plot-button" in ctx.triggered[0]["prop_id"]: + fig: FigureResampler = FigureResampler(create_overview=True) + + # Figure construction logic + fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=noisy_sin * 0.9999995**x) + fig.add_trace(go.Scattergl(name="exp"), hf_x=x, hf_y=noisy_sin * 1.000002**x) + + fig.update_layout(legend=dict(orientation="h", yanchor="bottom", y=1.02)) + fig.update_layout(margin=dict(b=10), template="plotly_white") + + coarse_fig = fig._create_overview_figure() + return fig, coarse_fig, Serverside(fig) + else: + return no_update + + +# --- Clientside callbacks used to bidirectionally link the overview and main graph --- +app.clientside_callback( + dash.ClientsideFunction(namespace="clientside", function_name="main_to_coarse"), + dash.Output(OVERVIEW_GRAPH_ID, "id", allow_duplicate=True), + dash.Input(GRAPH_ID, "relayoutData"), + [dash.State(OVERVIEW_GRAPH_ID, "id"), dash.State(GRAPH_ID, "id")], + prevent_initial_call=True, +) + +app.clientside_callback( + dash.ClientsideFunction(namespace="clientside", function_name="coarse_to_main"), + dash.Output(GRAPH_ID, "id", allow_duplicate=True), + dash.Input(OVERVIEW_GRAPH_ID, "selectedData"), + [dash.State(GRAPH_ID, "id"), dash.State(OVERVIEW_GRAPH_ID, "id")], + prevent_initial_call=True, +) + + +# --- FigureResampler update logic --- +@app.callback( + Output(TRACEUPDATER_ID, "updateData"), + Input(GRAPH_ID, "relayoutData"), + State(STORE_ID, "data"), # The server side cached FigureResampler per session + prevent_initial_call=True, +) +def update_fig(relayoutdata, fig): + if fig is None: + return no_update + return fig.construct_update_data(relayoutdata) + + +# --------------------------------- Running the app --------------------------------- +if __name__ == "__main__": + app.run_server(debug=False, port=9023, use_reloader=False) diff --git a/examples/dash_apps/05_cache_overview_subplots.py b/examples/dash_apps/05_cache_overview_subplots.py new file mode 100644 index 00000000..49763f04 --- /dev/null +++ b/examples/dash_apps/05_cache_overview_subplots.py @@ -0,0 +1,157 @@ +"""Minimal dash app example. + +Click on a button, and see a plotly-resampler graph of an exponential and log curve +(and combinations thereof) spread over 4 subplots. +In addition, another graph is shown below, which is an overview of subplot columns from +the main graph. This other graph is bidirectionally linked to the main graph; when you +select a region in the overview graph, the main graph will zoom in on that region and +vice versa. + +This example uses the dash-extensions its ServersideOutput functionality to cache +the FigureResampler per user/session on the server side. This way, no global figure +variable is used and shows the best practice of using plotly-resampler within dash-apps. + +""" + +import dash +import numpy as np +import plotly.graph_objects as go +from dash import Input, Output, State, callback_context, dcc, html, no_update +from dash_extensions.enrich import DashProxy, Serverside, ServersideOutputTransform +from plotly.subplots import make_subplots +from trace_updater import TraceUpdater + +# The overview figure requires clientside callbacks, whose JavaScript code is located +# in the assets folder. We need to tell dash where to find this folder. +from plotly_resampler import ASSETS_FOLDER, FigureResampler +from plotly_resampler.aggregation import MinMaxLTTB + +# -------------------------------- Data and constants --------------------------------- +# Data that will be used for the plotly-resampler figures +x = np.arange(2_000_000) +noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000 + +# The ids of the components used in the app (we put them here to avoid typos) +GRAPH_ID = "graph-id" +OVERVIEW_GRAPH_ID = "overview-graph" +STORE_ID = "store" +TRACEUPDATER_ID = "traceupdater" + + +# --------------------------------------Globals --------------------------------------- +# Remark how the assests folder is passed to the Dash(proxy) application +app = DashProxy( + __name__, transforms=[ServersideOutputTransform()], assets_folder=ASSETS_FOLDER +) + +app.layout = html.Div( + [ + html.H1("plotly-resampler + dash-extensions", style={"textAlign": "center"}), + html.Button("plot chart", id="plot-button", n_clicks=0), + html.Hr(), + # The graph and its needed components to serialize and update efficiently + # Note: we also add a dcc.Store component, which will be used to link the + # server side cached FigureResampler object + dcc.Graph(id=GRAPH_ID), + dcc.Graph(id=OVERVIEW_GRAPH_ID), + dcc.Loading(dcc.Store(id=STORE_ID)), + TraceUpdater(id=TRACEUPDATER_ID, gdID=GRAPH_ID), + ] +) + + +# ------------------------------------ DASH logic ------------------------------------- +# --- construct and store the FigureResampler on the serverside --- +@app.callback( + [ + Output(GRAPH_ID, "figure"), + Output(OVERVIEW_GRAPH_ID, "figure"), + Output(STORE_ID, "data"), + ], + Input("plot-button", "n_clicks"), + prevent_initial_call=True, +) +def plot_graph(_): + global app + ctx = callback_context + if len(ctx.triggered) and "plot-button" in ctx.triggered[0]["prop_id"]: + # NOTE: remark how the `overview_row_idxs` argument specifies the row indices + # (start at 0) of the subplots that will be used to construct the overview + # graph. In this list the position of the values indicate the column index of + # the subplot. In this case, the overview graph will show for the first column + # the second subplot row (1), and for the second column the first subplot row + # (0). + fig: FigureResampler = FigureResampler( + make_subplots( + rows=2, cols=2, shared_xaxes="columns", horizontal_spacing=0.03 + ), + create_overview=True, + overview_row_idxs=[1, 0], + default_downsampler=MinMaxLTTB(parallel=True), + ) + + # Figure construction logic + # fmt: off + log = noisy_sin * 0.9999995**x + exp = noisy_sin * 1.000002**x + fig.add_trace(go.Scattergl(name="log", legend='legend1'), hf_x=x, hf_y=log) + fig.add_trace(go.Scattergl(name="exp", legend='legend1'), hf_x=x, hf_y=exp) + + fig.add_trace(go.Scattergl(name="-log", legend='legend2'), hf_x=x, hf_y=-exp, row=1, col=2) + + fig.add_trace(go.Scattergl(name="log", legend='legend3'), hf_x=x, hf_y=-log, row=2, col=1) + fig.add_trace(go.Scattergl(name="3-exp", legend='legend3'), hf_x=x, hf_y=3 - exp, row=2, col=1) + + fig.add_trace(go.Scattergl(name="log", legend='legend4'), hf_x=x, hf_y=log**2, row=2, col=2) + + # fmt: on + fig.update_layout( + legend1=dict(orientation="h", yanchor="bottom", y=1.02), + legend2=dict(orientation="h", yanchor="bottom", y=1.02, x=0.52), + legend3=dict(orientation="h", y=0.51, x=0), + legend4=dict(orientation="h", y=0.51, x=0.52), + ) + fig.update_layout(margin=dict(b=10), template="plotly_white") + + coarse_fig = fig._create_overview_figure() + return fig, coarse_fig, Serverside(fig) + else: + return no_update + + +# --- Clientside callbacks used to bidirectionally link the overview and main graph --- +app.clientside_callback( + dash.ClientsideFunction(namespace="clientside", function_name="main_to_coarse"), + dash.Output( + OVERVIEW_GRAPH_ID, "id", allow_duplicate=True + ), # TODO -> look for clean output + dash.Input(GRAPH_ID, "relayoutData"), + [dash.State(OVERVIEW_GRAPH_ID, "id"), dash.State(GRAPH_ID, "id")], + prevent_initial_call=True, +) + +app.clientside_callback( + dash.ClientsideFunction(namespace="clientside", function_name="coarse_to_main"), + dash.Output(GRAPH_ID, "id", allow_duplicate=True), + dash.Input(OVERVIEW_GRAPH_ID, "selectedData"), + [dash.State(GRAPH_ID, "id"), dash.State(OVERVIEW_GRAPH_ID, "id")], + prevent_initial_call=True, +) + + +# --- FigureResampler update logic --- +@app.callback( + Output(TRACEUPDATER_ID, "updateData"), + Input(GRAPH_ID, "relayoutData"), + State(STORE_ID, "data"), # The server side cached FigureResampler per session + prevent_initial_call=True, +) +def update_fig(relayoutdata, fig): + if fig is None: + return no_update + return fig.construct_update_data(relayoutdata) + + +# --------------------------------- Running the app --------------------------------- +if __name__ == "__main__": + app.run_server(debug=True, port=9023, use_reloader=False) diff --git a/mkdocs/getting_started.md b/mkdocs/getting_started.md index 9485cda3..abb74b2e 100644 --- a/mkdocs/getting_started.md +++ b/mkdocs/getting_started.md @@ -107,6 +107,18 @@ fig.add_trace(go.Scattergl(name='noisy sine', showlegend=True), hf_x=x, hf_y=sin fig.show_dash(mode='inline') ``` +### Overview + +In the example below, we demonstrate the (x-axis)`overview` feature of plotly-ressampler. +For more information you can check out the [examples](https://github.com/predict-idlab/plotly-resampler/tree/main/examples) to find dash apps and in-notebook use-cases. + +> **Note**: +> * This overview is only available for the `FigureResampler` and not for the `FigureWidgetResampler`. +> * This is a rather new, experimental feature and may not work as expected. So please report any issue you encounter! + + +![FigureResampler overview](static/basic_example_overview.gif) + ### FigureWidget The gif below demonstrates the example usage of [`FigureWidgetResampler`][figure_resampler.FigureWidgetResampler], diff --git a/mkdocs/static/basic_example_overview.gif b/mkdocs/static/basic_example_overview.gif new file mode 100644 index 00000000..6eebb58e Binary files /dev/null and b/mkdocs/static/basic_example_overview.gif differ diff --git a/plotly_resampler/__init__.py b/plotly_resampler/__init__.py index 946eb8ac..8955ef00 100644 --- a/plotly_resampler/__init__.py +++ b/plotly_resampler/__init__.py @@ -3,7 +3,7 @@ import contextlib from .aggregation import LTTB, EveryNthPoint, MinMaxLTTB -from .figure_resampler import FigureResampler, FigureWidgetResampler +from .figure_resampler import ASSETS_FOLDER, FigureResampler, FigureWidgetResampler from .registering import register_plotly_resampler, unregister_plotly_resampler __docformat__ = "numpy" @@ -14,6 +14,7 @@ "__version__", "FigureResampler", "FigureWidgetResampler", + "ASSETS_FOLDER", "MinMaxLTTB", "LTTB", "EveryNthPoint", diff --git a/plotly_resampler/figure_resampler/__init__.py b/plotly_resampler/figure_resampler/__init__.py index 62202ec4..2b1e32db 100644 --- a/plotly_resampler/figure_resampler/__init__.py +++ b/plotly_resampler/figure_resampler/__init__.py @@ -10,10 +10,11 @@ """ -from .figure_resampler import FigureResampler +from .figure_resampler import ASSETS_FOLDER, FigureResampler from .figurewidget_resampler import FigureWidgetResampler __all__ = [ "FigureResampler", + "ASSETS_FOLDER", "FigureWidgetResampler", ] diff --git a/plotly_resampler/figure_resampler/assets/coarse_fine.js b/plotly_resampler/figure_resampler/assets/coarse_fine.js new file mode 100644 index 00000000..3d850f3b --- /dev/null +++ b/plotly_resampler/figure_resampler/assets/coarse_fine.js @@ -0,0 +1,243 @@ +function getGraphDiv(gdID) { + let graphDiv = document?.querySelectorAll('div[id*="' + gdID + '"][class*="dash-graph"]'); + graphDiv = graphDiv?.[0]?.getElementsByClassName("js-plotly-plot")?.[0]; + if (!_.isElement(graphDiv)) { + throw new Error(`Invalid gdID '${gdID}'`); + } + return graphDiv; +} + +/** + * + * @param {object} data The data of the graphDiv + * @returns {Array} An array containing all the unique axis keys of the graphDiv data + * [{x: x[ID], y: y[ID]}, {x: x[ID], y: y[ID]}] + */ +const getXYAxisKeys = (data) => { + return _.chain(data) + .map((obj) => ({ x: obj.xaxis || "x", y: obj.yaxis || "y" })) + .uniqWith(_.isEqual) + .value(); +}; + +const getAnchorT = (keys, anchor) => { + const obj_index = anchor.slice(0, 1); + const anchorT = _.chain(keys) + .filter((obj) => obj[obj_index] == anchor) + .value()[0][{ x: "y", y: "x" }[obj_index]]; + + return anchorT; +}; + +/** + * Get the corresponding axis name of the anchors + * + * @param {object} layout the layout of the graphDiv + * @returns {object} An object containing the anchor and its orthogonal axis name e.g. + * {x[ID]: yaxis[ID], y[ID]: xaxis[ID]} + */ +const getLayoutAxisAnchors = (layout) => { + var layout_axis_anchors = Object.assign( + {}, + ..._.chain(layout) + .map((value, key) => { + if (key.includes("axis")) return { [value.anchor]: key }; + }) + .without(undefined) + .value() + ); + // Edge case for non "make_subplot" figures; i.e. figures constructed with + // go.Figure + if (_.size(layout_axis_anchors) == 1 && _.has(layout_axis_anchors, undefined)) { + return { x: "yaxis", y: "xaxis" }; + } + return layout_axis_anchors; +}; + +/** + * Compare the equality of two arrays with a certain decimal point presiction + * @param {*} objValueArr An array with numeric values + * @param {*} othValueArr An array with numeray values + * @returns {boolean} true when all values are equal (to 5 decimal points) + */ +function rangeCustomizer(objValueArr, othValueArr) { + return _.every( + _.zipWith(objValueArr, othValueArr, (objValue, othValue) => { + if (_.isNumber(objValue) && _.isNumber(othValue)) { + objValue = _.round(objValue, 5); + othValue = _.round(othValue, 5); + return objValue === othValue; + } else { + alert(`not a number ${objValue} type:${typeof objValue} | ${othValue} type:${typeof othValue}`); + } + }) + ); +} + +window.dash_clientside = Object.assign({}, window.dash_clientside, { + clientside: { + coarse_to_main: function (selectedData, mainFigID, coarseFigID) { + // Base case + if (!selectedData.range) { + return mainFigID; + } + + main_graphDiv = getGraphDiv(mainFigID); + coarse_graphDiv = getGraphDiv(coarseFigID); + + const coarse_xy_axiskeys = getXYAxisKeys(coarse_graphDiv.data); + const main_xy_axiskeys = getXYAxisKeys(main_graphDiv.data); + const layout_axis_anchors = getLayoutAxisAnchors(main_graphDiv.layout); + + // Use the maingraphDiv its layout to obtain a list of a list of all shared (x)axis names + // in practice, these are the xaxis names that are linked to each other (i.e. the inner list is the + // xaxis names of the subplot columns) + // e.g.: [ [xaxis1, xaxis2], [xaxis3, xaxis4] ] + let shared_axes_list = _.chain(main_graphDiv.layout) + .map((value, key) => { + if (value.matches) return { anchor: value.matches, match: [key] }; + }) + .without(undefined) + // groupby same anchor and concat the match arrays + .groupBy("anchor") + .map( + _.spread((...values) => { + return _.mergeWith(...values, (objValue, srcValue) => { + if (_.isArray(objValue)) return objValue.concat(srcValue); + }); + }) + ) + // add the axis string to the match array and return the match array + .map((m_obj) => { + const anchorT = getAnchorT(main_xy_axiskeys, m_obj.anchor); + let axis_str = layout_axis_anchors[anchorT]; + m_obj.match.push(axis_str); + return m_obj.match; + }) + .value(); + // console.log("shared axes list", shared_axes_list); + + const relayout = {}; + + // Quick inline function to set the relayout range values + const setRelayoutRangeValues = (axisStr, values) => { + for (let rangeIdx = 0; rangeIdx < 2; rangeIdx++) { + relayout[axisStr + `.range[${rangeIdx}]`] = values[rangeIdx]; + } + }; + + // iterate over the selected data range + console.log('selected data range', selectedData.range); + for (const anchor_key in selectedData.range) { + const selected_range = selectedData.range[anchor_key]; + // Obtain the anchor key of the orthogonal axis (x or y), based on the coarse graphdiv anchor pairs + const anchorT = getAnchorT(coarse_xy_axiskeys, anchor_key); + const axisStr = layout_axis_anchors[anchorT]; + const mainLayoutRange = main_graphDiv.layout[axisStr].range; + const coarseFigRange = coarse_graphDiv.layout[axisStr].range; + + if (!_.isEqual(selected_range, mainLayoutRange)) { + const shared_axis_match = _.chain(shared_axes_list) + .filter((arr) => arr.includes(axisStr)) + .value()[0]; + if (axisStr.includes("yaxis") && _.isEqualWith(selected_range, coarseFigRange, rangeCustomizer)) { + continue; + } + + if (shared_axis_match) { + shared_axis_match.forEach((axisMStr) => { + setRelayoutRangeValues(axisMStr, selected_range); + }); + } else { + setRelayoutRangeValues(axisStr, selected_range); + } + } + } + + Object.keys(relayout).length > 0 ? Plotly.relayout(main_graphDiv, relayout) : null; + return mainFigID; + }, + main_to_coarse: function (mainRelayout, coarseFigID, mainFigID) { + const coarse_graphDiv = getGraphDiv(coarseFigID); + const main_graphDiv = getGraphDiv(mainFigID); + + const coarse_xy_axiskeys = getXYAxisKeys(coarse_graphDiv.data); + const layout_axis_anchors = getLayoutAxisAnchors(coarse_graphDiv.layout); + + const currentSelections = coarse_graphDiv.layout.selections; + const update = { selections: currentSelections || [] }; + + const getUpdateObj = (xy_pair, x_range, y_range) => { + return { + type: "rect", + xref: xy_pair.x, + yref: xy_pair.y, + line: { width: 1, color: "#352F44", dash: "solid" }, + x0: x_range[0], + x1: x_range[1], + y0: y_range[0], + y1: y_range[1], + }; + }; + + // Base case; no selections yet on the coarse graph + if (!currentSelections) { + // if current selections is None + coarse_xy_axiskeys.forEach((xy_pair) => { + console.log("xy pair", xy_pair); + const x_axis_key = _.has(layout_axis_anchors, xy_pair.y) ? layout_axis_anchors[xy_pair.y] : "xaxis"; + const y_axis_key = _.has(layout_axis_anchors, xy_pair.x) ? layout_axis_anchors[xy_pair.x] : "yaxis"; + // console.log('xaxis key', x_axis_key, main_graphDiv.layout[x_axis_key]); + const x_range = main_graphDiv.layout[x_axis_key].range; + const y_range = main_graphDiv.layout[y_axis_key].range; + + update["selections"].push(getUpdateObj(xy_pair, x_range, y_range)); + }); + Plotly.relayout(coarse_graphDiv, update); + return coarseFigID; + } + + // Alter the selections based on the relayout + let performed_update = false; + + for (let i = 0; i < coarse_xy_axiskeys.length; i++) { + const xy_pair = coarse_xy_axiskeys[i]; + // If else handles the edge case of a figure without subplots + const x_axis_key = _.has(layout_axis_anchors, xy_pair.y) ? layout_axis_anchors[xy_pair.y] : "xaxis"; + const y_axis_key = _.has(layout_axis_anchors, xy_pair.x) ? layout_axis_anchors[xy_pair.x] : "yaxis"; + // console.log('xaxis key', x_axis_key, main_graphDiv.layout[x_axis_key]); + + let x_range = main_graphDiv.layout[x_axis_key].range; + let y_range = main_graphDiv.layout[y_axis_key].range; + // If the y-axis autorange is true, we alter the y-range to the coarse graphdiv its y-range + // console.log('mainrelayout', mainRelayout); + if (main_graphDiv.layout[y_axis_key]["autorange"] === true) { + y_range = coarse_graphDiv.layout[y_axis_key].range; + } + if ( + mainRelayout[x_axis_key + ".autorange"] === true && + mainRelayout[y_axis_key + ".autorange"] === true + ) { + performed_update = true; + if ( + // mainRelayout[x_axis_key + ".showspikes"] === false && + // mainRelayout[y_axis_key + ".showspikes"] === false + // NOTE: for some reason, showspikes info is only availabel for the xaxis & yaxis keys + mainRelayout["xaxis.showspikes"] === false && + mainRelayout["yaxis.showspikes"] === false + ) { + // reset axis -> we use the coarse graphDiv layout + x_range = coarse_graphDiv.layout[x_axis_key].range; + } + } else if (mainRelayout[x_axis_key + ".range[0]"] || mainRelayout[y_axis_key + ".range[0]"]) { + // a specific range is set + performed_update = true; + } + + update["selections"][i] = getUpdateObj(xy_pair, x_range, y_range); + } + performed_update ? Plotly.relayout(coarse_graphDiv, update) : null; + return coarseFigID; + }, + }, +}); diff --git a/plotly_resampler/figure_resampler/figure_resampler.py b/plotly_resampler/figure_resampler/figure_resampler.py index b9beff5f..da06318e 100644 --- a/plotly_resampler/figure_resampler/figure_resampler.py +++ b/plotly_resampler/figure_resampler/figure_resampler.py @@ -10,8 +10,10 @@ __author__ = "Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost" +import os import warnings -from typing import List, Tuple +from pathlib import Path +from typing import List, Optional, Tuple import dash import plotly.graph_objects as go @@ -34,6 +36,15 @@ 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 = { + "showlegend": False, + "height": 120, + "activeselection": dict(fillcolor="#96C291", opacity=0.3), + "margin": {"t": 0, "b": 0}, +} + class FigureResampler(AbstractFigureAggregator, go.Figure): """Data aggregation functionality for ``go.Figures``.""" @@ -51,6 +62,9 @@ def __init__( ), show_mean_aggregation_size: bool = True, convert_traces_kwargs: dict | None = None, + create_overview: bool = False, + overview_row_idxs: list = None, + overview_kwargs: dict = {}, verbose: bool = False, show_dash_kwargs: dict | None = None, ): @@ -105,6 +119,29 @@ def __init__( !!! note This argument is only used when the passed ``figure`` contains data and ``convert_existing_traces`` is set to True. + create_overview: bool, optional + Whether an overview will be added to the figure (also known as rangeslider), + by default False. An overview is a bidirectionally linked figure that is + placed below the FigureResampler figure and shows a coarse version on which + the current view of the FigureResampler figure is highlighted. The overview + can be used to quickly navigate through the data by dragging the selection + box. + !!! note + - In the case of subplots, the overview will be created for each subplot + column. Only a single subplot row can be captured in the overview, + this is by default the first row. If you want to customize this + behavior, you can use the `overview_row_idxs` argument. + - This functionality is not yet extensively validated. Please report any + issues you encounter on GitHub. + overview_row_idxs: list, optional + A list of integers corresponding to the row indices (START AT 0) of the + subplots columns that should be linked with the column its corresponding + overview. By default None, which will result in the first row being utilized + for each column. + overview_kwargs: dict, optional + A dict of kwargs that will be passed to the `update_layout` method of the + overview figure, by default {}, which will result in utilizing the + [`default`][_DEFAULT_OVERVIEW_LAYOUT_KWARGS] overview layout kwargs. verbose: bool, optional Whether some verbose messages will be printed or not, by default False. show_dash_kwargs: dict, optional @@ -188,6 +225,17 @@ def __init__( for idx in update_indices: self.data[idx].update(graph_dict["data"][idx]) + self._create_overview = create_overview + # update the overview layout + overview_layout_kwargs = _DEFAULT_OVERVIEW_LAYOUT_KWARGS.copy() + overview_layout_kwargs.update(overview_kwargs) + self._overview_layout_kwargs = overview_layout_kwargs + + # array representing the row indices per column (START AT 0) of the subplot + # that should be linked with the columns corresponding overview. + # By default, the first row (i.e. index 0) will be utilized for each column + self._overview_row_idxs = self._parse_subplot_row_indices(overview_row_idxs) + # The FigureResampler needs a dash app self._app: dash.Dash | None = None self._port: int | None = None @@ -196,6 +244,225 @@ def __init__( # (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. + + Returns + ------- + Tuple[int, int] + The number of rows and columns of the figure's grid, respectively. + """ + if self._grid_ref is None: # case: go.Figure (no subplots) + return (1, 1) + # TODO: not 100% sure whether this is correct + return (len(self._grid_ref), len(self._grid_ref[0])) + + def _parse_subplot_row_indices(self, row_indices: list = None) -> List[int]: + """Verify whether the passed row indices are valid. + + Parameters + ---------- + row_indices: list, optional + A list of integers representing the row indices for which the overview + should be created. The length of the list should be equal to the number of + columns of the figure. Each element of the list should be smaller than the + number of rows of the figure (thus note that the row indices start at 0). By + default None, which will result in the first row being utilized for each + column. + !!! note + When you do not want to use an overview of a certain column (because + a certain subplot spans more than 1 column), you can specify this by + setting that respecive row_index value to `None`. + + For instance, the sbuplot on row 2, col 1 spans two coloms. So when you + intend to utilize that subplot within the overview, you want to specify + the row_indices as: `[1, None, ...]` + + Returns + ------- + List[int] + A list of integers representing the row indices per subplot column. + + """ + n_rows, n_cols = self._get_subplot_rows_and_cols_from_grid() + + # By default, the first row is utilized to set the row indices + if row_indices is None: + return [0] * n_cols + + # perform some checks on the row indices + assert isinstance(row_indices, list), "row indices must be a list" + assert ( + len(row_indices) == n_cols + ), "the number of row indices must be equal to the number of columns" + assert all( + [(li is None) or (0 <= li < n_rows) for li in row_indices] + ), "row indices must be smaller than the number of rows" + + return row_indices + + # determines which subplot data to take from main and put into coarse + def _remove_other_axes_for_coarse(self) -> go.Figure: + # base case: no rows and cols to filter + if self._grid_ref is None: # case: go.Figure (no subplots) + return self + + # Create the grid specification for the overview figure (in `reduced_grid_ref`) + # The trace_list and the 2 axis lists are 1D arrays holding track of the traces + # and axes to track. + reduced_grid_ref = [[]] + + # Store the xaxis keys (e.g., x2) of the traces to keep + trace_list = [] + # Store the xaxis and yaxis layout keys of the traces to keep (e.g., xaxis2) + layout_xaxis_list, layout_yaxis_list = [], [] + for col_idx, row_idx in enumerate(self._overview_row_idxs): + if row_idx is None: # skip None value + continue + + overview_grid_ref = self._grid_ref[row_idx][col_idx] + reduced_grid_ref[0].append(overview_grid_ref) # [0] bc 1 row in overview + for subplot in overview_grid_ref: + trace_list.append(subplot.trace_kwargs["xaxis"]) + + # store the layout keys so that we can retain the exact layout + xaxis_key, yaxis_key = subplot.layout_keys + layout_yaxis_list.append(yaxis_key) + layout_xaxis_list.append(xaxis_key) + # print("layout_list", l_xaxis_list, l_yaxis_list) + # print("trace_list", trace_list) + + fig_dict = self._get_current_graph() # a copy of the current graph + + # copy the data from the relevant overview subplots + reduced_fig_dict = { + "data": [], + "layout": {"template": fig_dict["layout"]["template"]}, + } + # NOTE: we enumerate over the data of the full figure so that we can utilize the + # trace index to mimic the colorway. + for i, trace in enumerate(fig_dict["data"]): + # NOTE: the interplay between line_color and marker_color seems to work in + # this implementation - a more thorough investigation might be needed + if trace.get("xaxis", "x") in trace_list: + if "line" not in trace: + trace["line"] = {} + # Ensure that the same color is utilized + trace["line"]["color"] = ( + self._layout_obj.template.layout.colorway[i] + if self.data[i].line.color is None + else self.data[i].line.color + ) + # add the trace to the reduced figure + reduced_fig_dict["data"].append(trace) + + # Add the relevant layout keys to the reduced figure + for k, v in fig_dict["layout"].items(): + if k in layout_xaxis_list: + reduced_fig_dict["layout"][k] = v + elif k in layout_yaxis_list: + v = v.copy() + # set the domain to [0, 1] to ensure that the overview figure has the + # global y-axis range + v.update({"domain": [0, 1]}) + reduced_fig_dict["layout"][k] = v + + # Create a figure object using the reduced figure dict + reduced_fig = go.Figure(layout=reduced_fig_dict["layout"]) + reduced_fig._grid_ref = reduced_grid_ref + # Ensure that the trace uid is not adjusted, this must be set prior to adding + # the trace data. Otherwise, data aggregation will not work. + reduced_fig._data_validator.set_uid = False + reduced_fig.add_traces(reduced_fig_dict["data"]) + return reduced_fig + + def _create_overview_figure(self) -> go.Figure: + # create a new coarse fig + reduced_fig = self._remove_other_axes_for_coarse() + + # Resample the coarse figure using 3x the default aggregation size to ensure + # that it contains sufficient details + coarse_fig_hf = FigureResampler( + reduced_fig, + default_n_shown_samples=3 * self._global_n_shown_samples, + ) + + # NOTE: this way we can alter props without altering the original hf data + # NOTE: this also copies the default aggregation functionality to the coarse figure + coarse_fig_hf._hf_data = {uid: trc.copy() for uid, trc in self._hf_data.items()} + for trace in coarse_fig_hf.hf_data: + trace["max_n_samples"] *= 3 + + coarse_fig_dict = coarse_fig_hf._get_current_graph() + # add the 3x max_n_samples coarse figure data to the coarse_fig_dict + coarse_fig_hf._check_update_figure_dict(coarse_fig_dict) + del coarse_fig_hf + + coarse_fig = go.Figure(layout=coarse_fig_dict["layout"]) + coarse_fig._grid_ref = reduced_fig._grid_ref + coarse_fig._data_validator.set_uid = False + coarse_fig.add_traces(coarse_fig_dict["data"]) + + # height of the overview scales with the height of the dynamic view + coarse_fig.update_layout( + **self._overview_layout_kwargs, + hovermode=False, + clickmode="event+select", + dragmode="select", + ) + # Hide the grid + hide_kwrgs = dict( + showgrid=False, + showticklabels=False, + zeroline=False, + title_text=None, + mirror=True, + ticks="", + showline=False, + linecolor="black", + ) + coarse_fig.update_yaxes(**hide_kwrgs) + coarse_fig.update_xaxes(**hide_kwrgs) + + vrect_props = dict( + **dict(line_width=0, x0=0, x1=1), + **dict(fillcolor="lightblue", opacity=0.25, layer="above"), + ) + + if self._grid_ref is None: # case: go.Figure (no subplots) + # set the fixed range to True + coarse_fig["layout"]["xaxis"]["fixedrange"] = True + coarse_fig["layout"]["yaxis"]["fixedrange"] = True + + # add a shading to the overview + coarse_fig.add_vrect(xref="x domain", **vrect_props) + return coarse_fig + + col_idx_overview = 0 + for col_idx, row_idx in enumerate(self._overview_row_idxs): + if row_idx is None: # skip the None value + continue + + # we will only use the first grid-ref (as we will otherwise have multiple + # overlapping selection boxes) + for subplot in self._grid_ref[row_idx][col_idx][:1]: + xaxis_key, yaxis_key = subplot.layout_keys + + # set the fixed range to True + coarse_fig["layout"][xaxis_key]["fixedrange"] = True + coarse_fig["layout"][yaxis_key]["fixedrange"] = True + + # add a shading to the overview + coarse_fig.add_vrect( + col=col_idx_overview + 1, + xref=f"{subplot.trace_kwargs['xaxis']} domain", + **vrect_props, + ) + + col_idx_overview += 1 # only increase the index when not None + + return coarse_fig + def show_dash( self, mode=None, @@ -278,25 +545,32 @@ def show_dash( self.data[trace_idx].update(updated_trace) # 1. Construct the Dash app layout + app_init_kwargs = {} + if self._create_overview: + app_init_kwargs["assets_folder"] = os.path.relpath( + ASSETS_FOLDER, os.getcwd() + ) + 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") + app = JupyterDashPersistentInlineOutput("local_app", **app_init_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") + app = dash.Dash("local_app", **app_init_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") - app.layout = dash.html.Div( + app = dash.Dash("local_app", **app_init_kwargs) + + div = dash.html.Div( [ dash.dcc.Graph( id="resample-figure", figure=self, config=config, **graph_properties @@ -306,7 +580,26 @@ def show_dash( ), ] ) - self.register_update_graph_callback(app, "resample-figure", "trace-updater") + if self._create_overview: + overview_config = config.copy() if config is not None else {} + overview_config["displayModeBar"] = False + coarse_fig = self._create_overview_figure() + div.children += [ + dash.dcc.Graph( + id="overview-figure", + figure=coarse_fig, + config=overview_config, + **graph_properties, + ), + ] + app.layout = div + + self.register_update_graph_callback( + app, + "resample-figure", + "trace-updater", + "overview-figure" if self._create_overview else None, + ) height_param = "height" if self._is_persistent_inline else "jupyter_height" @@ -366,7 +659,11 @@ def stop_server(self, warn: bool = True): ) def register_update_graph_callback( - self, app: dash.Dash, graph_id: str, trace_updater_id: str + self, + app: dash.Dash, + graph_id: str, + trace_updater_id: str, + coarse_graph_id: Optional[str] = None, ): """Register the [`construct_update_data`][figure_resampler.figure_resampler_interface.AbstractFigureAggregator.construct_update_data] method as callback function to the passed dash-app. @@ -382,11 +679,39 @@ def register_update_graph_callback( The id of the ``TraceUpdater`` component. This component is leveraged by ``FigureResampler`` to efficiently POST the to-be-updated data to the front-end. + coarse_graph_id: str, optional + The id of the ``dcc.Graph``-component which withholds the coarse overview + Figure, by default None. """ + if coarse_graph_id is not None: + # update pr graph range with overview selection + app.clientside_callback( + dash.ClientsideFunction( + namespace="clientside", function_name="coarse_to_main" + ), + dash.Output(graph_id, "id", allow_duplicate=True), + dash.Input(coarse_graph_id, "selectedData"), + dash.State(graph_id, "id"), + dash.State(coarse_graph_id, "id"), + prevent_initial_call=True, + ) + + # update selectbox with clientside callback + app.clientside_callback( + dash.ClientsideFunction( + namespace="clientside", function_name="main_to_coarse" + ), + dash.Output(coarse_graph_id, "id", allow_duplicate=True), + dash.Input(graph_id, "relayoutData"), + dash.State(coarse_graph_id, "id"), + dash.State(graph_id, "id"), + prevent_initial_call=True, + ) + app.callback( - dash.dependencies.Output(trace_updater_id, "updateData"), - dash.dependencies.Input(graph_id, "relayoutData"), + dash.Output(trace_updater_id, "updateData"), + dash.Input(graph_id, "relayoutData"), prevent_initial_call=True, )(self.construct_update_data) diff --git a/tests/conftest.py b/tests/conftest.py index 0cb83543..ff5df099 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -81,7 +81,7 @@ def driver(): def float_series() -> pd.Series: x = np.arange(_nb_samples).astype(np.uint32) y = np.sin(x / 50).astype(np.float32) + np.random.randn(_nb_samples) / 5 - return pd.Series(index=x, data=y) + return pd.Series(index=x, data=y, name="float_series") @pytest.fixture @@ -91,17 +91,21 @@ def cat_series() -> pd.Series: cats_list[i] = "b" for i in np.random.randint(0, len(cats_list), 3): cats_list[i] = "c" - return pd.Series(cats_list * (_nb_samples // len(cats_list) + 1), dtype="category")[ - :_nb_samples - ] + return pd.Series( + cats_list * (_nb_samples // len(cats_list) + 1), + dtype="category", + name="cat_series", + )[:_nb_samples] @pytest.fixture def bool_series() -> pd.Series: bool_list = [True, False, True, True, True, True] + [True] * 1000 - return pd.Series(bool_list * (_nb_samples // len(bool_list) + 1), dtype="bool")[ - :_nb_samples - ] + return pd.Series( + bool_list * (_nb_samples // len(bool_list) + 1), + dtype="bool", + name="bool_series", + )[:_nb_samples] @pytest.fixture diff --git a/tests/test_rangeslider.py b/tests/test_rangeslider.py new file mode 100644 index 00000000..84e48f96 --- /dev/null +++ b/tests/test_rangeslider.py @@ -0,0 +1,143 @@ +"""Code which tests the overview functionality.""" + +__author__ = "Jonas Van Der Donckt" + +import numpy as np +import plotly.graph_objects as go +import pytest +from plotly.subplots import make_subplots +from pytest_lazyfixture import lazy_fixture as lf + +from plotly_resampler import FigureResampler +from plotly_resampler.aggregation import ( + EveryNthPoint, + MedDiffGapHandler, + MinMaxLTTB, + NoGapHandler, +) + + +@pytest.mark.parametrize("figure_class", [go.Figure, make_subplots]) +@pytest.mark.parametrize( + "series", [lf("float_series"), lf("cat_series"), lf("bool_series")] +) +def test_overview_figure_type(figure_class, series): + """Test the overview functionality (i.e., whether the overview figure can be + constructed)""" + # Create a figure with a scatter plot + fig = FigureResampler(figure_class(), create_overview=True) + fig.add_trace(go.Scatter(x=series.index, y=series)) + fig.add_trace({}, hf_x=series.index, hf_y=series) + + overview_fig = fig._create_overview_figure() + assert len(overview_fig["data"]) == 2 + # fig.write_image(f"test_{figure_class.__name__}_{series.name}.png") + + +@pytest.mark.parametrize("n_cols", [1, 2, 3]) +def test_valid_row_indices_subplots(n_cols): + fig = FigureResampler( + make_subplots(rows=3, cols=n_cols, shared_xaxes="columns"), + create_overview=True, + overview_row_idxs=None, + ) + fig._create_overview_figure() + # by default, the overview row indices should be the first row of each subplot col + assert fig._overview_row_idxs == [0] * n_cols + + # this should not crash + fig = FigureResampler( + make_subplots(rows=3, cols=n_cols, shared_xaxes="columns"), + create_overview=True, + overview_row_idxs=[np.random.randint(0, 2) for _ in range(n_cols)], + ) + fig._create_overview_figure() + + # By adding None values, we can skip certain subplot columns + row_idxs = [np.random.randint(0, 2) for _ in range(n_cols)] + for _ in range(np.random.randint(0, n_cols)): + row_idxs[np.random.randint(0, n_cols)] = None + # print(row_idxs) + fig = FigureResampler( + make_subplots(rows=3, cols=n_cols, shared_xaxes="columns"), + create_overview=True, + overview_row_idxs=row_idxs, + ) + fig._create_overview_figure() + + +@pytest.mark.parametrize("n_cols", [1, 2, 3]) +def test_invalid_row_indices_subplots(n_cols): + with pytest.raises(AssertionError): + FigureResampler( + make_subplots(rows=3, cols=n_cols, shared_xaxes="columns"), + create_overview=True, + # row index 3 is too high (starts at 0, so [0, 1, 2]) + overview_row_idxs=[3 for _ in range(n_cols)], + ) + + with pytest.raises(AssertionError): + FigureResampler( + make_subplots(rows=3, cols=n_cols, shared_xaxes="columns"), + create_overview=True, + # n_cols -1 causes the overview to have one subplot column less + overview_row_idxs=[0 for _ in range(n_cols - 1)], + ) + + +@pytest.mark.parametrize("overview_kwargs", [{"height": 80}]) +@pytest.mark.parametrize("series", [lf("float_series")]) +def test_overview_kwargs(overview_kwargs, series): + fig = FigureResampler( + go.Figure(), + create_overview=True, + overview_kwargs=overview_kwargs, + ) + fig.add_trace(go.Scatter(x=series.index, y=series)) + + overview_fig = fig._create_overview_figure() + for key, value in overview_kwargs.items(): + assert overview_fig.layout[key] == value + + +@pytest.mark.parametrize("figure_class", [go.Figure, make_subplots]) +@pytest.mark.parametrize( + "series", [lf("float_series"), lf("cat_series"), lf("bool_series")] +) +@pytest.mark.parametrize("default_n_samples", [500, 1000, 1500]) +def test_coarse_figure_aggregation(figure_class, series, default_n_samples): + """Test whether the coarse figure aggregation works as expected""" + # Create a figure with a scatter plot + fig = FigureResampler( + figure_class(), create_overview=True, default_n_shown_samples=default_n_samples + ) + fig.add_trace(go.Scatter(x=series.index, y=series)) + fig.add_trace({}, hf_x=series.index, hf_y=series) + + overview_fig = fig._create_overview_figure() + for trace in overview_fig.data: + assert len(trace.y) == 3 * default_n_samples + + +@pytest.mark.parametrize("aggregator", [MinMaxLTTB, EveryNthPoint]) +def test_overview_figure_gap_handler_similarity(aggregator): + """Test whether the same gap handlers as those used in the figure are used in the + overview figure""" + fig = FigureResampler(create_overview=True, default_downsampler=aggregator()) + + # create uneven data which contains gaps + N = 20_000 + x = np.arange(N) + for idx in np.random.randint(0, N, size=4): + x[idx:] += np.random.randint(N / 10, N / 5) + y = np.random.normal(size=N) + + fig.add_trace(go.Scatter(x=x, y=y), gap_handler=NoGapHandler()) + fig.add_trace({}, hf_x=x, hf_y=y, gap_handler=MedDiffGapHandler()) + fig.add_trace({}, hf_x=x, hf_y=y, gap_handler=MedDiffGapHandler(fill_value=42)) + + overview_fig = fig._create_overview_figure() + assert len(overview_fig.data) == 3 + assert np.isnan(overview_fig.data[0]["y"]).sum() == 0 + assert np.isnan(overview_fig.data[1]["y"]).sum() == 4 + assert (overview_fig.data[2]["y"] == 42).sum() == 4