diff --git a/ehrapy/plot/__init__.py b/ehrapy/plot/__init__.py
index c6d24b4ec..a646442d8 100644
--- a/ehrapy/plot/__init__.py
+++ b/ehrapy/plot/__init__.py
@@ -8,6 +8,7 @@
missing_values_heatmap,
missing_values_matrix,
)
+from ehrapy.plot._sankey import plot_sankey, plot_sankey_time
from ehrapy.plot._scanpy_pl_api import * # noqa: F403
from ehrapy.plot._survival_analysis import cox_ph_forestplot, kaplan_meier, ols
from ehrapy.plot.causal_inference._dowhy import causal_effect
diff --git a/ehrapy/plot/_sankey.py b/ehrapy/plot/_sankey.py
new file mode 100644
index 000000000..fc176bc2f
--- /dev/null
+++ b/ehrapy/plot/_sankey.py
@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import holoviews as hv
+import numpy as np
+import pandas as pd
+from holoviews import opts
+
+if TYPE_CHECKING:
+ from ehrdata import EHRData
+
+
+def plot_sankey(
+ edata: EHRData,
+ *,
+ columns: list[str],
+ show: bool = False,
+ **kwargs,
+) -> hv.Sankey:
+ """Create a Sankey diagram showing relationships across observation columns.
+
+ Please call :func:`holoviews.extension` with ``"matplotlib"`` or ``"bokeh"`` before using this function to select the backend.
+
+ Args:
+ edata : Central data object containing observation data
+ columns : Column names from edata.obs to visualize
+ show: If True, display the plot immediately. If False, only return the plot object without displaying.
+ **kwargs: Additional styling options passed to `holoviews.opts.Sankey`. See HoloViews Sankey documentation for full list of options.
+
+ Returns:
+ holoviews.Sankey
+
+ Examples:
+ >>> import ehrdata as ed
+ >>> edata = ed.dt.diabetes_130_fairlearn(columns_obs_only=["gender", "race"])
+ >>> ep.pl.plot_sankey(edata, columns=["gender", "race"])
+
+ """
+ df = edata.obs[columns]
+
+ labels = []
+ for col in columns:
+ labels.extend([f"{col}: {val}" for val in df[col].unique()])
+ labels = list(dict.fromkeys(labels)) # keep order & unique
+
+ # Build links between consecutive columns
+ sources, targets, values = [], [], []
+ source_levels, target_levels = [], []
+ for i in range(len(columns) - 1):
+ col_from, col_to = columns[i], columns[i + 1]
+ flows = df.groupby([col_from, col_to]).size().reset_index(name="count")
+ for _, row in flows.iterrows():
+ source = f"{col_from}: {row[col_from]}"
+ target = f"{col_to}: {row[col_to]}"
+ sources.append(source)
+ targets.append(target)
+ values.append(row["count"])
+ source_levels.append(col_from)
+ target_levels.append(col_to)
+
+ sankey_df = pd.DataFrame(
+ {
+ "source": sources,
+ "target": targets,
+ "value": values,
+ "source_level": source_levels,
+ "target_level": target_levels,
+ }
+ )
+
+ sankey = hv.Sankey(sankey_df, kdims=["source", "target"], vdims=["value"])
+ default_opts = {"label_position": "right", "show_values": True, "title": f"Flow across: {columns}"}
+
+ default_opts.update(kwargs)
+
+ sankey = sankey.opts(opts.Sankey(**default_opts))
+
+ if show:
+ from IPython.display import display
+
+ display(sankey)
+
+ return sankey
+
+
+def plot_sankey_time(
+ edata: EHRData,
+ *,
+ columns: list[str],
+ layer: str,
+ state_labels: dict[int, str] | None = None,
+ show: bool = False,
+ **kwargs,
+) -> hv.Sankey:
+ """Create a Sankey diagram showing patient state transitions over time.
+
+ This function visualizes how patients transition between different states
+ (e.g., disease severity, treatment status) across consecutive time points.
+ Each node represents a state at a specific time point, and flows show the
+ number of patients transitioning between states.
+
+ Please call :func:`holoviews.extension` with ``"matplotlib"`` or ``"bokeh"`` before using this function to select the backend.
+
+ Args:
+ edata: Central data object containing observation data
+ columns: Column names from edata.var_names to visualize
+ layer: Name of the layer in `edata.layers` containing the feature data to visualize.
+ state_labels: Mapping from numeric state values to readable labels. If None, state values
+ will be displayed as strings of their numeric codes (e.g., "0", "1", "2"). Default: None
+ show: If True, display the plot immediately. If False, only return the plot object without displaying.
+ **kwargs: Additional styling options passed to `holoviews.opts.Sankey`. See HoloViews Sankey documentation for full list of options.
+
+ Returns:
+ holoviews.Sankey
+
+ Examples:
+ >>> import numpy as np
+ >>> import pandas as pd
+ >>> import ehrdata as ed
+ >>>
+ >>> layer = np.array(
+ ... [
+ ... [[1, 0, 1], [0, 1, 0]], # patient 1: treatment, disease_flare
+ ... [[0, 1, 1], [1, 0, 0]], # patient 2: treatment, disease_flare
+ ... [[1, 1, 0], [0, 0, 1]], # patient 3: treatment, disease_flare
+ ... ]
+ ... )
+ >>>
+ >>> edata = ed.EHRData(
+ ... layers={"layer_1": layer},
+ ... obs=pd.DataFrame(index=["patient_1", "patient_2", "patient_3"]),
+ ... var=pd.DataFrame(index=["treatment", "disease_flare"]),
+ ... tem=pd.DataFrame(index=["visit_0", "visit_1", "visit_2"]),
+ ... )
+ >>>
+ >>> plot_sankey_time(edata, columns=["disease_flare"], layer="layer_1", state_labels={0: "no flare", 1: "flare"})
+
+
+ """
+ flare_data = edata[:, edata.var_names.isin(columns), :].layers[layer][:, 0, :]
+
+ time_steps = edata.tem.index.tolist()
+
+ if state_labels is None:
+ unique_states = np.unique(flare_data)
+ unique_states = unique_states[~np.isnan(unique_states)]
+ state_labels = {int(state): str(state) for state in unique_states}
+
+ state_values = sorted(state_labels.keys())
+ state_names = [state_labels[val] for val in state_values]
+
+ sources, targets, values = [], [], []
+ for t in range(len(time_steps) - 1):
+ for s_from_idx, s_from_val in enumerate(state_values):
+ for s_to_idx, s_to_val in enumerate(state_values):
+ count = np.sum((flare_data[:, t] == s_from_val) & (flare_data[:, t + 1] == s_to_val))
+ if count > 0:
+ source_label = f"{state_names[s_from_idx]} ({time_steps[t]})"
+ target_label = f"{state_names[s_to_idx]} ({time_steps[t + 1]})"
+ sources.append(source_label)
+ targets.append(target_label)
+ values.append(int(count))
+
+ sankey_df = pd.DataFrame({"source": sources, "target": targets, "value": values})
+
+ sankey = hv.Sankey(sankey_df, kdims=["source", "target"], vdims=["value"])
+
+ default_opts = {"label_position": "right", "show_values": True, "title": f"Patient flows: {columns[0]} over time"}
+
+ default_opts.update(kwargs)
+
+ sankey = sankey.opts(opts.Sankey(**default_opts))
+
+ if show:
+ from IPython.display import display
+
+ display(sankey)
+
+ return sankey
diff --git a/tests/_scripts/sankey_bokeh_expected.ipynb b/tests/_scripts/sankey_bokeh_expected.ipynb
new file mode 100644
index 000000000..29fcca3b3
--- /dev/null
+++ b/tests/_scripts/sankey_bokeh_expected.ipynb
@@ -0,0 +1,775 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "42a55492",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.8.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.1.min.js\", \"https://cdn.holoviz.org/panel/1.8.3/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));",
+ "application/vnd.holoviews_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n })\n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n",
+ "application/vnd.holoviews_load.v0+json": ""
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.holoviews_exec.v0+json": "",
+ "text/html": [
+ "\n",
+ ""
+ ]
+ },
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "1ca26b78-2b9d-4973-bed6-c78e6bb73cd0"
+ }
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+ "AAAB+wAAAfsBxc2miwAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAA6zSURB\n",
+ "VHic7ZtpeFRVmsf/5966taWqUlUJ2UioBBJiIBAwCZtog9IOgjqACsogKtqirT2ttt069nQ/zDzt\n",
+ "tI4+CrJIREFaFgWhBXpUNhHZQoKBkIUASchWla1S+3ar7r1nPkDaCAnZKoQP/D7mnPOe9/xy76n3\n",
+ "nFSAW9ziFoPFNED2LLK5wcyBDObkb8ZkxuaoSYlI6ZcOKq1eWFdedqNzGHQBk9RMEwFAASkk0Xw3\n",
+ "ETacDNi2vtvc7L0ROdw0AjoSotQVkKSvHQz/wRO1lScGModBFbDMaNRN1A4tUBCS3lk7BWhQkgpD\n",
+ "lG4852/+7DWr1R3uHAZVQDsbh6ZPN7CyxUrCzJMRouusj0ipRwD2uKm0Zn5d2dFwzX1TCGhnmdGo\n",
+ "G62Nna+isiUqhkzuKrkQaJlPEv5mFl2fvGg2t/VnzkEV8F5ioioOEWkLG86fvbpthynjdhXYZziQ\n",
+ "x1hC9J2NFyi8vCTt91Fh04KGip0AaG9zuCk2wQCVyoNU3Hjezee9bq92duzzTmxsRJoy+jEZZZYo\n",
+ "GTKJ6SJngdJqAfRzpze0+jHreUtPc7gpBLQnIYK6BYp/uGhw9YK688eu7v95ysgshcg9qSLMo3JC\n",
+ "4jqLKQFBgdKDPoQ+Pltb8dUyQLpeDjeVgI6EgLIQFT5tEl3rn2losHVsexbZ3EyT9wE1uGdkIPcy\n",
+ "BGxn8QUq1QrA5nqW5i2tLqvrrM9NK6AdkVIvL9E9bZL/oyfMVd/jqvc8LylzRBKDJSzIExwhQzuL\n",
+ "QYGQj4rHfFTc8mUdu3E7yoLtbTe9gI4EqVgVkug2i5+uXGo919ixbRog+3fTbQ8qJe4ZOYNfMoTI\n",
+ "OoshUNosgO60AisX15aeI2PSIp5KiFLI9ubb1vV3Qb2ltwLakUCDAkWX7/nHKRmmGIl9VgYsUhJm\n",
+ "2NXjKYADtM1ygne9QQDIXlk49FBstMKx66D1v4+XuQr7vqTe0VcBHQlRWiOCbmmSYe2SqtL6q5rJ\n",
+ "zsTb7lKx3FKOYC4DoqyS/B5bvLPxvD9Qtf6saxYLQGJErmDOdOMr/zo96km1nElr8bmPOBwI9COv\n",
+ "HnFPRIwmkSOv9kcAS4heRsidOkpeWBgZM+UBrTFAXNYL5Vf2ii9c1trNzpYdaoVil3WIc+wdk+gQ\n",
+ "noie3ecCcxt9ITcLAPWt/laGEO/9U6PmzZkenTtsSMQ8uYywJVW+grCstAvCIaAdArAsIWkRDDs/\n",
+ "KzLm2YcjY1Lv0UdW73HabE9n6V66cxSzfEmuJssTpKGVp+0vHq73FwL46eOjpMpbRAnNmJFrGJNu\n",
+ "Ukf9Yrz+3rghiumCKNXXWPhLYcjxGsIpoCMsIRoFITkW8AuyM8jC1+/QLx4bozCEJIq38+1rtpR6\n",
+ "V/yzb8eBlRb3fo5l783N0CWolAzJHaVNzkrTzlEp2bQ2q3TC5gn6wpnoQAmwSiGh2GitnTmVMc5O\n",
+ "UyfKWUKCIsU7+fZDKwqdT6DDpvkzAX4/+AMFjk0tDp5GRXLpQ2MUmhgDp5gxQT8+Y7hyPsMi8uxF\n",
+ "71H0oebujHALECjFKaW9Lm68n18wXp2kVzIcABytD5iXFzg+WVXkegpAsOOYziqo0OkK76GyquC3\n",
+ "ltZAzMhhqlSNmmWTE5T6e3IN05ITFLM4GdN0vtZ3ob8Jh1NAKXFbm5PtLU/eqTSlGjkNAJjdgn/N\n",
+ "aedXa0tdi7+t9G0FIF49rtMSEgAs1kDLkTPO7ebm4IUWeyh1bKomXqlgMG6kJmHcSM0clYLJ8XtR\n",
+ "1GTnbV3F6I5wCGikAb402npp1h1s7LQUZZSMIfALFOuL3UUrfnS8+rez7v9qcold5tilgHbO1fjK\n",
+ "9ubb17u9oshxzMiUBKXWqJNxd+fqb0tLVs4lILFnK71H0Ind7uiPgACVcFJlrb0tV6DzxqqTIhUM\n",
+ "CwDf1/rrVhTa33/3pGPxJYdQ2l2cbgVcQSosdx8uqnDtbGjh9SlDVSMNWhlnilfqZk42Th2ZpLpf\n",
+ "xrHec5e815zrr0dfBZSwzkZfqsv+1FS1KUknUwPARVvItfKUY+cn57yP7qv07UE3p8B2uhUwLk09\n",
+ "e0SCOrK+hbdYHYLjRIl71wWzv9jpEoeOHhGRrJAzyEyNiJuUqX0g2sBN5kGK6y2Blp5M3lsB9Qh4\n",
+ "y2Ja6x6+i0ucmKgwMATwhSjdUu49tKrQ/pvN5d53ml2CGwCmJipmKjgmyuaXzNeL2a0AkQ01Th5j\n",
+ "2DktO3Jyk8f9vcOBQHV94OK+fPumJmvQHxJoWkaKWq9Vs+yUsbq0zGT1I4RgeH2b5wef7+c7bl8F\n",
+ "eKgoHVVZa8ZPEORzR6sT1BzDUAD/d9F78e2Tzv99v8D+fLVTqAKAsbGamKey1Mt9Ann4eH3gTXTz\n",
+ "idWtAJ8PQWOk7NzSeQn/OTHDuEikVF1R4z8BQCy+6D1aWRfY0tTGG2OM8rRoPaeIj5ZHzJxszElN\n",
+ "VM8K8JS5WOfv8mzRnQAKoEhmt8gyPM4lU9SmBK1MCQBnW4KONT86v1hZ1PbwSXPw4JWussVjtH9Y\n",
+ "NCoiL9UoH/6PSu8jFrfY2t36erQHXLIEakMi1SydmzB31h3GGXFDFNPaK8Rme9B79Ixrd0WN+1ij\n",
+ "NRQ/doRmuFLBkHSTOm5GruG+pFjFdAmorG4IXH1Qua6ASniclfFtDYt+oUjKipPrCQB7QBQ2lrgP\n",
+ "fFzm+9XWUtcqJ3/5vDLDpJ79XHZk3u8nGZ42qlj1+ydtbxysCezrydp6ugmipNJ7WBPB5tydY0jP\n",
+ "HaVNzs3QzeE4ZpTbI+ZbnSFPbVOw9vsfnVvqWnirPyCNGD08IlqtYkh2hjZ5dErEQzoNm+6ykyOt\n",
+ "Lt5/PQEuSRRKo22VkydK+vvS1XEKlhCJAnsqvcVvH7f/ZU2R67eXbMEGAMiIV5oWZWiWvz5Fv2xG\n",
+ "sjqNJQRvn3Rs2lji/lNP19VjAQDgD7FHhujZB9OGqYxRkZxixgRDVlqS6uEOFaJUVu0rPFzctrnF\n",
+ "JqijImVp8dEKVWyUXDk92zAuMZ6bFwpBU1HrOw6AdhQgUooChb0+ItMbWJitSo5Ws3IAOGEOtL53\n",
+ "0vHZih9sC4vtofZ7Qu6523V/fmGcds1TY3V36pUsBwAbSlxnVh2xLfAD/IAIMDf7XYIkNmXfpp2l\n",
+ "18rkAJAy9HKFaIr/qULkeQQKy9zf1JgDB2uaeFNGijo5QsUyacNUUTOnGO42xSnv4oOwpDi1zYkc\n",
+ "efUc3I5Gk6PhyTuVKaOGyLUAYPGIoY9Pu/atL/L92+4q9wbflRJ2Trpm/jPjdBtfnqB/dIThcl8A\n",
+ "KG7hbRuKnb8qsQsVvVlTrwQAQMUlf3kwJI24Z4JhPMtcfng5GcH49GsrxJpGvvHIaeem2ma+KSjQ\n",
+ "lIwUdYyCY8j4dE1KzijNnIP2llF2wcXNnsoapw9XxsgYAl6k+KzUXbi2yP3KR2ecf6z3BFsBICdW\n",
+ "nvnIaG3eHybqX7vbpEqUMT+9OL4Qpe8VON7dXuFd39v19FoAABRVePbGGuXTszO0P7tu6lghUonE\n",
+ "llRdrhArLvmKdh9u29jcFiRRkfLUxBiFNiqSU9icoZQHo5mYBI1MBgBH6wMNb+U7Pnw337H4gi1Y\n",
+ "ciWs+uks3Z9fztUvfzxTm9Ne8XXkvQLHNytOOZeiD4e0PgkAIAYCYknKUNUDSXEKzdWNpnil7r4p\n",
+ "xqkjTarZMtk/K8TQ6Qve78qqvXurGwIJqcOUKfUWHsm8KGvxSP68YudXq4pcj39X49uOK2X142O0\n",
+ "Tz5/u/7TVybqH0rSya6ZBwD21/gubbrgWdDgEOx9WUhfBaC2ibcEBYm7a7x+ukrBMNcEZggyR0TE\n",
+ "T8zUPjikQ4VosQZbTpS4vqizBKvqmvjsqnpfzaZyx9JPiz1/bfGKdgD45XB1zoIMzYbfTdS/NClB\n",
+ "Gct0USiY3YL/g0LHy/uq/Ef6uo5+n0R/vyhp17Klpge763f8rMu6YU/zrn2nml+2WtH+Z+5IAAFc\n",
+ "2bUTdTDOSNa9+cQY7YLsOIXhevEkCvzph7a8laecz/Un/z4/Ae04XeL3UQb57IwU9ZDr9UuKVajv\n",
+ "nxp1+1UVIo/LjztZkKH59fO3G/JemqCfmaCRqbqbd90ZZ8FfjtkfAyD0J/9+C2h1hDwsSxvGjNDc\n",
+ "b4zk5NfrSwiQblLHzZhg+Jf4aPlUwpDqkQqa9nimbt1/TDH8OitGMaQnj+RJS6B1fbF7SY1TqO5v\n",
+ "/v0WAADl1f7zokgS7s7VT2DZ7pegUjBM7mjtiDZbcN4j0YrHH0rXpCtY0qPX0cVL0rv5jv/ZXend\n",
+ "0u/EESYBAFBU4T4Qa5TflZOhTe7pmKpaP8kCVUVw1+yhXfJWvn1P3hnXi33JsTN6PnP3hHZ8Z3/h\n",
+ "aLHzmkNPuPj7Bc/F/Q38CwjTpSwQXgE4Vmwry9tpfq/ZFgqFMy4AVDtCvi8rvMvOmv0N4YwbVgEA\n",
+ "sPM72/KVnzfspmH7HQGCRLG2yL1+z8XwvPcdCbsAANh+xPzstgMtxeGKt+6MK3/tacfvwhWvIwMi\n",
+ "oKEBtm0H7W+UVfkc/Y1V0BhoPlDr/w1w/eu1vjIgAgDg22OtX6/eYfnEz/focrZTHAFR+PSs56/7\n",
+ "q32nwpjazxgwAQCwcU/T62t3WL7r6/jVRa6/byp1rei+Z98ZUAEAhEPHPc8fKnTU9nbgtnOe8h0l\n",
+ "9hcGIqmODLQAHCy2Xti6v/XNRivf43f4fFvIteu854+VHnR7q9tfBlwAAGz+pnndB9vM26UebAe8\n",
+ "SLHujPOTPVW+rwY+sxskAAC2HrA8t2Vvc7ffP1r9o+vwR2dcr92InIAbKKC1FZ5tB1tf+/G8p8sv\n",
+ "N/9Q5zd/XR34LYCwV5JdccMEAMDBk45DH243r/X4xGvqxFa/GNpS7n6rwOwNWwHVE26oAADYurf1\n",
+ "zx/utOzt+DMKYM0p17YtZZ5VNzqfsB2HewG1WXE8PoZ7gOclbTIvynZf9JV+fqZtfgs/8F/Nu5rB\n",
+ "EIBmJ+8QRMmpU7EzGRsf2FzuePqYRbzh/zE26EwdrT10f6r6o8HOYzCJB9Dpff8tbnGLG8L/A/WE\n",
+ "roTBs2RqAAAAAElFTkSuQmCC)
\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " 
\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
ⓘ\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
ⓘ\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
ⓘ\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
\n",
+ " ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+ "AAAFMAAABTABZarKtgAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAArNSURB\n",
+ "VFiFnVd5VFNXGv/ee0kgGyQhbFoXIKCFYEXEDVErTucMoKUOWA/VLsNSLPQgFTOdyrHPiIp1lFIQ\n",
+ "OlaPShEG3EpPcQmISCuV1bQ1CLKIULeQhJA9JO+9+UMT0x5aPfOdc895373f/e7v/t537/ddBF5Q\n",
+ "JBIJl81mJwCACEVRQBCEQhAEAQCgnghCURRCkmS7Wq2+WlJSYn0Rv8jzDHAcD0EQJIVGo5mFQuGF\n",
+ "jIyMu39kq1KpkOrq6gU6nS6aIAiGzWY7VVBQ0P9/AcjNzWXy+fxcOp2uiY+Przm0d6+n8dblv/Fo\n",
+ "kzM4SzYfPlRePvFnjnt6ehh1dXVv2mw2nlar/byoqMj8wgBwHBchCJIZEhJSeu1yHVi7vtu02t8+\n",
+ "NykQ7BMWoOUMhXQsXLv5IQAwSJJEEASxcDicoeTk5DtCoZBy9XX69Gnv3t7ebJIky3EcH3guAKlU\n",
+ "GoGiaOKWLVsOvhs7/9XXPMde3/IyIFbMnaPDuD5AUdQuOf2XlD0npTExMWYAgNbWVpZcLg8xGAzB\n",
+ "JEnSvby82tPT052LaTQatLy8fBtJkt/s3Lnz5h8CwHFcRKPRNu/YsePAjh072KTs0IGCxRg8RgUB\n",
+ "TGpSx6cmHgMAfNqN6Xa1GvJ/D35gYAAViURkcXHxUrPZHDRv3rxv4uLiDI7xPXv2bLdYLBUFBQWD\n",
+ "jj7M8ZGbm8tkMpmSrKysQiaTScXGxtpqL7dManT6tcu5mgEWWJyOhicozpk+c3NsbKzNFcBbWWEf\n",
+ "1Td9/upA30i3ZJv0h8bGxiSFQmFcuHDhOACAWCy+0d3dvX3lypUtzc3N9t8AiIuLk4SEhByLiooy\n",
+ "AgAcO3ZsNlPgH3Cttb35JZo+bCYXIQAA9MDiUW7sWS1KN687w6Mera2twa2trfMvXboUOS28Pyb1\n",
+ "U08McRtf/sXBSmt5cc35pqamVQqFwhoZGallMpnU/fv3e7RaberVq1d/AABAn1IfQqfTNRs3blQB\n",
+ "AFy+fJk7Nja2XCKRnD3dNSorusPq6NfTPR+gPiEEoLRFXO1tS2+zavv27ReftjNttyr0S1/j0rUP\n",
+ "PEJQwNwQYGgAACQSyXmNRhMtk8lYAAApKSlKDMP0+fn5QU4ACIKkxMfH1zjYuHnz5uspKSlOfdX7\n",
+ "u68fvOePcCzKQR4YVCgATGfa/F3pnzaHWOAXSDyaMCqH2+r8VXErP3D+snXr1tV2dXW94dATExOr\n",
+ "6XT6JgAAVCKRcDEMM4WHh9sAAHJyUqNu//wDymKx7AAAVVVVPiaTKXxByrYMvBsxEMSTwPXhuL+8\n",
+ "e/fu9fv371+flvbemogYNz+TnsBOFEwMFO8/KzEYDKFVVVX+AAChoaGT7u7ud48ePRro0DEMs+bl\n",
+ "5bFRNpud4O3tfdGBzq5uy/5wTUPM/q2zC9atmbVqeHg4Pi0t7WxGRoZFH5rw76I7LI8HqHfwPL7d\n",
+ "rfVagzw1NfW81t4ePUfsP/OrnWZ6fPSuUqFQSEkkkrOjo6OvuQR5q0ajiXLoPj4+lzgcTjwKACLH\n",
+ "9SqXy2kzhBO8haGo+UA2wZW+p880DxeveGt9aHx9fT09ctlq3sC0NT9e6xsbjuZblSxl7wKtVotM\n",
+ "m6PnXvlmZJBtX91CEMQsxyJsNlteXl4udugIghAajQYFAEhPTx9AEGQOimGY8y4oLt63KlJkdB4t\n",
+ "P282Z/c/dPrDH04ktJ9P2tfWXP3+2o1vHzunEp6Xq0lsGt08KzUrcSGTQ3n3XeefLCs5UqnT6Rap\n",
+ "VCoEACA7O/snvV4f5gJooLa2NsihoygKKEVRzquTND2OCpttGXdG1tOxwOlgzdvE9v30rV+m3W5I\n",
+ "2jfJNQmLH85QUUzPNTwvkAx0+vVGhq2/VV9fT+dyuZ01NTXOXQOA3fGxevXq2waDYY5r8KIoij5b\n",
+ "jzB5Cz2oKdOo0erOm+1tVuVtBMZXElNMRJR1fvvjx9iPLQ/RjpuB0Xu/Vp7YmH1864YNG3oNBkPw\n",
+ "VD7mzp1rJUnSzZUBmqsBggAgGFC/n6jVA+3WoN3tu1Gg39cg2tEx1Cg3CIJHsclxnl2HRorMN8Z0\n",
+ "fRW+vr7GJ36Q56Z5h9BIknzGAMJWtvdQYs0EZe3/FSwqk5tpXEMb1JoYD+n8xRdQJl/fMPEgzKhS\n",
+ "L40KCD7lGzg92qIyovpb3y/msT2un2psvFpWVvYyl8vtc1nDSXFXV5c7iqLOtEyS5LNBAADfWeKm\n",
+ "Ly4uuvR1++sfv51/P5sfnHm2/Iy+mBmwsaHJbpt+Q0jHSS7TZ/PSNVkNJ/973OxtemD1s91CPb12\n",
+ "h9MfvZsk5meo1eqo5ORkxTNWn7HR1tY2l8PhOAsUiqIolCRJcETtv/61qzNySYK5trZ2TCgUUiwW\n",
+ "S1FSUhLR+bA/kAzwXcAbHa/cFhrTXrJ/v+7IkSPu3Je4Xm5eboJv2wba5QbO5fQwxhsP679Y+nFO\n",
+ "jgAAoKSkJILFYjnBGI1G0YYNGwYBnqRoiqIQlKKojurq6gUAAAKBgKQoiuGYkJWVpTCZTOKmI1Xd\n",
+ "HwnDcm+cOnOMw+H0FxYWbqpvqv/r9EV+bky+O+/QoUPiqJRt9JphTLFHbKBCR87tWL9EPN9oNIZn\n",
+ "ZWUpXHaMCQQCEgCgsrIyEgBuoGq1+qpOp4t2GPH5/BvFxcVLHXpgYGDD8ePH/56Xl2cCAMjMzOxP\n",
+ "S0s7pWfow4RCbz/fAF9RT0+P9yeffHJySSqev+9nxLD1FaAlTR8vlJ8vxxzsFhUVLRMIBB0OvwaD\n",
+ "YRlFUdfQkpISK0EQ9J6eHgYAQEZGxl2z2Rw0MjJCBwBITk5+xOVyfzpw4ECSw5lQKKQIbxtJm4EN\n",
+ "8eZ7jPz0oNv+dK5FG/jq54eH+IFr/S1KabBy0UerAvI+++wzD4vFEpCWljYEACCTyVh2ux3FcXwS\n",
+ "BQCw2WxVdXV1bzrQRURE1FVVVTn1zMzM/pkzZ35/9OjRd0pLS19RqVQIy4/tCwDgOcPTQvFQEQBA\n",
+ "aWnpK0ERK2LbyVllN341GUJ4YDu8zD5bKyur7O+85tx9Z2fnO1ar9QjA04KkpaVFs2LFir8olcq7\n",
+ "YWFhJpFINNnX16drbGyMjY6Ovg0AIBaLjcuXL5d3d3d7XbhwIW704b3F479MeD1qVfJ5Og/bvb4R\n",
+ "LwaDMZabm9uwflNa/z/3HOIv5NsDEK7XS7FeevXPvYNLvm5S/GglCK5KpZorlUobXE8g5ObmMqVS\n",
+ "6UG1Wu1BURSHoijOiRMnwgoLC7coFAqBo+9Fm0KhEKStmvvto3TeucFN7pVJYbytarXaQyqVHsRx\n",
+ "3N15TF1BuBaljr4rV66wOzo63mAymXdzcnKuwwtIUVHRMqvVGkgQxMV7NXvyJijGvcNXB/7z5Zdf\n",
+ "bicI4gSO40NTAgD4bVnuODIAT2pElUq1FEEQO4fD6QsPD++fqixHEATj8/ntjoCrqKhwS0hIsJWV\n",
+ "leURBHEOx3G563pT3tn5+flBDAbjg6CgoMMpKSlK17GhoSFMJpMFPk04DJIkEQzDzCwW6+5UD5Oa\n",
+ "mhrfO3fufECS5GHXnf8pAAAAHMfdURTdimGYPjExsTo0NHTyj2ynEplMxurs7HyHIAiKJMlSHMct\n",
+ "U9k9N2vl5+cH0en0TRiGWX18fC65vnh+LxqNBq2oqFhgMpmi7XY7arVaj+zdu/fxn/l/4bSZl5fH\n",
+ "5nK5CQAQMtXznCRJePpEbwOAZhzHX4ix/wHzzC/tu64gcwAAAABJRU5ErkJggg==)
\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "
ⓘ\n",
+ "
"
+ ],
+ "text/plain": [
+ ":Sankey [source,target] (value)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {}
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sankey"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a59e9a13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "layer = np.array(\n",
+ " [\n",
+ " [[0, 1, 2, 1, 2], [1, 2, 1, 2, 0]],\n",
+ " [[1, 2, 0, 2, 1], [2, 1, 2, 1, 2]],\n",
+ " [[2, 0, 1, 2, 0], [2, 0, 1, 1, 2]],\n",
+ " [[1, 2, 1, 0, 1], [0, 2, 1, 2, 0]],\n",
+ " [[0, 2, 1, 2, 2], [1, 2, 1, 0, 2]],\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "\n",
+ "edata_time = ed.EHRData(\n",
+ " layers={\"layer_1\": layer},\n",
+ " obs=pd.DataFrame(index=[\"patient_1\", \"patient_2\", \"patient_3\", \"patient_4\", \"patient_5\"]),\n",
+ " var=pd.DataFrame(index=[\"treatment\", \"disease_flare\"]),\n",
+ " tem=pd.DataFrame(index=[\"visit_0\", \"visit_1\", \"visit_2\", \"visit_3\", \"visit_4\"]),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "dfb9988e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sankey_time = ep.pl.plot_sankey_time(\n",
+ " edata_time,\n",
+ " columns=[\"disease_flare\"],\n",
+ " layer=\"layer_1\",\n",
+ " state_labels={0: \"no flare\", 1: \"mid flare\", 2: \"severe flare\"},\n",
+ " backend=\"matplotlib\",\n",
+ ")\n",
+ "\n",
+ "fig = hv.render(sankey_time, backend=\"matplotlib\")\n",
+ "fig.savefig(f\"{_TEST_IMAGE_PATH}/sankey_time_expected.png\", dpi=80)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "c9b9fb79",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ":Sankey [source,target] (value)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {}
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sankey_time"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/plot/_images/sankey_expected.png b/tests/plot/_images/sankey_expected.png
new file mode 100644
index 000000000..5c16c9376
Binary files /dev/null and b/tests/plot/_images/sankey_expected.png differ
diff --git a/tests/plot/_images/sankey_time_expected.png b/tests/plot/_images/sankey_time_expected.png
new file mode 100644
index 000000000..ff3b74c5a
Binary files /dev/null and b/tests/plot/_images/sankey_time_expected.png differ
diff --git a/tests/plot/test_sankey.py b/tests/plot/test_sankey.py
new file mode 100644
index 000000000..f6065cb1f
--- /dev/null
+++ b/tests/plot/test_sankey.py
@@ -0,0 +1,157 @@
+from pathlib import Path
+
+import ehrdata as ed
+import holoviews as hv
+import numpy as np
+import pandas as pd
+import pytest
+
+import ehrapy as ep
+
+CURRENT_DIR = Path(__file__).parent
+_TEST_IMAGE_PATH = f"{CURRENT_DIR}/_images"
+
+
+@pytest.fixture
+def ehr_3d_mini():
+ layer = np.array(
+ [
+ [[0, 1, 2, 1, 2], [1, 2, 1, 2, 0]],
+ [[1, 2, 0, 2, 1], [2, 1, 2, 1, 2]],
+ [[2, 0, 1, 2, 0], [2, 0, 1, 1, 2]],
+ [[1, 2, 1, 0, 1], [0, 2, 1, 2, 0]],
+ [[0, 2, 1, 2, 2], [1, 2, 1, 0, 2]],
+ ]
+ )
+
+ edata = ed.EHRData(
+ layers={"layer_1": layer},
+ obs=pd.DataFrame(index=["patient 1", "patient 2", "patient 3", "patient 4", "patient 5"]),
+ var=pd.DataFrame(index=["treatment", "disease_flare"]),
+ tem=pd.DataFrame(index=["visit_0", "visit_1", "visit_2", "visit_3", "visit_4"]),
+ )
+ return edata
+
+
+@pytest.fixture
+def diabetes_130_fairlearn_sample_100():
+ edata = ed.dt.diabetes_130_fairlearn(
+ columns_obs_only=[
+ "race",
+ "gender",
+ ]
+ )[:100]
+
+ return edata
+
+
+def test_sankey_plot(diabetes_130_fairlearn_sample_100, check_same_image):
+ hv.extension("matplotlib")
+ edata = diabetes_130_fairlearn_sample_100.copy()
+
+ sankey = ep.pl.plot_sankey(edata, columns=["gender", "race"])
+ fig = hv.render(sankey, backend="matplotlib")
+
+ check_same_image(
+ fig=fig,
+ base_path=f"{_TEST_IMAGE_PATH}/sankey",
+ tol=2e-1,
+ )
+
+
+def test_sankey_time_plot(ehr_3d_mini, check_same_image):
+ hv.extension("matplotlib")
+ edata = ehr_3d_mini
+ sankey_time = ep.pl.plot_sankey_time(
+ edata,
+ columns=["disease_flare"],
+ layer="layer_1",
+ state_labels={0: "no flare", 1: "mid flare", 2: "severe flare"},
+ )
+
+ fig = hv.render(sankey_time, backend="matplotlib")
+
+ check_same_image(
+ fig=fig,
+ base_path=f"{_TEST_IMAGE_PATH}/sankey_time",
+ tol=2e-1,
+ )
+
+
+def test_sankey_bokeh_plot(diabetes_130_fairlearn_sample_100):
+ hv.extension("bokeh")
+ edata = diabetes_130_fairlearn_sample_100.copy()
+
+ sankey = ep.pl.plot_sankey(edata, columns=["gender", "race"])
+
+ assert isinstance(sankey, hv.Sankey)
+
+ data = sankey.data
+ required_columns = ["source", "target", "value"]
+ for col in required_columns:
+ assert col in data.columns
+
+ assert len(data) > 0 # at least one flow
+ assert (data["value"] > 0).all() # flow values positive
+ assert data["value"].sum() == len(edata.obs) # total flow must match total obs
+
+ # each flow matches the expected count
+ for _, row in data.iterrows():
+ gender_value = row["source"].split(": ")[1]
+ race_value = row["target"].split(": ")[1]
+ flow_value = row["value"]
+
+ expected_count = len(edata.obs[(edata.obs["gender"] == gender_value) & (edata.obs["race"] == race_value)])
+
+ assert flow_value == expected_count
+
+ for source in data["source"].unique():
+ assert source.startswith("gender:") # sources have the correct prefix
+ for target in data["target"].unique():
+ assert target.startswith("race:") # targets have the correct prefix
+
+
+def test_sankey_time_bokeh_plot(ehr_3d_mini):
+ hv.extension("bokeh")
+ edata = ehr_3d_mini
+ sankey = ep.pl.plot_sankey_time(
+ edata,
+ columns=["disease_flare"],
+ layer="layer_1",
+ state_labels={0: "no flare", 1: "mid flare", 2: "severe flare"},
+ )
+ assert isinstance(sankey, hv.Sankey)
+
+ data = sankey.data
+ required_columns = ["source", "target", "value"]
+ for col in required_columns:
+ assert col in data.columns
+
+ assert len(data) > 0
+ assert (data["value"] > 0).all()
+
+ # check that sources and targets contain state labels
+ state_labels = ["no flare", "mid flare", "severe flare"]
+ for source in data["source"].unique():
+ assert any(label in source for label in state_labels)
+ assert "(" in source and ")" in source
+ for target in data["target"].unique():
+ assert any(label in target for label in state_labels)
+ assert "(" in target and ")" in target
+
+ # check conservation of patients across time points
+ time_steps = edata.tem.index.tolist()
+ first_time = time_steps[0]
+ last_time = time_steps[-1]
+
+ outflow_first = data[data["source"].str.contains(f"\\({first_time}\\)", regex=True)]["value"].sum()
+ inflow_last = data[data["target"].str.contains(f"\\({last_time}\\)", regex=True)]["value"].sum()
+
+ assert outflow_first == inflow_last
+
+ # total flow equals number of transitions
+ n_patients = edata.n_obs
+ n_transitions = len(time_steps) - 1
+ expected_total_flow = n_patients * n_transitions
+
+ assert data["value"].sum() == expected_total_flow