diff --git a/auxiliary_tools/LCF_mixed-phase_partition_diagram.ipynb b/auxiliary_tools/LCF_mixed-phase_partition_diagram.ipynb
index a89ca6f15..60f83cc0c 100644
--- a/auxiliary_tools/LCF_mixed-phase_partition_diagram.ipynb
+++ b/auxiliary_tools/LCF_mixed-phase_partition_diagram.ipynb
@@ -220,7 +220,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:e3sm_unified_1.6.0_nompi] *",
+ "display_name": "Python [conda env:e3sm_unified_1.6.0_nompi]",
"language": "python",
"name": "conda-env-e3sm_unified_1.6.0_nompi-py"
},
diff --git a/auxiliary_tools/debug/968-native-grid-vis/TGCLDLWP.cfg b/auxiliary_tools/debug/968-native-grid-vis/TGCLDLWP.cfg
new file mode 100644
index 000000000..cdd57eb9b
--- /dev/null
+++ b/auxiliary_tools/debug/968-native-grid-vis/TGCLDLWP.cfg
@@ -0,0 +1,12 @@
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["TGCLDLWP"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+#regions = ["global"]
+regions = ["global", "30S30N-150E90W"]
+#test_colormap = "Blues"
+#reference_colormap = "Blues"
+diff_colormap = "RdBu"
+contour_levels = [10, 25, 50, 75, 100, 125, 150, 175, 200,225, 250]
+diff_levels = [-35, -30, -25, -20, -15, -10, -5, 5, 10, 15, 20, 25, 30, 35]
diff --git a/auxiliary_tools/debug/968-native-grid-vis/run_lat_lon_native.cfg b/auxiliary_tools/debug/968-native-grid-vis/run_lat_lon_native.cfg
new file mode 100644
index 000000000..d41ceaa3d
--- /dev/null
+++ b/auxiliary_tools/debug/968-native-grid-vis/run_lat_lon_native.cfg
@@ -0,0 +1,11 @@
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["PRECT"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+regions = ["global", "60S60N", "30S30N-150E90W"]
+test_colormap = "WhiteBlueGreenYellowRed.rgb"
+reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+diff_colormap = "BrBG"
+contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
+diff_levels = [-2.5, -2, -1.5, -1, -0.5, -0.25, 0.25, 0.5, 1, 1.5, 2, 2.5]
\ No newline at end of file
diff --git a/auxiliary_tools/debug/968-native-grid-vis/run_lat_lon_native.py b/auxiliary_tools/debug/968-native-grid-vis/run_lat_lon_native.py
new file mode 100644
index 000000000..5364eeaee
--- /dev/null
+++ b/auxiliary_tools/debug/968-native-grid-vis/run_lat_lon_native.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""
+This script runs e3sm_diags with the lat_lon_native set to visualize native grid data.
+"""
+
+import os
+import sys
+
+from e3sm_diags.parameter.lat_lon_native_parameter import LatLonNativeParameter
+from e3sm_diags.run import runner
+
+# Auto-detect username
+username = os.environ.get('USER', 'unknown_user')
+
+# Create parameter objects for 3 different runs
+params = []
+
+## (1) First test configuration
+#param1 = LatLonNativeParameter()
+#param1.results_dir = f"/lcrc/group/e3sm/public_html/diagnostic_output/{username}/tests/lat_lon_native_test_1"
+#param1.test_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+#param1.test_name = "v3.LR.amip_0101"
+#param1.short_test_name = "v3.LR.amip_0101"
+#param1.reference_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+#param1.ref_name = "v3.HR.test4"
+#param1.short_ref_name = "v3.HR.test4"
+#param1.seasons = ["DJF"]
+#param1.test_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne30pg2.nc"
+#param1.ref_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne120pg2.nc"
+#param1.case_id = "model_vs_model"
+#param1.run_type = "model_vs_model"
+#params.append(param1)
+#
+## (2) Second test configuration
+#param2 = LatLonNativeParameter()
+#param2.results_dir = f"/lcrc/group/e3sm/public_html/diagnostic_output/{username}/tests/lat_lon_native_test_2"
+#param2.test_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+#param2.test_file = "v3.LR.amip_0101_DJF_climo.nc"
+#param2.short_test_name = "v3.LR.amip_0101"
+#param2.reference_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+#param2.ref_file = "v3.HR.test4_DJF_climo.nc"
+#param2.short_ref_name = "v3.HR.test4"
+#param2.seasons = ["DJF"]
+#param2.test_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne30pg2.nc"
+#param2.ref_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne120pg2.nc"
+#param2.case_id = "model_vs_model"
+#param2.run_type = "model_vs_model"
+#params.append(param2)
+
+# (3) Third test configuration
+param3 = LatLonNativeParameter()
+param3.results_dir = f"/lcrc/group/e3sm/public_html/diagnostic_output/{username}/tests/lat_lon_native_test_3"
+param3.test_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+param3.test_file = "v3.LR.amip_0101.eam.h0.1989-12.nc"
+param3.reference_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+param3.ref_file = "v3.LR.amip_0101.eam.h0.1989-12.nc"
+param3.short_ref_name = "v3.HR.test4"
+param3.time_slices = ["0"]
+param3.test_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne30pg2.nc"
+param3.ref_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne30pg2.nc"
+param3.case_id = "model_vs_model"
+param3.run_type = "model_vs_model"
+params.append(param3)
+
+# Run the single diagnostic, comment out for complete diagnostics.
+cfg_path = "auxiliary_tools/debug/968-native-grid-vis/TGCLDLWP.cfg"
+sys.argv.extend(["--diags", cfg_path])
+
+runner.sets_to_run = ["lat_lon_native"]
+
+# Run each test sequentially
+for i, param in enumerate(params, 1):
+ print(f"\n{'='*60}")
+ print(f"Running Test {i}: {param.results_dir}")
+ print(f"{'='*60}")
+
+ # Create results directory
+ if not os.path.exists(param.results_dir):
+ os.makedirs(param.results_dir)
+
+ # Run the diagnostic
+ runner.run_diags([param])
+ print(f"Test {i} completed!")
+
+print(f"\n{'='*60}")
+print("All tests completed!")
+print(f"{'='*60}")
+
+
diff --git a/auxiliary_tools/plot_native_grid_data.ipynb b/auxiliary_tools/plot_native_grid_data.ipynb
new file mode 100644
index 000000000..c3586cad5
--- /dev/null
+++ b/auxiliary_tools/plot_native_grid_data.ipynb
@@ -0,0 +1,1454 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "2ccccc79",
+ "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 = '3.7.2'.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",
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+ " 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",
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+ " 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/1.6.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.2.min.js\", \"https://cdn.holoviz.org/panel/1.6.2/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",
+ " },\n",
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+ "\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",
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+ " var NewBokeh = root.Bokeh;\n",
+ " if (Bokeh.versions === undefined) {\n",
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+ " 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",
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+ " // to determine whether there is an ongoing attempt to initialize\n",
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+ " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
+ " // before older versions are fully initialized.\n",
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+ " // 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",
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+ " 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));"
+ ],
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+ " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
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+ " 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",
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+ " return comm;\n",
+ " }\n",
+ " window.PyViz.comm_manager = new JupyterCommManager();\n",
+ " \n",
+ "\n",
+ "\n",
+ "var JS_MIME_TYPE = 'application/javascript';\n",
+ "var HTML_MIME_TYPE = 'text/html';\n",
+ "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+ "var CLASS_NAME = 'output';\n",
+ "\n",
+ "/**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function handle_update_output(event, handle) {\n",
+ " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+ " handle_add_output(event, handle)\n",
+ "}\n",
+ "\n",
+ "function 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",
+ "\n",
+ "if (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": "\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 console.log(message)\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 comm.open();\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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.holoviews_exec.v0+json": "",
+ "text/html": [
+ "\n",
+ ""
+ ]
+ },
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "98fe1713-4c31-4f6c-b6e0-80b2ff02d6c3"
+ }
+ },
+ "output_type": "display_data"
+ },
+ {
+ "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 = false;\n",
+ " const py_version = '3.7.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+ " const reloading = true;\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/1.6.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.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",
+ " },\n",
+ "function(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": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.7.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\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/1.6.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.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));"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "\n",
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+ " 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",
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+ " console.log(message)\n",
+ " var content = {data: message.data, comm_id};\n",
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+ " for (var buffer of message.buffers || []) {\n",
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+ " }\n",
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+ " 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",
+ " comm.open();\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",
+ "\n",
+ "var JS_MIME_TYPE = 'application/javascript';\n",
+ "var HTML_MIME_TYPE = 'text/html';\n",
+ "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+ "var CLASS_NAME = 'output';\n",
+ "\n",
+ "/**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function handle_update_output(event, handle) {\n",
+ " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+ " handle_add_output(event, handle)\n",
+ "}\n",
+ "\n",
+ "function 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",
+ "\n",
+ "if (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": "\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 console.log(message)\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 comm.open();\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 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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-04-23 16:31:45,401 is when this event was logged.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import uxarray as ux\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "\n",
+ "import logging\n",
+ "logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)\n",
+ "logging.warning('is when this event was logged.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdf2c250",
+ "metadata": {},
+ "source": [
+ "This notebook demonstrates using matplotlib's PolyCollection method to visualize native datasets read in with uxarray. It follows the uxarray user guide: https://uxarray.readthedocs.io/en/latest/user-guide/mpl.html with consideration of the discussion from E3SM documentation: https://acme-climate.atlassian.net/wiki/spaces/DOC/pages/1210023949/Plotting+data+on+SE+native+grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "dbd2a9e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-04-23 16:31:45,405 open datasets with uxarray\n"
+ ]
+ }
+ ],
+ "source": [
+ "base_path = \"/lcrc/group/e3sm/public_html/\"\n",
+ "grid_info = \"ne120pg2\"\n",
+ "grid_path = base_path + f\"diagnostics/grids/{grid_info}.nc\"\n",
+ "data_path = base_path + f\"e3sm_diags_test_data/native_grid/PRECC.{grid_info}.nc\"\n",
+ "\n",
+ "base_path = \"/Users/zhang40/Documents/ACME_simulations/E3SM_v2/native_grid_data/\"\n",
+ "grid_info = \"ne120pg2\"\n",
+ "grid_path = base_path + f\"{grid_info}.nc\"\n",
+ "data_path = base_path + f\"PRECC.{grid_info}.nc\"\n",
+ "\n",
+ "logging.info(\"open datasets with uxarray\")\n",
+ "uxds = ux.open_dataset(grid_path, data_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f1fefbf4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-04-23 16:31:45,606 convert a UxDataArray containing a face-centered data variable into a matplotlib.collections.PolyCollection instance\n"
+ ]
+ }
+ ],
+ "source": [
+ "logging.info(\"convert a UxDataArray containing a face-centered data variable into a matplotlib.collections.PolyCollection instance\")\n",
+ "#pc = uxds[\"PRECC\"].squeeze().to_polycollection()\n",
+ "pc = uxds[\"PRECC\"].squeeze().to_polycollection(periodic_elements=\"split\") # option to treat data cross date-time/antimeridian, which will results in 20x performance hit\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "30c3e6a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-04-23 16:32:45,994 start creating plot\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'PolyCollection Plot with Projection & Features')"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# disables grid lines\n",
+ "pc.set_antialiased(False)\n",
+ "\n",
+ "pc.set_cmap(\"plasma\")\n",
+ "\n",
+ "logging.info(\"start creating plot\")\n",
+ "fig, ax = plt.subplots(\n",
+ " 1,\n",
+ " 1,\n",
+ " figsize=(10, 5),\n",
+ " facecolor=\"w\",\n",
+ " constrained_layout=True,\n",
+ " subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)),\n",
+ ")\n",
+ "\n",
+ "ax.add_feature(cfeature.COASTLINE)\n",
+ "ax.add_feature(cfeature.BORDERS)\n",
+ "\n",
+ "ax.add_collection(pc)\n",
+ "ax.set_global()\n",
+ "plt.title(\"PolyCollection Plot with Projection & Features\")\n",
+ "#logging.info(\"save plot in png format\")\n",
+ "#plt.savefig(f'/lcrc/group/e3sm/public_html/diagnostic_output/ac.zhang40/tests/PRECC_{grid_info}.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "266af17e-cefd-4d7b-88bd-e875cd670e02",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.562738, 0.051545, 0.641509, 1. ],\n",
+ " [0.551715, 0.043136, 0.645277, 1. ],\n",
+ " [0.600266, 0.081516, 0.625342, 1. ],\n",
+ " ...,\n",
+ " [0.050383, 0.029803, 0.527975, 1. ],\n",
+ " [0.086222, 0.026125, 0.542658, 1. ],\n",
+ " [0.050383, 0.029803, 0.527975, 1. ]], shape=(346078, 4))"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pc.get_fc()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a6dba4e4-4393-4844-81e2-137aea014c86",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# remapping from ne120 to ne30\n",
+ "grid_info = \"ne30pg2\"\n",
+ "grid_path = base_path + f\"{grid_info}.nc\"\n",
+ "data_path = base_path + f\"PRECC.{grid_info}.nc\"\n",
+ "uxds_ne30 = ux.open_dataset(grid_path, data_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "d92a13f0-0c46-4382-b4b1-9a355f8006d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "remapped = uxds[\"PRECC\"].squeeze().remap.nearest_neighbor(uxds_ne30.uxgrid, remap_to=\"face centers\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "27823e1a-1913-4c94-9568-b3f4b59c3811",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pc_r = (uxds_ne30[\"PRECC\"].squeeze() - remapped).to_polycollection()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "be06bbfa-0db8-4311-965c-32073eacee92",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-04-23 16:33:23,796 start creating plot\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'PolyCollection Plot with Projection & Features')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# disables grid lines\n",
+ "pc_r.set_antialiased(False)\n",
+ "\n",
+ "pc_r.set_cmap(\"plasma\")\n",
+ "\n",
+ "logging.info(\"start creating plot\")\n",
+ "fig, ax = plt.subplots(\n",
+ " 1,\n",
+ " 1,\n",
+ " figsize=(10, 5),\n",
+ " facecolor=\"w\",\n",
+ " constrained_layout=True,\n",
+ " subplot_kw=dict(projection=ccrs.PlateCarree(central_longitude=180)),\n",
+ ")\n",
+ "\n",
+ "ax.add_feature(cfeature.COASTLINE)\n",
+ "ax.add_feature(cfeature.BORDERS)\n",
+ "\n",
+ "ax.add_collection(pc_r)\n",
+ "ax.set_global()\n",
+ "plt.title(\"PolyCollection Plot with Projection & Features\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "c3df0efb-f5d9-4e64-8368-36049505472e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Size: 86kB\n",
+ "array([4.8524551e-08, 3.2724763e-08, 6.5138693e-08, ..., 9.9643827e-10,\n",
+ " 4.2643347e-09, 9.1901931e-10], shape=(21600,), dtype=float32)\n",
+ "Coordinates:\n",
+ " time object 8B 0001-02-01 00:00:00\n",
+ "Dimensions without coordinates: n_face\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(remapped )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6847a487-0b6e-4ac7-8f90-b0af68b900fa",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.13.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/auxiliary_tools/plot_native_grid_data.py b/auxiliary_tools/plot_native_grid_data.py
new file mode 100644
index 000000000..61e2097dc
--- /dev/null
+++ b/auxiliary_tools/plot_native_grid_data.py
@@ -0,0 +1,35 @@
+import uxarray as ux
+import matplotlib.pyplot as pl
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+
+base_path = "/Users/zhang40/Documents/ACME_simulations/E3SM_v2/native_grid_data/"
+grid_info = "ne30pg2"
+grid_path = base_path + f"{grid_info}.nc"
+data_path = base_path + f"PRECC.{grid_info}.nc"
+
+uxds = ux.open_dataset(grid_path, data_path)
+
+pc = uxds["PRECT"].to_polycollection()
+#pc = uxds["PRECT"].to_polycollection(periodic_elements="split")
+
+# disables grid lines
+pc.set_antialiased(False)
+
+pc.set_cmap("plasma")
+
+fig, ax = plt.subplots(
+ 1,
+ 1,
+ figsize=(10, 5),
+ facecolor="w",
+ constrained_layout=True,
+ subplot_kw=dict(projection=ccrs.PlateCarree()),
+)
+
+ax.add_feature(cfeature.COASTLINE)
+ax.add_feature(cfeature.BORDERS)
+
+ax.add_collection(pc)
+ax.set_global()
+plt.title("PolyCollection Plot with Projection & Features")
diff --git a/conda-env/ci.yml b/conda-env/ci.yml
index 42fb6e61c..13ad3e0f2 100644
--- a/conda-env/ci.yml
+++ b/conda-env/ci.yml
@@ -24,6 +24,7 @@ dependencies:
- numpy >=2.0.0,<3.0.0
- pywavelets
- scipy
+ - uxarray >=2023.3.0
- xarray >=2024.3.0
- xcdat >=0.10.0,<1.0.0
- xesmf >=0.8.7
diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index f56b30158..b8c51abdb 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -22,6 +22,7 @@ dependencies:
- numpy >=2.0.0,<3.0.0
- pywavelets
- scipy
+ - uxarray >=2023.3.0
- xcdat >=0.10.0,<1.0.0
- xesmf >=0.8.7
- xskillscore >=0.0.20
diff --git a/e3sm_diags/derivations/default_regions_xr.py b/e3sm_diags/derivations/default_regions_xr.py
index b5e0c251c..41a6740e5 100644
--- a/e3sm_diags/derivations/default_regions_xr.py
+++ b/e3sm_diags/derivations/default_regions_xr.py
@@ -18,6 +18,8 @@
"20N50N": {"lat": (20.0, 50)},
"50N90N": {"lat": (50.0, 90)},
"60S90N": {"lat": (-60.0, 90)},
+ "45S45N-120E60W": {"lat": (-45.0, 45), "lon": (120, 300)},
+ "30S30N-150E90W": {"lat": (-30.0, 30), "lon": (150, 270)},
"60S60N": {"lat": (-60.0, 60)},
"75S75N": {"lat": (-75.0, 75)},
"ocean": {"value": 0.65},
diff --git a/e3sm_diags/derivations/derivations.py b/e3sm_diags/derivations/derivations.py
index 37e99962f..a54b1f628 100644
--- a/e3sm_diags/derivations/derivations.py
+++ b/e3sm_diags/derivations/derivations.py
@@ -624,6 +624,18 @@
("hfss",): rename,
("surf_sens_flux",): rename, # EAMxx
},
+ "TGCLDLWP": OrderedDict(
+ [
+ (
+ ("TGCLDLWP",),
+ lambda x: convert_units(x, target_units="g/m^2"),
+ ),
+ (
+ ("LiqWaterPath",),
+ lambda x: convert_units(x, target_units="g/m^2"),
+ ), # EAMxx
+ ]
+ ),
"TGCLDLWP_OCN": OrderedDict(
[
(
diff --git a/e3sm_diags/driver/__init__.py b/e3sm_diags/driver/__init__.py
index 630d0c539..4a617d686 100644
--- a/e3sm_diags/driver/__init__.py
+++ b/e3sm_diags/driver/__init__.py
@@ -9,3 +9,9 @@
# The keys for the land and ocean fraction variables in the
# `LAND_OCEAN_MASK_PATH` file.
FRAC_REGION_KEYS = {"land": ("LANDFRAC", "landfrac"), "ocean": ("OCNFRAC", "ocnfrac")}
+
+# The default value for metrics if it is not calculated. This value was
+# preserved from the legacy CDAT codebase because the viewer expects this
+# value for metrics that aren't calculated.
+# TODO: Update `lat_lon_viewer.py` to handle missing metrics with None value.
+METRICS_DEFAULT_VALUE = 999.999
diff --git a/e3sm_diags/driver/default_diags/lat_lon_native_model_vs_model.cfg b/e3sm_diags/driver/default_diags/lat_lon_native_model_vs_model.cfg
new file mode 100644
index 000000000..ab33c3607
--- /dev/null
+++ b/e3sm_diags/driver/default_diags/lat_lon_native_model_vs_model.cfg
@@ -0,0 +1,119 @@
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["PRECT"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+regions = ["global", "60S60N", "30S30N-150E90W"]
+test_colormap = "WhiteBlueGreenYellowRed.rgb"
+reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+diff_colormap = "BrBG"
+contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
+diff_levels = [-2.5, -2, -1.5, -1, -0.5, -0.25, 0.25, 0.5, 1, 1.5, 2, 2.5]
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["PRECC"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+regions = ["global", "60S60N", "30S30N-150E90W"]
+test_colormap = "WhiteBlueGreenYellowRed.rgb"
+reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+diff_colormap = "BrBG"
+contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
+diff_levels = [-2.5, -2, -1.5, -1, -0.5, -0.25, 0.25, 0.5, 1, 1.5, 2, 2.5]
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["TGCLDLWP"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+regions = ["global", "60S60N", "30S30N-150E90W"]
+#test_colormap = "Blues"
+#reference_colormap = "Blues"
+diff_colormap = "RdBu"
+#contour_levels = [10, 25, 50, 75, 100, 125, 150, 175, 200,225, 250]
+diff_levels = [-35, -30, -25, -20, -15, -10, -5, 5, 10, 15, 20, 25, 30, 35]
+
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["TREFHT"]
+regions = ["global"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [-35, -30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40]
+diff_levels = [-5, -4, -3, -2, -1, -0.5, -0.2, 0.2, 0.5, 1, 2, 3, 4, 5]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["SWCFSRF"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [-170, -150, -135, -120, -105, -90, -75, -60, -45, -30, -15, 0, 15, 30, 45]
+diff_levels = [-30, -25, -20, -15, -10, -5, -2, 2, 5, 10, 15, 20, 25, 30]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["LWCFSRF"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [0, 10, 20, 30, 40, 50, 60, 70, 80]
+diff_levels = [-30, -25, -20, -15, -10, -5, -2, 2, 5, 10, 15, 20, 25, 30]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["LHFLX"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [0,5, 15, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300]
+diff_levels = [-75, -50, -25, -10, -5, -2, 2, 5, 10, 25, 50, 75]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["SHFLX"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [-100, -75, -50, -25, -10, 0, 10, 25, 50, 75, 100, 125, 150]
+diff_levels = [-75, -50, -25, -10, -5, -2, 2, 5, 10, 25, 50, 75]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["NET_FLUX_SRF"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [-200, -160, -120, -80, -40, 0, 40, 80, 120, 160, 200]
+diff_levels = [-75, -50, -25, -10, -5, -2, 2, 5, 10, 25, 50, 75]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["TMQ"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60]
+diff_levels = [-12, -9, -6, -4, -3, -2, -1, 1, 2, 3, 4, 6, 9, 12]
+
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["QREFHT"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+contour_levels = [0.2, 0.5, 1, 2.5, 5, 7.5, 10, 12.5, 15, 17.5]
+diff_levels = [-5, -4, -3, -2, -1, -0.25, 0.25, 1, 2, 3, 4, 5]
+
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["U10"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+test_colormap = "PiYG_r"
+reference_colormap = "PiYG_r"
+contour_levels = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
+diff_levels = [-8, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 8]
diff --git a/e3sm_diags/driver/default_diags/lat_lon_native_model_vs_obs.cfg b/e3sm_diags/driver/default_diags/lat_lon_native_model_vs_obs.cfg
new file mode 100644
index 000000000..6c870b589
--- /dev/null
+++ b/e3sm_diags/driver/default_diags/lat_lon_native_model_vs_obs.cfg
@@ -0,0 +1,15 @@
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_obs"
+variables = ["PRECT"]
+seasons = ["ANN", "DJF", "MAM", "JJA", "SON"]
+regions = ["global"]
+test_colormap = "WhiteBlueGreenYellowRed.rgb"
+reference_colormap = "WhiteBlueGreenYellowRed.rgb"
+diff_colormap = "BrBG"
+contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
+diff_levels = [-2.5, -2, -1.5, -1, -0.5, -0.25, 0.25, 0.5, 1, 1.5, 2, 2.5]
+
+# Native grid settings
+grid_file = ""
+antialiased = False
diff --git a/e3sm_diags/driver/lat_lon_driver.py b/e3sm_diags/driver/lat_lon_driver.py
index 791f3d390..7c45f16df 100755
--- a/e3sm_diags/driver/lat_lon_driver.py
+++ b/e3sm_diags/driver/lat_lon_driver.py
@@ -4,6 +4,7 @@
import xarray as xr
+from e3sm_diags.driver import METRICS_DEFAULT_VALUE
from e3sm_diags.driver.utils.climo_xr import ClimoFreq
from e3sm_diags.driver.utils.dataset_xr import Dataset
from e3sm_diags.driver.utils.io import _save_data_metrics_and_plots
@@ -25,12 +26,6 @@
if TYPE_CHECKING:
from e3sm_diags.parameter.core_parameter import CoreParameter
-# The default value for metrics if it is not calculated. This value was
-# preserved from the legacy CDAT codebase because the viewer expects this
-# value for metrics that aren't calculated.
-# TODO: Update `lat_lon_viewer.py` to handle missing metrics with None value.
-METRICS_DEFAULT_VALUE = 999.999
-
def run_diag(parameter: CoreParameter) -> CoreParameter:
"""Get metrics for the lat_lon diagnostic set.
diff --git a/e3sm_diags/driver/lat_lon_native_driver.py b/e3sm_diags/driver/lat_lon_native_driver.py
new file mode 100644
index 000000000..d2029532b
--- /dev/null
+++ b/e3sm_diags/driver/lat_lon_native_driver.py
@@ -0,0 +1,779 @@
+from __future__ import annotations
+
+import traceback
+from typing import TYPE_CHECKING, Sequence
+
+import uxarray as ux
+
+from e3sm_diags.derivations.default_regions_xr import REGION_SPECS
+from e3sm_diags.driver import METRICS_DEFAULT_VALUE
+from e3sm_diags.driver.utils.dataset_native import NativeDataset
+from e3sm_diags.driver.utils.type_annotations import MetricsDict
+from e3sm_diags.logger import _setup_child_logger
+from e3sm_diags.metrics.metrics import native_correlation, native_rmse
+from e3sm_diags.plot.lat_lon_native_plot import plot as plot_func
+
+logger = _setup_child_logger(__name__)
+
+if TYPE_CHECKING:
+ from e3sm_diags.driver.utils.type_annotations import TimeSelection
+ from e3sm_diags.parameter.lat_lon_native_parameter import LatLonNativeParameter
+
+
+def run_diag(parameter: LatLonNativeParameter) -> LatLonNativeParameter: # noqa: C901
+ """Get metrics for the lat_lon_native diagnostic set.
+
+ This function loops over each variable, season/time_slice, pressure level (if 3-D),
+ and region.
+
+ Parameters
+ ----------
+ parameter : LatLonNativeParameter
+ The parameter for the diagnostic.
+
+ Returns
+ -------
+ LatLonNativeParameter
+ The parameter for the diagnostic with the result (completed or failed).
+
+ Raises
+ ------
+ RuntimeError
+ If the dimensions of the test and reference datasets are not aligned
+ (e.g., one is 2-D and the other is 3-D).
+ """
+ variables = parameter.variables
+ ref_name = getattr(parameter, "ref_name", "")
+ regions = parameter.regions
+
+ # Determine whether to use seasons or time_slices
+ if len(parameter.time_slices) > 0:
+ time_periods: Sequence["TimeSelection"] = parameter.time_slices
+ use_time_slices = True
+ logger.info(f"Using time_slices: {time_periods}")
+ else:
+ time_periods = parameter.seasons
+ use_time_slices = False
+ logger.info(f"Using seasons: {time_periods}")
+
+ test_ds = NativeDataset(parameter, data_type="test")
+ ref_ds = NativeDataset(parameter, data_type="ref")
+
+ for var_key in variables:
+ logger.info("Variable: {}".format(var_key))
+ parameter.var_id = var_key
+
+ for time_period in time_periods:
+ if use_time_slices:
+ logger.info(f"Processing time slice: {time_period}")
+ parameter._set_time_slice_attrs(
+ test_ds.dataset, ref_ds.dataset, time_period
+ )
+ else:
+ logger.info(f"Processing season: {time_period}")
+ parameter._set_name_yrs_attrs(
+ test_ds.dataset, ref_ds.dataset, time_period
+ )
+
+ ds_xr_test = test_ds.get_native_dataset(
+ var_key, time_period, use_time_slices
+ )
+ ds_xr_ref = ref_ds.get_native_dataset(
+ var_key, time_period, use_time_slices, allow_missing=True
+ )
+
+ # Log basic dataset info
+ if ds_xr_test is not None:
+ logger.debug(f"Test dataset variables: {list(ds_xr_test.variables)}")
+
+ uxds_test_grid = None
+ if parameter.test_grid_file:
+ logger.info(f"Loading test native grid: {parameter.test_grid_file}")
+
+ uxds_test_grid = test_ds.get_grid_dataset()
+
+ # Apply variable derivations if needed
+ test_ds._process_variable_derivations(var_key)
+
+ if ds_xr_ref is not None:
+ uxds_ref = ref_ds.get_grid_dataset()
+
+ if uxds_ref is not None:
+ # Apply variable derivations if needed
+ ref_ds._process_variable_derivations(var_key)
+
+ logger.debug(
+ f"Reference dataset variables: {list(uxds_ref.data_vars)}"
+ )
+
+ if ds_xr_ref is None:
+ if uxds_test_grid is not None:
+ _run_diags_2d_model_only(
+ parameter,
+ time_period,
+ regions,
+ var_key,
+ ref_name,
+ uxds_test_grid,
+ )
+ else:
+ logger.warning(
+ "Skipping native grid diagnostics: uxds_test is None"
+ )
+ else:
+ _run_diags_2d(
+ parameter,
+ time_period,
+ regions,
+ var_key,
+ ref_name,
+ uxds_test_grid,
+ uxds_ref,
+ )
+
+ return parameter
+
+
+def _run_diags_2d_model_only(
+ parameter: LatLonNativeParameter,
+ season: str,
+ regions: list[str],
+ var_key: str,
+ ref_name: str,
+ uxds_test: ux.UxDataset,
+):
+ """Run a model-only diagnostics on a 2D variable using native grid.
+
+ This function plots the native grid data directly using uxarray dataset.
+
+ Parameters
+ ----------
+ parameter : LatLonNativeParameter
+ The parameter object.
+ season : str
+ The season.
+ regions : list[str]
+ The list of regions.
+ var_key : str
+ The key of the variable.
+ ref_name : str
+ The reference name.
+ uxds_test : ux.UxDataset
+ The uxarray dataset containing the test native grid information.
+ """
+ # Process each region
+ for region in regions:
+ parameter._set_param_output_attrs(var_key, season, region, ref_name, ilev=None)
+ logger.info(f"Processing {var_key} for region {region}")
+
+ # Apply regional subsetting before metrics calculation
+ uxds_test_subset = _apply_regional_subsetting(uxds_test, var_key, region)
+
+ # Create a minimal metrics_dict for model-only mode
+ # Only need test metrics, no reference or diff metrics
+ parameter.metrics_dict = _create_metrics_dict(
+ var_key,
+ uxds_test_subset,
+ uxds_ref=None,
+ uxds_test_remapped=uxds_test_subset, # For model-only, test_regrid = test
+ uxds_ref_remapped=None,
+ uxds_diff=None,
+ )
+
+ # Create plot with model-only mode
+ plot_func(
+ parameter,
+ var_key,
+ region,
+ uxds_test=uxds_test,
+ uxds_ref=None,
+ uxds_diff=None,
+ )
+
+
+def _run_diags_2d(
+ parameter: LatLonNativeParameter,
+ season: str,
+ regions: list[str],
+ var_key: str,
+ ref_name: str,
+ uxds_test: ux.UxDataset = None,
+ uxds_ref: ux.UxDataset = None,
+):
+ """Run diagnostics on a 2D variable using native grid.
+
+ This function creates plots for each region using the native grid datasets.
+
+ Parameters
+ ----------
+ parameter : LatLonNativeParameter
+ The parameter object.
+ season : str
+ The season.
+ regions : list[str]
+ The list of regions.
+ var_key : str
+ The key of the variable.
+ ref_name : str
+ The reference name.
+ uxds_test : ux.UxDataset, optional
+ The uxarray dataset containing the test native grid information.
+ uxds_ref : ux.UxDataset, optional
+ The uxarray dataset containing the reference native grid information.
+ """
+ # Check if we have valid reference data
+ has_valid_ref = uxds_ref is not None and var_key in uxds_ref
+
+ for region in regions:
+ parameter._set_param_output_attrs(var_key, season, region, ref_name, ilev=None)
+
+ if has_valid_ref:
+ logger.info(f"Processing {var_key} for region {region} (model vs model)")
+
+ uxds_diff, uxds_test_remapped, uxds_ref_remapped = (
+ _compute_diff_between_grids(uxds_test, uxds_ref, var_key)
+ )
+
+ # Apply regional subsetting to all datasets before metrics calculation
+ uxds_test_subset = _apply_regional_subsetting(uxds_test, var_key, region)
+ uxds_ref_subset = _apply_regional_subsetting(uxds_ref, var_key, region)
+ uxds_test_remapped_subset = _apply_regional_subsetting(
+ uxds_test_remapped, var_key, region
+ )
+ uxds_ref_remapped_subset = _apply_regional_subsetting(
+ uxds_ref_remapped, var_key, region
+ )
+ uxds_diff_subset = _apply_regional_subsetting(uxds_diff, var_key, region)
+
+ # Create metrics dictionary using regionally subsetted datasets
+ metrics_dict = _create_metrics_dict(
+ var_key,
+ uxds_test_subset,
+ uxds_ref_subset,
+ uxds_test_remapped_subset,
+ uxds_ref_remapped_subset,
+ uxds_diff_subset,
+ )
+
+ # Store metrics in parameter for plot function to access
+ parameter.metrics_dict = metrics_dict
+
+ parameter._set_param_output_attrs(
+ var_key, season, region, ref_name, ilev=None
+ )
+
+ # Call plot function with original datasets for visualization
+ plot_func(
+ parameter,
+ var_key,
+ region,
+ uxds_test=uxds_test,
+ uxds_ref=uxds_ref,
+ uxds_diff=uxds_diff,
+ )
+ else:
+ logger.info(f"Processing {var_key} for region {region} (model-only)")
+
+ # Apply regional subsetting to test dataset before metrics calculation
+ uxds_test_subset = _apply_regional_subsetting(uxds_test, var_key, region)
+
+ # Create metrics dictionary for model-only run using regionally subsetted dataset
+ metrics_dict = _create_metrics_dict(
+ var_key,
+ uxds_test_subset,
+ None, # No reference dataset
+ None, # No remapped test dataset (not needed for model-only)
+ None, # No remapped reference dataset
+ None, # No difference dataset
+ )
+
+ # Store metrics in parameter for plot function to access
+ parameter.metrics_dict = metrics_dict
+
+ parameter._set_param_output_attrs(
+ var_key, season, region, ref_name, ilev=None
+ )
+
+ # Call plot function with original dataset for visualization
+ plot_func(
+ parameter,
+ var_key,
+ region,
+ uxds_test=uxds_test,
+ uxds_ref=None,
+ uxds_diff=None,
+ )
+
+
+def _compute_diff_between_grids(
+ uxds_test: ux.UxDataset, uxds_ref: ux.UxDataset, var_key: str
+) -> tuple[ux.UxDataset | None, ux.UxDataset, ux.UxDataset]:
+ """Compute the difference between two native grid datasets.
+
+ This function handles the remapping between different grids if needed,
+ and computes the difference between test and reference data.
+
+ FIXME: This function has too many nested blocks and should be refactored.
+ The broad exception handling may hide bugs and makes debugging difficult.
+
+ Parameters
+ ----------
+ uxds_test : ux.UxDataset
+ The test dataset on native grid
+ uxds_ref : ux.UxDataset
+ The reference dataset on native grid
+ var_key : str
+ The variable key to compute difference for
+
+ Returns
+ -------
+ tuple[ux.UxDataset | None, ux.UxDataset, ux.UxDataset]
+ A tuple containing (difference_dataset, remapped_test, remapped_ref).
+ The difference dataset can be None if computation fails.
+ """
+ try:
+ # Check if variables exist in both datasets
+ if var_key not in uxds_test or var_key not in uxds_ref:
+ if var_key not in uxds_test:
+ logger.error(f"Variable {var_key} not found in test dataset")
+ if var_key not in uxds_ref:
+ logger.error(f"Variable {var_key} not found in reference dataset")
+
+ return None, uxds_test, uxds_ref
+
+ # Determine if both grids are identical by comparing properties and
+ # create a difference dataset accordingly. Otherwise return None.
+ same_grid, test_face_count, ref_face_count = _compare_grids(uxds_test, uxds_ref)
+
+ if same_grid:
+ uxds_diff = _compute_direct_difference(uxds_test, uxds_ref, var_key)
+ # For same grid, no remapping needed
+ remapped_test = uxds_test
+ remapped_ref = uxds_ref
+ else:
+ # Determine which grid to use as target (prefer lower resolution grid)
+ target_is_test = ref_face_count >= test_face_count
+
+ uxds_diff, remapped_test, remapped_ref = _compute_remapped_difference(
+ uxds_test, uxds_ref, var_key, target_is_test
+ )
+
+ if uxds_diff is None:
+ return None, uxds_test, uxds_ref
+
+ # Copy attributes and add diff metadata
+ if var_key in uxds_diff and var_key in uxds_test:
+ for attr, value in uxds_test[var_key].attrs.items():
+ uxds_diff[var_key].attrs[attr] = value
+
+ # Add metadata indicating this is a difference field
+ uxds_diff[var_key].attrs["long_name"] = (
+ f"Difference in {uxds_diff[var_key].attrs.get('long_name', var_key)}"
+ )
+
+ return uxds_diff, remapped_test, remapped_ref
+
+ except Exception as e:
+ logger.error(f"Error in compute_diff_between_grids: {e}")
+
+ return None, uxds_test, uxds_ref
+
+
+def _compare_grids(
+ uxds_test: ux.UxDataset, uxds_ref: ux.UxDataset
+) -> tuple[bool, int, int]:
+ """Compare two grids to determine if they're identical.
+
+ This function compares the grid properties of the test and reference datasets
+ to determine if they are on the same grid.
+
+ Parameters
+ ----------
+ uxds_test : ux.UxDataset
+ The test dataset on native grid.
+ uxds_ref : ux.UxDataset
+ The reference dataset on native grid.
+ Returns
+ -------
+ tuple[bool, int, int]
+ A tuple containing (same_grid, test_face_count, ref_face_count).
+ """
+ test_sizes = uxds_test.uxgrid.sizes
+ ref_sizes = uxds_ref.uxgrid.sizes
+
+ test_face_count = test_sizes.get("face", 0)
+ ref_face_count = ref_sizes.get("face", 0)
+
+ same_grid = test_face_count == ref_face_count and test_face_count > 0
+
+ if same_grid:
+ logger.debug(f"Same grid detected with {test_face_count} faces")
+ else:
+ logger.debug(
+ f"Different grids: test ({test_face_count} faces), ref ({ref_face_count} faces)"
+ )
+
+ return same_grid, test_face_count, ref_face_count
+
+
+def _compute_direct_difference(
+ uxds_test: ux.UxDataset, uxds_ref: ux.UxDataset, var_key: str
+) -> ux.UxDataset | None:
+ """Compute direct difference when grids are identical.
+
+ This function computes the difference directly without remapping.
+
+ FIXME: This function has too many nested blocks and should be refactored.
+ The broad exception handling may hide bugs and makes debugging difficult.
+
+ Parameters
+ ----------
+ uxds_test : ux.UxDataset
+ The test dataset on native grid.
+ uxds_ref : ux.UxDataset
+ The reference dataset on native grid.
+ var_key : str
+ The variable key to compute difference for.
+
+ Returns
+ -------
+ ux.UxDataset or None
+ A dataset containing the difference data, or None if computation fails.
+ """
+ try:
+ # Extract the variable data arrays and handle time dimension if present
+ test_var = uxds_test[var_key]
+ ref_var = uxds_ref[var_key]
+
+ # Handle multiple time points in test data
+ if "time" in test_var.dims and test_var.sizes["time"] > 1:
+ logger.info(
+ f"Test variable {var_key} has multiple time points. Using first time point for difference calculation."
+ )
+ test_var = test_var.isel(time=0)
+
+ # Handle multiple time points in reference data
+ if "time" in ref_var.dims and ref_var.sizes["time"] > 1:
+ logger.info(
+ f"Reference variable {var_key} has multiple time points. Using first time point for difference calculation."
+ )
+ ref_var = ref_var.isel(time=0)
+
+ # Squeeze any remaining singleton dimensions
+ test_var = test_var.squeeze()
+ ref_var = ref_var.squeeze()
+
+ # Create a copy of the test dataset to store the difference
+ uxds_diff = uxds_test.copy()
+
+ # Compute the difference
+ uxds_diff[var_key] = test_var - ref_var
+ logger.debug("Difference computed using direct subtraction")
+ return uxds_diff
+
+ except Exception as e:
+ logger.error(f"Error computing direct difference: {e}")
+ logger.debug(
+ f"Test var shape: {uxds_test[var_key].shape}, dims: {uxds_test[var_key].dims}"
+ )
+ logger.debug(
+ f"Ref var shape: {uxds_ref[var_key].shape}, dims: {uxds_ref[var_key].dims}"
+ )
+
+ return None
+
+
+def _compute_remapped_difference(
+ uxds_test: ux.UxDataset, uxds_ref: ux.UxDataset, var_key: str, target_is_test: bool
+) -> tuple[ux.UxDataset | None, ux.UxDataset, ux.UxDataset]:
+ """Compute difference with remapping for different grids.
+
+ FIXME: This function has too many nested blocks and should be refactored.
+ The broad exception handling may hide bugs and makes debugging difficult.
+
+ Parameters
+ ----------
+ uxds_test : ux.UxDataset
+ The test dataset on native grid.
+ uxds_ref : ux.UxDataset
+ The reference dataset on native grid.
+ var_key : str
+ The variable key to compute difference for.
+ target_is_test : bool
+ If True, remap reference to test grid; otherwise remap test to reference
+ grid.
+
+ Returns
+ -------
+ ux.UxDataset or None
+ A dataset containing the difference data, or None if computation fails.
+ """
+ try:
+ # Extract variables and handle time dimension
+ test_var = uxds_test[var_key]
+ ref_var = uxds_ref[var_key]
+
+ # Handle multiple time points in test data
+ if "time" in test_var.dims and test_var.sizes["time"] > 1:
+ logger.info(
+ f"Test variable {var_key} has multiple time points. Using first time point for remapping."
+ )
+ test_var = test_var.isel(time=0)
+
+ # Handle multiple time points in reference data
+ if "time" in ref_var.dims and ref_var.sizes["time"] > 1:
+ logger.info(
+ f"Reference variable {var_key} has multiple time points. Using first time point for remapping."
+ )
+ ref_var = ref_var.isel(time=0)
+
+ # Squeeze any remaining singleton dimensions
+ test_var = test_var.squeeze()
+ ref_var = ref_var.squeeze()
+
+ if target_is_test:
+ # Remap reference to test grid
+ logger.info("Remapping reference data to test grid")
+ uxds_diff = uxds_test.copy()
+ remapped_test = uxds_test
+
+ ref_remapped = ref_var.remap.nearest_neighbor(
+ uxds_test.uxgrid, remap_to="face centers"
+ )
+ uxds_diff[var_key] = test_var - ref_remapped
+
+ # Create remapped reference dataset
+ remapped_ref = uxds_test.copy()
+ remapped_ref[var_key] = ref_remapped
+
+ else:
+ # Remap test to reference grid
+ logger.info("Remapping test data to reference grid")
+ uxds_diff = uxds_ref.copy()
+ remapped_ref = uxds_ref
+
+ test_remapped = test_var.remap.nearest_neighbor(
+ uxds_ref.uxgrid, remap_to="face centers"
+ )
+ uxds_diff[var_key] = test_remapped - ref_var
+
+ # Create remapped test dataset
+ remapped_test = uxds_ref.copy()
+ remapped_test[var_key] = test_remapped
+
+ return uxds_diff, remapped_test, remapped_ref
+
+ except Exception as e:
+ logger.error(f"Error during remapping and difference computation: {e}")
+ logger.debug(
+ f"Test var shape: {uxds_test[var_key].shape}, dims: {uxds_test[var_key].dims}"
+ )
+ logger.debug(
+ f"Ref var shape: {uxds_ref[var_key].shape}, dims: {uxds_ref[var_key].dims}"
+ )
+ logger.debug(traceback.format_exc())
+
+ return None, uxds_test, uxds_ref
+
+
+def _create_metrics_dict(
+ var_key: str,
+ uxds_test: ux.UxDataset,
+ uxds_ref: ux.UxDataset | None,
+ uxds_test_remapped: ux.UxDataset | None,
+ uxds_ref_remapped: ux.UxDataset | None,
+ uxds_diff: ux.UxDataset | None,
+) -> MetricsDict:
+ """Create a metrics dictionary for native grid datasets.
+
+ This function follows the same pattern as lat_lon_driver._create_metrics_dict
+ but uses uxarray datasets and native grid operations.
+
+ Parameters
+ ----------
+ var_key : str
+ The variable key.
+ uxds_test : ux.UxDataset
+ The original test uxarray dataset.
+ uxds_ref : ux.UxDataset | None
+ The original reference uxarray dataset.
+ uxds_test_remapped : ux.UxDataset | None
+ The remapped test uxarray dataset.
+ uxds_ref_remapped : ux.UxDataset | None
+ The remapped reference uxarray dataset.
+ uxds_diff : ux.UxDataset | None
+ The difference uxarray dataset.
+
+ Returns
+ -------
+ MetricsDict
+ The metrics dictionary.
+ """
+ # Basic test metrics using original dataset
+ var_test = uxds_test[var_key]
+ metrics_dict: MetricsDict = {
+ "test": {
+ "min": [var_test.min().item()],
+ "max": [var_test.max().item()],
+ "mean": [var_test.weighted_mean().item()],
+ "std": METRICS_DEFAULT_VALUE, # Not implemented yet for native grids
+ },
+ "unit": uxds_test[var_key].attrs.get("units", ""),
+ }
+
+ # Set default values for all optional metrics
+ metrics_dict = _set_default_metric_values(metrics_dict)
+
+ # Add reference metrics if available (using original dataset)
+ if uxds_ref is not None and var_key in uxds_ref:
+ var_ref = uxds_ref[var_key]
+ metrics_dict["ref"] = {
+ "min": [var_ref.min().item()],
+ "max": [var_ref.max().item()],
+ "mean": [var_ref.weighted_mean().item()],
+ "std": METRICS_DEFAULT_VALUE, # Not implemented yet for native grids
+ }
+
+ # Add remapped test metrics if available
+ if uxds_test_remapped is not None and var_key in uxds_test_remapped:
+ var_test_remapped = uxds_test_remapped[var_key]
+ metrics_dict["test_regrid"] = {
+ "min": [var_test_remapped.min().item()],
+ "max": [var_test_remapped.max().item()],
+ "mean": [var_test_remapped.weighted_mean().item()],
+ "std": METRICS_DEFAULT_VALUE, # Not implemented yet for native grids
+ }
+
+ # Add remapped reference metrics if available
+ if uxds_ref_remapped is not None and var_key in uxds_ref_remapped:
+ var_ref_remapped = uxds_ref_remapped[var_key]
+ metrics_dict["ref_regrid"] = {
+ "min": [var_ref_remapped.min().item()],
+ "max": [var_ref_remapped.max().item()],
+ "mean": [var_ref_remapped.weighted_mean().item()],
+ "std": METRICS_DEFAULT_VALUE, # Not implemented yet for native grids
+ }
+
+ # Calculate RMSE and correlation on remapped datasets (following lat_lon pattern)
+ if uxds_test_remapped is not None and uxds_ref_remapped is not None:
+ try:
+ rmse_val = native_rmse(uxds_test_remapped, uxds_ref_remapped, var_key)
+ corr_val = native_correlation(
+ uxds_test_remapped, uxds_ref_remapped, var_key
+ )
+
+ metrics_dict["misc"] = {
+ "rmse": [rmse_val],
+ "corr": [corr_val],
+ }
+ except Exception as e:
+ logger.warning(f"Failed to calculate RMSE/correlation: {e}")
+ # Keep default NaN values for misc metrics
+
+ # For model-only run, copy test metrics to test_regrid
+ if uxds_test is not None and uxds_ref_remapped is None:
+ metrics_dict["test_regrid"] = metrics_dict["test"]
+
+ # Add difference metrics if available
+ if uxds_diff is not None and var_key in uxds_diff:
+ var_diff = uxds_diff[var_key]
+ metrics_dict["diff"] = {
+ "min": [var_diff.min().item()],
+ "max": [var_diff.max().item()],
+ "mean": [var_diff.weighted_mean().item()],
+ "std": METRICS_DEFAULT_VALUE, # Not implemented yet for native grids
+ }
+
+ return metrics_dict
+
+
+def _set_default_metric_values(metrics_dict: MetricsDict) -> MetricsDict:
+ """Set default values for optional metrics in the dictionary.
+
+ This function follows the same pattern as lat_lon_driver._set_default_metric_values.
+ """
+ var_keys = ["test_regrid", "ref", "ref_regrid", "diff"]
+ metric_keys = ["min", "max", "mean", "std"]
+
+ for var_key in var_keys:
+ if var_key not in metrics_dict:
+ metrics_dict[var_key] = {
+ metric_key: METRICS_DEFAULT_VALUE for metric_key in metric_keys
+ }
+
+ if "misc" not in metrics_dict:
+ metrics_dict["misc"] = {
+ "rmse": METRICS_DEFAULT_VALUE,
+ "corr": METRICS_DEFAULT_VALUE,
+ }
+
+ return metrics_dict
+
+
+def _apply_regional_subsetting(
+ uxds: ux.UxDataset | None, var_key: str, region: str
+) -> ux.UxDataset | None:
+ """Apply regional subsetting to a uxarray dataset based on region specification.
+
+ This function follows the same pattern as the regional subsetting in
+ lat_lon_native_plot.py but moves it to the driver for consistency.
+
+ Parameters
+ ----------
+ uxds : ux.UxDataset or None
+ The uxarray dataset to subset.
+ var_key : str
+ The variable key to subset.
+ region : str
+ The region specification (e.g., "global", "CONUS", etc.).
+
+ Returns
+ -------
+ ux.UxDataset or None
+ The regionally subsetted dataset, or None if input was None.
+ """
+ if uxds is None:
+ return uxds
+
+ # Get region specs (same logic as in plot function)
+ region_specs = REGION_SPECS.get(region, None)
+
+ if region_specs is None:
+ # Unknown region, return original dataset
+ logger.warning(
+ f"Region '{region}' not found in REGION_SPECS. Using global dataset."
+ )
+ return uxds
+
+ # Get bounds (same logic as in plot function)
+ lat_bounds = region_specs.get("lat", (-90, 90)) # type: ignore
+ lon_bounds = region_specs.get("lon", (0, 360)) # type: ignore
+ is_global_domain = lat_bounds == (-90, 90) and lon_bounds == (0, 360)
+
+ if is_global_domain:
+ # Global domain, no subsetting needed
+ return uxds
+
+ try:
+ # Check if target variable exists
+ if var_key not in uxds.data_vars:
+ logger.warning(
+ f"Variable '{var_key}' not found in dataset. Available vars: {list(uxds.data_vars)}"
+ )
+ return uxds
+
+ # Apply subsetting to the specific variable
+ var_subset = uxds[var_key].subset.bounding_box(lon_bounds, lat_bounds)
+
+ # Create new dataset from subsetted variable
+ uxds_subset = var_subset.to_dataset()
+ uxds_subset.attrs.update(uxds.attrs)
+ uxds_subset[var_key].attrs.update(uxds[var_key].attrs)
+ return uxds_subset
+
+ except Exception as e:
+ logger.warning(
+ f"Failed to apply regional subsetting for region '{region}': {e}"
+ )
+ logger.warning("Using global dataset instead.")
+ return uxds
diff --git a/e3sm_diags/driver/mp_partition_driver.py b/e3sm_diags/driver/mp_partition_driver.py
index 67b29cc24..b4b27b522 100644
--- a/e3sm_diags/driver/mp_partition_driver.py
+++ b/e3sm_diags/driver/mp_partition_driver.py
@@ -117,7 +117,7 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
)
raise
- parameter.test_name_yrs = test_data.get_name_yrs_attr(season) # type: ignore
+ parameter.test_name_yrs = test_data.get_name_yrs_attr(season)
metrics_dict["test"] = {}
metrics_dict["test"]["T"], metrics_dict["test"]["LCF"] = compute_lcf(
@@ -166,7 +166,7 @@ def run_diag(parameter: MPpartitionParameter) -> MPpartitionParameter:
# cliq = ref_data.get_timeseries_variable("CLDLIQ")(
# cdutil.region.domain(latitude=(-70.0, -30, "ccb"))
# )
- parameter.ref_name_yrs = ref_data.get_name_yrs_attr(season) # type: ignore
+ parameter.ref_name_yrs = ref_data.get_name_yrs_attr(season)
metrics_dict["ref"] = {}
metrics_dict["ref"]["T"], metrics_dict["ref"]["LCF"] = compute_lcf(
cice, cliq, temp, landfrac
diff --git a/e3sm_diags/driver/utils/dataset_native.py b/e3sm_diags/driver/utils/dataset_native.py
new file mode 100644
index 000000000..8778220f6
--- /dev/null
+++ b/e3sm_diags/driver/utils/dataset_native.py
@@ -0,0 +1,344 @@
+from __future__ import annotations
+
+import os
+import traceback
+from typing import TYPE_CHECKING, Literal, get_args
+
+import uxarray as ux
+import xarray as xr
+
+from e3sm_diags.derivations.derivations import DERIVED_VARIABLES, FUNC_NEEDS_TARGET_VAR
+from e3sm_diags.driver.utils.climo_xr import ClimoFreq
+from e3sm_diags.driver.utils.dataset_xr import Dataset
+from e3sm_diags.logger import _setup_child_logger
+
+if TYPE_CHECKING:
+ from collections.abc import Callable
+
+ from e3sm_diags.driver.utils.type_annotations import TimeSelection
+ from e3sm_diags.parameter.lat_lon_native_parameter import LatLonNativeParameter
+
+logger = _setup_child_logger(__name__)
+
+
+class NativeDataset:
+ """
+ A class for handling native grid datasets using xarray for raw data
+ and uxarray for grid-aware operations.
+
+ NOTE: NativeDataset uses composition instead of inheritance to wrap Dataset
+ with additional native-grid specific functionalities. It does not inherit
+ from Dataset to avoid confusion with existing Dataset methods and prevent
+ tight coupling. If needed, we can refactor to inherit from Dataset or
+ create a parent abstract class in the future.
+ """
+
+ def __init__(
+ self,
+ parameter: LatLonNativeParameter,
+ data_type: Literal["test", "ref"],
+ ):
+ # The dataset object (test or reference).
+ self.dataset = Dataset(parameter, data_type)
+
+ # The uxarray dataset for grid operations.
+ self.grid_dataset: ux.Dataset | None = None
+
+ @property
+ def dataset_name(self) -> str:
+ return "reference" if self.dataset.data_type == "ref" else "test"
+
+ # --------------------------------------------------------------------------
+ # Native-dataset related methods
+ # --------------------------------------------------------------------------
+ def get_native_dataset(
+ self,
+ var_key: str,
+ season: TimeSelection,
+ is_time_slice: bool = False,
+ allow_missing: bool = False,
+ ) -> xr.Dataset | None:
+ """Get the climatology dataset for the variable and season for native grid processing.
+
+ This function handles both test and reference datasets. For reference datasets,
+ if the data cannot be found and allow_missing=True, it will return None to
+ enable model-only runs.
+
+ This function also stores the data file path in the parameter object
+ for native grid visualization.
+
+ Parameters
+ ----------
+
+ var_key : str
+ The key of the variable.
+ season : TimeSelection
+ The climatology frequency or time slice string.
+ is_time_slice : bool, optional
+ If True, treat season as a time slice string rather than climatology
+ frequency. Default is False.
+ allow_missing : bool, optional
+ If True, return None when dataset cannot be loaded instead of raising
+ an exception. This enables model-only runs when reference data is
+ missing. Default is False.
+
+ Returns
+ -------
+ xr.Dataset | None
+ The climatology dataset if it exists, or None if allow_missing=True
+ and the dataset cannot be loaded.
+
+ Raises
+ ------
+ RuntimeError, IOError
+ If the dataset cannot be loaded and allow_missing=False.
+ """
+ try:
+ if is_time_slice:
+ ds = self._get_full_native_dataset()
+ ds = self._apply_time_slice(ds, season)
+ else:
+ if season in get_args(ClimoFreq):
+ ds = self.dataset.get_climo_dataset(var_key, season) # type: ignore
+ else:
+ raise ValueError(f"Invalid season for climatology: {season}")
+
+ # Store file path in parameter for native grid processing.
+ # Note: For climatology case, get_climo_dataset() already handles file
+ # path storage.
+ if is_time_slice:
+ # For time slices, we know the exact file path we used.
+ filepath = self.dataset._get_climo_filepath_with_params()
+
+ if filepath:
+ if self.dataset.data_type == "test":
+ self.dataset.parameter.test_data_file_path = filepath
+ elif self.dataset.data_type == "ref":
+ self.dataset.parameter.ref_data_file_path = filepath
+
+ return ds
+ except (RuntimeError, IOError) as e:
+ if allow_missing:
+ logger.info(
+ f"Cannot process {self.dataset.data_type} data: {e}. Using model-only mode."
+ )
+ return None
+ else:
+ raise
+
+ def _get_full_native_dataset(self) -> xr.Dataset:
+ """Get the full native dataset without any time averaging.
+
+ This function uses the dataset's file path parameters to directly open
+ the raw data file for time slicing operations.
+
+ Parameters
+ ----------
+ dataset : Dataset
+ The dataset object (test or reference).
+ var_key : str
+ The key of the variable.
+
+ Returns
+ -------
+ xr.Dataset
+ The full dataset with all time steps.
+
+ Raises
+ ------
+ RuntimeError
+ If unable to get the full dataset.
+ """
+ filepath = self.dataset._get_climo_filepath_with_params()
+
+ if filepath is None:
+ raise RuntimeError(
+ f"Unable to get file path for {self.dataset.data_type} dataset. "
+ f"For time slicing, please ensure that "
+ f"{'ref_file' if self.dataset.data_type == 'ref' else 'test_file'} parameter is set."
+ )
+
+ if not os.path.exists(filepath):
+ raise RuntimeError(f"File not found: {filepath}")
+
+ logger.info(f"Opening full native dataset from: {filepath}")
+
+ try:
+ # Open the dataset directly without any averaging
+ ds = xr.open_dataset(filepath, decode_times=True)
+ logger.info(
+ f"Successfully opened dataset with time dimension size: {ds.sizes.get('time', 'N/A')}"
+ )
+
+ return ds
+ except Exception as e:
+ raise RuntimeError(f"Failed to open dataset {filepath}: {e}") from e
+
+ def _apply_time_slice(self, ds: xr.Dataset, time_slice: str) -> xr.Dataset:
+ """Apply time slice selection to a dataset.
+
+ Parameters
+ ----------
+ ds : xr.Dataset
+ The input dataset with time dimension.
+ time_slice : str
+ The time slice specification (e.g., "0:10:2", "5:15", "7").
+
+ Returns
+ -------
+ xr.Dataset
+ The dataset with time slice applied.
+ """
+ # Parse the time slice string
+ time_dim = None
+
+ for dim in ds.dims:
+ if str(dim).lower() in ["time", "t"]:
+ time_dim = dim
+ break
+
+ if time_dim is None:
+ logger.warning(
+ "No time dimension found in dataset. Returning original dataset."
+ )
+ return ds
+
+ # Parse slice notation
+ if ":" in time_slice:
+ # Handle slice notation like "0:10:2" or "5:15" or ":10" or "5:" or "::2"
+ parts = time_slice.split(":")
+
+ start = int(parts[0]) if parts[0] else None
+ end = int(parts[1]) if len(parts) > 1 and parts[1] else None
+ step = int(parts[2]) if len(parts) > 2 and parts[2] else None
+
+ # Apply the slice
+ ds_sliced = ds.isel({time_dim: slice(start, end, step)})
+ else:
+ # Single index
+ index = int(time_slice)
+ ds_sliced = ds.isel({time_dim: index})
+
+ logger.info(
+ f"Applied time slice '{time_slice}' to dataset. "
+ f"Original time length: {ds.sizes[time_dim]}, "
+ f"Sliced time length: {ds_sliced.sizes.get(time_dim, 1)}"
+ )
+
+ return ds_sliced
+
+ # --------------------------------------------------------------------------
+ # Grid dataset related methods
+ # --------------------------------------------------------------------------
+ def get_grid_dataset(self) -> ux.Dataset:
+ """Open the dataset using uxarray.
+
+ Returns
+ -------
+ ux.Dataset
+ The opened dataset.
+ """
+ uxds = None
+
+ try:
+ if self.dataset.data_type == "test":
+ uxds = ux.open_dataset(
+ self.dataset.parameter.test_grid_file, # type: ignore
+ self.dataset.parameter.test_data_file_path,
+ )
+ elif self.dataset.data_type == "ref":
+ has_ref_grid = (
+ hasattr(self.dataset.parameter, "ref_grid_file")
+ and self.dataset.parameter.ref_grid_file is not None
+ )
+
+ if not has_ref_grid:
+ logger.info(
+ "No ref_grid_file specified. Skipping reference grid loading."
+ )
+ else:
+ grid_file = self.dataset.parameter.ref_grid_file # type: ignore
+
+ # Use ref_data_file_path if available, otherwise use ds_ref
+ if (
+ hasattr(self.dataset.parameter, "ref_data_file_path")
+ and self.dataset.parameter.ref_data_file_path
+ ):
+ data_source = self.dataset.parameter.ref_data_file_path
+ else:
+ data_source = uxds # type: ignore
+
+ uxds = ux.open_dataset(grid_file, data_source)
+ except Exception as e:
+ logger.error(f"Failed to load {self.dataset.data_type} native grid: {e}")
+
+ logger.debug(traceback.format_exc())
+
+ self.grid_dataset = uxds
+
+ return uxds
+
+ def _process_variable_derivations(self, var_key: str) -> bool:
+ """Process variable derivations following dataset_xr approach."""
+ name_suffix = f" in {self.dataset_name} dataset" if self.dataset_name else ""
+
+ # Follow dataset_xr._get_climo_dataset logic:
+ # 1. If var is in derived_vars_map, try to derive it
+ if var_key in DERIVED_VARIABLES:
+ target_var_map = DERIVED_VARIABLES[var_key]
+ matching_target_var_map = self._get_matching_src_vars(
+ self.grid_dataset, target_var_map
+ )
+
+ if matching_target_var_map is not None:
+ # Get derivation function and source variables
+ derivation_func = list(matching_target_var_map.values())[0]
+ src_var_keys = list(matching_target_var_map.keys())[0]
+
+ logger.info(
+ f"Deriving {var_key}{name_suffix} using source variables: {src_var_keys}"
+ )
+
+ try:
+ self._apply_derivation_func(
+ self.grid_dataset, derivation_func, src_var_keys, var_key
+ )
+ return True
+ except Exception as e:
+ logger.warning(f"Failed to derive {var_key}{name_suffix}: {e}")
+
+ # 2. Check if variable exists directly in dataset
+ if var_key in self.grid_dataset.data_vars: # type: ignore
+ return True
+
+ # 3. Variable not found and couldn't be derived
+ logger.warning(
+ f"Variable {var_key} not found{name_suffix} and could not be derived"
+ )
+ return False
+
+ def _get_matching_src_vars(self, dataset, target_var_map):
+ """Get matching source variables following dataset_xr pattern."""
+ for src_vars, func in target_var_map.items():
+ if all(v in dataset for v in src_vars):
+ return {src_vars: func}
+
+ return None
+
+ def _apply_derivation_func(
+ self,
+ dataset: ux.Dataset,
+ func: Callable,
+ src_var_keys: list[str],
+ target_var_key: str,
+ ):
+ """Apply derivation function following dataset_xr pattern."""
+ func_args = [dataset[var] for var in src_var_keys]
+
+ if func in FUNC_NEEDS_TARGET_VAR:
+ func_args = [target_var_key] + func_args
+
+ derived_var = func(*func_args)
+ dataset[target_var_key] = derived_var
+
+ return dataset
diff --git a/e3sm_diags/driver/utils/dataset_xr.py b/e3sm_diags/driver/utils/dataset_xr.py
index 986f1aa06..38f24d9fa 100644
--- a/e3sm_diags/driver/utils/dataset_xr.py
+++ b/e3sm_diags/driver/utils/dataset_xr.py
@@ -37,6 +37,7 @@
from e3sm_diags.logger import _setup_child_logger
if TYPE_CHECKING:
+ from e3sm_diags.driver.utils.type_annotations import TimeSelection
from e3sm_diags.parameter.core_parameter import CoreParameter
logger = _setup_child_logger(__name__)
@@ -213,7 +214,7 @@ def _get_derived_vars_map(self) -> DerivedVariablesMap:
# --------------------------------------------------------------------------
def get_name_yrs_attr(
self,
- season: ClimoFreq | None = None,
+ season: TimeSelection | None = None,
default_name: str | None = None,
) -> str:
"""Get the diagnostic name and 'yrs_averaged' attr as a single string.
@@ -227,8 +228,9 @@ def get_name_yrs_attr(
Parameters
----------
- season : CLIMO_FREQ | None, optional
- The climatology frequency, by default None.
+ season : TimeSelection | None
+ The optional frequency for climatology or time slice, by default
+ None.
Returns
-------
@@ -322,7 +324,7 @@ def _get_ref_name(self, default_name: str | None = None) -> str:
return self.parameter.ref_name
def _get_global_attr_from_climo_dataset(
- self, attr: str, season: ClimoFreq
+ self, attr: str, season: TimeSelection
) -> str | None:
"""Get the global attribute from the climo file based on the season.
@@ -330,8 +332,8 @@ def _get_global_attr_from_climo_dataset(
----------
attr : str
The attribute to get (e.g., "Convention").
- season : CLIMO_FREQ
- The climatology frequency.
+ season : TimeSelection
+ The frequency or time slice for the climatology.
Returns
-------
@@ -398,10 +400,21 @@ def get_climo_dataset(self, var: str, season: ClimoFreq) -> xr.Dataset:
ds_climo = climo(ds, self.var, season).to_dataset()
ds_climo = ds_climo.bounds.add_missing_bounds(axes=["X", "Y"])
+ self.parameter._add_time_series_file_path_attr(self.data_type, ds)
+
return ds_climo
ds = self._get_climo_dataset(season)
+ # Store the filepath used for the dataset in the parameter object for debugging
+ try:
+ filepath = self._get_climo_filepath(season)
+
+ self.parameter._add_climatology_file_path_attr(self.data_type, filepath)
+
+ except Exception as e:
+ logger.warning(f"Failed to store absolute file path: {e}")
+
return ds
def _get_climo_dataset(self, season: str) -> xr.Dataset:
diff --git a/e3sm_diags/driver/utils/type_annotations.py b/e3sm_diags/driver/utils/type_annotations.py
index ca035ce8e..b0c6bc300 100644
--- a/e3sm_diags/driver/utils/type_annotations.py
+++ b/e3sm_diags/driver/utils/type_annotations.py
@@ -2,6 +2,15 @@
# type of metrics and the value is a sub-dictionary of metrics (key is metrics
# type and value is float). There is also a "unit" key representing the
# units for the variable.
+from e3sm_diags.driver.utils.climo_xr import ClimoFreq
+
UnitAttr = str
MetricsSubDict = dict[str, float | None | list[float]]
MetricsDict = dict[str, UnitAttr | MetricsSubDict]
+
+# Type for time slice specification: index-based with optional stride
+# Examples: "0:10:2" (start:end:stride), "5:15" (start:end), "7" (single index)
+TimeSlice = str
+
+# Union type for time selection - can be either climatology season or time slice
+TimeSelection = ClimoFreq | TimeSlice
diff --git a/e3sm_diags/metrics/metrics.py b/e3sm_diags/metrics/metrics.py
index d5cba4fbe..3a518039d 100644
--- a/e3sm_diags/metrics/metrics.py
+++ b/e3sm_diags/metrics/metrics.py
@@ -1,6 +1,8 @@
"""This module stores functions to calculate metrics using Xarray objects."""
-from typing import Literal
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Literal
import numpy as np
import xarray as xr
@@ -9,6 +11,9 @@
from e3sm_diags.logger import _setup_child_logger
+if TYPE_CHECKING:
+ import uxarray as ux
+
logger = _setup_child_logger(__name__)
Axis = Literal["X", "Y", "Z"]
@@ -301,3 +306,107 @@ def _get_dims(da: xr.DataArray, axis: list[Axis]) -> list[str]:
dims.append(dim_key)
return dims
+
+
+def native_rmse(uxds_a: "ux.UxDataset", uxds_b: "ux.UxDataset", var_key: str) -> float:
+ """Calculate RMSE for native grid datasets using uxarray and xskillscore.
+
+ Parameters
+ ----------
+ uxds_a : ux.UxDataset
+ The first uxarray dataset.
+ uxds_b : ux.UxDataset
+ The second uxarray dataset.
+ var_key : str
+ The key of the variable.
+
+ Returns
+ -------
+ float
+ The root mean square error.
+
+ Raises
+ ------
+ RuntimeError
+ If RMSE calculation fails.
+ """
+ try:
+ import xskillscore as xs
+
+ var_a = uxds_a[var_key]
+ var_b = uxds_b[var_key]
+
+ # Get spatial dimensions
+ var_dims = list(var_a.dims)
+ spatial_dims = [
+ dim for dim in var_dims if "face" in dim or "node" in dim or "edge" in dim
+ ]
+ if not spatial_dims:
+ spatial_dims = [dim for dim in var_dims if dim != "time"]
+
+ # Get appropriate weights
+ weights = None
+ if var_a._face_centered():
+ weights = var_a.uxgrid.face_areas
+ elif var_a._edge_centered():
+ weights = var_a.uxgrid.edge_node_distances
+
+ return xs.rmse(
+ var_a, var_b, dim=spatial_dims, weights=weights, skipna=True
+ ).item()
+
+ except Exception as e:
+ raise RuntimeError(f"Failed to calculate native grid RMSE: {e}") from e
+
+
+def native_correlation(
+ uxds_a: "ux.UxDataset", uxds_b: "ux.UxDataset", var_key: str
+) -> float:
+ """Calculate Pearson correlation for native grid datasets using uxarray and xskillscore.
+
+ Parameters
+ ----------
+ uxds_a : ux.UxDataset
+ The first uxarray dataset.
+ uxds_b : ux.UxDataset
+ The second uxarray dataset.
+ var_key : str
+ The key of the variable.
+
+ Returns
+ -------
+ float
+ The Pearson correlation coefficient.
+
+ Raises
+ ------
+ RuntimeError
+ If correlation calculation fails.
+ """
+ try:
+ import xskillscore as xs
+
+ var_a = uxds_a[var_key]
+ var_b = uxds_b[var_key]
+
+ # Get spatial dimensions
+ var_dims = list(var_a.dims)
+ spatial_dims = [
+ dim for dim in var_dims if "face" in dim or "node" in dim or "edge" in dim
+ ]
+ if not spatial_dims:
+ spatial_dims = [dim for dim in var_dims if dim != "time"]
+
+ # Get appropriate weights
+ weights = None
+ if var_a._face_centered():
+ weights = var_a.uxgrid.face_areas
+ elif var_a._edge_centered():
+ weights = var_a.uxgrid.edge_node_distances
+
+ return xs.pearson_r(
+ var_a, var_b, dim=spatial_dims, weights=weights, skipna=True
+ ).item()
+
+ except Exception as e:
+ raise RuntimeError(f"Failed to calculate native grid correlation: {e}") from e
diff --git a/e3sm_diags/parameter/__init__.py b/e3sm_diags/parameter/__init__.py
index d37d0bccd..fcbb5a49f 100644
--- a/e3sm_diags/parameter/__init__.py
+++ b/e3sm_diags/parameter/__init__.py
@@ -5,6 +5,7 @@
from .diurnal_cycle_parameter import DiurnalCycleParameter
from .enso_diags_parameter import EnsoDiagsParameter
from .lat_lon_land_parameter import LatLonLandParameter
+from .lat_lon_native_parameter import LatLonNativeParameter
from .lat_lon_river_parameter import LatLonRiverParameter
from .meridional_mean_2d_parameter import MeridionalMean2dParameter
from .mp_partition_parameter import MPpartitionParameter
@@ -21,6 +22,7 @@
"zonal_mean_2d_stratosphere": ZonalMean2dStratosphereParameter,
"meridional_mean_2d": MeridionalMean2dParameter,
"lat_lon": CoreParameter,
+ "lat_lon_native": LatLonNativeParameter,
"polar": CoreParameter,
"cosp_histogram": CoreParameter,
"area_mean_time_series": AreaMeanTimeSeriesParameter,
diff --git a/e3sm_diags/parameter/core_parameter.py b/e3sm_diags/parameter/core_parameter.py
index d542b4693..ce4139393 100644
--- a/e3sm_diags/parameter/core_parameter.py
+++ b/e3sm_diags/parameter/core_parameter.py
@@ -2,8 +2,9 @@
import copy
import importlib
+import os
import sys
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
import numpy as np
@@ -13,6 +14,11 @@
from e3sm_diags.driver.utils.regrid import REGRID_TOOLS
from e3sm_diags.logger import _setup_child_logger
+if TYPE_CHECKING:
+ import xarray as xr
+
+ from e3sm_diags.driver.utils.type_annotations import TimeSelection
+
logger = _setup_child_logger(__name__)
# FIXME: There is probably a better way of defining default sets because most of
@@ -25,6 +31,7 @@
"zonal_mean_2d_stratosphere",
"meridional_mean_2d",
"lat_lon",
+ "lat_lon_native",
"polar",
"area_mean_time_series",
"cosp_histogram",
@@ -74,6 +81,11 @@ def __init__(self):
# (REQUIRED) Path to the test (model) data.
self.test_data_path: str = ""
+ # File paths for data files (set dynamically during processing using
+ # the dataset's file_path attribute).
+ self.test_data_file_path = "" # Path to test data file
+ self.ref_data_file_path = "" # Path to reference data file
+
# (REQUIRED) The name of the folder where all runs will be stored.
self.results_dir: str = ""
@@ -288,7 +300,7 @@ def _set_param_output_attrs(
self.main_title = main_title
def _set_name_yrs_attrs(
- self, ds_test: Dataset, ds_ref: Dataset, season: ClimoFreq | None
+ self, ds_test: Dataset, ds_ref: Dataset, season: TimeSelection | None
):
"""Set the test_name_yrs and ref_name_yrs attributes.
@@ -298,8 +310,8 @@ def _set_name_yrs_attrs(
The test dataset object used for setting ``self.test_name_yrs``.
ds_ref : Dataset
The ref dataset object used for setting ``self.ref_name_yrs``.
- season : ClimoFreq | None
- The optional climatology frequency.
+ season : TimeSelection | None
+ The optional frequency for climatology or time slice.
"""
self.test_name_yrs = ds_test.get_name_yrs_attr(season)
self.ref_name_yrs = ds_ref.get_name_yrs_attr(season)
@@ -357,6 +369,63 @@ def _run_diag(self) -> list[Any]:
return results
+ def _add_time_series_file_path_attr(
+ self,
+ data_type: Literal["test", "ref"],
+ ds: xr.Dataset,
+ ):
+ """Add file path attributes to the parameter object.
+
+ Parameters
+ ----------
+ data_type : Literal["test", "ref"]
+ The type of data, either "test" or "ref".
+ ds : xr.Dataset
+ The dataset object containing the file path attribute.
+
+ Raises
+ ------
+ ValueError
+ If `data_type` is not "test" or "ref".
+ """
+ if data_type not in {"test", "ref"}:
+ raise ValueError("data_type must be either 'test' or 'ref'.")
+
+ file_path_attr = f"{data_type}_data_file_path"
+
+ setattr(self, file_path_attr, getattr(ds, "file_path", "Unknown"))
+
+ def _add_climatology_file_path_attr(
+ self,
+ data_type: Literal["test", "ref"],
+ filepath: str | None = None,
+ ):
+ """Add file path attributes to the parameter object.
+
+ Parameters
+ ----------
+ data_type : Literal["test", "ref"]
+ The type of data, either "test" or "ref".
+ filepath : str | None, optional
+ The file path for climatology data.
+
+ Raises
+ ------
+ ValueError
+ If `data_type` is not "test" or "ref".
+ ValueError
+ If `filepath` is not provided for climatology data.
+ """
+ if data_type not in {"test", "ref"}:
+ raise ValueError("data_type must be either 'test' or 'ref'.")
+
+ file_path_attr = f"{data_type}_data_file_path"
+
+ if not filepath:
+ raise ValueError("Filepath must be provided for climatology data.")
+
+ setattr(self, file_path_attr, os.path.abspath(filepath))
+
def __setattr__(self, name: str, value: Any) -> None:
"""Override setattr to ensure year attributes are padded when set."""
if name in YEAR_ATTRIBUTES and value not in [None, ""]:
diff --git a/e3sm_diags/parameter/lat_lon_native_parameter.py b/e3sm_diags/parameter/lat_lon_native_parameter.py
new file mode 100644
index 000000000..78a7da395
--- /dev/null
+++ b/e3sm_diags/parameter/lat_lon_native_parameter.py
@@ -0,0 +1,188 @@
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+from e3sm_diags.parameter.core_parameter import CoreParameter
+
+if TYPE_CHECKING:
+ from e3sm_diags.driver.utils.dataset_xr import Dataset
+ from e3sm_diags.driver.utils.type_annotations import TimeSelection, TimeSlice
+
+
+class LatLonNativeParameter(CoreParameter):
+ """Parameters for the lat_lon_native diagnostic set.
+
+ This diagnostic set allows displaying data on native grids (e.g., cubed-sphere)
+ using uxarray's visualization capabilities.
+ """
+
+ def __init__(self):
+ super(LatLonNativeParameter, self).__init__()
+
+ # Override existing attributes
+ # =============================
+ # Path to the grid files for the native grids
+ self.test_grid_file = "" # Grid file for test data
+ self.ref_grid_file = "" # Grid file for reference data
+
+ # Style options for native grid visualization
+ self.edge_color = None # Set to a color string to show grid edges
+ self.edge_width = 0.3 # Width of grid edges when displayed
+
+ # Option to disable the grid antialiasing (may improve performance)
+ self.antialiased = False
+
+ # Time selection parameters (mutually exclusive with seasons)
+ # Either use seasons (inherited from CoreParameter) OR time_slices
+ # Index-based time selection with stride support
+ # Examples: ["0:10:2", "5:15", "7"] for start:end:stride, start:end, or single index
+ self.time_slices: list[TimeSlice] = []
+
+ def check_values(self):
+ """Verifies that required values are properly set.
+
+ Raises
+ ------
+ RuntimeError
+ If no grid files are provided or set.
+ RuntimeError
+ If neither seasons nor time_slices are specified.
+ """
+ has_seasons = len(self.seasons) > 0
+ has_time_slices = len(self.time_slices) > 0
+
+ if not has_seasons and not has_time_slices:
+ raise RuntimeError(
+ "Must specify either 'seasons' or 'time_slices'. "
+ "Use 'seasons' for climatological analysis (e.g., ['ANN', 'DJF']) "
+ "or 'time_slices' for index-based selection (e.g., ['0:10:2', '5:15'])."
+ )
+
+ # Validate time_slice format if provided
+ if has_time_slices:
+ for time_slice in self.time_slices:
+ self._validate_time_slice_format(time_slice)
+
+ # TODO: For now, we'll make grid file check a soft check. In the future,
+ # we may want to require at least test_grid_file
+ pass
+
+ def _validate_time_slice_format(self, time_slice: str):
+ r"""Validate that time_slice follows the expected format.
+
+ This regex pattern for slice notation is designed to match a
+ latitude/longitude-like format with optional degrees, minutes, and
+ seconds.
+ - ^: Matches the start of the string.
+ - (-?\d+|): Matches an optional integer (can be negative) for degrees.
+ - (?::(-?\d+|): Matches an optional colon followed by an optional
+ integer (can be negative) for minutes.
+ - (?::(-?\d+|)): Matches an optional colon followed by an optional
+ integer (can be negative) for seconds.
+ - )?: Makes the minutes and seconds groups optional.
+ - $: Matches the end of the string.
+
+ Valid formats:
+ - "index" (single index): "5"
+ - "start:end" (range): "0:10"
+ - "start:end:stride" (range with stride): "0:10:2"
+ - ":end" (from beginning): ":10"
+ - "start:" (to end): "5:"
+ - "::stride" (full range with stride): "::2"
+
+ Parameters
+ ----------
+ time_slice : str
+ The time slice string to validate
+
+ Raises
+ ------
+ ValueError
+ If the time slice format is invalid
+ """
+ pattern = r"^(-?\d+|)(?::(-?\d+|)(?::(-?\d+|))?)?$"
+
+ if not re.match(pattern, time_slice.strip()):
+ raise ValueError(
+ f"Invalid time_slice format: '{time_slice}'. "
+ f"Expected formats: 'index', 'start:end', 'start:end:stride', "
+ f"':end', 'start:', or '::stride'. Examples: '5', '0:10', '0:10:2'"
+ )
+
+ def _set_name_yrs_attrs(
+ self, test_ds: Dataset, ref_ds: Dataset, season: TimeSelection | None
+ ):
+ """Override parent method to handle both ClimoFreq and time slice strings.
+
+ Parameters
+ ----------
+ test_ds : Dataset
+ The test dataset object.
+ ref_ds : Dataset
+ The reference dataset object.
+ season : TimeSelection | None
+ The season or time slice string.
+ """
+ from e3sm_diags.driver.utils.climo_xr import CLIMO_FREQS
+
+ if season is None or season in CLIMO_FREQS:
+ # Standard climatology season, use parent implementation.
+ super()._set_name_yrs_attrs(test_ds, ref_ds, season)
+ else:
+ # This is a time slice string, handle it specially.
+ self._set_time_slice_attrs(test_ds, ref_ds, season)
+
+ def _set_time_slice_attrs(self, test_ds: Dataset, ref_ds: Dataset, time_slice: str):
+ """Set attributes for time slice-based processing.
+
+ This method sets up the necessary attributes for file naming and
+ processing when using time_slices instead of seasons.
+
+ Store the time slice info but keep current_set as the diagnostic set name
+ current_set should remain as "lat_lon_native" for proper directory structure
+ The time slice will be used in filename generation via other attributes
+
+ Parameters
+ ----------
+ test_ds : Dataset
+ The test dataset object.
+ ref_ds : Dataset
+ The reference dataset object.
+ time_slice : str
+ The time slice specification.
+ """
+ # Set the time slice info for potential use in plotting/output
+ self.current_time_slice = time_slice
+
+ # For time slices, we manually set the name_yrs attributes instead of
+ # calling parent method to avoid issues with the dataset's get_name_yrs_attr
+ # expecting a valid season
+
+ # Set test_name_yrs - use test dataset years if available, otherwise use
+ # time slice info
+ try:
+ # Try to get year range from test dataset start/end years
+ if hasattr(test_ds, "start_yr") and hasattr(test_ds, "end_yr"):
+ test_years = f"{test_ds.start_yr:04d}-{test_ds.end_yr:04d}"
+ else:
+ test_years = f"timeslice_{time_slice}"
+
+ self.test_name_yrs = f"{getattr(self, 'test_name', 'test')}_{test_years}"
+ except AttributeError:
+ self.test_name_yrs = (
+ f"{getattr(self, 'test_name', 'test')}_timeslice_{time_slice}"
+ )
+
+ # Set ref_name_yrs - use ref dataset years if available, otherwise use time slice info
+ try:
+ if hasattr(ref_ds, "start_yr") and hasattr(ref_ds, "end_yr"):
+ ref_years = f"{ref_ds.start_yr:04d}-{ref_ds.end_yr:04d}"
+ else:
+ ref_years = f"timeslice_{time_slice}"
+
+ self.ref_name_yrs = f"{getattr(self, 'ref_name', 'ref')}_{ref_years}"
+ except AttributeError:
+ self.ref_name_yrs = (
+ f"{getattr(self, 'ref_name', 'ref')}_timeslice_{time_slice}"
+ )
diff --git a/e3sm_diags/parser/__init__.py b/e3sm_diags/parser/__init__.py
index 781531d36..f078e6e7e 100644
--- a/e3sm_diags/parser/__init__.py
+++ b/e3sm_diags/parser/__init__.py
@@ -3,6 +3,7 @@
from e3sm_diags.parser.core_parser import CoreParser
from e3sm_diags.parser.diurnal_cycle_parser import DiurnalCycleParser
from e3sm_diags.parser.enso_diags_parser import EnsoDiagsParser
+from e3sm_diags.parser.lat_lon_native_parser import LatLonNativeParser
from e3sm_diags.parser.meridional_mean_2d_parser import MeridionalMean2dParser
from e3sm_diags.parser.mp_partition_parser import MPpartitionParser
from e3sm_diags.parser.qbo_parser import QboParser
@@ -20,6 +21,7 @@
"zonal_mean_2d_stratosphere": ZonalMean2dStratosphereParser,
"meridional_mean_2d": MeridionalMean2dParser,
"lat_lon": CoreParser,
+ "lat_lon_native": LatLonNativeParser,
"polar": CoreParser,
"cosp_histogram": CoreParser,
"area_mean_time_series": AreaMeanTimeSeriesParser,
diff --git a/e3sm_diags/parser/core_parser.py b/e3sm_diags/parser/core_parser.py
index 1a0a45d91..07a2fe6ce 100644
--- a/e3sm_diags/parser/core_parser.py
+++ b/e3sm_diags/parser/core_parser.py
@@ -1031,7 +1031,26 @@ def _granulate(self, parameters):
delattr(param, module)
# Granulate param.
- vars_to_granulate = param.granulate # Ex: ['seasons', 'plevs']
+ # Make a copy of param.granulate to avoid mutating the original list.
+ # This is necessary because we may remove 'seasons' from the
+ # granulation variables for special cases (e.g., lat_lon_native with
+ # time_slices), and modifying the original would cause side effects
+ # for other uses of the parameter object.
+ vars_to_granulate = param.granulate.copy() # Ex: ['seasons', 'plevs']
+
+ # Special handling for lat_lon_native: if time_slices is specified,
+ # remove seasons from granulation
+ if (
+ hasattr(param, "time_slices")
+ and hasattr(param, "seasons")
+ and len(param.time_slices) > 0
+ and "seasons" in vars_to_granulate
+ ):
+ vars_to_granulate.remove("seasons")
+
+ # Also clear default seasons to avoid conflicts
+ param.seasons = []
+
# Check that all of the vars_to_granulate are iterables.
# Ex: {'season': ['ANN', 'DJF', 'MAM'], 'plevs': [850.0, 250.0]}
vals_to_granulate = collections.OrderedDict()
diff --git a/e3sm_diags/parser/lat_lon_native_parser.py b/e3sm_diags/parser/lat_lon_native_parser.py
new file mode 100644
index 000000000..be5ec8bbc
--- /dev/null
+++ b/e3sm_diags/parser/lat_lon_native_parser.py
@@ -0,0 +1,58 @@
+from e3sm_diags.parameter.lat_lon_native_parameter import LatLonNativeParameter
+from e3sm_diags.parser.core_parser import CoreParser
+
+
+class LatLonNativeParser(CoreParser):
+ def __init__(self, *args, **kwargs):
+ # FIXME: B026 Star-arg unpacking after a keyword argument is strongly discouraged
+ super().__init__(parameter_cls=LatLonNativeParameter, *args, **kwargs) # type: ignore # noqa: B026
+
+ def add_arguments(self):
+ super().add_arguments()
+
+ self.parser.add_argument(
+ "--test_grid_file",
+ dest="test_grid_file",
+ help="Path to the native grid file for test data visualization",
+ required=False,
+ )
+
+ self.parser.add_argument(
+ "--ref_grid_file",
+ dest="ref_grid_file",
+ help="Path to the native grid file for reference data visualization",
+ required=False,
+ )
+
+ self.parser.add_argument(
+ "--antialiased",
+ dest="antialiased",
+ help="Whether to use antialiasing for grid edges",
+ action="store_true",
+ default=False,
+ required=False,
+ )
+
+ self.parser.add_argument(
+ "--edge_color",
+ dest="edge_color",
+ help="Color for grid edges (None for no edges)",
+ required=False,
+ )
+
+ self.parser.add_argument(
+ "--edge_width",
+ dest="edge_width",
+ type=float,
+ default=0.3,
+ help="Width of grid edges when displayed",
+ required=False,
+ )
+
+ self.parser.add_argument(
+ "--time_slices",
+ dest="time_slices",
+ nargs="+",
+ help="Time slices for snapshot-based analysis (e.g., '0', '0:10:2', '5:15'). Mutually exclusive with seasons.",
+ required=False,
+ )
diff --git a/e3sm_diags/plot/lat_lon_native_plot.py b/e3sm_diags/plot/lat_lon_native_plot.py
new file mode 100644
index 000000000..9b0a491c9
--- /dev/null
+++ b/e3sm_diags/plot/lat_lon_native_plot.py
@@ -0,0 +1,613 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Optional
+
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import matplotlib
+import matplotlib.colors as mcolors
+import numpy as np
+import uxarray as ux
+
+from e3sm_diags.derivations.default_regions_xr import REGION_SPECS
+from e3sm_diags.logger import _setup_child_logger
+from e3sm_diags.plot.utils import (
+ DEFAULT_PANEL_CFG,
+ _add_min_mean_max_text,
+ _add_rmse_corr_text,
+ _get_c_levels_and_norm,
+ _get_colormap,
+ _save_plot,
+)
+
+if TYPE_CHECKING:
+ from e3sm_diags.parameter.lat_lon_native_parameter import LatLonNativeParameter
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt # isort:skip # noqa: E402
+
+logger = _setup_child_logger(__name__)
+
+
+def plot( # noqa: C901
+ parameter: LatLonNativeParameter,
+ var_key: str,
+ region: str,
+ uxds_test: ux.dataset.UxDataset,
+ uxds_ref: Optional[ux.dataset.UxDataset] = None,
+ uxds_diff: Optional[ux.dataset.UxDataset] = None,
+):
+ """Create visualization of data on native (unstructured) grids using uxarray.
+
+ This function creates plots without regridding to a regular lat-lon grid.
+ The layout matches the standard lat_lon_plot with 3 panels (test, ref, diff).
+
+ Parameters
+ ----------
+ parameter : LatLonNativeParameter
+ The parameter object.
+ var_key : str
+ The variable key.
+ region : str
+ The region name, used to determine map extents.
+ uxds_test : ux.dataset.UxDataset
+ The test native grid dataset.
+ uxds_ref : ux.dataset.UxDataset, optional
+ The reference native grid dataset.
+ uxds_diff : ux.dataset.UxDataset, optional
+ The difference native grid dataset.
+ ilev : float, optional
+ The pressure level to visualize for 3D variables.
+ """
+ logger.info(f"Creating native grid plot for {var_key}, region={region}")
+
+ if uxds_test is None or var_key not in uxds_test:
+ logger.error(
+ f"Cannot plot native grid data. Either uxds_test is None or {var_key} not in dataset"
+ )
+ if uxds_test is not None:
+ logger.error(f"Available variables: {list(uxds_test.data_vars)}")
+ return
+
+ has_reference = uxds_ref is not None and var_key in uxds_ref
+ if has_reference:
+ logger.info(
+ f"Reference data available for {var_key}, implementing model vs model visualization"
+ )
+
+ # Set the viewer description to the "long_name" attr of the variable
+ if "long_name" in uxds_test[var_key].attrs:
+ parameter.viewer_descr[var_key] = uxds_test[var_key].attrs["long_name"]
+ else:
+ parameter.viewer_descr[var_key] = var_key
+
+ # Create figure with standard layout
+ fig = plt.figure(figsize=parameter.figsize, dpi=parameter.dpi)
+
+ # Use the same title formatting as in lat_lon_plot (fontsize=18, y=0.96)
+ fig.suptitle(parameter.main_title, x=0.5, y=0.96, fontsize=18)
+
+ # Get region information for setting map extents
+ region_specs = REGION_SPECS.get(region, None)
+
+ # Set map bounds based on region
+ if region_specs is not None:
+ lat_bounds = region_specs.get("lat", (-90, 90)) # type: ignore
+ lon_bounds = region_specs.get("lon", (0, 360)) # type: ignore
+ is_global_domain = lat_bounds == (-90, 90) and lon_bounds == (0, 360)
+ else:
+ lat_bounds = (-90, 90)
+ lon_bounds = (0, 360)
+ is_global_domain = True
+
+ # Get the cartopy projection based on region info.
+ # --------------------------------------------------------------------------
+ # Determine projection and extents based on region
+ projection = ccrs.PlateCarree()
+ if is_global_domain:
+ projection = ccrs.PlateCarree(central_longitude=180)
+
+ logger.info(f"Region: {region}, lat_bounds: {lat_bounds}, lon_bounds: {lon_bounds}")
+
+ # Extract metrics from parameter.metrics_dict (calculated in driver with regional subsetting)
+ if uxds_test is not None and var_key in uxds_test:
+ units = uxds_test[var_key].attrs.get("units", "")
+
+ # Get test metrics from parameter.metrics_dict
+ try:
+ test_min = parameter.metrics_dict["test_regrid"]["min"][0] # type: ignore
+ test_max = parameter.metrics_dict["test_regrid"]["max"][0] # type: ignore
+ test_mean = parameter.metrics_dict["test_regrid"]["mean"][0] # type: ignore
+ except (KeyError, IndexError, TypeError) as e:
+ logger.warning(
+ f"Failed to get test metrics from metrics_dict: {e}, using NaN"
+ )
+ test_min = test_max = test_mean = float("nan")
+ else:
+ # This should not happen since we check earlier, but just in case
+ logger.error(f"Missing test data for variable {var_key} in native grid dataset")
+ return
+
+ # Extract metrics for reference data if available
+ ref_min = ref_max = ref_mean = diff_min = diff_max = diff_mean = None
+ if has_reference and uxds_ref is not None:
+ ref_units = uxds_ref[var_key].attrs.get("units", "")
+
+ # Check if units match between test and reference
+ if units != ref_units:
+ logger.warning(
+ f"Units mismatch between test ({units}) and reference ({ref_units})"
+ )
+
+ # Get reference metrics from parameter.metrics_dict
+ try:
+ ref_min = parameter.metrics_dict["ref"]["min"][0] # type: ignore
+ ref_max = parameter.metrics_dict["ref"]["max"][0] # type: ignore
+ ref_mean = parameter.metrics_dict["ref"]["mean"][0] # type: ignore
+ except (KeyError, IndexError, TypeError):
+ logger.warning(
+ "Failed to get reference metrics from metrics_dict, using NaN"
+ )
+ ref_min = ref_max = ref_mean = float("nan")
+
+ # Get difference metrics from parameter.metrics_dict
+ if uxds_diff is not None and var_key in uxds_diff:
+ try:
+ diff_min = parameter.metrics_dict["diff"]["min"][0] # type: ignore
+ diff_max = parameter.metrics_dict["diff"]["max"][0] # type: ignore
+ diff_mean = parameter.metrics_dict["diff"]["mean"][0] # type: ignore
+ except (KeyError, IndexError, TypeError):
+ logger.warning(
+ "Failed to get diff metrics from metrics_dict, using NaN"
+ )
+ diff_min = diff_max = diff_mean = float("nan")
+
+ # Create panels following the lat_lon_plot layout
+ # Panel 1: Test data (always created)
+ ax1 = fig.add_axes(DEFAULT_PANEL_CFG[0], projection=projection)
+
+ # Use the standard title configuration from utils._configure_titles
+ # Format: (years_text on left, main title in center, units on right)
+ ax1.set_title(parameter.test_name_yrs, loc="left", fontdict={"fontsize": 9.5})
+ ax1.set_title(parameter.test_title, fontdict={"fontsize": 11.5})
+ ax1.set_title(units, loc="right", fontdict={"fontsize": 9.5})
+
+ # Initialize ax2 and ax3 as None - they'll only be created when reference data exists
+ ax2 = None
+ ax3 = None
+
+ # Only create panels 2 and 3 when reference data is available
+ if has_reference:
+ # Panel 2: Reference data
+ ax2 = fig.add_axes(DEFAULT_PANEL_CFG[1], projection=projection)
+ ax2.set_title(parameter.ref_name_yrs, loc="left", fontdict={"fontsize": 9.5})
+ ax2.set_title(parameter.reference_title, fontdict={"fontsize": 11.5})
+ ax2.set_title(units, loc="right", fontdict={"fontsize": 9.5})
+
+ # Panel 3: Difference plot
+ ax3 = fig.add_axes(DEFAULT_PANEL_CFG[2], projection=projection)
+ ax3.set_title("", loc="left", fontdict={"fontsize": 9.5})
+ ax3.set_title(parameter.diff_title, fontdict={"fontsize": 11.5})
+ ax3.set_title(units, loc="right", fontdict={"fontsize": 9.5})
+
+ # Configure map settings for all created panels
+ panels = [ax for ax in [ax1, ax2, ax3] if ax is not None]
+ _configure_map_panels(
+ panels, region, region_specs, lat_bounds, lon_bounds, is_global_domain
+ )
+
+ # Create the test panel visualization
+ _create_panel_visualization(
+ uxds_test,
+ var_key,
+ ax1,
+ fig,
+ DEFAULT_PANEL_CFG[0],
+ units,
+ parameter.test_colormap,
+ parameter.contour_levels,
+ test_min,
+ test_max,
+ test_mean,
+ 0, # subplot_num
+ False,
+ parameter.antialiased,
+ )
+
+ # Create reference panel visualization if available
+ if has_reference and ax2 is not None:
+ _create_panel_visualization(
+ uxds_ref,
+ var_key,
+ ax2,
+ fig,
+ DEFAULT_PANEL_CFG[1],
+ units,
+ parameter.reference_colormap,
+ parameter.contour_levels,
+ ref_min, # type: ignore
+ ref_max, # type: ignore
+ ref_mean, # type: ignore
+ 1, # subplot_num
+ False,
+ parameter.antialiased,
+ )
+
+ # Create difference panel visualization if available
+ if ax3 is not None:
+ try:
+ if uxds_diff is not None and var_key in uxds_diff:
+ _create_panel_visualization(
+ uxds_diff,
+ var_key,
+ ax3,
+ fig,
+ DEFAULT_PANEL_CFG[2],
+ units,
+ parameter.diff_colormap,
+ parameter.diff_levels
+ if hasattr(parameter, "diff_levels")
+ else None,
+ diff_min, # type: ignore
+ diff_max, # type: ignore
+ diff_mean, # type: ignore
+ 2, # subplot_num
+ True,
+ parameter.antialiased,
+ )
+
+ # Get metrics from parameter (calculated in driver on remapped datasets)
+ try:
+ rmse_val = parameter.metrics_dict["misc"]["rmse"][0] # type: ignore
+ corr_val = parameter.metrics_dict["misc"]["corr"][0] # type: ignore
+ except (KeyError, IndexError, TypeError):
+ rmse_val = float("nan")
+ corr_val = float("nan")
+
+ diff_metrics: tuple[float, float, float, float, float] = (
+ float(diff_max) if diff_max is not None else float("nan"),
+ float(diff_mean) if diff_mean is not None else float("nan"),
+ float(diff_min) if diff_min is not None else float("nan"),
+ rmse_val,
+ corr_val,
+ )
+
+ # Add RMSE/correlation text for difference panel
+ _add_rmse_corr_text(
+ fig, 2, DEFAULT_PANEL_CFG, diff_metrics, fontsize=9.5
+ )
+ # ------------------------------------------------------------
+
+ else:
+ # If difference calculation failed, show a message
+ ax3.text(
+ 0.5,
+ 0.5,
+ "Could not calculate difference data for native grids",
+ transform=ax3.transAxes,
+ ha="center",
+ va="center",
+ fontsize=11,
+ )
+ except Exception as e:
+ # Fallback if there's an error in diff calculation or visualization
+ logger.error(f"Error calculating or visualizing difference data: {e}")
+ import traceback
+
+ logger.error(traceback.format_exc())
+ ax3.text(
+ 0.5,
+ 0.5,
+ f"Error in difference calculation:\n{str(e)}",
+ transform=ax3.transAxes,
+ ha="center",
+ va="center",
+ fontsize=11,
+ )
+
+ # Save the plot using the standard output path structure
+ _save_plot(fig, parameter)
+ plt.close(fig)
+
+
+def _configure_map_panels(
+ panels, region, region_specs, lat_bounds, lon_bounds, is_global_domain
+):
+ """Configure map settings (projection, extent, features) for all panels.
+
+ FIXME: Refactor this function for readability.
+
+ Parameters
+ ----------
+ panels : list
+ List of matplotlib axes to configure
+ region : str
+ Region name
+ region_specs : dict
+ Region specifications from REGION_SPECS
+ lat_bounds : tuple
+ Latitude bounds (south, north)
+ lon_bounds : tuple
+ Longitude bounds (west, east)
+ is_global_domain : bool
+ Whether this is a global domain
+ """
+ # Determine X and Y ticks
+ lat_south, lat_north = lat_bounds
+ lon_west, lon_east = lon_bounds
+
+ for ax in panels:
+ # Handle global domain specially - don't use set_extent for global domain
+ if is_global_domain:
+ logger.info("Using global view")
+ ax.set_global()
+ else:
+ try:
+ # More robust longitude handling for map extents
+ lon_west_orig, lon_east_orig = lon_west, lon_east
+
+ # For regions that don't specify longitude (like 60S60N), use the full longitude range
+ if region_specs and "lon" not in region_specs:
+ logger.info(
+ f"Region {region} only specifies latitude bounds, using full longitude range"
+ )
+ lon_west = 0
+ lon_east = 360
+
+ # Now determine the best projection based on the region
+ # For full longitude range or close to it, use central_longitude=180
+ is_lon_full = lon_east - lon_west >= 350
+
+ # Set up appropriate projection
+ if is_lon_full:
+ logger.info(
+ "Using central longitude 180 for full/near-full longitude range"
+ )
+ projection = ccrs.PlateCarree(central_longitude=180)
+ ax.projection = projection
+ # For full longitude, use simplified extent setting
+ ax.set_extent(
+ [-180, 180, lat_south, lat_north], crs=ccrs.PlateCarree()
+ )
+ else:
+ # For partial longitude ranges, we need to handle differently
+ # Normalize to [-180, 180] range for consistency with cartopy
+ if lon_west > 180:
+ lon_west -= 360
+ if lon_east > 180:
+ lon_east -= 360
+
+ # Handle cases where the region crosses the dateline
+ if lon_east < lon_west:
+ # This is a dateline-crossing region (e.g., Pacific)
+ logger.info(
+ f"Detected dateline crossing region: lon=[{lon_west}, {lon_east}]"
+ )
+
+ # For dateline-crossing regions, adjust the central longitude of projection
+ center_lon = (lon_west + lon_east + 360) / 2.0
+ if center_lon > 180:
+ center_lon -= 360
+
+ logger.info(f"Using central longitude: {center_lon}")
+
+ # Create a new projection with the adjusted central longitude
+ ax.projection = ccrs.PlateCarree(central_longitude=center_lon)
+
+ # When using a central_longitude, we need to transform our coordinates
+ # Adjust longitudes for the new central longitude
+ if lon_west < 0:
+ lon_west += 360
+ if lon_east < 0:
+ lon_east += 360
+
+ logger.info(
+ f"Transformed coordinates for central_longitude={center_lon}: lon=[{lon_west}, {lon_east}]"
+ )
+
+ # Make sure longitudes are properly ordered
+ if lon_east < lon_west:
+ logger.warning(
+ "East longitude still less than west after transforms - swapping values"
+ )
+ lon_west, lon_east = lon_east, lon_west
+
+ logger.info(
+ f"Final map extent: lon=[{lon_west}, {lon_east}], lat=[{lat_south}, {lat_north}]"
+ )
+
+ # Set the extent using the adjusted longitude values
+ ax.set_extent(
+ [lon_west, lon_east, lat_south, lat_north],
+ crs=ccrs.PlateCarree(),
+ )
+
+ except Exception as e:
+ # Comprehensive error handling
+ logger.error(f"Error setting map extent: {e}")
+ logger.error(f"Original lon bounds: [{lon_west_orig}, {lon_east_orig}]")
+ logger.error(f"Transformed lon bounds: [{lon_west}, {lon_east}]")
+ import traceback
+
+ logger.error(traceback.format_exc())
+
+ # Fallback to global view
+ logger.info("Falling back to global view due to extent error")
+ ax.set_global()
+
+ # Add map features
+ ax.coastlines(linewidth=0.5)
+ ax.add_feature(cfeature.BORDERS, linewidth=0.3)
+
+ # Configure gridlines and labels
+ gl = ax.gridlines(
+ crs=ccrs.PlateCarree(),
+ draw_labels=True,
+ linewidth=0.5,
+ color="gray",
+ alpha=0.5,
+ linestyle="--",
+ )
+ gl.top_labels = False
+ gl.right_labels = False
+
+ # Set aspect ratio only for non-global views
+ if not is_global_domain:
+ # Ensure we have a valid aspect ratio by clamping to reasonable values
+ width = lon_east - lon_west
+ height = lat_north - lat_south
+ if width <= 0:
+ width += 360 # Handle wraparound cases
+ aspect_ratio = width / (
+ 2 * max(height, 1)
+ ) # Avoid division by zero or negative values
+ logger.info(f"Setting aspect ratio: {aspect_ratio}")
+ ax.set_aspect(aspect_ratio)
+
+
+def _create_panel_visualization(
+ dataset: ux.UxDataset,
+ var_key: str,
+ ax: matplotlib.axes.Axes,
+ fig: matplotlib.figure.Figure,
+ panel_cfg: tuple[float, float, float, float],
+ units: str,
+ colormap_name: str,
+ contour_levels: list[float] | None,
+ min_value: float,
+ max_value: float,
+ mean_value: float,
+ subplot_num: int,
+ is_diff: bool = False,
+ antialiased: bool = True,
+) -> matplotlib.collections.PolyCollection:
+ """Create a panel visualization with PolyCollection for native grid data.
+
+ Parameters
+ ----------
+ dataset : ux.UxDataset
+ The uxarray dataset
+ var_key : str
+ The variable key
+ ax : matplotlib.axes.Axes
+ The axis to draw on
+ fig : matplotlib.figure.Figure
+ The figure for adding colorbars and text
+ panel_cfg : tuple
+ The panel configuration (x, y, width, height)
+ units : str
+ The units string
+ colormap_name : str
+ The name of the colormap
+ contour_levels : list or None
+ List of contour levels or None
+ min_value, max_value, mean_value : float
+ The min, max, and mean values
+ is_diff : bool
+ Whether this is a difference plot
+ antialiased : bool
+ Whether to antialias the PolyCollection
+
+ Returns
+ -------
+ pc : matplotlib.collections.PolyCollection
+ The created PolyCollection
+ """
+ # Get the data array and handle time dimension if present
+ var_data = dataset[var_key]
+
+ # Check if time dimension exists and has more than one point
+ if "time" in var_data.dims and var_data.sizes["time"] > 1:
+ logger.warning(
+ f"Variable {var_key} has multiple time points. Using first time point only."
+ )
+ # Select first time point
+ var_data = var_data.isel(time=0)
+
+ # Squeeze to remove any remaining singleton dimensions
+ var_data = var_data.squeeze()
+
+ # Log shape information for debugging
+ logger.info(f"Variable {var_key} shape: {var_data.shape}")
+ logger.info(f"Variable {var_key} dims: {var_data.dims}")
+
+ # Get colormap
+ cmap = _get_colormap(colormap_name)
+
+ # Configure contour levels and boundary norm
+ c_levels, norm = _get_c_levels_and_norm(contour_levels or [])
+
+ # If no contour levels provided, create normalization manually
+ if norm is None:
+ # For difference plots, use symmetric normalization
+ if is_diff:
+ max_abs = max(abs(min_value), abs(max_value))
+ vmin, vmax = -max_abs, max_abs
+ else:
+ vmin, vmax = min_value, max_value
+
+ # Add buffer for constant values
+ if vmin == vmax:
+ buffer = max(0.1, abs(vmin * 0.1))
+ vmin -= buffer
+ vmax += buffer
+ logger.warning(f"Data has constant value, adding buffer: [{vmin}, {vmax}]")
+
+ norm = mcolors.Normalize(vmin=vmin, vmax=vmax)
+
+ # Create the PolyCollection
+ pc = var_data.to_polycollection()
+
+ # Set visualization properties
+ pc.set_cmap(cmap)
+ pc.set_norm(norm)
+ pc.set_antialiased(antialiased)
+
+ # Add to panel
+ ax.add_collection(pc)
+
+ # Add colorbar (custom implementation for PolyCollection)
+ cbax_rect = (
+ panel_cfg[0] + 0.6635, # Position relative to the panel
+ panel_cfg[1] + 0.0215,
+ 0.0326, # Width
+ 0.1792, # Height
+ )
+ cbax = fig.add_axes(cbax_rect)
+ cbar = fig.colorbar(pc, cax=cbax, extend="both")
+
+ # Configure colorbar ticks
+ if contour_levels and len(contour_levels) > 0:
+ cbar.set_ticks(contour_levels)
+
+ # Format tick labels
+ maxval = np.amax(np.absolute(contour_levels))
+ if maxval < 0.01:
+ fmt, pad = "%.1e", 35
+ elif maxval < 0.2:
+ fmt, pad = "%5.3f", 28
+ elif maxval < 10.0:
+ fmt, pad = "%5.2f", 25
+ elif maxval < 100.0:
+ fmt, pad = "%5.1f", 25
+ elif maxval > 9999.0:
+ fmt, pad = "%.0f", 40
+ else:
+ fmt, pad = "%6.1f", 30
+
+ labels = [fmt % level for level in contour_levels]
+ cbar.ax.set_yticklabels(labels, ha="right")
+ cbar.ax.tick_params(labelsize=9.0, pad=pad, length=0)
+ else:
+ cbar.ax.tick_params(labelsize=9.0, length=0)
+
+ # Add units label
+ cbar.set_label(units, fontsize=9.5)
+
+ # Add metrics text (max, mean, min)
+ metrics = (max_value, mean_value, min_value)
+
+ # Add min/mean/max text using utility function
+ _add_min_mean_max_text(fig, subplot_num, DEFAULT_PANEL_CFG, metrics, fontsize=9.5)
+
+ return pc
diff --git a/e3sm_diags/viewer/default_viewer.py b/e3sm_diags/viewer/default_viewer.py
index d1a77ca58..7b03b90bb 100644
--- a/e3sm_diags/viewer/default_viewer.py
+++ b/e3sm_diags/viewer/default_viewer.py
@@ -3,10 +3,13 @@
E3SM Diagnostics as of v1.7.0.
"""
+from __future__ import annotations
+
import collections
import json
import os
from collections import OrderedDict
+from typing import TYPE_CHECKING
import numpy
@@ -16,6 +19,9 @@
from . import lat_lon_viewer, utils
+if TYPE_CHECKING:
+ from e3sm_diags.parameter.core_parameter import CoreParameter
+
logger = _setup_child_logger(__name__)
# A dictionary of the sets to a better name which
# is displayed in the viewer.
@@ -25,6 +31,7 @@
"lat_lon": "Latitude-Longitude contour maps",
"lat_lon_land": "Latitude-Longitude contour maps (land variables)",
"lat_lon_river": "Latitude-Longitude contour maps (river variables)",
+ "lat_lon_native": "Latitude-Longitude native grid maps",
"polar": "Polar contour maps",
"cosp_histogram": "CloudTopHeight-Tau joint histograms",
"diurnal_cycle": "Diurnal cycle phase maps",
@@ -58,7 +65,7 @@
]
-def create_viewer(root_dir, parameters):
+def create_viewer(root_dir, parameters): # noqa: C901
"""
Given a set of parameters for a certain set of diagnostics,
create a single page.
@@ -98,7 +105,16 @@ def create_viewer(root_dir, parameters):
# ref_name-variable-plev'mb'-season-region
ref_name = getattr(parameter, "ref_name", "")
for var in parameter.variables:
- for season in parameter.seasons:
+ # Handle either seasons or time_slices
+ time_periods = (
+ parameter.time_slices
+ if (
+ hasattr(parameter, "time_slices") and len(parameter.time_slices) > 0
+ )
+ else parameter.seasons
+ )
+
+ for season in time_periods:
for region in parameter.regions:
# Since some parameters have plevs, there might be
# more than one row_name, filename pair.
@@ -175,6 +191,29 @@ def create_viewer(root_dir, parameters):
] = os.path.join(
"..", "{}".format(set_name), parameter.case_id, fnm
)
+
+ logger.debug(
+ f"DEBUG VIEWER: var={var}, season={season}, region={region}"
+ )
+ logger.debug(f"DEBUG VIEWER: fnm={fnm}")
+ logger.debug(
+ "DEBUG VIEWER: expected_path="
+ + os.path.join(
+ "..", "{}".format(set_name), parameter.case_id, fnm
+ )
+ )
+
+ # Check what files actually exist
+ actual_dir = os.path.join(
+ parameter.results_dir, set_name, parameter.case_id
+ )
+ if os.path.exists(actual_dir):
+ actual_files = os.listdir(actual_dir)
+ logger.debug(f"DEBUG VIEWER: actual files: {actual_files}")
+ else:
+ logger.debug(
+ f"DEBUG VIEWER: directory missing: {actual_dir}"
+ )
ROW_INFO[set_name][parameter.case_id][row_name][season][
"metadata"
] = create_metadata(parameter)
@@ -216,12 +255,36 @@ def create_viewer(root_dir, parameters):
return (name, url)
-def seasons_used(parameters):
+def seasons_used(parameters: list[CoreParameter]) -> list[str]:
"""
- Get a list of the seasons used for this set of parameters.
+ Determine the seasons or time slices used based on the provided parameters.
+
+ Parameters
+ ----------
+ parameters : list[CoreParameter]
+ A list of CoreParameter objects, each potentially containing `time_slices`
+ and `seasons` attributes.
+ Returns
+ -------
+ list[str]
+ A sorted list of time slices if `time_slices` are used in any parameter;
+ otherwise, a list of seasons used, ordered by the predefined `SEASONS`.
"""
- seasons_used = set([s for p in parameters for s in p.seasons])
- # Make sure this list is ordered by SEASONS.
+ # Determine if time_slices are used in any parameter
+ time_slices_used = {
+ time_slice
+ for p in parameters
+ if hasattr(p, "time_slices") and p.time_slices
+ for time_slice in p.time_slices
+ }
+
+ # Return sorted time slices if they are used
+ if time_slices_used:
+ return sorted(time_slices_used)
+
+ # Otherwise, collect and return seasons used, ordered by SEASONS
+ seasons_used = {season for p in parameters for season in p.seasons}
+
return [season for season in SEASONS if season in seasons_used]
@@ -271,14 +334,33 @@ def create_metadata(parameter):
if "parameters" in supported_cmd_args:
supported_cmd_args.remove("parameters")
+ # Exclude parameters not used by this diagnostic set
+ exclude_params = {"granulate", "selectors"}
+ if set_name == "lat_lon_native":
+ exclude_params.update({"regrid_tool", "regrid_method"})
+
+ # Get default parameter values to skip parameters equal to defaults
+ default_param = parameter.__class__()
+
for param_name in parameter.__dict__:
param = parameter.__dict__[param_name]
+
+ # Skip excluded parameters
+ if param_name in exclude_params:
+ continue
+
# We don't want to include blank values.
if (isinstance(param, numpy.ndarray) and not param.all()) or (
not isinstance(param, numpy.ndarray) and not param
):
continue
+ # Skip if parameter value equals default value
+ if hasattr(default_param, param_name):
+ default_value = getattr(default_param, param_name)
+ if param == default_value:
+ continue
+
if param_name in supported_cmd_args:
if (
isinstance(param, list)
diff --git a/e3sm_diags/viewer/main.py b/e3sm_diags/viewer/main.py
index a8b3c5e9d..0ee0aa98b 100644
--- a/e3sm_diags/viewer/main.py
+++ b/e3sm_diags/viewer/main.py
@@ -31,6 +31,7 @@
"lat_lon": default_viewer.create_viewer,
"lat_lon_land": default_viewer.create_viewer,
"lat_lon_river": default_viewer.create_viewer,
+ "lat_lon_native": default_viewer.create_viewer,
"polar": default_viewer.create_viewer,
"zonal_mean_xy": default_viewer.create_viewer,
"zonal_mean_2d": mean_2d_viewer.create_viewer,
@@ -105,6 +106,7 @@ def insert_data_in_row(row_obj, name, url):
name, url = row
insert_data_in_row(tr, name, url)
+ # FIXME: Item "PageElement" of "PageElement | Tag | NavigableString" has no attribute "append"Mypyunion-attr
table.append(tr)
html = soup.prettify("utf-8")
diff --git a/examples/lat_lon_native/TGCLDLWP.cfg b/examples/lat_lon_native/TGCLDLWP.cfg
new file mode 100644
index 000000000..cdd57eb9b
--- /dev/null
+++ b/examples/lat_lon_native/TGCLDLWP.cfg
@@ -0,0 +1,12 @@
+[#]
+sets = ["lat_lon_native"]
+case_id = "model_vs_model"
+variables = ["TGCLDLWP"]
+seasons = ["ANN", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "DJF", "MAM", "JJA", "SON"]
+#regions = ["global"]
+regions = ["global", "30S30N-150E90W"]
+#test_colormap = "Blues"
+#reference_colormap = "Blues"
+diff_colormap = "RdBu"
+contour_levels = [10, 25, 50, 75, 100, 125, 150, 175, 200,225, 250]
+diff_levels = [-35, -30, -25, -20, -15, -10, -5, 5, 10, 15, 20, 25, 30, 35]
diff --git a/examples/lat_lon_native/run_native_grid_test.py b/examples/lat_lon_native/run_native_grid_test.py
new file mode 100755
index 000000000..1774d12d5
--- /dev/null
+++ b/examples/lat_lon_native/run_native_grid_test.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+"""
+This script runs e3sm_diags with the lat_lon_native set to visualize native grid data.
+"""
+
+import os
+
+from e3sm_diags.parameter.lat_lon_native_parameter import LatLonNativeParameter
+from e3sm_diags.run import runner
+
+# Create parameter object
+param = LatLonNativeParameter()
+
+# Auto-detect username
+username = os.environ.get('USER', 'unknown_user')
+
+# Basic parameters
+param.results_dir = f"/lcrc/group/e3sm/public_html/diagnostic_output/{username}/tests/lat_lon_native_test_TGCLDLWP"
+
+# Create results directory if it doesn't exist
+if not os.path.exists(param.results_dir):
+ os.makedirs(param.results_dir)
+
+# Model data
+param.test_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+param.test_file = "v3.LR.amip_0101.eam.h0.1989-12.nc"
+
+param.reference_data_path = "/lcrc/group/e3sm/public_html/e3sm_diags_test_data/native_grid"
+param.ref_file = "v3.LR.amip_0101.eam.h0.1989-12.nc"
+param.short_ref_name = "v3.HR.test4"
+
+param.case_id = "model_vs_model"
+
+# Time slices for snapshot-based analysis
+param.time_slices = ["0"]
+
+# Native grid settings
+param.test_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne30pg2.nc"
+param.ref_grid_file = "/lcrc/group/e3sm/diagnostics/grids/ne30pg2.nc"
+
+param.antialiased = False
+
+## If no reference data for this test - model only
+# param.model_only = True
+param.run_type = "model_vs_model"
+
+# Run the diagnostic
+runner.sets_to_run = ["lat_lon_native"]
+runner.run_diags([param])
diff --git a/pyproject.toml b/pyproject.toml
index c275a478a..3e0280199 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,6 +41,7 @@ dependencies = [
"numpy >=2.0.0,<3.0.0",
"pywavelets",
"scipy",
+ "uxarray >=2023.3.0",
"xarray >=2024.03.0",
"xcdat >=0.10.0,<1.0.0",
"xesmf >=0.8.7",
@@ -98,6 +99,9 @@ version = { attr = "e3sm_diags.__version__" }
"e3sm_diags/driver/default_diags/lat_lon*",
"e3sm_diags/driver/default_diags/legacy_diags/lat_lon*",
]
+"share/e3sm_diags/lat_lon_native" = [
+ "e3sm_diags/driver/default_diags/lat_lon_native*",
+]
"share/e3sm_diags/polar" = [
"e3sm_diags/driver/default_diags/polar*",
"e3sm_diags/driver/default_diags/legacy_diags/polar*",
diff --git a/tests/e3sm_diags/test_parameters.py b/tests/e3sm_diags/test_parameters.py
index 52f9ad2f1..6261abf11 100644
--- a/tests/e3sm_diags/test_parameters.py
+++ b/tests/e3sm_diags/test_parameters.py
@@ -1,5 +1,8 @@
+import os
+
import numpy as np
import pytest
+import xarray as xr
from e3sm_diags.parameter.annual_cycle_zonal_mean_parameter import ACzonalmeanParameter
from e3sm_diags.parameter.area_mean_time_series_parameter import (
@@ -141,6 +144,58 @@ def test_logs_exception_if_driver_run_diag_function_fails(self, caplog):
assert "TypeError: 'NoneType' object is not iterable" in caplog.text
+class TestCoreParameterAdditionalMethods:
+ def test_add_time_series_file_path_attr_valid(self):
+ param = CoreParameter()
+ ds = xr.Dataset(attrs={"file_path": "/path/to/test/file.nc"})
+
+ param._add_time_series_file_path_attr("test", ds)
+
+ assert param.test_data_file_path == "/path/to/test/file.nc"
+
+ def test_add_time_series_file_path_attr_invalid_data_type(self):
+ param = CoreParameter()
+ ds = xr.Dataset(attrs={"file_path": "/path/to/test/file.nc"})
+
+ with pytest.raises(
+ ValueError, match="data_type must be either 'test' or 'ref'."
+ ):
+ param._add_time_series_file_path_attr("invalid", ds) # type: ignore
+
+ def test_add_time_series_file_path_attr_missing_file_path(self):
+ param = CoreParameter()
+ ds = xr.Dataset()
+
+ param._add_time_series_file_path_attr("test", ds)
+
+ assert param.test_data_file_path == "Unknown"
+
+ def test_add_climatology_file_path_attr_valid(self):
+ param = CoreParameter()
+ filepath = "/path/to/climatology/file.nc"
+
+ param._add_climatology_file_path_attr("ref", filepath)
+
+ assert param.ref_data_file_path == os.path.abspath(filepath)
+
+ def test_add_climatology_file_path_attr_invalid_data_type(self):
+ param = CoreParameter()
+ filepath = "/path/to/climatology/file.nc"
+
+ with pytest.raises(
+ ValueError, match="data_type must be either 'test' or 'ref'."
+ ):
+ param._add_climatology_file_path_attr("invalid", filepath) # type: ignore
+
+ def test_add_climatology_file_path_attr_missing_filepath(self):
+ param = CoreParameter()
+
+ with pytest.raises(
+ ValueError, match="Filepath must be provided for climatology data."
+ ):
+ param._add_climatology_file_path_attr("test", None)
+
+
def test_ac_zonal_mean_parameter():
param = ACzonalmeanParameter()