diff --git a/examples/Jupyter/XAFS_Processing_bokeh.ipynb b/examples/Jupyter/XAFS_Processing_bokeh.ipynb new file mode 100644 index 000000000..3dac14e3f --- /dev/null +++ b/examples/Jupyter/XAFS_Processing_bokeh.ipynb @@ -0,0 +1,867 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "59499dc6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8980.5\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import larch\n", + "from larch.xafs import pre_edge, autobk\n", + "from larch.io import read_ascii\n", + "from larch.plot.bokeh_xafsplots import plot_mu, plot_bkg\n", + "\n", + "cu = read_ascii('../xafsdata/cu_metal_rt.xdi')\n", + "cu.mu = -np.log(cu.itrans/cu.i0)\n", + "pre_edge(cu)\n", + "autobk(cu, rbkg=1, kw=2)\n", + "print(cu.e0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c431a710", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "'use strict';\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + "\n", + " // Clean up Bokeh references\n", + " if (id != null) {\n", + " drop(id)\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\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", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const 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", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded(error = null) {\n", + " const el = document.getElementById(\"e09c16ba-71ab-4169-b9bc-2a63b3b7b182\");\n", + " if (el != null) {\n", + " const html = (() => {\n", + " if (typeof root.Bokeh === \"undefined\") {\n", + " if (error == null) {\n", + " return \"BokehJS is loading ...\";\n", + " } else {\n", + " return \"BokehJS failed to load.\";\n", + " }\n", + " } else {\n", + " const prefix = `BokehJS ${root.Bokeh.version}`;\n", + " if (error == null) {\n", + " return `${prefix} successfully loaded.`;\n", + " } else {\n", + " return `${prefix} encountered errors while loading and may not function as expected.`;\n", + " }\n", + " }\n", + " })();\n", + " el.innerHTML = html;\n", + "\n", + " if (error != null) {\n", + " const wrapper = document.createElement(\"div\");\n", + " wrapper.style.overflow = \"auto\";\n", + " wrapper.style.height = \"5em\";\n", + " wrapper.style.resize = \"vertical\";\n", + " const content = document.createElement(\"div\");\n", + " content.style.fontFamily = \"monospace\";\n", + " content.style.whiteSpace = \"pre-wrap\";\n", + " content.style.backgroundColor = \"rgb(255, 221, 221)\";\n", + " content.textContent = error.stack ?? error.toString();\n", + " wrapper.append(content);\n", + " el.append(wrapper);\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(() => display_loaded(error), 100);\n", + " }\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, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\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", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\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", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\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", + " };\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.bokeh.org/bokeh/release/bokeh-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.5.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " try {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "\n", + " } catch (error) {display_loaded(error);throw error;\n", + " }if (force === true) {\n", + " display_loaded();\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", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"e09c16ba-71ab-4169-b9bc-2a63b3b7b182\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(\"e09c16ba-71ab-4169-b9bc-2a63b3b7b182\");\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\n }\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, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\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\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 };\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.bokeh.org/bokeh/release/bokeh-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.5.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {display_loaded(error);throw error;\n }if (force === true) {\n display_loaded();\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 } else if (force !== true) {\n const cell = $(document.getElementById(\"e09c16ba-71ab-4169-b9bc-2a63b3b7b182\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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norm}(E)/dE$$\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1048\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1012\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1013\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1014\"},\"axis_label\":\"$$E\\\\rm\\\\,(eV)$$\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1015\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1016\",\"attributes\":{\"axis\":{\"id\":\"p1012\"},\"grid_line_color\":\"#D8D8D8\"}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1021\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1017\"},\"grid_line_color\":\"#D8D8D8\"}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1042\",\"attributes\":{\"click_policy\":\"hide\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1043\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"mu\"},\"renderers\":[{\"id\":\"p1039\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1058\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"$$\\\\mu(E)$$\"},\"renderers\":[{\"id\":\"p1055\"}]}}]}}]}}]}};\n", + " const render_items = [{\"docid\":\"fbaa21c7-450d-47a4-b889-7c4a9c1e21d6\",\"roots\":{\"p1001\":\"be923ce9-85a6-48e8-8cab-e50467a5cc8a\"},\"root_ids\":[\"p1001\"]}];\n", + " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1001" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_mu(cu, with_deriv=True, show_e0=True, emin=8900, emax=9100)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3c3c1148", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "'use strict';\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + "\n", + " // Clean up Bokeh references\n", + " if (id != null) {\n", + " drop(id)\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\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", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const 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", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded(error = null) {\n", + " const el = document.getElementById(\"f4f9a6fd-8736-4e2a-9ec8-b7e61fcd1702\");\n", + " if (el != null) {\n", + " const html = (() => {\n", + " if (typeof root.Bokeh === \"undefined\") {\n", + " if (error == null) {\n", + " return \"BokehJS is loading ...\";\n", + " } else {\n", + " return \"BokehJS failed to load.\";\n", + " }\n", + " } else {\n", + " const prefix = `BokehJS ${root.Bokeh.version}`;\n", + " if (error == null) {\n", + " return `${prefix} successfully loaded.`;\n", + " } else {\n", + " return `${prefix} encountered errors while loading and may not function as expected.`;\n", + " }\n", + " }\n", + " })();\n", + " el.innerHTML = html;\n", + "\n", + " if (error != null) {\n", + " const wrapper = document.createElement(\"div\");\n", + " wrapper.style.overflow = \"auto\";\n", + " wrapper.style.height = \"5em\";\n", + " wrapper.style.resize = \"vertical\";\n", + " const content = document.createElement(\"div\");\n", + " content.style.fontFamily = \"monospace\";\n", + " content.style.whiteSpace = \"pre-wrap\";\n", + " content.style.backgroundColor = \"rgb(255, 221, 221)\";\n", + " content.textContent = error.stack ?? error.toString();\n", + " wrapper.append(content);\n", + " el.append(wrapper);\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(() => display_loaded(error), 100);\n", + " }\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, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\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", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\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", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\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", + " };\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.bokeh.org/bokeh/release/bokeh-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.5.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " try {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "\n", + " } catch (error) {display_loaded(error);throw error;\n", + " }if (force === true) {\n", + " display_loaded();\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", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"f4f9a6fd-8736-4e2a-9ec8-b7e61fcd1702\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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$$\\\\mu(E)$$\"},\"renderers\":[{\"id\":\"p1108\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1122\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"bkg\"},\"renderers\":[{\"id\":\"p1119\"}]}}]}}]}}]}};\n", + " const render_items = [{\"docid\":\"06c46ab4-a63b-4765-8978-80714783a245\",\"roots\":{\"p1070\":\"e4c2ec05-163f-4fe9-be3e-899055595785\"},\"root_ids\":[\"p1070\"]}];\n", + " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1070" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_bkg(cu, emin=8900, emax=9600)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b8cddd7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "253905ac-0f46-4fd7-9c53-4982df96a27c", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Jupyter/XAFS_Processing_matplotlib.ipynb b/examples/Jupyter/XAFS_Processing_matplotlib.ipynb index 936a91382..6a2b93736 100644 --- a/examples/Jupyter/XAFS_Processing_matplotlib.ipynb +++ b/examples/Jupyter/XAFS_Processing_matplotlib.ipynb @@ -23,16 +23,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "fe2o3_rt1_xmu \n", - "fe3c_rt_xdi \n", - "feo_rt1_xmu \n" + "fe2o3_rt1_xmu \n", + "fe3c_rt_xdi \n", + "feo_rt1_xmu \n" ] } ], @@ -40,7 +40,7 @@ "from larch.io import read_athena\n", "project = read_athena('../xafsdata/fe_athena.prj')\n", "\n", - "for name, group in project._athena_groups.items():\n", + "for name, group in project.groups.items():\n", " print(name, group)" ] }, @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -72,14 +72,17 @@ "atsym \n", "callargs \n", "d2mude \n", + "datatype \n", "dmude \n", "e0 \n", "edge \n", "edge_step \n", "edge_step_poly \n", "energy \n", + "energy_shift \n", "epsk \n", "epsr \n", + "filename \n", "flat \n", "i0 \n", "journal \n", @@ -87,11 +90,16 @@ "mu \n", "norm \n", "norm_poly \n", + "plot_xlabel \n", + "plot_ylabel \n", "post_edge \n", "pre_edge \n", "pre_edge_details \n", "sel \n", - "signal \n" + "signal \n", + "xdat \n", + "ydat \n", + "yerr \n" ] } ], @@ -114,22 +122,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -151,7 +159,65 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's remove the XAFS background and extract the EXAFS $\\chi(k)$. We'll use\n", + "Let's now subtract the pre-edge and normalize all the Fe datasets\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fe2o3_rt1_xmu 7125.6176 1.2930835174651394\n", + "fe3c_rt_xdi 7122.5 2.42352172994476\n", + "feo_rt1_xmu 7122.9272 1.354364206322157\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, '$\\\\mu(E)$')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from larch.xafs import pre_edge\n", + "\n", + "for name, group in project.groups.items():\n", + " pre_edge(group)\n", + " print(name, group.e0, group.edge_step)\n", + " \n", + "for name, group in project.groups.items():\n", + " plt.plot(group.energy, group.norm, label=name)\n", + " \n", + "plt.legend()\n", + "plt.xlim(7090, 7190)\n", + "plt.xlabel('Energy (eV)')\n", + "plt.ylabel(r'$\\mu(E)$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's focus on the Fe2O3 data and remove the XAFS background and extract the EXAFS $\\chi(k)$. We'll use\n", "the `autobk()` function (https://xraypy.github.io/xraylarch/xafs/autobk.html)\n", "\n", "Note that we pass in the *Group* as the first argument. The `autobk()` function will add several attributes to this Group -- it will be the container for this dataset." @@ -159,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -168,13 +234,13 @@ "Text(0, 0.5, '$\\\\mu(E)$')" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -202,15 +268,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No conda env active, defaulting to base\n", - "['k', 'r', 'energy', 'ewithk', 'i0', 'mu', 'norm', 'flat', 'deconv', 'dmude', 'd2mude', 'chie', 'chie0', 'chie1', 'chiew', 'chikw', 'chi0', 'chi1', 'chi2', 'chi3', 'chir', 'chirmag', 'chirre', 'chirim', 'chirpha', 'e0color', 'chirlab']\n" + "['k', 'r', 'energy', 'ewithk', 'i0', 'mu', 'norm', 'flat', 'deconv', 'dmude', 'd2mude', 'chie', 'chie0', 'chie1', 'chiew', 'chikw', 'chi0', 'chi1', 'chi2', 'chi3', 'chir', 'chirmag', 'chirre', 'chirim', 'chirpha', 'e0color', 'chirlab', 'x', 'y', 'xdat', 'ydat', 'xplot', 'yplot', 'ynorm', 'xshift', 'dydx', 'd2ydx']\n" ] }, { @@ -219,13 +284,13 @@ "Text(0, 0.5, '$\\\\chi(k)$')" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -252,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -261,13 +326,13 @@ "Text(0, 0.5, '$k^{2}\\\\chi(k) \\\\rm\\\\,(\\\\AA^{-2})$')" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -291,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -301,22 +366,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -342,22 +407,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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4+/sjIyMDixcvtnjus88+i169eiEwMBDdu3fHCy+84DDImjx5MlavXi34+pxFk7mJV/H9ST2i22xLYr7qzZBR8tOzzFNBVRM4jjOtkOtkDhbW4KGvctCi0UMmlaBFo8eKbfnIr2jEsmlZUMjp9xdCPIbjAI2XstJ+gYDA/7fmzp2LTz/9FO+//z7GjBmD4uJiHD9+3OKYefPm4d1330V6ejrmzZuHO+64A6dOnYJc3v5H+9atWxEYGIjMzMx2j7300kuYP38+3n//fchkMgCAQqHAwIEDsWXLFlxxxRWivs1nn30W7733HlasWAGlUolPP/0UL730EhYtWoTBgwdj//79mDlzJoKCgnD33XcDAEJCQrBy5UokJCTg8OHDmDlzJkJCQvDMM8/YfJ1hw4ahsLAQ586ds7t3nasoUCJedbqcZY56RAdbPmC+hYkhoyTVtkAiAZo1OlQ2tiIqWNmRl+oWaq3OGCRd1jsaS+8agq0nK/Do6n3YmFeOF348grduGeDtyySk69I0AW8meOe1/6+I/fLnQH19PT744AMsWrTIGEj06NEDY8aMsTjuqaeewjXXXAMAeOWVV9C3b1+cOnUKGRkZ7c6Zn5+P2NhYq2W3qVOn4r777mt3f2JiIvLz84V8ZxaeeOIJ3HTTTcavX3vtNbz33nvG+9LS0pCbm4tly5YZv7/nn3/eeHy3bt3w5JNPYs2aNXYDpcTEROP35slAiX51JV7FZ5S628womXqUJNpmxIew8lxnXfn24/4LKKptQVyoPxZNvQT+fjJc1ScWy6ZlQSIB1uwtxJ+5pd6+TEKIFx07dgxqtRpXXnml3eMGDDD9UhUfHw8AKCsrs3psc3Mz/P39rT6WlZVl9f6AgAA0NYn/v9b8fOXl5SgsLMSMGTMQHBxs/Hj99ddx+vRp43H//e9/MWbMGMTFxSE4OBgvvPACCgoK7L5OQAD7JdqZaxSDMkrEq87YyiiZr3ozZJQAoHuEHEV1rSisasIlKeEddZluoddzWLb5DABgxpg0BCtN//zG9YrGzLHd8cnmM3juh8P4u3sEQv39vHWphHRdfoEss+Ot1xaADwAcns7P9H8E34qg11vfuSAqKgrV1dVWHwsKsp7lqqqqQo8ePQRdi63z8dfz6aefYvjw4RbH8WW+nTt3YsqUKXjllVcwYcIEqFQqfPPNN3jvvffsvk5VVRUAIDo6WvQ1ikGBEvEavZ7D2Qo+o2Sj9NY2UAqVYis658q3TSfLcaa8ESH+ckwZltzu8Tnje+HP3FKcqWjExxtPYe6k9r0EhBAXSSSCyl/elJ6ejoCAAPz111+4//773XLOwYMHo6SkBNXV1QgPF/ZL5pEjR3DLLbe49LqxsbFITEzEmTNncOedd1o9Ztu2bUhNTcW8efOM9507d07Q9fn5+aFv374uXaMjVHojXlNWrzY2NCeHt/kNynzVm1QGyFg/Umoo+63pfHXnW/m2/nAJAODGwYkIsZIt8veT4f+uZsHRiq35NIGckIuUv78/nn32WTzzzDP44osvcPr0aezcuRPLly93+pyDBw9GdHQ0tm3bJuj4/Px8XLhwAVdddZXTr8l7+eWXMX/+fHzwwQc4ceIEDh8+jBUrVmDBggUAgJ49e6KgoADffPMNTp8+jQ8//BBr1651eN4tW7Zg7NixgjNwzqJAiXgN32eUGBYAuazNX0XzHiXAmFVKCOIAAMWG2UudhU7P4c9jrPcou0+czeOuzIzB6J6RaNXp8eHfJzvq8gghPuaFF17Ak08+iRdffBGZmZm4/fbbbfYfCSGTyXDfffdh1apVgo5fvXo1srOzLZqkX375ZXTr1k30a99///347LPPsHLlSvTv3x/jxo3DypUrkZaWBgC4/vrrMXv2bDz66KMYNGgQtm/fjhdeeEHQNc6cOVP09Ygl4TiO8/irdFF1dXVQqVSora1FaGio4ycQC9/tLcTT/z2EMT2j8NX9ZrVrjgNeiwL0WmB2LqBKBN7LAOqLsW/ij7jpxyb0jg3B77Mv9d7Fi7Q3vwq3LN2BEH859r0wHn5tA0MzBwprcMPH2yCTSvD3k+OQGunbZQJCfFlLSwvOnj2LtLQ0m83MF4vS0lL07dsXOTk5dleJqdVqpKenY/Xq1Rg9erTx/nvuuQcAm9Hkbb/++iuefvppHDp0yOo4BMD+n72Yn9+UUSJew/cZpUS2aXDUtbIgCWDjAQBjRinanzUGFtd2rtLbBsNKtisyYuwGSQAwKDkM43pFQ6fnsHjjabvHEkKIULGxsVi+fLnD1WTnzp3DvHnzLIIkANi0aRNee+01T16iYI2NjVixYoXNIMmdqJmbeA1fekuJaBMotZptxOhnyKYYVotEKlmgVNeiRaNaiyBl5/grvOVkBQDgysxYQcc/dmU6Np0ox/f7zuPRK3oiue17RAghTrj++usdHtOrVy/06tWr3f0dsa+aULfddluHvRZllIjXnLMVKKnr2a3cH5AZAiFDRilQ0mpcVl9S1zn6lOpbNDheUgcAGJEWIeg5Q1LDMaZnFLR6Dks2UVaJEEK8hQIl4jWFjjJKCrORAfyIAE0z4lWs1lzSSRq69xfUQM8ByREBiAkV3iPx2JXpAFgvV2crNRJCSFdBgRLxika1FhUNbJPbdj1K5jOUePygNk0T4gyBUmdZ+bY3nw1FG5oqLJvEG5YWgWFpEdDoOHyxw/FMEUKIbbRu6eLjrj9zCpSIV/D9SWGBfu0nULcaSm8OMkrFNZ0jy7L3HJuGO6Sb+Eni949hy2e/3lWA5ladW6+LkIsBP73a09tcEN/T2sp+GecngDurc3TCCrR48WK88847KC4uRt++fbFw4UKMHTvW5vFqtRqvvvoqvvrqK5SUlCApKQnz5s2zujkgcS8+UEq11qRsviEuzyKjxIKm4k7Qo6TR6XGgsAYAkCUyowSw5u+UiEAUVDXhh/3ncedwz238SEhXJJPJEBYWZpxBFBgYaNzug3Rder0e5eXlCAwMdHllXJcJlNasWYMnnngCixcvxujRo7Fs2TJMmjQJubm5SElJsfqc2267DaWlpVi+fDl69uyJsrIyaLXaDr7yixPfn2R1NZfV0lvn7FE6VdaAplYdQpRypMcEO35CGzKpBPeM6oZXf8nFim35mDoshf6TJ0SkuDg25NWVgY2k85FKpUhJcf3/zC4TKC1YsAAzZsww7ouzcOFC/P7771iyZAnmz5/f7vj169dj06ZNOHPmDCIi2G/6zkwcJc45V2mjkRswrXqzGig1mUpvnSBQyi1iq90yE0IhlTr3j/XWrCQs2HACp8oasOVkBS7t5dkNIAnpaiQSCeLj4xETEwONRuPtyyEdRKFQQCp1vcOoSwRKra2tyMnJwXPPPWdxf3Z2NrZv3271OT/99BOysrLw9ttv48svv0RQUBAmT56M1157zea+MWq1Gmq12vh1XV2d+76Ji4yx9Na2kRsANIZeAj9rzdzNiOdLb51gJVhuMfs70jfB+cntIf5+uDUrCSu25WP51rMUKBHiJJlM5nK/Crn4dIlm7oqKCuh0OsTGWg7zi42NRUlJidXnnDlzBlu3bsWRI0ewdu1aLFy4EP/973/xyCOP2Hyd+fPnQ6VSGT+Sk9vvAE+EsVt60xqCUT+zpfRmpbfYULZBbk2TBmqtbzc48xmlPvGubXFzz6hukEiATSfKabNcQgjpQF0iUOK1rUNyHGezNqnX6yGRSLBq1SoMGzYMV199NRYsWICVK1eiudl6pmLu3Lmora01fhQWFrr9e7gY6PQcCqvtlN74QEluHiiZmrlVAX5QGLYB4UcM+CKO43C0qBYA0MeFjBIApEYGYWw6yySt2Wt/+wFCCCHu0yUCpaioKMhksnbZo7KysnZZJl58fDwSExOhUqmM92VmZoLjOJw/f97qc5RKJUJDQy0+iHgldS3Q6Dj4ySTGMpoFraH3SK403ccHTZoWSCQSRIewx8p8eOXbhZpm1LVo4SeTID0mxOXzTRnKMpjf7T0PrU7v8vkIIYQ41iUCJYVCgSFDhmDDhg0W92/YsAGjRo2y+pzRo0ejqKgIDQ0NxvtOnDgBqVSKpKQkj17vxY4vHSWFB0JmrcHZQUYJgDFQKq9Xw1fxZbeeMSFQyF3/p3ZVZiwigxQoq1fjn7xyl89HCCHEsS4RKAHAnDlz8Nlnn+Hzzz/HsWPHMHv2bBQUFGDWrFkAWNls+vTpxuOnTp2KyMhI3HvvvcjNzcXmzZvx9NNP47777rPZzE3cw25/EmA9o2TWowSYAqUyHw6UjhWz1XuZ8a5nkwBAIZfi6bSz+MJvPv7ec8At5ySEEGJfl1j1BgC33347Kisr8eqrr6K4uBj9+vXDunXrkJrKBvQVFxejoMDU2xEcHIwNGzbgX//6F7KyshAZGYnbbrsNr7/+ure+hYvGuSo2J8nqsEnAlFGS2Q6UYjpBoHSqtBY9JBfQO6b9LtzOmnLqaUAG6E7PR33LFQhpO9WcEEKIW3WZQAkAHn74YTz88MNWH1u5cmW7+zIyMtqV64jnFVSxYMdqIzdgI6PU+UpvPc6vxUfKRThd9SSAF9167n6SM/j9aCluGUJlYkII8aQuU3ojnUeBw9KbtR6lthkl9lh5vW82c+v0HLo1HgQAxDfkuv38kajD/w5ccPt5CSGEWKJAiXS4gkpD6c3asEnALKPUeZu5C6ua0B1s9WRAk/sDGqmEw47TFahtpinDhBDiSRQokQ5V16JBdRP74e44o9R5e5ROldahh6QIACCpcdPco1bLQZMR+mpsPE57VxFCiCdRoEQ6FL/iLTJIgWCljRY5exklnRrQ64wZpYoGNfR6zlOX67SS86cQJDEEcS01QEut6ydtrrL4Ml16Hn/kWp88TwghxD0oUCIdip+hlGKr7AbYzygBgKYZUcHsMY2OQ40Plp+az7fpS6pxwxT3pkqLL9MlF/BPXjlaNL69jQshhHRmFCiRDsU3cttc8QZYzyiZf65phkIuRXggWxrvi31K0qoTlne4o/zWWGHx5QBlKZpaddh1tsrGEwghhLiKAiXSoYQFSlYySlIpIDdklbSWK9/KfHDlW3jjGcs73BEoNVkGRAP9SwEAm0/QlG5CCPEUCpRIhxIUKOmsjAcAbE7n9rWMUm2TBql6VmrThyayO2vOuX5ivvTmz/YnjPZj7wMFSoQQ4jkUKJEO5XRGCTDbGJedw1dXvp2rakSyhAUv0u6XszvdklEyBEphKQCAIIkaUglwsqwBxbXNrp+fEEJIOxQokQ6j1elxodowldtuM7eVHiUAkCsMj7cCMNvvrc63AqWCqiaEwLCUP34gu3VLRsnQo6RigZJM24wBSWEAgC0nKmw8iRBCiCsoUCIdpri2BVo9B4VcitgQf+sH6bSAXss+t5VRMpTmjKW3Bt8KlM5X1CJAwoI5xPVjt27NKCWz29YmjE2PAgBsO02BEiGEeAIFSqTDGLcuCQ+AVCqxfpDOLOhpm1GSWWaUYkINzdx1vtXMXVZu1jMUnsZuW2oBvd61E/PN3IbSGzSNGN4tAgCwh1a+EUKIR1CgRDrMOcMMpdTIINsHac0DpbYZJcPXhtJcdLBvZpSqqlh2RysLAPxDTQ9oXewjatOjBE6PS5ICIJdKUFTbgvPVTbafSwghxCkUKJEOc66K7fEmaIaSVA5IZZaPtSm9xYQaAiUf61Gqq2EBjV4ZahppAACtja6dmJ+jpEo23hWIVvRLZKvgdlNWiRBC3I4CJdJhjFO5xQ6b5MmsN3PXq7VobvWN6dQanR4t9dUAAKl/KJv/xG+/4kqgxHGmjFJwDCBTGs85PI2V3yhQIoQQ96NAiXQYvkcpVez2Jbw2pbcQpRz+fuyvsK/MUrpQ3Yxgw4o3WQDL9EBhKDVqXCiNtdQCnCEYDIgAFKbgaxgfKOVToEQIIe5GgRLpEBzHGTNK9gMlOxklPlDSsYySRCIxW/nmGw3dbDQA60WS8P1J7sgo8UGWVA74+QN+fPDViCGp4QCAM+WNqGlqdf41CCGEtEOBEukQ1U0a1KvZsv+kcCczSny5yazh27iNiY/0KRVUNSFEYghqlIZAic8ouRQoGRrB+Z4nY0apCWGBCqRFsdc4eL7W+dcghBDSDgVKpEPwZbe4UH/4+8lsH2g3o8T3KJmCIl9b+VZY1YRgQ0bJuOLNHaU3/n3xM7wvfJbKcM6BSazMd7CwxvnXIIQQ0g4FSqRDnKs0rHizV3YDHPQoWa56A0wr33wlo3Su0kpGyR2lN2MAyWeULLNUA5PDAFCgRAgh7kaBEukQgla8AWaBkr1Vb+0zSmX1vtSjZAiU/Ns0c7tUeuMDJUMA2TajZAiUDhTWgOM451+HEEKIBQqUSIcwrngTHCjZW/VmJaPkA6veOI5DYVUTQiWG0lvbHiWXSm+Gc/KlN2Pwxc7ZJz4UfjIJKhtbcb6aNsglhBB3oUCJdIhzhkDJcenNXo9S+9Ibv+qtwgd6lPiGdVNGqW3pzYVASWOj9KZhWSp/Pxky49nrHTxf4/zrEEIIsUCBEukQBUK2LwHMAiVrq94sB04CQHQwC558YY4SnzWLkBu+B2UIuzVmfxqcP3nb98VK8NU3gZX6covqnH8dQgghFihQIh7XotGhxLBxrUs9Sm0GTgJAVAgLniobWqHXe7c3hw+UwqVtSm9t+omcYlz11nY8gKnvqU8Ce73cYgqUCCHEXShQIh7Hb9YaopQjPNDP/sF8QMBnj8y1GTgJAJFB7D6tnkNNs8bla3VFoSFQCpHYGA/gUumNn6PEjwewLL0BrE8JoIwSIYS4EwVKxOPOVZr6kyQSif2D7a56a9/MrZBLjcGXt8tv/AiEAL0heGk3cNINpbd2GSVT8JUZHwKJhDW2e/u9IISQroICJeJx54SOBgDs9ygZM0qWQYBxGxMvBwcFVU2QQQeFns8oGcYDuLP0ZmM8AAAEKuTGCd1UfiOEEPegQIl4XIHQFW+AwB4ly4AoKtg3Vr4VVjUjCGZL89tllDyw6q3NbCYqvxFCiHtRoEQ8zjRDycGKN8D+eAArpTfANzJKrVo9imqbTTOU5P6mLVfcWnqzvoUJj1/5drSI9nwjhBB3oECJeBzfu5MqKqNkrfRmCDzMmrkB39jv7Xx1EzgOiDKOBgg1PeiO0pudTXHNZcSzkQQnS10IygghhBhRoEQ8SqfnUGiYFC2uR8nOwEmt5XYlxqGTXswo8VmzHqF6doe/WaDkjtJb2wBSEcxuNZalt/QYdv+ZigZodHrnX48QQggACpSIh12obkarVg+FXIqEsADHT7CXUbIycBIw9Sh5M6PEjwZICTYEJ3wgA7Sbou0U4xYmhvfQxrTvBFUAAhUyaHScsYmeEEKI8yhQIh51uoKVgLpHBUEmdTAaADCtaLPXzO2Dq974oCQpiA+UzPqx/NoPhxRN0ybTZmP/OKlUYswqnSqrd/71CCGEAKBAiXjYmXIWHHSPFtDIDTjoUfK3PMbAFwIlvvQWG2CYDu5nlj3jgxpdK6DTOvcCNjNKDQBnOZG8ZwzrUzpBfUqEEOIyCpSIR50p5zNKwQ6ONLC76o0vvVkfD1DV1Aqtl/py+EApxl/H7vAz68cyzy45W35r16NkOD+nb/d+pMey9/pkGQVKhBDiqi4VKC1evBhpaWnw9/fHkCFDsGXLFkHP27ZtG+RyOQYNGuTZC7wIuTejZFZ6M8uiRAQpIJWwu6oaW9s/z8M4jjP2KEUoDIGSeXAkUwASGfvc2fJb21VvfubBl2X5jS+9nSyl0hshhLiqywRKa9aswRNPPIF58+Zh//79GDt2LCZNmoSCggK7z6utrcX06dNx5ZVXdtCVXlzO8D1K0WIzSnYCJcBiRIBMKkGkIatU5oXyW2VjKxpbdZBIgDC54brMS28Siesr39rOUZLJTRm2NsFXr1hWejtT0ei1DBshhHQVXSZQWrBgAWbMmIH7778fmZmZWLhwIZKTk7FkyRK7z3vwwQcxdepUjBw5soOu9OLRoNaitI4FLsIzSoZAQ2Zt1ZvZfW37lLy48o0vu8WF+kOu4wOaNqMQXF351jajZP4abTJKiWEB8PeTolWrN14bIYQQ53SJQKm1tRU5OTnIzs62uD87Oxvbt2+3+bwVK1bg9OnTeOmllwS9jlqtRl1dncUHse2soewWFaxEqL+fsCfxmSKZleP5DIr5cQZRXpylxJfdkiMCTUFL20DJ1ZVv1kqSfNaqzVwpqVSCnjHUp0QIIe7QJQKliooK6HQ6xMbGWtwfGxuLkpISq885efIknnvuOaxatQpyuVzQ68yfPx8qlcr4kZyc7PK1d2WmspvAbBJgNh7ASkZJKjVr6G4zdNKLGSV+NEBqRKAp86Nom1GyPvdIsLar3gCbc6UAoJdh5dspCpQIIcQlXSJQ4kkklnN6OI5rdx8A6HQ6TJ06Fa+88gp69eol+Pxz585FbW2t8aOwsNDla+7KTht+SPcQEygZS28K64/74H5vxk1/IwJNGaN2pTfrk7QFaztHyfzzNkEjAPQ0rHw7QQ3dhBDiEmGpFB8XFRUFmUzWLntUVlbWLssEAPX19di7dy/279+PRx99FACg1+vBcRzkcjn++OMPXHHFFe2ep1QqoVRayXQQq05XGFa8CR0NAJgySrYCJbkCaEX70lswO96rgVJkIFDCZ37alt4MmSBnMkocZ8ooWQRK/N537b/n9Bja840QQtyhS2SUFAoFhgwZgg0bNljcv2HDBowaNard8aGhoTh8+DAOHDhg/Jg1axZ69+6NAwcOYPjw4R116V2a6NEAej2gNwxktFZ6A8wySjb2e/NC6c16j1Kb7VpkZqMNxDIPCv2sZZSsBUosOD1d3gCdnmv3OCGEEGG6REYJAObMmYNp06YhKysLI0eOxCeffIKCggLMmjULACubXbhwAV988QWkUin69etn8fyYmBj4+/u3u584R6/ncFbsaADzgMBmRokPlCwzSt4qvbVodCipY0FbqnmgpGgTHNq4bkH4vifActWbjTIkwII2pVwKtVaPwqomdIsSUf4khBBi1GUCpdtvvx2VlZV49dVXUVxcjH79+mHdunVITU0FABQXFzucqUTcp7iuBS0aPfxkEiSHC9gMF7DMttjKKNnY7y3GS4HS+epmcBwQpJAhIkhhKq21zSjZuG5B+OyZRGq5GpAvvVkJlGRSCXpEByO3uA4nyxooUCKEECd1idIb7+GHH0Z+fj7UajVycnJw6aWXGh9buXIl/vnnH5vPffnll3HgwAHPX+RFgt+6JCUiEHKZwL9mOo3pc6mNcQIOtjGpa9FCrdWJulZXmJfdJBKJqVnbr01gYmO1niAas/4k88UJfOnNRvCVTg3dhBDisi4VKBHfYepPEtHIzQc/Uj82CsAaG305qgA/+MlYEFHR0HHbmFiseANMQU27jBJ/3U5cm3GGUpv97+yMBwCAHob3nv+zIIQQIh4FSsQjjJvhumuGEs9GCUsikZhmKXVg+c04QymyzZyktnOUXCq9OQq+rGep+Peen2dFCCFEPAqUiEecNmQxeogZDaC1M5WbZ6P0Bninodsio8RxZqvebDVzO3Ft1mYoAXbHAwCmsQxnyhvBcbTyjRBCnEGBEvEIvi+mV1yI8CcZty+xl1GyvSSe71PqyBEBFqMBtC0ADAGJrfEAzgRK1mYomX9t45xphgbu2mYNqho7rhxJCCFdCQVKxO2qG1tRZsjq8PN8BOEDJbmN0QDmj+na/+DnM0pldR0TKHEcZ8wopUYGWS7jbztw0s4KNYf45/jZ6lGyfs4AhQyJYSxgO1NBfUqEEOIMCpSI2/HZpOSIAAQpRUyg4H/g28so2Rg4CZhGBJTVO7GyzAnlDWo0a3SQSMACEn77EpkCkLX5vh2sULPLuOrNVo+S7XPyfUpnqaGbEEKcQoEScTs+UOodK6LsBpiV3uxllGwPboxVscChtIMySnzZLUEVAIVcarbiLbD9wQ6yP3bxQWHbjJKABvHuhvLbaWroJoQQp1CgRNwuj+9PcjZQslt6sx0cxIbwgVLHZJQKjP1JhkyPxsaGuICLzdw2epQcjAcATOMZaEQAIYQ4hwIl4nYnSlj2oreYRm5AYOnN9uDGOFXHBkrG0QARhhVufEDTdjQA4FrpzdYcJQfjAQCzEQHllFEihBBnUKBE3IrjONczSvbGA9gZ3BgTalr1ptXpxb22E4yjAdrOUGq74g1wsfRma9Wb7cZ2Hp9RKqhq6pD3hBBCuhoKlIhbldapUdusgUwqETdsEjArvYkfOAkAkUFKyKQS6LmOmc5tMRoAsD1DCXCt9MZv7dK2JCkgoxQf6g9/Pyk0Og6F1c02jyOEEGIdBUrErY4W1QIAekYHQymXiXuyqNKb9Y1g+ZVvHVF+yzeW3toGSlYySsYAz4kAzlaTu4AslVQqQZpx8CSV3wghRCwKlIhbHblQBwDomxAq/smiSm/Wg4OYUPZ4iYcDpQa11jgBvFsU36PEb19iJaNkZ6yBQ7YCJQHjAQDzPiVq6CaEELEoUCJuxWeU+rgSKNktvdnvy4k1Dp30bKCUbxjgGBGkgCrAENjZ61GyM9bAIa2tQEnY/nE9omjPN0IIcRYFSsStjhaxjFK/RJX4JxtLb3bGAzjIzMR10Cyl/EoWKHWLNFvhZiy92RkP4MyqN5sZJWF9T2mGjNJpyigRQohoFCgRt6lpasWFGtYw7LmMkv3gILaDSm98RqlbpFmZzV6g5Mpeb7aauQWe03xzXEIIIeJQoETcJteQTUqJCESov50+I1vETOa2VXoL7ZhZSnwjt7E/CTCV3qzOUXIlULKRaRMwHgAw9ShVNKhR16IR//qEEHIRo0CJuA1fdnOqkRuw3YtjzkHpLTa0YzbGNWaUogRmlMxLbxwn7sUcNnPbDwpD/P2MGwZTVokQQsShQIm4zRFDI7fTgRKfORFUerOfUfJ46c3Qo5QmuPRmFuSIHRHAl95sjgdwfD5+zzcaEUAIIeJQoETcxpRRcqKRGzBr5rY3HsB+UzQfKNU2a9Ci0Tl3HQ7Ut2iMAy27RZk3cwvYwgQQX36z1eQuMKME0J5vhBDiLAqUiFs0t+qM2QrnM0p85kTIwEnrWZRQfzn8/dhfa0/1KeVXsMxRVLACIea9WK12NsU1D3LEBkq25kvxQaNeA+jtb0/SI5pGBBBCiDMoUCJucaykDnoOiApWGoc+iiao9GY/iyKRSMwauj3Tp3S20sqKN8CUUbIWKEmlpmBJ7IgA46q3Nu+L+dcOzklDJwkhxDkUKBG3cLmRGxDWzC1gKxBPr3w7Z62RG7C/hQng/IgAW6vezDNvAkcEnK1ohF4vspmcEEIuYhQoEbc4esHFRm5A2HgAAfubeTpQOmtt2CRgfwsTwLScX3SgxJck25TeZH4AJILOmRQeAD+ZBGqt3jjrihBCiGMUKBG3cGkiN09M6c3OMvtYD2+Ma3U0AGB/CxPA8trFsBVASiSCJ37LZVKkRvJ9SlR+I4QQoShQIi5Ta3U4XmIIlJxd8QYILL05Xmbv6W1MjMMmbfYo2cgoCciGWWUMlKwEkDL74xLM0YgAQggRjwIl4rLcojpodBwighRIjrCRTRFCUOnNcV9OjAdnKdU2a1DVyI8GaBso8avebGWUnOxR0tpY9WZxThoRQAghnkCBEnHZwcIaAMDAJBUkEonzJzLu9SagmRuwGXDEGQKlMg8ESnzZLTpEiWCl3PSATgPotexza3OUAEGN6FbZCyBFbLbbnUYEEEKIaBQoEZcdPM8auQcmh7l2IuNgRTs9ShKJw2X2/DYmpXVqcGK3C3HA6kRuwDRDCbA+HgBwuP2KTbbGA5jfJyBL1YNGBBBCiGgUKBGX8RmlQa4GSraWwbflYJk9v+qtWaNDXYvWtWtqgx82aTGRGzCteJPIbF+/s6U34/tipfQmYuQAPyKguLYFTa3ufV8IIaSrokCJuKS2SWNcRTUwKcy1kxkzJw4CJQcBh7+fDKoAFlS4u/zGl63a9yfx25cEsayXNU4HSgJKbwLOGR6kQHgge18oq0QIIcJQoERccvB8DQAgNTIQ4UEOAhxHhJTeAEF9OXz5zd0N3afKWKCUHhNi+UCrg0ZuwPR9iRkPoNcBnGF7Ehd7lACzhm4aEUAIIYJQoERc4rayGyCsmRtwuN8bAMSrWMBS5Mbhijo9ZxYoBVs+aG/7Ep5c+FJ+I/NMkbVASeTIARoRQAgh4lCgRFxywLjiLcz1kwkZDwAIWhKfGM4CpQs17ssona9uglqrh0IuRXJE2x4lOxvi8kQs5TcyXyFnNaPE730nMqNEpTdCCBGEAiXiNI7jjKU3l1e8cZz9wYrmBCyzTwwzBErV7ssonSxlWZge0cGQSdv0IRl7lOwESsbVeiIySnzfFmBjjpK4jXb5EQFnqfRGCCGCUKBEnHahphkVDa2QSyWu7fEGWAYPDktvjhuYjYFSTZNr12XmpK2yG+B4+xLALPvjREZJprDeJC4yo2QaEdDg9tEJhBDSFXWpQGnx4sVIS0uDv78/hgwZgi1bttg89ocffsD48eMRHR2N0NBQjBw5Er///nsHXm3nx5fdMuND4e8nc+1kjnpxzAkoYSWE8T1K7iu9nSyrB2AjUOLHA9javgRwblNcRyMTRIwHAICUiCDIpBI0tupQXOuZvfAIIaQr6TKB0po1a/DEE09g3rx52L9/P8aOHYtJkyahoKDA6vGbN2/G+PHjsW7dOuTk5ODyyy/Hddddh/3793fwlXdexoncyS7s78azKDG5ofRm6FEqrm2GXu+ezImxkTvWXqAkZNWbE6U3a2U3QPTIAYVcijRDQ/eJ0nrh10EIIRepLhMoLViwADNmzMD999+PzMxMLFy4EMnJyViyZInV4xcuXIhnnnkGQ4cORXp6Ot58802kp6fj559/7uAr77wOFrKJ3IOSw10/GZ85kcoBqYO/lgKyKLEhSsikEmh0HMobXN8cV2+24q2nvYySvR4ll0pvNoJHkeMBAKCXIdDje64IIYTY1iUCpdbWVuTk5CA7O9vi/uzsbGzfvl3QOfR6Perr6xEREWHzGLVajbq6OouPi5VWp8fhC3yg5IaMktAZSoCgEpZcJjXu+XbeDQ3dhdVNaGrVQSGXolvb7UsAsx4le4GS47EG7WgdrAQUOR4AAHrFshlQlFEihBDHukSgVFFRAZ1Oh9jYWIv7Y2NjUVJSIugc7733HhobG3HbbbfZPGb+/PlQqVTGj+TkZJeuuzM7UdqAZo0OwUq5cWsMlzgqMZnjMzMOsiiJYe6bpXSsmAXFvWKDIZdZ+WcjZI6SMwMnjRklB6U3EeU8CpQIIUS4LhEo8druXM9xnKDd7FevXo2XX34Za9asQUxMjM3j5s6di9raWuNHYWGhy9fcWfFjAQYkqSBtu1TeGXzwYG3j17YEDJwEgIQwFlBdcEOglFvMgoqMOBur+0TNUXIiULL1vjiVUTKU3soa3Na/RQghXZXc2xfgDlFRUZDJZO2yR2VlZe2yTG2tWbMGM2bMwHfffYerrrrK7rFKpRJKpYAf5BcBt07kBsxKTEJKb8IGN/IN3e7MKGXG2wqUBMxRciVQcmNGKTUyCAqZFE2tOlyoaW4/PJMQQohRl8goKRQKDBkyBBs2bLC4f8OGDRg1apTN561evRr33HMPvv76a1xzzTWevswuxTiR212BkqOAwJzABubEMBYAFFa5PkvpeAkfKIVYP0DQXm/OjAdwf4+Sn0xqHDxJ5TdCCLHP5UBJo9GgsLAQeXl5qKqqcsc1OWXOnDn47LPP8Pnnn+PYsWOYPXs2CgoKMGvWLACsbDZ9+nTj8atXr8b06dPx3nvvYcSIESgpKUFJSQlqa2u99S10Go1qrfEHrNsySqJKb8L2TEsxZEoKXAyU6ls0KKxiGaNMm6U3IXOUDNet19g+pi1Hq96M075FnBOmPqXjJRQoEUKIPU4FSg0NDVi2bBkuu+wyqFQqdOvWDX369EF0dDRSU1Mxc+ZM7Nmzx93Xatftt9+OhQsX4tVXX8WgQYOwefNmrFu3DqmpqQCA4uJii5lKy5Ytg1arxSOPPIL4+Hjjx+OPP96h190ZHblQCz0HxKv8EWtYWeYyR6u7zAksvaVGGjJK1a7NUuKDibhQf4QH2bg+MVuYiMkoaYWW3sSNQOhjmKSeW3TxrtwkhBAhRPcovf/++3jjjTfQrVs3TJ48Gc899xwSExMREBCAqqoqHDlyBFu2bMH48eMxYsQIfPTRR0hPT/fEtbfz8MMP4+GHH7b62MqVKy2+/ueffzx/QV2UWzfC5TmaQG1OYF9OvMofcqkErVo9SutbEK+yUxaz46hhDILNshtgVnqzFygZgh0x2R8PlN4AGLecOVpEGVRCCLFHdKC0fft2bNy4Ef3797f6+LBhw3Dfffdh6dKlWL58OTZt2tRhgRLpGG7bCNeco9Vd5gRu2yGXSZEYHoBzlU04V9nkdKB06DwLJgbYCwwFjQdwZlNc/n2xESg50cwNAH0T2Oyr/Mom1LdoEOIvoDeMEEIuQqIDpe+++07QcUql0mZ2h3RuponcYe47qajSm/AsSkpEIM5VNqGgqgkjukc6dWmHDBklu1u1CNrCxJlAiZ8v5SCjJDJQighSIF7lj+LaFhwrrsewNNuDVgkh5GLWJVa9kY5TVt+CCzXNkEiA/klumMjNE9PMLXDgJGDW0F3pXEN3g1qL0+Vsq4/+iWG2DzRuYWKnmduZxmuHm+I6Me3bgMpvhBDimKhAqbq62riyrby8HN9//z2OHDnikQsjvonPJqXHBCNY6cYxXGImc4voy+Ebup1d+Xb4fC04DkhQ+SM6xE4Q1+qpjJJnmrkBoI+h/HaUGroJIcQmwYHSZ599hqysLAwZMgRLlizBjTfeiL/++gtTpkzBJ5984slrJD5kf0E1ADeX3QCRe70JH9zIZ5TOORkoHTJOIA+zfZBeZwpU7I0HcCqjxAeQtsYDCBuVYA2fUTpygTJKhBBii+CUwEcffYSjR4+iqakJKSkpOHv2LKKjo1FXV4dLL70UDzzwgCevk/iIQ/mleED2M0YEZwMY6L4TG0tvQnqUxJTeWOBSUNno1GUZG7mF9CcBDjJK/Ko3MZviOiq9OXFOg8GGYDevtJ4augkhxAbBGSWZTAZ/f39ERESgZ8+eiI6OBgCEhoYK2k+NdH4anR5Di77E//mtxhU77wV+fATg3LRXmJhmbhF9OSmG0lt1kwa1zeKGMnIch5xzAjJofNkNEoGlNzGTuR2UJF0ovcWE+iM5IgAcZxr5QAghxJLgQEkul6OlhQ3427Rpk/H++nqa7HuxOF5Uh2uwzXTHga+AqjPuObmjCdTmBA6cBIBgpRwxht6isxXiskoXappRUtcCuVTiIFBizd5QBAH2fmngAyVOz8p1QgjeFFd8RgkAhqSEA4AxICSEEGJJcKD0999/GzeEValMZYjm5mYsX77c/VdGfM7ZozvQU1qEVokCiOnD7izc7Z6TO5oXZE7k7KAe0cEAgNNlDaIuaW8+Cx76JqoQqLBTpTaOBnCwuax5Vkhoqcy46s39GSUAGNKNjQWgQIkQQqwTHCgFBwdblNhKSkoAADExMbjkkkvcf2XE5wSc+B8AoCBiDNDzSnZn4S73nFxMM7fAgZM8fgPYMxXiAqU9+WyF59DUcPsHtgoYDQBYZoUEB0oC5yjptYBeL+ycZviM0v6CGuhc2OaFEEK6KqfnKGVnZ7vzOkgn0LN6KwBA1+dGIHk4u9PdGSU3bmHCM2WUxJXe+IzSUEfDGM1Lb/ZIzTNKAvulhG6Ka36sCL3jQhCslKNBrcWxYhoTQAghbTkdKHHuauIlnUJpRSVS9OcBAMmDrgSShrEHynKBFjcsL3em9CagRwkAesQYAqVy4Rml2iYN8kpZ/12Wo4yS0NKbVApIDSU8oUGNVmDpDXCq/CaTSjCiOwsEN50oF/18Qgjp6pyeGEgr3S4up4/sQqyEQ5UkHBGRiezO8G5AdT5wfq+pFOcsZ0pvula26s7B38XuUSzTc66yCVqdHnKZ498PdpypBAD0iA5CZLCDa+I3xHWUUQJYBkivdX/pDRDf0N3aBPz8OF5pKMZgeTg2HXsAj1zeU9w5fEB1Yyu+33ceW05W4ExFA5pb9YgNVaJfggpXZsbg8owY+An4MyeEEGvcOFqZdGU1Z3IAABXBvWEsRCVmsUCp5JDrgZKjCdTm5G3KTQ62PUkMC4BSLoVaq8f56mZ0i3Ic0Gw7VQEAGJse7fh6RAVKfoAGwoMaR6veJBJW0tNrxGeUcn8EDn+LRACPyIHiC1GoaRqOsEABWT0f0KLR4aO/T2L51rNo0Vj2Z1U0qHG0qA5r9hYiXuWPWeN6YOrwFAqYCCGiUaBEBJGXHQYAcPFmQyYj0thtTYHrL+AoILC4GH/T59oWh8+RSiXoHh2MY8V1OF3eICpQGtVDwEa6QvZ544ndxkTI1i5yJdCqEd+jdHIDuw1PA6rP4nH5f7Ht6CxcM7S3uPN4wbHiOjy8ap9x5EPfhFDcODgRA5PDEOAnQ1FNM3acqcTPB4tQXNuCl346iv/syMfcSZm4KjOGMuKEEMGcDpQUis7xWydxXYtGh4TmE4AEiOyZZXpAlcxuawpdfxFRpTfx5aYe0UE4VlyHU2UNuDIz1u6xF2qacaaiEVIJMEJIoMRnlBz1KAGWZUMhHG2Ka/6YmNKbTguc/pt9fv0iVH0zC9EtheC2fQgM/Vj4ebzgj6MleOyb/WjR6BEX6o+XJ/fFhL6xFsFPv0QVsvvG4blJGfh2TyHC//gXhtUdQsU3KsyNexZ333gdMuNDvfhdEEI6C6fz0Hv37rX7+J49e5w9NfExh/LLkA4WDEX2HGp6ICyF3bozoySk9CaRiJ5y3Ts2BABwvMTxgFQ+mzQwOQyhQrb1EFt6A5xY9WYnUHJmltKFHKClBvBXAckj0HLpPADA2Oq1KCn33abutfvP46FV+9Ci0WNsehR+e3wsJvaLs5khUsplmNZLh2u5zYiR1KCP9BwGFP0X13y4Bc/+9xDOObm1DSHk4uHWgn1ZWRkWLFiAfv36YcSIEe48NfGiU7n7oJRo0SQNgiS8m+kBPlCqLXR9KxMxpTfAVH4TOEuJzx4IWQLPr/4a0zNK2LV0SOnNXkbJEHyJySid+pPd9rgCkMmRMOI2FMkSoZI0Iu/XRcLP04G+2nkOc749CJ2ew82XJGHFPUMRHiQgs81nzgwrDif4H4We47BmbyEuf/cf3PnZTizfehbbT1cgt6gOBZVNKKtvQXVjK+paNNDTfClCLmou9yjpdDr88ssvWLFiBX777Td0794dN998M7788kt3XB/xAdX5BwEA9apeCDT/zV2VxG41TUBTJRAkMLCwRsxeb+bHCQyUMuJZRul0eQNatXoo5NZ/R9Do9NicxwKlyzNihF2LqNKbyEDJ0XgAQHw5D2AN+ADQbQy7lcpQ1v8BJBx4CRn5X6C+4SmEBIcIP5+HLdt0GvN/Ow4AuHtkKl66ri+kUoF9Rqf+Yrdj5gDbFiJSW4qf74jDe/v0+CevHNtOVWLbqUqbT1fKpegVG4IrM2Nwa1YyEsPs7OdHCOlyBGeUysvLMXfuXLzzzjvQaDQ4cuQInnzySSQkJOCBBx5AcnIy9Ho9vv/+e7z++usYPHiwJ6+bdBC1VgdZJfsB5R/f1/JBuRIIjmOf15xz7YX4spHgjJK4clNiWABClHJodJzdeUp786tRr9YiIkiBgUlhwq7Fk6U3PZ9RsvO+OFN6q73AbsNSjXf1mfggyiSRiEUVtnz9b+Hn8iCO4/Du73nGIOnhy3rg5ckigiRtK5C/hX2eeS2QwjLd/VtysPLeYdj89OWYOykDV2XGIi0qCFHBSgT4ySxOodbqcfhCLRb+eRLj3/0T7/2yH2qtwL36CCGdnuCM0tSpU9GnTx9EREQgMTERTU1NuO666/D5559j4sSJkMlkWLp0qSevlXjBwcJadOfYoMnQlP7tDwhLARpKWEN34hDnX0jMZG7AbOiksOBAIpEgIz4Ee/KrcbykzmYj78a8MgDAZb2iIRP6w1jsHCVAeFAjZNWbM83cdezPFKGJxrsU/gGoG/4UYnbOxYgLK/HTrmmYPDxD+DndTKvTY97aI1izl/XHPT2ht/g5T4W72OT0oGggtj8rNZ7dzLJMwx9ESmQgHhzXAw+Oa/9UnZ6DRqdHaV0L9uRX47vd5/BS8UNI2lOOf598DrNmPIjYUP/2TySEdCmCM0rHjx/HnXfeifvuuw9VVVV44IEH8Oqrr+Kaa66BTCZzfALSKe04XYl0CfuhKonJbH9AGL/yzcWGbtGlN3GBEmDqUzpebLuh++/jLFASXHYDhE/mBlxo5hYQKAkNvlobgWbDJriqRIuHeo5/AOXKFERIGnDh59fxwo9HUFTTLOy8btTUqsWsr/Zhzd5CSCXA/Jv6OzcMs2gfu00dzSajp13Kvr5gfzEKwKaW+/vJkBoZhFuGJOGbq5rRR3oOoZImPF/zEt5dvASldcKmwxNCOi/BgdLzzz+PG2+8EePGjcO///1v5Ofno1+/fhg+fDgWLVqEch9eKUOct+vEeaRKWPAAq4GSWUO3K0SX3kT2+gDIiDM0dNtY+XaqrAGnyhrgJ5Pg0l4CBk3yjBmlYMfHitynTlAzt/G9EBh88WU3RQhb9WZOJkfkDW8BAO6T/YYtu3ZizFt/467PduHbvYWoaxH4Gi44XlCMhz/4Fn8eK4FCLsWSu4bgjmEpzp2s4iS7jTbMhooy3DZVAo22+5KskexbCQDQ+wVDJuEwrnE97vpsV4e8J4QQ7xEcKD344IPIy8vD/v378dRTT+GHH37A+fPnMWXKFHz66adISEiAXq/Hhg0bUF/veAk28X21TRrUnc+FVMJB5x/OyhdtqbydURL+G32fBBYoHblQa3WvwvVHigEAo3tGQRUgYCwAzxgoeaCZW1BGSWR2jS+78c34bUgzJgE9r4JSosU7qv9CzwFbT1Xgmf8eQtbrf+Khr3KQc65K2GuJUFLTjP+teAsxy7OwsvEh7PB/HD9PbMGEvnHOn7TyFLuN6sVulcFAqOH7rjwp/Dz1JUDebwAA6YTXAQBjZEdxqqwOj63eDx2tjCOkyxI1HiA4OBh+fqb/sKOjozF79mwcPHgQO3fuxEMPPYTXXnsNMTExmDx5stsvlnSsracq0BPsh6osJtP6nmrGWUquZpQ826MEAJnxIVDIpKhqbEVBVVO7x387UgIAmNRP5A9mUeMBxJbehGSURDZz8xmlNmU3I4kEmDAfkEgxtGUHdk5T4ekJvZEeE4xWrR6/HSnBzUt24PZlO7D5RLnTG2Tr9RzySuqxencB7v/PXnz07vO4/tybiJA0QA8J4lGB3ttmAw0uZKv5jFKkWdku2hA0VZwQfp6Tf7A9+hKHAIPvAhQhCEM9BvsV4p+8cnzwl4igixDSqbhtjtKQIUOwaNEiFBUV4T//+Q+0Wq27Tk285J+8MvSSGrIPMTaaeoMNU64by5x/IY5zYdWb8NKbUi5Dv0SWVdpXUG3xWEFlE44W1UEmlWB8H5GBknE8gJvnKOl1AKezfJ7Vc4qco1RnCJRCbQRKAAsmBt4BAIjLeQePXN4Tf8y+FOseG4vbs5LhJ5Ng19kqTP98NyYv2obfDhc7nDdU09SKjXllWPBHHqYt34WBr/yBCQs3Y+4Ph1F6fAdelK0EABT2eQDS5wqAuP6sl2r9c8K+r7aaqoAmNjzUIlCKciJQKmFb+CBlJHu/08YCAF7rz4K4j/4+ic0nqP2AkK7I7Xu9KRQK3HbbbbjtttvcfWrSgfR6DptOlONqiWHZf0wf6wcGG5qemyrZD3apE4395tkVD5beAGBwSjj2FdRgf0ENbhxsKj39Zii7DU+LQISQIYY8jvNc6c38fZHa+acqdo5Srf3Sm9G4Z4FD3wJn/gHO50CSNAR9EkLx1i0D8MT4dHy6+SxW7y7A4Qu1eGjVPsSGKnFFRiz6JoQiPFABtVaH4toW5BbV4WhRLfIr22fxAvxkGJQUgvfrV0HZoAV6X4PkW99mWa3rPgQ+uxI48l/gyhcA82GnQvBlt9BEVnLjRaWz2woRWaCSI+w2zrDys/tlQN469G3Zh6nDb8LXuwrwxJoDWPfYWMSpaCUcIV2J6ECpoKAAKSnCGysvXLiAxEQ7v7kSn7S/sBpl9Wr0VRp6j8w3wzUXGAVAAnB6FiwFi1gtxjP/AS+69CZuI9jBKWEAgP0FNRb3O11207Wasj7uLr0JfV/kIle91bYfDWBVeCrQ7ybg0Bpg7+dAkmn8Q7wqAC9e1wePXtETK7edxX92nENpnRqrd9vvVUuLCsLglDBckhKOwSlh6B0bAvnR74EfjrHm8us+MJV4Ey9hq9XytwDHfgZG/UvY98ezVnYDxGeUOA4oNQRKsf0M34hh9VzhHrz4dAYOFNQgt7gO/1q9D1/PHAE/mVs3PSCEeJHoQGno0KGYPHkyZs6ciWHDhlk9pra2Ft9++y0++OADPPjgg/jXv0T+B0e87pdDxYhCLWIk1QAkQGxf6wfK5EBgBAuSGspcD5Q8NHCSNzglHADbyqS5VYcAhQzFtc04UFjDWnPENg63mu0VJqb0JqS3Sm9WvhbUzC2y9GarR8lc1n0sUDryPTDhdSAg3OLhiCAF5mT3xiNX9MS2UxXYdaYKJ8sa0NCihZ9cgpgQf2TEhaBvgoplmtpm63Ra4O9X2edjHgeC2ywY6HM9C5Ry/+dEoGQIhPgMEo8PlKrzAU0L4OcgA1RzDlDXsT87/rlRvQB5AKBphH99ARbfeQmu/Wgr9uRX490/8jB3kpUVomADNHPOVWN3fhXK69UIUcoxvHskhqdFQN7BwVVtkwZnKhrAAUgOD0R0iMB/e4RcZEQHSseOHcObb76JiRMnws/PD1lZWUhISIC/vz+qq6uRm5uLo0ePIisrC++88w4mTZrkiesmHqTXc1h3uBh9pPnsjsie9rMlQTGG5dZO9inxQYNEJrx0J3ILE16Cyh8xIUqU1auxr6Aao3tGYb0hm5SVGo4YsQME+UBJpmBBoyOiSm+GYxy9L2LmKHGcqZk71EHpDQCShwMxfYGyo6wMN/xBq4cp5TJckRGLKzJiHZ/T3LH/sRWTgVHAiEfaP555HbDuaeD8HpYJc1QuNNd2xRsvOBZQhrLgp+q07V8CeHzZLTrDlL2TyoDYPmxz4ZJD6Na3B96+ZQAeXrUPyzadQa+YENw8xHStej2HzZv/RMmOb7Cg9nKUwSzg/PsUksID8NykDFzTP97mBr/u0KrV47ucQqzeXYAjFyz3PewVqcBDPStxxRVXQ6WyPpC1qymsasLOM5UoqGpCi0aHYKUfescFY1hapLgSPOnSRAdKERERePfdd/H6669j3bp12LJlC/Lz89Hc3IyoqCjceeedmDBhAvr16+eJ6yUdIKegGqV1atyhNKxki7MykdtccDRQfgxorHDuBcWueANEb4rLk0gkGNcrGt/lnMcfR0swumcUfjxQBACY2C9e1LkAiJvKDZgFNSJKb/aySYC4OUrqekBjuOZQAd+vRAJcMh1Y/yxw+L82AyWncByw3bAB77CZ1nu8QuLYtiMFO9jy/GEzhZ+/6iy7jehheb9EwoL/on1A1RkBgZKhkbvtv4PYfoZA6TDQ90Zc3T8e941Ow+fbzuLJ7w7idHkDrh2QgBOl9fhh024srH4Ul0kacJnybyxPfQvyhAEorWvBxuNlOF/djEe/3o/1A0rw75sHIFjp9vZR7C+oxrPfH8KJUtMWPnGh/pBJJehTvxUvN6xE4sFKHD3YA3+PX4HrRw4QvlVMJ6LTc/h+33l8sSO/XbDYV3IWtygWoBL++Ds8GzETn8PY3rEeDV6J73P6X6O/vz9uuukm3HTTTe68HuIDvt7F+kzGqUqAOgDxA+w/IchQbmtwMqPEBwRyMYGSyL4cMxP6xrFAKbcUt2Yl42BhDfxkElw/KEH0uYxBh5CyG+BcM7ejAFLMHCV+IrfcX3hw1+d6tvLs/G7xWR17CnezYEWmBLJm2D6u++UsUCrcJTxQ4jjT/oPhqe0fD+/GXrs63/G5Sts0cvP4r/mME4Dnr8mEnuOwcns+Fv9zGov/OQ2AwxrFvxEhbQAHCeIkVZjXshCYsA2QSNDUqsWyTWewaOMp/HKoGCdLG7Di3qFIcNPmu02tWrz7+wms2H4WHAdEBinw8OU9cf2gBEQFK4G6YnAf3QmJYcxFX5yG5PdpuHn/Irx240D0S1Q5eIXOY19BNV748QiOFrEASS6VYFByGHrHhSBZfwF35b6NYF0tACC9ZgVe/7IVHyXfhZeu69ul3gcijvt/bSGdWkWDGr8eYivAMvkVbw4zSoZAydXSm72NX9tyYgsT3pj0KAQqZCiubcG8H9kPuQl949gPDbFaRcxQAsyaucUESkIzSgLei5Yadusf5vhYXmi8KauT+xMw8mHhz7Vn33/Ybf9b2/cmmUvKYrfn9wg/d3M12+MNMA1FNReRxm75rJM9fK8TP92bF2f4BYLPOAGQSiV46bo+yOoWjhXb8nGytB6XBRVieONxcH6BkNz9M7DyGlbKvJADJGUhUCHH7PG9cGmvKMz6ah/ySutx4+JtWHHPMOOQVGdtPlGO/1t7GOer2TY0Nw1OxAvX9rHsFdv4BguSkoZCM+k96FZchz7ac0gt+g2TF9XjvtFpmD2+F4I8kOXiFVY14eD5GlyoboaO46AK8EP3qGBckhoGpdz1LbKqGlvx9vrj+GYPy5KH+svx8OU9cXtWsum9+M//AbpaIH4QqpOvQvjud/GU/FtMPHcJrltUjVuHJOGp7N7iy/Ok06NAiVhYs6cQrTo9xiYA/lVn2J3xg+w/iZ/Y7exgQP4HfAeU3gDA30+Gaam16J3/Bb4+fwWADNw53ErWQQgxowEAJ0tvQjNKAoIvPqPUpinbob43skDp6Fr3BEotdexcACvt2ZOUBUDCsj8N5faDKh6fTQqOs96szY8acJRR0utMx7Qt4cUaRmbUF7HtUIIiAbDy7rUDEnDtAEOG8s+Xga2ApNcE9r30uQE49A2w7wtTEAhgSGoE1j48Cveu2IOTZQ24bdkOLL7zEnHb6RiU1rXg378dx9r9rB8tMSwAb9zYD5f1brPYouIksP8r9nn2G/BLHAi/cY8Df72K54J/wf/qRuGzrWfxy6FiPHBpd9x0SSLCAt3Tu9PcqsN/953Hqp3ncNzGtkIBfjKM6B6BKzNjce2AeNGvrddz+GZPId7+/Thqmti/uVuGJOG5SRmWvxhd2Mc2S5bKgdu/RLgqGajYC/8z/+D9qJ9xY/kD+HbvefxyqBgPX9YD94/tDn8/2wFci0aHU2UNqGhQo75Fi6ZWLYKVfggP8kNUsBKpkYFuCQBJx6BAiRjVNmvw2RYWHD3a7TxQxbEd14Oi7D+RD5SczSjxQYNTpTdx4wEAAGe34Jni2ZDJGpEt3Yv3ExdgRPcI8ecBOqb0JnWQURJzzuYadis2UMqcDPz2DCu/1Zew3iFXHF3LJppHpgPJ1lfPGvmrWDan/DjbzLa3gAUi1YZAKczGKJNwQ0bJUaBUW8jeV5mifclRGQJEdGd9TqWH2Wwla47/ym4zrmW3l0xjgdKR74EJb1rMeEoKD8R/HxqFWV/mYMeZSty7cg/m39gftw21khVrQ6fncKCwBj/sO4/v951Hi0YPiQS4e2Q3PD2ht/WM0P4vAXBAejaQMpzdN3QmsO0DxLUU4KeravFQTgLOVzfj1V9y8ca6Y8iMD0FqRBCSIwKRGhmI1IhApEUHIV4lrFTIcRx2r/8KdXtWo6+2FON0Q1EsvQrdEuORFhkIP5kUlY2tOHyhFuX1amzMK8fGvHK8+nMursiIwc1DknBZ72iHIxgOna/BC/87ioOFNQCAjLgQvHZDPwztZuXf+rYP2G2/W0x/Z7LfAJaOxuCGLfhp+ny8uLEaBwpr8O4fJ/DFjnO4NSsJI7tHITzIDzVNGuQW1UF9ajOGFH2NbpqTOKwbiCW6ySjg2i9wkEklSI0MRGZ8KLJSwzG0WwQy40Mh64I9YV2By4GSRqNBSUkJmpqaEB0djYgIJ3/gEK/7eOMpVDdp0DMmGEN1B9idPS53/ERj6c3JjJJLpTeRu7dzHPDjQ5BpGwFFMIJbG/BC/euA7lZxgRpPdDO3mNKb2GZuMRmlMMfHmguNZ9t3XMhhTdVZ94p7flt8FuOSada3xmkrKYsFSoW7hQVK/N6DNgOlbqbj7A1KrTzNbiO6Wz8mrj8LlEpsBErlJ1jpTuoHpI9n96WOBsJSWdbrzEa2ss+MKsAP/7lvGJ79/hDW7r+AZ74/hIKqJjx2ZToUclNwUN+iwYHCGuScq8a+ghocLKxBbbMpUzkkNRwvXtsHA5PDrH9vOi1w8Bv2uXlWzz+UjYXY+j76l6zFn3O+Zc3P288hr7QeRy7UtWuClkGHW1R5GJoSihFjrkJSapvsm0FNXT0OfzYLY+t+YXdIgUukp/BM1H7I7v2NjRox4DgOx0vq8U9eOX46WIRjxXVYf7QE64+WICJIgckDE5DdNxZDUsON2Rm9nsPec9X4z/Z8/HqYtRAEK+WYM74Xpo9MtT6Coa4IOPYT+9x8BEVcP/ZndW4bBpT8gB8emoefDxXhrd+Oo6i2BR9vPI2PN542Hj5Weggr/N6GXKIHJMAd8o24xm8vXo16B1VBPdHQokV1UytK6lpQ36LFmfJGnClvNLY6BCvlGJwShtE9ozCmZxT6xId2yWb6zsipQKmhoQGrVq3C6tWrsXv3bqjVpvJHUlISsrOz8cADD2Do0KFuu1AhFi9ejHfeeQfFxcXo27cvFi5ciLFjx9o8ftOmTZgzZw6OHj2KhIQEPPPMM5g1a1YHXrHv2F9QjRXbWL/GvKszIP11I3ugxxWOn+xy6c2ZZm7nBk6i5DDLEvgFAo/tB5aOZaWTE+uBPk7sT2jsURJYehOz9Yreg83cYjNKAJBxDQuUjv/qWqBUnscyUxIZMGCKsOckDWPBldA+JT5QstbIDQChCex91bWyuVK2Aiq+/Ny27MaL7c9mPJn1KVk4wTbSRdqlLDMGsMCw99XAriXs712bQAkAFHIpFtw2EEnhAfjo71NYuvE4Kvb+gFERDaiUReG7xkE4VtaEttvshSjluCwjBlOHpWBE9wj7q7VO/wU0lAKBkUD6BMvHBk8Dtr4PnPoL/o1FuHN4Ku4cnorCqiYcL6nHucpGFFQ14VxlE4LK9+PppgVIU5cAJwHtCSk2Rd+GXlPeRHxUpPGUO4/lQ/btNIzlDkHPSXAwcQr6DciC39Z3IKs4DqyeAtz9i/H/AYlEgsz4UGTGh+Khy3ogt6gOa/efx9r9RahoUGPl9nys3J6PQKkGd4fm4ArtNmg0GuTqErFdez0kklDcMCgRcydl2O8rOvgNG5ibMpIFR+aGPQCc2wbkrIT00qdx/aBETOwXhz9zy7DuSDFyi+rQqNYiw68Ey5o/hJzToyzxKvgNmYawvQsRWrQf7za9CNz5NxDGsoIcx6GsXo28knocvlCL3WersO9cNerVWmw5WYEtJ9nq4bBAP4zqEYnRPaMwukcUUiMDfX71nU7PoaCqCafKGowfZbUN6F2zBZc3/4EU/QVIoUeeJA1b/cchVzUO4cEBCA9SICpIgagQJaKC2UeIvxx+MikUMikClTLnekjdRHSg9P777+ONN95At27dMHnyZDz33HNITExEQEAAqqqqcOTIEWzZsgXjx4/HiBEj8NFHHyE9Pd3xiV20Zs0aPPHEE1i8eDFGjx6NZcuWYdKkScjNzbU6Sfzs2bO4+uqrMXPmTHz11VfYtm0bHn74YURHR+Pmm2/2+PXac/7McRT/+gZaAmLRHNINNbEjERqViHiVP+JU/ogKVro1RVtQ2YQHvsyBRsdhQt9YXBZeCdQXsz6glJGOT2CeUeI4YRkCc06NB3Bu4CRO/s5u08ax6x44Bdi2EDiwyrlASW3orVCGCDvemTlK7swoOdPMzcu4FvjrVeDsJtZj5O9kozGfTeo1AQgROHcp8RJ2W3JI2N+xGgelN6mMPVZ5ijV02zqOzyhFdrf+uJWVbxbObmG3Pa+yvL/XBEOg9Aeg1wPS9pkOiUSCJ7N7o7dKg9j1szBUcxgoZY9doY/Bc5KZOB+ehUtSwo0fGfEhwqeC838OA25v/0tKZA+g21g27PPA18BlzwIAkiMCkRxh9ktBxSngszcBSQ1aFWEo4SKQojmDcRXfoPCjv/F9r6eQNf52/LnxT4w++jIypIVohj9KJi7F4JE3snOkjQJWTGSrGje/A1wxz+rl9kkIRZ+EPnh2Yga2nKrAzweKcOHEXrymWYBeLYbZYBJglPwQpii3o/KaT5F6ySD77wHHse8PAAZNbf94xrVsgn3dBVYuHjgFSrkM1wyIxzUD4k3n+GIycLYJSB2NmGlfs/+fMscBK69lqyZ/mMmCQJkcEokEsaH+iA31x6W9ovHI5SzAyCupx84zldh+ugI7z1ShpkmDdYdLsOnwWfSUXECvoCakRAYhKTwQ0ZGRCItOQmR8CmKioiFz8GfeqNbiQnUTmsrzIak8BV1rE3QKFTShKUBIAsKClIgIUiA8yE9Q71Rzqw75lY2mgKi8AafLGnCmohGtWr3xuF6SQrzjtwwDpWcsnp/IleGK5l0oaFyB97S3YrV+FDg7W8+O6RmFr+4f7vC6PEV0oLR9+3Zs3LgR/ftbXwk1bNgw3HfffVi6dCmWL1+OTZs2dUigtGDBAsyYMQP3338/AGDhwoX4/fffsWTJEsyfP7/d8UuXLkVKSgoWLlwIAMjMzMTevXvx7rvvej1Qqi44iqGVP5nuOAYc1adii34Aduj7oARRiAxSIDGYQ0Igh0R/NYK5evi11kGhYR9KbS0UuiZweg56cNBzgJ4DdHpAwbVAyamhkweiOLgvPq4cjPKWePSODcF7tw2C5Pc57HW7jXU8tRgwZZT0GpaxCBRZfnWq9ObcwEmc+IPd9spmt4PvYoHSyT+c671Rs6XEUAoMGkRtYSJw1ZuoHiUXMkpRvdj8ocpTwKk/2fYmYuk0pnLP4LvEvbZEBrTUslKJo6nijkpvACu/VZ4y9CmNs35MFV96s5FR4gOlirz2U751WtYADwDdxlg+L3U0oAhmfX3F+1lZ0xqdFtceehzgDkMrD8TZsNFIrN2LVE0Zvla8Ccmw54BLnxa/x2JjJSuhAsCgO60fc8l0FijlrATGzmn/97C1EVh9Owu+E7OgmP4jUpQhOLXte4T99SyS9WVIPvkMtCeewwyJHpAC9fJw+N31LdK6mfWlxfZhW9d8dw+w5T0g42ogYbDNS5fLpLi8dwwuV54Ed+pFSKSNaPWPwvle0xEQkYjY3OUILstF8B8PAKl/s6DPlvN7gcqTLMPc98b2j8vkLHv69+vA7k/YL1Zt5f7IGsFlSuCGxaZf4gLCgdu/BJZeyv4ebHrLZhAok0oMgWAo7huTBo1Oj9P7/oZyx/tIrtoJObSAFixQLrV8bjOnQLk0EuWKZNQHpaIhOA3VijiUNeqhqStDeOMZ9NKdRH/pWfSS1LV77RouCLn6VOzgUpGrT0WBXzcEBgUjKlCG2EAgSdGMYF01tM31qG5sxdkmJXKaYnCcS7Ya3CjlUvSOUuIB+S+YVPkFZJwGWr8QVPa9Gy2pl0Gi1yOgcDPCjn2NFHU5PlAsxvOBG7Ahejq2yIajrFGHigY1mlp10Oj00Gj1dhvnO4LoQOm7774TdJxSqcTDD7tpGbEDra2tyMnJwXPPWe4ynp2dje3bt1t9zo4dO5CdnW1x34QJE7B8+XJoNBr4+bX/4aRWqy3KjHV17f/SuUNYQg9sT54J/6YSxDTmIanlBPpKz6Gv9Bxm4Wd2kAZAteHDWTqgn3o/xuMr/B12FfrdMh/B1ccNDZ4Axj4p7DxyJSsrtNSyrJLYQMmp0psTq94aK02lm3TDn31UOps+XbiLLX0f/oDw8wEsswKYyiqOOJVRcmfprYbdiu1RAlgWJ+Ma1vh6/FfnAqWTf7DgICja9GcghFzJgqXyY0DpUfuBEseZBUp2VjMKaejmp3vb+mEbmsB+IDZXsx6qhEGmx4oPshEF/mGmPeKM34+ClbWP/cQCFluB0s6PWQO7UgX5veuQHtePBSi/PQvJ/i+Bf+az0tBNnwnPzgHA4e/YLzbxA9uXm3h9rgd+/z9Wmj7+S/tA4s9X2PsTkgDcsdqYVe05+mZwQ8Yjf+2rUJ34FuGohRYyVKRMQtztH1hfHNL3RuDojyzo+PFh4IF/7G9nlL8VWHUrG2uQdikUN3+O7vxqyNF3Af+5lv1b/2Yq8OBm2+c6sIrdZk62nRW+5B5g09us7Fy4B0g2aylpbQR+NwQ/Y2a337g5ojtw3ULg+xksW5Z2KZBmux0EAKBugN/fryNj11IArLbKBcWiMSAOzRo9Wlo1kLXWI1RXjWA0IUDSihSuGCnqYkC9G6iyck5DnKGFDBekiVBLAxDM1SNGV4owSSNGyXIxCrmm45sMH7YogVoEIU/RH0WRw6FJHoXolEz0VEmQULEF0q0LWLYRAHpNgvza9xFrPuB2yATg6v8Ddi4Btn2A6KaTmHruBUwNjgV6TWT/J8f1t5yG70VON3OfO3cOeXl56N+/P+Lj20/4LSoqQkKCEwP8nFBRUQGdTofYWMv/KGJjY1FSUmL1OSUlJVaP12q1qKiosPo9zZ8/H6+88or7LtyG5F6DkNxrkOmOhnK2g/vpv8Gd3wuuoRR6SKGR+kMtUaJBEoxmWQjUfqHQ+IVC46eCVqmCXhECmUwGuZQNVpNLJZBJJZD6+UMrD0RLbRkiz/+FtMpNuKLlT+DzjYBEyur1fa4HUgWU3XhB0YZAqaL9vBlHtM6MB3Ci9HZ+DwAOiOptuYKp9yQWKJ3ZKD5QUhsCJY+U3sTOUfJwRglgpYhtH7CAR9sq/j8xvtwzcIrj76ut2D4sUCo7asoIWtNYwVbUQWJ/OKZxRICNWUo6jWn1nK2MkkTC/kM/u5mVWMwDpXxD2S11tNXSGjKuYYHSsZ+BK55v/3j1OWDjm+zzCW+YAhpFEHD9Ipal+mU2e+2lY4DJHwprdOc44IDhz2GQnayeXAkMuRfY/Dawa5lloHR2C7B7Gfv8+o/a7fEo8Q9FtzvehU47H7UlZxAa1w1xjvZxvOY9FgCV5bLsy5UvWj/u7Bbg69vYn3GPK4EpX1tm8vz8gdtXsfek/Diw7UNg3NPtz6NpBo78YHgfrJTdeMHRQP/b2Hu28XVg+v9Mj21ZYOpxG/OE9ef3v4X937L/K+D7+4H7/zT2K7Vz+m/g58dNgf7AO4AxsyGJ7o1gAMFtDtepG1FVUoiq4tNoKTkBVJxEcEM+QlpLoZDogYBwSCLSoEzNgn/qUMhj+yHV/L3SqlnPYMlhcCWHoCs6BFTkgdProYMMWsjRIAtFszwMOr9gBCrlCNXVIKjqKFSaRgxr3QkU7wSKPwB2t7m4oBj297b/rdZL5Yog4NKn2MKBnYuBvStYz9y+/5hmrElk7N9w98vY328vcSpQWr16NaZPnw6dTgd/f38sW7YM06ZNw7lz57B69WqsXbsWOTk50Gq1jk/mRm0b3TiOs9v8Zu14a/fz5s6dizlz5hi/rqurQ3Ky42W7LguOBgbcCgy4FRIAEgBSsD+8AABhLp38cZZ+5ntPOB0bpDfhTXGnCTBkkZqt/TrjgDM9Ss6U3sqPs9u2v0Hzq5XObmE/HMX8AOczSh4tvflIRgkAErPYf4CNZcC5rcKa/Xn1pcAJQ4/Y4GniXzu2L1tSX3rU/nH8D5mQePtZiQgHGaWaAvbvQR7AzmVL3AAWrBQfAswrRvlb2W3bshuv10S2Gq78OFsdF91mT7pNb7FVnaljrJcpB05hJarv7mHBxeoprEl84r9tN7EDrAxUcpj9vel/i+3jAMPqtwXsOSf+YAGquh74n6FaMOSe9v1XZmRyP6iSBP7iFBQFXLsA+HY6sHUhC8r53jSeoyCJFxILTJxvyuT0u6l9VvD4r6x0rkpmbQb2jHuabQ595h/g9Ea2GvhCDrDd8MN7wnzAz854hElvs1lNZbnAVzcBd/9sWeZvrgb+eN70i4QqmWWi7Ly3ACBTBiE6NQPRqRkArrH/PVgjV7KdF+IHQII7LQIC/n/BtsEZAFZWLj4I5G9mwd2F/UCroV8zqjcLjoY/KKyPMTCC/aJw6TPsfKf+Zr2IJYfYL98155xfUe0mTgVKr732Gv71r39hxowZ+L//+z889NBDOH78ON566y307NkT48ePx7x51muxnhAVFQWZTNYue1RWVtYua8SLi4uzerxcLkdkZKTV5yiVSiiVXXCH7aQs4O6fzBpX7dT0beHLbU0uBEqOfuM050zpzbibfNsJywNZoNdcxf7zSxkh/Jx8RkloY7MzpTfBc5QEBF98M7ezGSWplGUt9v2HZULEBEoHVrHAI2mY+KwjwDbnBYDSXPvHOWrk5vEZJVvTuS1GA9hpluUHshbuMt2naWElMcB2oBQQxoL0UxvY5sDRZlmPipPAwdXs8/Gv2G5ej+4N3P8XsOnfwI6Pgbx17Af5pU8Cox6z/m+Knxk0aKrjMnloPFv5tXMx8NO/gOk/sn6dmgJAlQJkv27/+WL1uR7odzMLiH98iH1v/JypYz8D388EtM0sgLh9lf0eyn43szaCM/8A654C7vrB8n3MWcluB95h/88XYH9Xhs4Adi0F1s5ifyZ/vsz+jfa+hmUH7VEEAXd+ByzPZv8PLRkFjH2KBceFe4A9n7KNxSFh2/Rc+aLwLLU3yORA0hD2MWY2y1K21LC+O7GZYp5cwf5c+eCQ49iioupzXi+/CVweYen06dN4/PHH0bdvX3z88cdoamrCjh07cPjwYRw/fhwfffQRJk92YgWRkxQKBYYMGYINGzZY3L9hwwaMGjXK6nNGjhzZ7vg//vgDWVlZVvuTLgqRPZwLkgDXMkrONHM7M3CyPI/dtv3NXSoFuhuaeU9vFH4+wGzVm9BAScRYA9Gr3kSMB3Bm1Ruvz/Xs9sgPLCAQQqcBdn/KPh9yj3Ovy29eW5Fn//1zNBqAxwdKLTWm98Uc38hta8Ubj+85KT5o+kXh3FaW9QiJt78FkPG9XAuLtf5/vcJK4L2vtpjebZUiEBj/KjBrG8s+aZtZMPPZlaZgj1dyhI0kgMRyZpA9V77IBoM2lACLR7B+JakcuOFjz/wwn/QOK+WXHwc+vRzYs5wFJ2vuYt9berbjIAlgQdE1C9i/udN/s+CLd2YTK41K/dgsLyHGPQtEZ7L3Ye2D7Id4dAZw41JhK31VSSyTFNufBUW/zwW+upkFuU2V7D2+9zfg6nd8O0iyRiJhv3w5GyTZOmdoAmsBsdXD10GcCpQ0Gg0CAliaMSkpCQEBAXj33XeRmZnp1osTY86cOfjss8/w+eef49ixY5g9ezYKCgqMc5Hmzp2L6dNNQ9VmzZqFc+fOYc6cOTh27Bg+//xzLF++HE899ZS3voXOzaWMksBeHHNiB05ynO2MEsA2XgVY+VGMFrEZJREDJ/WG0rW7Sm86jWn/M2czSgDLgoQmsQDj+C/CnpP7P9YUHBTjuNxjiyoJUKrY+1J50vZxQjNKiiDThs58L5I5Y0bJwS8PIXFATB8AHCvBAabVlenj7f8QzbiGlfbKjrK+L4CVl479zPoFbfXpWBOTAdzzC3DTp2w2UslhYNk401Yx6npWigLYKAyhvxT5BbDVW8kjAEjYcvl7fmWNyZ4QFAnc8Q0LMitOAL/OMWXXhj0ITFktbDUuwL5HflHKL7OBsuOsbPTny+y+rPsc/z3hBUYAM35nwWt4N7Yq8K4fxI3JiOwBzPyLlerSxrFAq9ck4OblwMM7xfWFkg7jdDP3119/jYkTJyIjIwNSqRTh4S78x+sGt99+OyorK/Hqq6+iuLgY/fr1w7p165Cayn6rLC4uRkFBgfH4tLQ0rFu3DrNnz8bHH3+MhIQEfPjhh14fDdBp8T94nepRMvyAF1V6Ezlwsr6YlckkUus/IFINmccL+8Q1KRubuT1YenMUKAnNrvH9SYDwVXrWSGWsbLP5bVbacBT46PWmcs+wB8T9OZuTSFhDd8EO1qfEZ5jaEjIagBfejfVbVZ+1bMQGzDJKAgKK7pex/pMzG1mWiJ/X1XaQY1uBEazUsv1D4O/XgIRLWJkIYD/EY0T+8imRAANuY+W+/84ACrazHqYdH7OsWeUpFoBc/a6488ZksiChqYoFTvb6cdwhKQt4cAt7XypOsMAvawYr9Yg1Zjb7BejcNjbvKDQRKNrHth261EqTtz3+KrbCzxVyJdsv0V2bSxOPcypQGjNmDF566SU8+eSTCA8PR0tLCz744AOMGjUK/fr1Q69evSCXd/w2cg8//LDNkQQrV65sd9+4ceOwb98+D1/VRcKYUXJiXkFHrHrjy24R3a3/oI7saVrmXXpYWKpXrzNlaASPBxDTzM0HSg7+LRn7tVrsD2M0lt1U4ufutDX4ThYondlkP2gBgMPfssZMRQj74e+KGLNAyRYhowF4EWlsSri1hm6hGSWABUo7F7PSbfEBdj6Zwvb+b+ZGP8FW/JQcBhZksmX7QdHAZf/n+Lm2hCawMs/GN1iQyo/FUIYCt33RbpWaYGJHf7giOBrIfs3188gVwG1fAsvHs+C3oZT10tz0qbANlslFz6loZvNmll4+efIkcnJysG/fPuTk5OCLL75ATU0N/Pz80Lt3bxw6dMitF0t8mEur3gSu7jLHl5t0rcImNdsruwHs+UlDWfmjcI+wQInPJgEeHg/gKKNkeC84PStL2SphutrIbS68G5s9c+wn4K/XgKnfWD9O3cDm7QBsaGGQ9YUSgvEBma1AyWKGksCMEtA+UNKq2VY3gLCMUuooFrDWnAO+uIHdl3GtxYa3NgVFAte+D6x7kq3yCU0Epv3o+nslkwNXvcRWHx37mf0d7TXR+RWPnVlQJDBrC3B8HZtLlTWjfa8iITa4lPZJT09Heno6pkwxTSs9e/Ys9u7di/3797t8caQTcalHyYXSG8B+qDnqWbDVyG0uaRgLlM7vBiBgzz++P0mmFH7t5oGSowBPcOmtzVwUW4GSOxq5zV35IltifeI3llniG+J5HAf89CjrTQpLAUa4odTAB0plNla+NZSxzJpEygIOR/ihk21XvlXns8BTEQwECxjkqAxhM2N+fZIFpAHhbJm+UANuZasJz2xkvUDuzHSExLHy3sVOEWQcs0KIGE41c9uTlpaGW2+9FW++KXIOD+ncXFr15sJeb4Cw8ptxJVSa7WP4ibuFAjdeFTsaADALYjhWurNH8BYmbYJGW4wzlNzUTxiVDgy5m33+3d1sFhBPpwHWz2WNxFI/4MZPhDfg2sP37NRdsB6UG2coJQjrM7OVUTIfDSB078Kh9wNj5rCG88kfiZuUDbDsU+Z1VA4ixMeIzigVFBRY3WTWlgsXLiAxUcBvdqRz4zNKzdXiN8blV67JRfwgNQ+qhDR01xk2zbS39UXiEJaJqC0Qtu+b2GGTQPsAz17/kdDSm1TKghG9xv4qQGPpLUzIlQqT/QYbtHhhL/DJZWyptb+KlXr4rM/V77hvNY+/is3vqS1g5287o4hf8eZoNACPHzpZe54Fmfyfj5hGbnNXvQRcPs9xXxkhpNMQnVEaOnQoZs6cid27284rN6mtrcWnn36Kfv364YcffnDpAkknwWeU9FrL3h0htE6U3iQScSMC+EAp1M6WFsoQUw9T8UHH5+RnKInKKJkFPY76lIQOnARMQaa97JrY7VaEUAQCU9ew/i5NIxvIt+ktFsQoVaxxOOte970eYNanZKX8JnQ0AC84lmXYOB1Qdsx0P98DFZ0h/vooSCKkSxH9L/rYsWN48803MXHiRPj5+SErKwsJCQnw9/dHdXU1cnNzcfToUWRlZeGdd97BpEkC9h4inZ+fP9uBW9PESiJilp8be5RElmbkSvZcRwGHuoE1yQJsNZA9cf3ZfmIlh4BeDpZ2ix0NALBBfTxHK9/EzJeSK4BW2C+98YGdws3D7IKigBkbWL/SmX/YRqFJWWyZvLUNUF0V24f1RZUeaf9Y5Rl223ZzUlskErYx7Jl/WHDMjwgoMvRY2tnFnhBycRAdKEVERODdd9/F66+/jt9++w2bN29Gfn4+mpubERUVhTvvvBMTJkxAv342dqQmXVdABAuUmqsA2OkFasuZjBIgfL+3uiJ2qwx1nP2JH8CWsxcLWLHJB19iMkoSCbtuXavjAE8vYjWg+YgAW9SGUQaemPorkQCZ17IPT7PX0M3v5ydmixTzQAlg7xPf/M9vT0IIuWg5nSP29/dHQEAA3n//fXdeD+nMAsOBuvPiZykZe5REBkpCggPArOzmIJsEsA1OAZZRcsSYURI5vFFooCRms2DjAE47QSM/80nIknVfZr7nm15v2qeL48xWN4oY1Bg/kN0WH2C3JYcAcKwhXGxDNiGky3Fp1dsNN9yAxx9/HGq1iI1JSdfl7Mo3pwMlgTOJjIGSgEUF/L5c1fmmjJEtYrcv4QkdOmksvQn4fUbIJsHG0lsnD5Qie7LgUdNo6kkC2J9zaz0rb0Y42J/NHJ81KjnC3nMquxFCzLgUKG3duhW///47hgwZYnO4ZFFREa6//npXXoZ0Fs7OUtI62aMkdI8zvvQmJKMUGAGoktnnJVZ6YMw506MECB86KSajJKQMadzAt5NtuNmWTG4qrZkPnuTLbhE9xO02Hp7G/gx1ajaYlAIlQogZlwKlrKws7N+/H6NGjcLw4cOxYMEC42N6vR65ubl48cUXsWPHDpcvlHQCTmeUXGjmNn++LbXn2a3Kzoo3c0LLby1OriIzBniOAiU39yi1erBHqaPFGnogzfuUjGU3Ef1JACvd8X/mZ/4BCnayzylQIoTAxcncABAQEIA33ngDCoUCTz/9NFavXm0MktRqNVJTUzF//nx3XCvxda5mlMQMnASE7/cmJqMEsPJb3q+OG7qdGTgJmJXehAZKQla9CQga+Wbuzl56A8xGBJhl/YyN3E4s6e8zGTi3FdjwIhtxERAOpAx3/ToJIZ2eSxmlZcuWISEhAXFxcVi5ciWGDh0KuVyO/fv34/7770d1dTXOnj2LGTNmuOt6iS/jt8ZodraZW2zpTeiqNxE9SgBb+QawTUrtMZayPFx6c9scJf56u0CgFNOH3Za6IaMEAEPuYbOX9Fr29bjnukbmjRDiMpcCpeeffx7XX389cnNzUV9fj507d2LHjh1477338Nlnn2H27Nloampy17USX8dPfOYnQAvl7HgAv0B2q3Hwd0xsoMSXYcqP2Q/CnBkPAIho5haz6o0PGi+S0hvfdF95CmgoZ2VMPmiKEbHijSdXAle+xD6P7Alk3eee6ySEdHouBUqXXXYZXn75ZfTu3RsSsy0rZs+ejd27d2Pv3r0YMGAAdu3a5fKFkk7AmFGqEf4cjnM+o6QwBEqtdgKl1iazYZPxws6rSmLfi15rOa25rcZydhsocqii0IwSn90QVHpzsOpNrzcFSu4eOOkNwTGG1WockLcOyN/MVrwFx4obDWCu/y3AtLXA3T+LawYnhHRpLgVK3333HWJjrc8Z6d+/P/bs2YNrr70Wl156qSsvQzoLZzJKei0Ajn0uOqMUxG7tZZQaywzn9hdeIpNIHJff9DqgqZJ9Hhwj7Lw8T6x6kzvYzoUPkoCuUXoD2AayANtX7tgv7PPeV5vmKjmjxxXCe9kIIRcFlwIlR5RKJRYuXIhffvnFky9DfIUzGSXzH+yiA6UAdms3UKpgt0HR4jbqdbTyrakS4PQAJE5klMSW3sRklGwEX3ygJJWLz9z5qszJ7PbMP8DRtYb7OmAyOCHkouLRQIk3fvz4jngZ4m3mGSWOE/Yc81KRTGSgJKT0xpfHxO45xgdKtla+NRgyVYGR4jdBNa7Wc+OqN0cbBJuveBMTMPqy6F5AVC+21UtLDZuQ3o2y14QQ9+qQQIlcJPiMkl7ruMGax/9glynEl0yElN74gCZIZHmML72VHmH9Pe3OW8puxZbdALPSm4PVeqLmKDkYD9BVhk22ddXLbAWcXxAwYhb1FhFC3M7lOUqEGCmCWGlHr2XlN0WQ4+c4O2wSEFh64zNK0eLOHZnOsjStDUBNfvstMfjzOhUoeWLVm4OBk61dZPuStjKuYR+EEOIhlFEi7iORmLJKQhu6nR0NAJiV3hptH2PsURLbRyQ3LTO31tDtbKYKENHMbQikpEL2enNQzlN3odEAhBDSgShQIu7F9ykJbeg2lt6cCJSMpbdm28fwq96cyfzEGbbJsLbnmyvnFTyZ2wOr3rrKijdCCOkgFCgR9+rIjJInS2+A2co3exklJ85rzCjZKb1xHGtSNj/eHqE9Sl2t9EYIIR5GgRJxL2czSs70KPE9UJ4ovQGmjVdLrWSU+EAp2PocMbuElN74YZOAyPEAtla9ObndCiGEXOQoUCLuJTajxAcLTmWU+C1M7JTeXOkl4ktvtYXt968zNnO7klGyEyiZPyYko+Ro3zsqvRFCiFMoUCLu5XRGyQOlN/Pp2c6UyPxVbKNUoH35jR8P4Eozt63hkECbQMkNW5hQ6Y0QQpxCgRJxrw5d9eag9NZUBbY9ioQNhnRG/EB2W7TfdJ8r25cAAjNKZv1Lgla9OSq90ao3QghxBgVKxL06skfJmFGyUXrjV6YFRoifns1LHMJuL+SY7nNl+xJA2Bwl82GTQiZpOxoPwM9RotIbIYSIQoEScS9/FbvtkFVvhoySTs2yPG25suKNl5jFbs+bBUp82c2Z7UsAcT1KQvqTAMfjAYxbmFBGiRBCxKBAibiX2I1xXZnMzQ+cBKyX38w3xHVWwmBAIgXqzgP1Jey+8jx2G97NuXOKKb0JKbsBIrYwoYwSIYSIQYEScS/zjXGFMN/rTSy5PwBDWcpa+c2VWUc8ZTAQncE+58tvfL9SwiDnzimo9CY2o+RoCxPqUSKEEGdQoETcqyMzShKJ2YgAaxklN5TeAFOf0vm97Lb4ILuNH+Tc+Rz1EwHihk2an9PWSjpj6Y0ySoQQIgYFSsS9zDNKHOf4eFfGAwBm+71ZGRHgrkApaSi7PbuJfU/Fh9jX/Io4sYylNxtlMsCsmVvAaADAtAWMw4GTlFEihBAxKFAi7sVnlHSt9gdB8owDJ53IKAH2V77xPUrODIU012si61O6kAOc+QdQ17Jgh980VyxRpTeBgRIfaHI6QKe1fIzjzFa9UaBECCFiUKBE3EsZAkhk7HMhfUquZpSMG+NaK725oUcJAEJigW5j2ecbXmC3sX2FBzFtiVr1JvB9MQ8022aVNM2GcQag0hshhIhEgRJxL4nENCJASJ+SK+MBgI4pvQFA/1vYLT+h29myG2CWUbITKGmdzChZOy9fdoPENKSTEEKIIF0iUKqursa0adOgUqmgUqkwbdo01NTU2Dxeo9Hg2WefRf/+/REUFISEhARMnz4dRUVFHXfRXZmYlW+uDJwEzJq5rQVKbhgPwMu8znSNUj+gz/XOn8uYUXLjqjepzDRKoG1GqdWskVvI8EpCCCFGXSJQmjp1Kg4cOID169dj/fr1OHDgAKZNm2bz+KamJuzbtw8vvPAC9u3bhx9++AEnTpzA5MmTO/CquzAxK99czSjZCpTUDab73BEoBYQD034EblgCPHUC6HGF8+cSU3qTixibYGtEADVyE0KI05zc18F3HDt2DOvXr8fOnTsxfPhwAMCnn36KkSNHIi8vD7179273HJVKhQ0bNljc99FHH2HYsGEoKChASkpKh1x7l2XMKNU6PtaV8QCA7dIbX3aTB7iv3JQ6kn24yhMZJYAFm60N7YdOGmcoUX8SIYSI1ekzSjt27IBKpTIGSQAwYsQIqFQqbN++XfB5amtrIZFIEBYWZvMYtVqNuro6iw9ihZiNcfkf6s4MnARsZ5TMV7z5WrnJE1uYAGYjAtoESnxGiRq5CSFEtE4fKJWUlCAmpv0O7jExMSgpKRF0jpaWFjz33HOYOnUqQkNDbR43f/58Yx+USqVCcnKy09fdpYnZGNdTPUruWvHmCXzwY2u7EcByU1yhbG1joqap3IQQ4iyfDZRefvllSCQSux9797JJyRIrGQOO46ze35ZGo8GUKVOg1+uxePFiu8fOnTsXtbW1xo/CwkLnvrmuzpmMkrtXvblzxZu7CZmj5EymzVaPEs1QIoQQp/lsj9Kjjz6KKVOm2D2mW7duOHToEEpLS9s9Vl5ejtjYWLvP12g0uO2223D27Fn8/fffdrNJAKBUKqFUOvkD/WLiExklXw6UPFR6s5lRotIbIYQ4y2cDpaioKERFRTk8buTIkaitrcXu3bsxbNgwAMCuXbtQW1uLUaNG2XweHySdPHkSGzduRGRkpNuu/aInJqOkc7GZ21ag1NAJAiW9hk3Ntpb55LNNola92djGhEpvhBDiNJ8tvQmVmZmJiRMnYubMmdi5cyd27tyJmTNn4tprr7VY8ZaRkYG1a9cCALRaLW655Rbs3bsXq1atgk6nQ0lJCUpKStDaaue3fCKMqIwSHyg52cxtLL21mcztyxkl8+/VVvlN50Tpjd/OxdYcJVr1RgghonX6QAkAVq1ahf79+yM7OxvZ2dkYMGAAvvzyS4tj8vLyUFvLlqufP38eP/30E86fP49BgwYhPj7e+CFmpRyxQVSPkodLb8HtG/29zjz4sVV+E7vXG2DazoUPjHhqw+pMKr0RQohoPlt6EyMiIgJfffWV3WM4s53su3XrZvE1cTOnMkpO9n7x26W0ndnUwK96c1y+7XCCAiV+1ZuI94WfF9W2sZ1Kb4QQ4rQukVEiPkZoRonjzFZ3ORkoBUSw26Yqy/sbDKMhQuKdO68nSWWAxPBPz1ag5MyqN2Og1KYM2UqBEiGEOIsCJeJ+fJZH2wJoWmwfp20BYMjs8f01YgUamvDNAyVNsynDFGx/5aPXOFr5ZswoiSi98YGSpk2gpDbb640QQogoFCgR91OGAjCs5LKXVdI0mz7ne43E4gMlda0puKg3ZJPk/qagzdc42sbEuNebM6W3toESP0eJAiVCCBGLAiXiflKpKUCx16fE/0CXKQCZk+1yAWEwBmXN1ey2wTBXKyTO97Yv4RmHTtrKKDmz6s3GCkDjwEn7c8IIIYS0R4ES8Qzjxrg1to/hM0rOZpMA1u/Dv1ZTJbutL2a3wXHOn9fT+J4sT5Te2mWUqPRGCCHOokCJeAbf0G0vo8T30rgSKAHt+5Tq+YySj/YnAY63MTGOB6DSGyGEeBMFSsQzxGSUFC4GSsaVb4aMki+veOM5auY2rnpzppnbbDyAVs0mgAO06o0QQpxAgRLxDCEZJX7ej7Mr3njGjBJfejNklHx1xRtgCpTa7svGM5bexPQoWRk4qTb7nEpvhBAiGgVKxDMEZZT4QCnItdfiA6VmvvRm6FEK8eUeJYGlN6dWvZlllPhGbr9A1s9FCCFEFAqUiGcI6lFyV0YpnN3yPUrmq958lcM5Sk5sYWKtR4nvT6JsEiGEOIUCJeIZYjJKCjdllIzN3IYeJZ9e9SY0UHJxMneLYZ83fxoNQAghzqBAiXiGcRuTWtvHeKJHSas2leB8OaMkFzhw0plVb5pGtj0MYApU+T8PQggholCgRDxDyMa47pijBFiueuPLbjIFEBDu2nk9yeGqNydKb/z7yOkN28PA9P7zfx6EEEJEoUCJeIaQjXGNPUpumqPUXAVU57PPQxN9dyo3IGAytwulN8CUraOMEiGEuIQCJeIZgjJKfI+SuwZOVgIlh9nncf1cO6eneWKvN6mM7W8HmEYEUEaJEEJcQoES8QxRGSVXe5QMpbeWWqBoP/s8boBr5/Q0Y6Bka46SE6U3oP3QSb5HzFc3ByaEEB9HgRLxDD6DoWky9du01eqm0pt/GIwb457dzG5jfT2jJLT0JiKjBJgNnTSsfKPSGyGEuIQCJeIZShWMwYutrJK7mrllciAqnX3ON3PH9XftnJ5mr/Sm07KGbMD5jBIfKFHpjRBCXEKBEvEMqdQ0u8dWn5K7NsUFgIF3mD73DwNUSa6f05P4TJG1LUzMs0ximrmB9oESZZQIIcQlFCgRz3HUp+SuTXEBFihJDH+d4/r79oo3wGyOkpXSm/l9Ypq5AdN7SRklQghxCwqUiOc4WvnmroGTABAaD/S4kn3u643cgGl1Gj/vyJx5oCSVizsvv1WJhjJKhBDiDiL/FyZEBH6llc2Mkps2xeVNegvYFg+MfMQ95/MkvqRmrdHdfIaS2MyYX5uMEq16I4QQl1CgRDzH0ca47hoPwIvsAUz+yD3n8jS+pGZtPICzK94Asx6lJkDTYspYUemNEEKcQqU34jmONsZ116a4nREfKFkrvTmzfQnPGCg1mN53iRRQhIg/FyGEEAqUiAc5yii5s0epszGuenNQehPLfOAk/777q9gqREIIIaLR/57Ec+xllHQaQG+YIeSO8QCdjd1mbsP7InciUDLvUaJGbkIIcRkFSsRz7GWU+LIbcJEGSvbGAxj6lpzKKBlWvbU2UCM3IYS4AQVKxHPsZZT4GUoSqfhZQV2BkIGTrpTe1PU0Q4kQQtyAAiXiOYGR7Lapqv1jrWZTuX19OKQnyO0FSobSmzOBUmg8u60rotIbIYS4AQVKxHOMgVJl+8fctc9bZ2VvPIDWhdJbWCq7rSmgjBIhhLgBBUrEc8wDJY6zfMzdM5Q6G0+V3lRJACTs/a08xe6jjBIhhDiNAiXiOQER7JbTmRqLeRfzDCVAWOnNmVVvciUQYii/nd3MbvmvCSGEiEaBEvEcP3/TKqy25beLeYYS4GAytwulNwAIS2G3DSXsNq6fc+chhBBCgRLxsEBDVqltQ7ex9HaR9yjZLb05MZkbMAVKvNi+zp2HEEIIBUrEw2w1dF/sgZJ5j1Lb/i3jqjcnxyaYB0qhSUBAuHPnIYQQQoES8TC+T6m5TUbpYl+RZew/4kyBEc+VVW8AEJ5q+pyySYQQ4pIuEShVV1dj2rRpUKlUUKlUmDZtGmpqagQ//8EHH4REIsHChQs9do0XLVsZpeZqdnuxrsjitzAB2vcpGTNKbii9UX8SIYS4pEsESlOnTsWBAwewfv16rF+/HgcOHMC0adMEPffHH3/Erl27kJCQ4OGrvEjZCpT4YYgXa1nIvKzWdmNcvkfJ2Ynl5oESZZQIIcQlcm9fgKuOHTuG9evXY+fOnRg+fDgA4NNPP8XIkSORl5eH3r1723zuhQsX8Oijj+L333/HNddc4/C11Go11GrTb/91dXWufwNdnaOM0sVaepNKAakf2xi47ca4rq56C00CJDI2liG2v2vXSQghF7lOn1HasWMHVCqVMUgCgBEjRkClUmH79u02n6fX6zFt2jQ8/fTT6NtX2G/d8+fPN5b3VCoVkpOTXb7+Ls/Wqjdjj9JFmlECbI8IcLX0JlcAE94Exj4JRKU7f32EEEI6f6BUUlKCmJiYdvfHxMSgpKTE5vPeeustyOVyPPbYY4Jfa+7cuaitrTV+FBYWOnXNFxXqUbKNzxi1HRFgHA/gwmbBI2YBV754ce6jRwghbuSzgdLLL78MiURi92Pv3r0AAImVHwYcx1m9HwBycnLwwQcfYOXKlTaPsUapVCI0NNTigzhAPUq28Q3dbQMl46o3JzNKhBBC3MZne5QeffRRTJkyxe4x3bp1w6FDh1BaWtrusfLycsTGxlp93pYtW1BWVoaUFFPTq06nw5NPPomFCxciPz/fpWsnZhz2KF3MgZIho6Sz0cztbI8SIYQQt/HZQCkqKgpRUVEOjxs5ciRqa2uxe/duDBs2DACwa9cu1NbWYtSoUVafM23aNFx11VUW902YMAHTpk3Dvffe6/rFExM+UGquBvQ6QCpjty2GRviLtZkbMMsotWnm5r++WLd3IYQQH+KzgZJQmZmZmDhxImbOnIlly5YBAB544AFce+21FiveMjIyMH/+fNx4442IjIxEZGSkxXn8/PwQFxdnd5UccQLfzM3p2ca4gRGGDXIN06ipR6n9eACNIVAyn7VECCHEK3y2R0mMVatWoX///sjOzkZ2djYGDBiAL7/80uKYvLw81NbW2jgD8RiZH+CvYp83lrNbvj/JL8hsQvVFyLjfW9uMUjO7pYwSIYR4XafPKAFAREQEvvrqK7vHcG3302qD+pI8KCSBZZHqLgDRvak/icdnjNqOB+CbuZ0dOEkIIcRtukRGifi4UMPU87oidnuxD5vk2Sy9GTJKcsooEUKIt1GgRDxPlchuay+wWxo2yThs5qYeJUII8TYKlIjnhSax27rz7NY4bFLlnevxFbbGA1AzNyGE+AwKlIjntS290bBJhp+83W7gJAVKhBDiKyhQIp5HpTfr5A4CJVr1RgghXkeBEvE8Y+mNb+auYbcXezO3rU1xjc3clFEihBBvo0CJeB5felPXAup6Gg/AszZHSa8D9BrD4xQoEUKIt1GgRDxPGWxq3K69ADRXsc8v5qncgFmPklkzt3nQRKveCCHE6yhQIh3DfOVb+XH2eXg3r12OT7BWetOYBUo0R4kQQryOAiXSMfjyW8EuVnqTyoHYvt69Jm+z1szNb18iUwBS+udJCCHeRv8Tk47Br3w79jO7jcmkLTqsjQcwbl9CZTdCCPEFFCiRjpEwmN2WH2O38QO9dy2+wlpGiVa8EUKIT6FAiXSMjOtYuY0XP8hrl+IzrPUo0fYlhBDiUyhQIh0jKBLofpnpa8oomW2KayVQoowSIYT4BAqUSMfpdzO7lUipkRsw2xTXyqo3CpQIIcQnyB0fQoibZF4H5KwEYvoAiiBvX433WS29GXqUaPsSQgjxCRQokY6jDAFm/OHtq/Ad1kpvlFEihBCfQqU3QrzFWumNepQIIcSnUKBEiLfIDRklnZUtTGjVGyGE+AQKlAjxFn6LEk2T6T7jHCXqUSKEEF9AgRIh3sI3tLeaBUrGydwX+dRyQgjxERQoEeItfKCkUwM6DfucVr0RQohPoUCJEG9RBJs+b21kt7TqjRBCfAoFSoR4i1wBSP3Y53ygRBklQgjxKRQoEeJNxj4lPlCiHiVCCPElFCgR4k18+a21gd3SqjdCCPEpFCgR4k3tMko0R4kQQnwJBUqEeJOtQIkySoQQ4hMoUCLEm4yBEl964wMl6lEihBBfQIESId5k7FGiVW+EEOKLKFAixJtsrnqjHiVCCPEFFCgR4k1tAyXjqjcKlAghxBdQoESIN7UdD0Cr3gghxKdQoESIN9nMKFGPEiGE+AIKlAjxJls9SpRRIoQQn0CBEiHeZD4egONMq96oR4kQQnxClwiUqqurMW3aNKhUKqhUKkybNg01NTUOn3fs2DFMnjwZKpUKISEhGDFiBAoKCjx/wYTwzMcD8FklwBRAEUII8aouEShNnToVBw4cwPr167F+/XocOHAA06ZNs/uc06dPY8yYMcjIyMA///yDgwcP4oUXXoC/P/0mTzqQeemtuYp9LlMCfoHeuyZCCCFGcm9fgKuOHTuG9evXY+fOnRg+fDgA4NNPP8XIkSORl5eH3r17W33evHnzcPXVV+Ptt9823te9e3e7r6VWq6FWq41f19XVueE7IBc180CpyRAoBUYAEon3rokQQohRp88o7dixAyqVyhgkAcCIESOgUqmwfft2q8/R6/X49ddf0atXL0yYMAExMTEYPnw4fvzxR7uvNX/+fGN5T6VSITk52Z3fCrkYmfco8RmlgAjvXQ8hhBALnT5QKikpQUxMTLv7Y2JiUFJSYvU5ZWVlaGhowL///W9MnDgRf/zxB2688UbcdNNN2LRpk83Xmjt3Lmpra40fhYWFbvs+yEXKVkaJEEKIT/DZ0tvLL7+MV155xe4xe/bsAQBIrJQpOI6zej/AMkoAcP3112P27NkAgEGDBmH79u1YunQpxo0bZ/V5SqUSSiVtVkrcyKJHqZp9HhDuveshhBBiwWcDpUcffRRTpkyxe0y3bt1w6NAhlJaWtnusvLwcsbGxVp8XFRUFuVyOPn36WNyfmZmJrVu3On/RhIjFr3rTNAJNlexzCpQIIcRn+GygFBUVhaioKIfHjRw5ErW1tdi9ezeGDRsGANi1axdqa2sxatQoq89RKBQYOnQo8vLyLO4/ceIEUlNTXb94QoQyHwNQe57dUumNEEJ8RqfvUcrMzMTEiRMxc+ZM7Ny5Ezt37sTMmTNx7bXXWqx4y8jIwNq1a41fP/3001izZg0+/fRTnDp1CosWLcLPP/+Mhx9+2BvfBrlYyQMAGErEtYaeN2rmJoQQn9HpAyUAWLVqFfr374/s7GxkZ2djwIAB+PLLLy2OycvLQ21trfHrG2+8EUuXLsXbb7+N/v3747PPPsP333+PMWPGdPTlk4uZVGrKKlFGiRBCfI6E4zjO2xfRWdXV1UGlUqG2thahoaHevhzSWb3bC2goZYMmdWpgymog42pvXxUhhHRZYn5+d4mMEiGdGp9R0hmGmVJGiRBCfAYFSoR4W9t93ahHiRBCfAYFSoR4W0QPy69pPAAhhPgMCpQI8baUEZZfU6BECCE+gwIlQrwteZjpc2UoIPPZ8WaEEHLRoUCJEG+LG2D6XF3nvesghBDSDgVKhHibzM/bV0AIIcQGCpQI8QXRGd6+AkIIIVZQoESIL7h1JRCWAkxe5O0rIYQQYoa6RgnxBTGZwBOHvX0VhBBC2qCMEiGEEEKIDRQoEUIIIYTYQIESIYQQQogNFCgRQgghhNhAgRIhhBBCiA0UKBFCCCGE2ECBEiGEEEKIDRQoEUIIIYTYQIESIYQQQogNFCgRQgghhNhAgRIhhBBCiA0UKBFCCCGE2ECBEiGEEEKIDRQoEUIIIYTYIPf2BXRmHMcBAOrq6rx8JYQQQggRiv+5zf8ct4cCJRfU19cDAJKTk718JYQQQggRq76+HiqVyu4xEk5IOEWs0uv1KCoqQkhICCQSiVvPXVdXh+TkZBQWFiI0NNSt5yYm9D53HHqvOwa9zx2H3uuO4Yn3meM41NfXIyEhAVKp/S4kyii5QCqVIikpyaOvERoaSv8AOwC9zx2H3uuOQe9zx6H3umO4+312lEniUTM3IYQQQogNFCgRQgghhNhAgZKPUiqVeOmll6BUKr19KV0avc8dh97rjkHvc8eh97pjePt9pmZuQgghhBAbKKNECCGEEGIDBUqEEEIIITZQoEQIIYQQYgMFSoQQQgghNlCg5IMWL16MtLQ0+Pv7Y8iQIdiyZYu3L6nLmT9/PoYOHYqQkBDExMTghhtuQF5enrcvq8ubP38+JBIJnnjiCW9fSpd04cIF3HXXXYiMjERgYCAGDRqEnJwcb19Wl6LVavH8888jLS0NAQEB6N69O1599VXo9XpvX1qnt3nzZlx33XVISEiARCLBjz/+aPE4x3F4+eWXkZCQgICAAFx22WU4evSox6+LAiUfs2bNGjzxxBOYN28e9u/fj7Fjx2LSpEkoKCjw9qV1KZs2bcIjjzyCnTt3YsOGDdBqtcjOzkZjY6O3L63L2rNnDz755BMMGDDA25fSJVVXV2P06NHw8/PDb7/9htzcXLz33nsICwvz9qV1KW+99RaWLl2KRYsW4dixY3j77bfxzjvv4KOPPvL2pXV6jY2NGDhwIBYtWmT18bfffhsLFizAokWLsGfPHsTFxWH8+PHGfVc9hiM+ZdiwYdysWbMs7svIyOCee+45L13RxaGsrIwDwG3atMnbl9Il1dfXc+np6dyGDRu4cePGcY8//ri3L6nLefbZZ7kxY8Z4+zK6vGuuuYa77777LO676aabuLvuustLV9Q1AeDWrl1r/Fqv13NxcXHcv//9b+N9LS0tnEql4pYuXerRa6GMkg9pbW1FTk4OsrOzLe7Pzs7G9u3bvXRVF4fa2loAQEREhJevpGt65JFHcM011+Cqq67y9qV0WT/99BOysrJw6623IiYmBoMHD8ann37q7cvqcsaMGYO//voLJ06cAAAcPHgQW7duxdVXX+3lK+vazp49i5KSEoufj0qlEuPGjfP4z0faFNeHVFRUQKfTITY21uL+2NhYlJSUeOmquj6O4zBnzhyMGTMG/fr18/bldDnffPMN9u3bhz179nj7Urq0M2fOYMmSJZgzZw7+7//+D7t378Zjjz0GpVKJ6dOne/vyuoxnn30WtbW1yMjIgEwmg06nwxtvvIE77rjD25fWpfE/A639fDx37pxHX5sCJR8kkUgsvuY4rt19xH0effRRHDp0CFu3bvX2pXQ5hYWFePzxx/HHH3/A39/f25fTpen1emRlZeHNN98EAAwePBhHjx7FkiVLKFByozVr1uCrr77C119/jb59++LAgQN44oknkJCQgLvvvtvbl9fleePnIwVKPiQqKgoymaxd9qisrKxdFE3c41//+hd++uknbN68GUlJSd6+nC4nJycHZWVlGDJkiPE+nU6HzZs3Y9GiRVCr1ZDJZF68wq4jPj4effr0sbgvMzMT33//vZeuqGt6+umn8dxzz2HKlCkAgP79++PcuXOYP38+BUoeFBcXB4BlluLj4433d8TPR+pR8iEKhQJDhgzBhg0bLO7fsGEDRo0a5aWr6po4jsOjjz6KH374AX///TfS0tK8fUld0pVXXonDhw/jwIEDxo+srCzceeedOHDgAAVJbjR69Oh2Iy5OnDiB1NRUL11R19TU1ASp1PJHp0wmo/EAHpaWloa4uDiLn4+tra3YtGmTx38+UkbJx8yZMwfTpk1DVlYWRo4ciU8++QQFBQWYNWuWty+tS3nkkUfw9ddf43//+x9CQkKMWTyVSoWAgAAvX13XERIS0q7vKygoCJGRkdQP5mazZ8/GqFGj8Oabb+K2227D7t278cknn+CTTz7x9qV1Kddddx3eeOMNpKSkoG/fvti/fz8WLFiA++67z9uX1uk1NDTg1KlTxq/Pnj2LAwcOICIiAikpKXjiiSfw5ptvIj09Henp6XjzzTcRGBiIqVOnevbCPLqmjjjl448/5lJTUzmFQsFdcskltGTdAwBY/VixYoW3L63Lo/EAnvPzzz9z/fr145RKJZeRkcF98skn3r6kLqeuro57/PHHuZSUFM7f35/r3r07N2/ePE6tVnv70jq9jRs3Wv1/+e677+Y4jo0IeOmll7i4uDhOqVRyl156KXf48GGPX5eE4zjOs6EYIYQQQkjnRD1KhBBCCCE2UKBECCGEEGIDBUqEEEIIITZQoEQIIYQQYgMFSoQQQgghNlCgRAghhBBiAwVKhBBCCCE2UKBECCGEEGIDBUqEEEIIITZQoEQIIYQQYgMFSoQQIsKyZcuQlJSEK6+8EqWlpd6+HEKIh9Feb4QQIlB9fT169+6N77//HqtXr0ZAQADeeustb18WIcSDKKNECCFtVFZWIiYmBvn5+Rb3K5VKhIWFIT09HUlJSYiIiLB4/JZbbsGCBQs68EoJIZ5GgRIh5KJw6aWXQiKRQCKRQKFQIDMzE19//bXVY+fPn4/rrrsO3bp1s7hfoVDg3nvvRWxsLN5++2088cQTFo+/+OKLeOONN1BXV+eh74IQ0tEoUCKEdHkcx+HAgQN49913UVxcjLy8PEycOBHTp0/H2bNnLY5tbm7G8uXLcf/991s91/bt2/Gvf/0LTU1NyMvLs3hswIAB6NatG1atWuWx74UQ0rEoUCKEdHknT55EfX09Jk6ciLi4OKSlpWHGjBnQ6XTtgp3ffvsNcrkcI0eObHee8vJy/Prrr3jooYcwefJkrFixot0xkydPxurVqz32vRBCOhYFSoSQLi8nJwfh4eHo06cPAOD8+fOYN28elEol+vfvb3Hs5s2bkZWVZfU8X331FQYOHIjevXvjrrvuwqpVq6DRaCyOGTZsGHbv3g21Wu2Zb4YQ0qEoUCKEdHn79u1DbW0tQkJCEBgYiOTkZGzYsAFLly5FYmKixbH5+flISEiwep4VK1bgrrvuAgBMnDgRHMfhl19+sTgmMTERarUaJSUlnvlmCCEdigIlQkiXl5OTg0ceeQQHDhzA5s2bMW7cODz++OO455572h3b3NwMf39/q+fIzc3FlClTAAByuRy33357u/JbQEAAAKCpqcn93wghpMPJvX0BhBDiafv378cDDzyAnj17AgAWL16M/v3744EHHkBaWprFsVFRUaiurm53jhUrVkCn01lkoDiOg1QqRUlJCeLi4gAAVVVVAIDo6GhPfTuEkA5EGSVCSJd25swZ1NTUoF+/fsb7+vTpg549e1ptuh48eDByc3Mt7lOr1Vi9ejXee+89HDhwwPhx8OBBdO/eHV999ZXx2CNHjiApKQlRUVGe+6YIIR2GAiVCSJeWk5MDuVyOXr16Wdw/fvx4rF27tt3xEyZMwNGjRy2ySv/73//Q0NCAGTNmoF+/fhYft9xyi0X5bcuWLcjOzvbcN0QI6VAUKBFCurR9+/ahV69eUCgUFvePHz8eOTk5OH/+vMX9/fv3R1ZWFr799lvjfStWrMBVV10FlUrV7vw333wzcnNzsWvXLrS0tGDt2rWYOXOmZ74ZQkiHo73eCCGkjXXr1uGpp57CkSNHIJUK/33y448/xv/+9z/88ccfHrw6QkhHomZuQghp4+qrr8bJkydx4cIFJCcnC36en58fPvroIw9eGSGko1FGiRBCCCHEBupRIoQQQgixgQIlQgghhBAbKFAihBBCCLGBAiVCCCGEEBsoUCKEEEIIsYECJUIIIYQQGyhQIoQQQgixgQIlQgghhBAbKFAihBBCCLHh/wFXs6lBCA39mAAAAABJRU5ErkJggg==", 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", 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -449,6 +514,13 @@ "plt.xlabel(plab.r)\n", "plt.ylabel(plab.chir.format(3))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -467,7 +539,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/larch/apps.py b/larch/apps.py index 6c9d2ff6b..1ef446efd 100644 --- a/larch/apps.py +++ b/larch/apps.py @@ -55,16 +55,16 @@ def make_desktop_shortcut(self, folder='Larch'): bindir = Path(sys.prefix, bindir).absolute() script = self.script if not self.script.startswith('_'): - script = Path(bindir, self.script).absolute() + script = Path(bindir, self.script).absolute().as_posix() - icon = Path(icondir, self.icon) + icon = Path(icondir, self.icon).absolute() if isinstance(ico_ext, (list, tuple)): for ext in ico_ext: - ticon = f"{self.icon:s}.{ext:s}" - if Path(ticon).exists(): + ticon = Path(f"{self.icon:s}.{ext:s}").absolute() + if ticon.exists(): icon = ticon - - make_shortcut(script, name=self.name, folder=folder, icon=icon, + make_shortcut(script, name=self.name, folder=folder, + icon=icon.as_posix(), description=self.description, terminal=(not self.is_wxapp)) diff --git a/larch/inputText.py b/larch/inputText.py index 7ade143a6..4e415f70d 100644 --- a/larch/inputText.py +++ b/larch/inputText.py @@ -2,14 +2,13 @@ # # InputText for Larch -from __future__ import print_function import os -from pathlib import Path import sys +import io import time +from pathlib import Path from collections import deque from copy import copy -import io from .utils import read_textfile diff --git a/larch/interpreter.py b/larch/interpreter.py index b78962c73..8ca991bde 100644 --- a/larch/interpreter.py +++ b/larch/interpreter.py @@ -7,12 +7,12 @@ numpy functions are imported if available and used. """ import os -from pathlib import Path import sys import types import ast import math import numpy +from pathlib import Path from copy import deepcopy from . import site_config @@ -1071,7 +1071,7 @@ def import_module(self, name, asname=None, continue if larchname in sorted(os.listdir(dirname)): islarch = True - modname = Path(dirname, larchname).absolute() + modname = Path(dirname, larchname).absolute().as_posix() try: thismod = self.runfile(modname, new_module=name) except: diff --git a/larch/larchlib.py b/larch/larchlib.py index ee155ae41..5bace430e 100644 --- a/larch/larchlib.py +++ b/larch/larchlib.py @@ -353,7 +353,7 @@ def add2path(envvar='PATH', dirname='.'): os.environ[envvar] = dirname else: paths = oldpath.split(sep) - paths.insert(0, Path(dirname).absolute()) + paths.insert(0, Path(dirname).absolute().as_posix()) os.environ[envvar] = sep.join(paths) return oldpath @@ -378,14 +378,14 @@ def get_dll(libname): # normally, we expect the dll to be here in the larch dlls tree # if we find it there, use that one fname = _dylib_formats[uname] % libname - dllpath = Path(bindir, fname) - if Path(dllpath).exists(): - return loaddll(dllpath) + dllpath = Path(bindir, fname).absolute() + if dllpath.exists(): + return loaddll(dllpath.as_posix()) # if not found in the larch dlls tree, try your best! - dllpath = ctypes.util.find_library(libname) - if dllpath is not None and Path(dllpath).exists(): - return loaddll(dllpath) + dllpath = Path(ctypes.util.find_library(libname)).absolute() + if dllpath is not None and dllpath.exists(): + return loaddll(dllpath.as_posix()) return None @@ -399,7 +399,7 @@ def read_workdir(conffile): try: w_file = Path(user_larchdir, conffile).absolute() - if Path(w_file).exists(): + if w_file.exists(): line = open(w_file, 'r').readlines() workdir = line[0][:-1] os.chdir(workdir) @@ -431,7 +431,7 @@ def read_config(conffile): """ cfile = Path(user_larchdir, conffile).absolute() out = None - if Path(cfile).exists(): + if cfile.exists(): data = read_textfile(cfile) try: out = toml.loads(data) diff --git a/larch/plot/bokeh_xafsplots.py b/larch/plot/bokeh_xafsplots.py new file mode 100644 index 000000000..67d9dcb2c --- /dev/null +++ b/larch/plot/bokeh_xafsplots.py @@ -0,0 +1,1373 @@ +#!/usr/bin/env python +""" +Plotting macros for XAFS data sets and fits + + Function Description of what is plotted + ---------------- ----------------------------------------------------- + plot_mu() mu(E) for XAFS data group in various forms + plot_bkg() mu(E) and background mu0(E) for XAFS data group + plot_chik() chi(k) for XAFS data group + plot_chie() chi(E) for XAFS data group + plot_chir() chi(R) for XAFS data group + plot_chifit() chi(k) and chi(R) for fit to feffit dataset + plot_path_k() chi(k) for a single path of a feffit dataset + plot_path_r() chi(R) for a single path of a feffit dataset + plot_paths_k() chi(k) for model and all paths of a feffit dataset + plot_paths_r() chi(R) for model and all paths of a feffit dataset + plot_diffkk() plots from DIFFKK + ---------------- ----------------------------------------------------- +""" + +import os +import numpy as np +import time +import logging +from copy import deepcopy + +from larch import Group +from larch.math import index_of +from larch.xafs import cauchy_wavelet, etok + +def nullfunc(*args, **kws): + pass + +get_display = _plot = _oplot = _newplot = _fitplot = _plot_text = nullfunc + +HAS_BOKEH = True +try: + import bokeh +except ImportError: + HAS_BOKEH = False + +if HAS_BOKEH: + from bokeh import plotting as bplot + from bokeh.plotting import figure, show + from bokeh.io import output_notebook, curdoc + from bokeh.io import show as bokeh_show + + from bokeh.core.properties import field + from bokeh.models import ColumnDataSource, LinearAxis, Grid, VSpan, Range1d + + + +LineColors = ('#1f77b4', '#d62728', '#2ca02c', '#ff7f0e', '#9467bd', + '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf') +LineStyles = ('solid', 'dashed', 'dotted') +NCOLORS = len(LineColors) +NSTYLES = len(LineStyles) + +FIGSTYLE = dict(width=800, height=500, + toolbar_location='above', + tools="pan,box_zoom,save,reset", + + # showlegend=True, hovermode='closest', + # legend=dict(borderwidth=0.5, bgcolor='#F2F2F2'), + # # orientation='v') #, x=0.1, y=1.15)# , yanchor='top'), + #plot_bgcolor='#FDFDFF', + #xaxis=dict(showgrid=True, gridcolor='#D8D8D8', + # color='#004', zerolinecolor='#DDD'), + # + # yaxis=dict(showgrid=True, gridcolor='#D8D8D8', + # color='#004', zerolinecolor='#DDD') + ) + +def set_label_weight(label, w): + return label.replace('_w_', '{0:g}'.format(w)) + +# common XAFS plot labels +def chirlab(kweight, show_mag=True, show_real=False, show_imag=False): + """generate chi(R) label for a kweight + + Arguments + ---------- + kweight k-weight to use (required) + show_mag bool whether to plot |chi(R)| [True] + show_real bool whether to plot Re[chi(R)] [False] + show_imag bool whether to plot Im[chi(R)] [False] + """ + ylab = [] + if show_mag: ylab.append(plotlabels.chirmag) + if show_real: ylab.append(plotlabels.chirre) + if show_imag: ylab.append(plotlabels.chirim) + if len(ylab) > 1: ylab = [plotlabels.chir] + return set_label_weight(ylab[0], kweight+1) +#enddef + +# note: +# to make life easier for MathJax/Plotly/Bokeh/IPython +# we have just replaced "\AA" with "\unicode{x212B}" + +plotlabels = Group(k = r'$$k \rm\,(\unicode{x212B}^{-1})$$', + r = r'$$R \rm\,(\unicode{x212B})$$', + energy = r'$$E\rm\,(eV)$$', + ewithk = r'$$E\rm\,(eV)$$' + '\n' + r'$$[k \rm\,(\unicode{x212B}^{-1})]$$', + mu = r'$$\mu(E)$$', + norm = r'normalized $$\mu(E)$$', + flat = r'flattened $$\mu(E)$$', + deconv = r'deconvolved $$\mu(E)$$', + dmude = r'$$d\mu_{\mathrm norm}(E)/dE$$', + d2mude = r'$$d^2\mu_{\rm norm}(E)/dE^2$$', + chie = r'$$\chi(E)$$', + chie0 = r'$$\chi(E)$$', + chie1 = r'$$E\chi(E) \rm\, (eV)$$', + chiew = r'$$E^{{_w_}\chi(E) \rm\,(eV^{_w_})$$', + chikw = r'$$k^{{_w_}}\chi(k) \rm\,(\unicode{x212B}^{{-_w_}})$$', + chi0 = r'$$\chi(k)$$', + chi1 = r'$$k\chi(k) \rm\,(\unicode{x212B}^{-1})$$', + chi2 = r'$$k^2\chi(k) \rm\,(\unicode{x212B}^{-2})$$', + chi3 = r'$$k^3\chi(k) \rm\,(\unicode{x212B}^{-3})$$', + chir = r'$$\chi(R) \rm\,(\unicode{x212B}^{{-_w_}})$$', + chirmag = r'$$|\chi(R)| \rm\,(\unicode{x212B}^{{-_w_}})$$', + chirre = r'$${{\rm Re}}[\chi(R)] \rm\,(\unicode{x212B}^{{-_w_}})$$', + chirim = r'$${{\rm Im}}[\chi(R)] \rm\,(\unicode{x212B}^{{-_w_}})$$', + chirpha = r'$${{\rm Phase}}[\chi(R)] \rm\,(\unicode{x212B}^{{-_w_}})$$', + e0color = '#B2B282', + chirlab = chirlab) + + +def safetitle(t): + if "'" in t: + t = t.replace("'", "\\'") + return t + +def _get_title(dgroup, title=None): + """get best title for group""" + if title is not None: + return safetitle(title) + data_group = getattr(dgroup, 'data', None) + + for attr in ('title', 'plot_title', 'filename', 'name', '__name__'): + t = getattr(dgroup, attr, None) + if t is not None: + if attr == 'filename': + folder, file = os.path.split(t) + if folder == '': + t = file + else: + top, folder = os.path.split(folder) + t = '/'.join((folder, file)) + return safetitle(t) + if data_group is not None: + t = getattr(data_group, attr, None) + if t is not None: + return t + return safetitle(repr(dgroup)) + + +def _get_kweight(dgroup, kweight=None): + if kweight is not None: + return kweight + callargs = getattr(dgroup, 'callargs', None) + ftargs = getattr(callargs, 'xftf', {'kweight':0}) + return ftargs['kweight'] + +def _get_erange(dgroup, emin=None, emax=None): + """get absolute emin/emax for data range, allowing using + values relative to e0. + """ + dat_emin, dat_emax = min(dgroup.energy)-100, max(dgroup.energy)+100 + e0 = getattr(dgroup, 'e0', 0.0) + if emin is not None: + if not (emin > dat_emin and emin < dat_emax): + if emin+e0 > dat_emin and emin+e0 < dat_emax: + emin += e0 + else: + emin = dat_emin + if emax is not None: + if not (emax > dat_emin and emax < dat_emax): + if emax+e0 > dat_emin and emax+e0 < dat_emax: + emax += e0 + else: + emax = dat_emax + return emin, emax + +def extend_plotrange(x, y, xmin=None, xmax=None, extend=0.10): + """return plot limits to extend a plot range for x, y pairs""" + xeps = min(np.diff(x)) / 5. + if xmin is None: + xmin = min(x) + if xmax is None: + xmax = max(x) + + xmin = max(min(x), xmin-5) + xmax = min(max(x), xmax+5) + + i0 = index_of(x, xmin + xeps) + i1 = index_of(x, xmax + xeps) + 1 + + xspan = x[i0:i1] + xrange = max(xspan) - min(xspan) + yspan = y[i0:i1] + yrange = max(yspan) - min(yspan) + + return (min(xspan) - extend * xrange, + max(xspan) + extend * xrange, + min(yspan) - extend * yrange, + max(yspan) + extend * yrange) + + +def redraw(win=1, xmin=None, xmax=None, ymin=None, ymax=None, + dymin=None, dymax=None, + show_legend=True, stacked=False): + pass + + +class BokehFigure: + """wrapping of Bokeh Figure + """ + def __init__(self, style=None, **kws): + + try: + self.in_ipython = __IPYTHON__ + except NameError: + self.in_ipython = False + if self.in_ipython: + output_notebook() + style = self.style = deepcopy(FIGSTYLE) + if style is not None: + self.style.update(style) + + + self.fig = bplot.figure(width=style['width'], height=style['height'], + toolbar_location=style['toolbar_location'], + tools=style['tools']) + + self.fig.xgrid.grid_line_color = '#D8D8D8' + self.fig.ygrid.grid_line_color = '#D8D8D8' + self.fig.legend.click_policy = 'hide' + self.fig + self.clear() + + + def clear(self): + self.y2_axes = None + self.traces = [] + + def add_plot(self, x, y, label=None, color=None, linewidth=3, + style='solid', marker=None, y2label=None, side='left'): + itrace = len(self.traces) + + if label is None: + label = "trace %d" % (1+itrace) + if color is None: + color = LineColors[itrace % NCOLORS] + if style is None: + style = LineStyles[ int(itrace*1.0 / NCOLORS) % NSTYLES] + + opts = {'line_color': color, 'line_width': linewidth, + 'legend_label': label} + + if side == 'right': + if y2label is None: + y2label = label + + ymin, ymax = min(y), max(y) + yr = abs(ymax-ymin) + try: + if yr/(ymin+ymax) > 1.e-18: + ymin, ymax = ymin-0.02*yr, ymax+0.02*yr + except: + pass + self.fig.extra_y_ranges['y2'] = Range1d(ymin, ymax) + self.y2_axes = LinearAxis(axis_label=y2label, y_range_name='y2') + + self.fig.add_layout(self.y2_axes, 'left') + opts['y_range_name'] = 'y2' + + trace = self.fig.line(x, y, **opts) + self.traces.append((trace, x, y, opts)) + self.fig.legend.click_policy='hide' + + def add_vline(self, x=None, line_width=1, line_color='#666666', **kws): + if x is None: + return + + source = ColumnDataSource({'x': [x], 'y': [line_width]}) + glyph = VSpan(x=x, line_width=line_width, line_color=line_color) + self.fig.add_glyph(source, glyph) + + def set_xrange(self, xmin, xmax): + + if xmin is not None: + self.fig.x_range.start = xmin + if xmax is not None: + self.fig.x_range.end = xmax + + + def set_yrange(self, ymin, ymax): + if ymin is not None: + self.fig.y_range.start = ymin + if ymax is not None: + self.fig.y_range.end = ymax + + + def set_ylog(self, ylog=True): + ytype = 'log' if ylog else 'linear' + self.fig.y_axis_type = ytype + + + def set_style(self, title=None, xlabel=None, ylabel=None, y2label=None): + if title is not None: + self.fig.title.text = title + if xlabel is not None: + self.fig.xaxis.axis_label = xlabel + if ylabel is not None: + self.fig.yaxis.axis_label = ylabel + if y2label is not None and self.y2_axes is not None: + self.y2_axes.axis_label = y2label + + + def show(self, title=None, xlabel=None, ylabel=None, y2label=None, + xmin=None, xmax=None, ymin=None, ymax=None, show=True): + self.set_style(title=title, xlabel=xlabel, ylabel=ylabel, y2label=y2label) + self.set_xrange(xmin, xmax) + self.set_yrange(ymin, ymax) + + if show: + curdoc().add_root(self.fig) + bokeh_show(self.fig) + return self + +def plot(xdata, ydata, dy=None, fig=None, label=None, xlabel=None, + ylabel=None, y2label=None, title=None, side='left', ylog_scale=None, + xlog_scale=None, grid=None, xmin=None, xmax=None, ymin=None, + ymax=None, color=None, style='solid', alpha=None, fill=False, + drawstyle=None, linewidth=2, marker=None, markersize=None, + show_legend=None, bgcolor=None, framecolor=None, gridcolor=None, + textcolor=None, labelfontsize=None, titlefontsize=None, + legendfontsize=None, fullbox=None, axes_style=None, zorder=None, show=True): + """emulate wxmplot plot() function, probably incompletely""" + + if fig is None: + fig = BokehFigure() + + if xmin is None: + xmin = min(xdata) + if xmax is None: + xmax = max(xdata) + if ymin is None: + ymin = min(ydata) + if ymax is None: + ymax = max(ydata) + + if xmin is not None and xmax is not None: + xr = abs(xmax-xmin) + try: + if xr/(xmin+xmax) > 1.e-18: + xmin, xmax = xmin-0.02*xr, xmax+0.02*xr + except: + pass + if ymin is not None and ymay is not None: + yr = abs(ymax-ymin) + try: + if yr/(ymin+ymax) > 1.e-18: + ymin, ymax = ymin-0.02*yr, ymax+0.02*yr + except: + pass + + fig.add_plot(xdata, ydata, label=label, color=color, linewidth=linewidth, + style=style, marker=marker, side=side) + + return fig.show(title=title, xlabel=xlabel, ylabel=ylabel, y2label=y2label, + xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, show=show) + + +def multi_plot(plotsets): + """plot multiple traces with an array of dictionaries emulating + multiplot calls to plot: + + instead of + + >>> plot(x1, y1, label='thing1', color='blue') + >>> plot(x2, y2, label='thing2', color='red') + + you can do + + >>> multi_plot([dict(xdata=x1, ydata=y1, label='thing1', color='blue'), + dict(xdata=x2, ydata=y2, label='thing2', color='red')]) + + """ + for pset in plotsets[:]: + side = pset.get('side', None) + fig = BokehFigure() + fig.clear() + + sopts = dict(title=None, xlabel=None, ylabel=None) + ropts = dict(xmin=None, xmax=None, ymin=None, ymax=None) + + for pset in plotsets[:]: + xdata = pset['xdata'] + ydata = pset['ydata'] + popts = dict(label=None, color=None, side='left', style=None, + linewidth=3, marker=None) + for w in ('label', 'color', 'style', 'linewidth', 'marker', 'side'): + if w in pset: + popts[w] = pset[w] + for w in ('title', 'xlabel', 'ylabel'): + if w in pset: + sopts[w] = pset[w] + + for w in ('xmin', 'xmax', 'ymin', 'ymax'): + if w in pset: + ropts[w] = pset[w] + + fig.add_plot(xdata, ydata, **popts) + + sopts['xaxis_title'] = sopts.pop('xlabel') + sopts['yaxis_title'] = sopts.pop('ylabel') + fig.style.update(sopts) + return fig.show(**ropts) + +def plot_mu(dgroup, show_norm=False, show_flat=False, show_deriv=False, + show_pre=False, show_post=False, show_e0=False, with_deriv=False, + emin=None, emax=None, label='mu', offset=0, title=None, fig=None, show=True): + """ + plot_mu(dgroup, norm=False, deriv=False, show_pre=False, show_post=False, + show_e0=False, show_deriv=False, emin=None, emax=None, label=None, + show=True, fig=None) + + Plot mu(E) for an XAFS data group in various forms + + Arplguments + ---------- + dgroup group of XAFS data after pre_edge() results (see Note 1) + show_norm bool whether to show normalized data [False] + show_flat bool whether to show flattened, normalized data [False] + show_deriv bool whether to show derivative of normalized data [False] + show_pre bool whether to show pre-edge curve [False] + show_post bool whether to show post-edge curve [False] + show_e0 bool whether to show E0 [False] + with_deriv bool whether to show deriv (dmu/de) together with mu [False] + emin min energy to show, absolute or relative to E0 [None, start of data] + emax max energy to show, absolute or relative to E0 [None, end of data] + label string for label [None: 'mu', `dmu/dE', or 'mu norm'] + title string for plot title [None, may use filename if available] + offset vertical offset to use for y-array [0] + show display the BokehFig now [True] + fig BokehFig to reuse [None] + + Notes + ----- + 1. The input data group must have the following attributes: + energy, mu, norm, e0, pre_edge, edge_step + """ + if not HAS_BOKEH: + logging.getLogger().error('Need bokeh installed') + return + + if hasattr(dgroup, 'mu'): + mu = dgroup.mu + elif hasattr(dgroup, 'mutrans'): + mu = dgroup.mutrans + elif hasattr(dgroup, 'mufluor'): + mu = dgroup.mufluor + else: + raise ValueError("XAFS data group has no array for mu") + #endif + ylabel = plotlabels.mu + if label is None: + label = getattr(dgroup, 'filename', 'mu') + #endif + if show_deriv: + mu = dgroup.dmude + ylabel = f"{ylabel} (deriv)" + dlabel = plotlabels.dmude + elif show_norm: + mu = dgroup.norm + ylabel = plotlabels.norm + dlabel = plotlabels.norm + #endif + elif show_flat: + mu = dgroup.flat + ylabel = f"{ylabel} (flat)" + dlabel = plotlabels.flat + #endif + emin, emax = _get_erange(dgroup, emin, emax) + title = _get_title(dgroup, title=title) + + if fig is None: + fig = BokehFigure() + fig.add_plot(dgroup.energy, mu+offset, label=label) + + y2label = None + if with_deriv: + y2label = plotlabels.dmude + fig.add_plot(dgroup.energy, dgroup.dmude+offset, label=ylabel, y2label=y2label, side='right') + else: + if not show_norm and show_pre: + fig.add_plot(dgroup.energy, dgroup.pre_edge+offset, label='pre_edge') + if not show_norm and show_post: + fig.add_plot(dgroup.energy, dgroup.post_edge+offset, label='post_edge') + + if show_e0: + fig.add_vline(x=dgroup.e0, line_width=2, line_dash="dash", line_color="#AAC") + + return fig.show(title=title, xlabel=plotlabels.energy, ylabel=ylabel, + y2label=y2label, xmin=emin, xmax=emax, show=show) + + +def plot_bkg(dgroup, norm=True, emin=None, emax=None, show_e0=False, + label=None, title=None, offset=0): + """ + plot_bkg(dgroup, norm=True, emin=None, emax=None, show_e0=False, label=None, new=True) + + Plot mu(E) and background mu0(E) for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after autobk() results (see Note 1) + norm bool whether to show normalized data [True] + emin min energy to show, absolute or relative to E0 [None, start of data] + emax max energy to show, absolute or relative to E0 [None, end of data] + show_e0 bool whether to show E0 [False] + label string for label [``None``: 'mu'] + title string for plot titlte [None, may use filename if available] + offset vertical offset to use for y-array [0] + + Notes + ----- + 1. The input data group must have the following attributes: + energy, mu, bkg, norm, e0, pre_edge, edge_step, filename + """ + if hasattr(dgroup, 'mu'): + mu = dgroup.mu + elif hasattr(dgroup, 'mutrans'): + mu = dgroup.mutrans + else: + raise ValueError("XAFS data group has no array for mu") + + bkg = dgroup.bkg + ylabel = plotlabels.mu + if label is None: + label = 'mu' + + emin, emax = _get_erange(dgroup, emin, emax) + if norm: + mu = dgroup.norm + bkg = (dgroup.bkg - dgroup.pre_edge) / dgroup.edge_step + ylabel = plotlabels.norm + label = ylabel + #endif + title = _get_title(dgroup, title=title) + + fig = BokehFigure() + fig.add_plot(dgroup.energy, mu+offset, label=label) + fig.add_plot(dgroup.energy, bkg+offset, label='bkg') + + if show_e0: + fig.add_vline(x=dgroup.e0, line_width=2, line_dash="dash", line_color="#AAC") + + return fig.show(title=title, xlabel=plotlabels.energy, ylabel=ylabel, xmin=emin, xmax=emax) + + +def plot_chie(dgroup, emin=-5, emax=None, label=None, title=None, + eweight=0, offset=0, how_k=False, fig=None, show=True): + """ + plot_chie(dgroup, emin=None, emax=None, label=None, new=True, fig=None): + + Plot chi(E) for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after autobk() results (see Note 1) + emin min energy to show, absolute or relative to E0 [-25] + emax max energy to show, absolute or relative to E0 [None, end of data] + label string for label [``None``: 'mu'] + title string for plot title [None, may use filename if available] + eweight energy weightingn for energisdef es>e0 [0] + offset vertical offset to use for y-array [0] + show display the BokehFigure now [True] + fig BokehFigure to re-use [None] + + Notes + ----- + 1. The input data group must have the following attributes: + energy, mu, bkg, norm, e0, pre_edge, edge_step, filename + """ + if hasattr(dgroup, 'mu'): + mu = dgroup.mu + elif hasattr(dgroup, 'mutrans'): + mu = dgroup.mutrans + else: + raise ValueError("XAFS data group has no array for mu") + #endif + e0 = dgroup.e0 + chie = (mu - dgroup.bkg) + ylabel = plotlabels.chie + if abs(eweight) > 1.e-2: + chie *= (dgroup.energy-e0)**(eweight) + ylabel = set_label_weight(plotlabels.chiew, eweight) + xlabel = plotlabels.energy + + emin, emax = _get_erange(dgroup, emin, emax) + if emin is not None: + emin = emin - e0 + if emax is not None: + emax = emax - e0 + + title = _get_title(dgroup, title=title) + def ek_formatter(x, pos): + ex = float(x) + if ex < 0: + s = '' + else: + s = f"\n[{etok(ex):.2f}]" + return r"%1.4g%s" % (x, s) + + if fig is None: + fig = BokehFigure() + fig.add_plot(dgroup.energy-e0, chie+offset, label=label) + return fig.show(title=title, xlabel=xlabel, ylabel=ylabel, xmin=emin, xmax=emax, show=show) + +def plot_chik(dgroup, kweight=None, kmax=None, show_window=True, + scale_window=True, label=None, title=None, offset=0, show=True, fig=None): + """ + plot_chik(dgroup, kweight=None, kmax=None, show_window=True, label=None, + fig=None) + + Plot k-weighted chi(k) for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after autobk() results (see Note 1) + kweight k-weighting for plot [read from last xftf(), or 0] + kmax max k to show [None, end of data] + show_window bool whether to also plot k-window [True] + scale_window bool whether to scale k-window to max |chi(k)| [True] + label string for label [``None`` to use 'chi'] + title string for plot title [None, may use filename if available] + offset vertical offset to use for y-array [0] + show display the BokehFig now [True] + fig BokehFigure to re-use [None] + + Notes + ----- + 1. The input data group must have the following attributes: + k, chi, kwin, filename + """ + kweight = _get_kweight(dgroup, kweight) + chi = dgroup.chi * dgroup.k ** kweight + + if label is None: + label = 'chi' + + title = _get_title(dgroup, title=title) + + if fig is None: + fig = BokehFigure() + fig.add_plot(dgroup.k, chi+offset, label=label) + + if show_window and hasattr(dgroup, 'kwin'): + kwin = dgroup.kwin + if scale_window: + kwin = kwin*max(abs(chi)) + fig.add_plot(dgroup.k, kwin+offset, label='window') + + return fig.show(title=title, xlabel=plotlabels.k, xmin=0, xmax=kmax, + ylabel=set_label_weight(plotlabels.chikw, kweight), + show=show) + +def plot_chir(dgroup, show_mag=True, show_real=False, show_imag=False, + show_window=False, rmax=None, label=None, title=None, + offset=0, show=True, fig=None): + """ + plot_chir(dgroup, show_mag=True, show_real=False, show_imag=False, + rmax=None, label=None, fig=None) + + Plot chi(R) for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after xftf() results (see Note 1) + show_mag bool whether to plot |chi(R)| [True] + show_real bool whether to plot Re[chi(R)] [False] + show_imag bool whether to plot Im[chi(R)] [False] + show_window bool whether to R-windw for back FT (will be scaled) [False] + label string for label [``None`` to use 'chir'] + title string for plot title [None, may use filename if available] + rmax max R to show [None, end of data] + offset vertical offset to use for y-array [0] + show display the BokehFig now [True] + fig BokehFigure to re-use [None] + + Notes + ----- + 1. The input data group must have the following attributes: + r, chir_mag, chir_im, chir_re, kweight, filename + """ + kweight = _get_kweight(dgroup, None) + + title = _get_title(dgroup, title=title) + + ylabel = plotlabels.chirlab(kweight, show_mag=show_mag, + show_real=show_real, show_imag=show_imag) + + if not hasattr(dgroup, 'r'): + print("group does not have chi(R) data") + return + #endif + if label is None: + label = 'chir' + + if fig is None: + fig = BokehFigure() + if show_mag: + fig.add_plot(dgroup.r, dgroup.chir_mag+offset, label=f'{label} (mag)') + if show_real: + fig.add_plot(dgroup.r, dgroup.chir_re+offset, label=f'{label} (real)') + + if show_imag: + fig.add_plot(dgroup.r, dgroup.chir_im+offset, label=f'{label} (imag)') + + if show_window and hasattr(dgroup, 'rwin'): + rwin = dgroup.rwin * max(dgroup.chir_mag) + fig.add_plot(dgroup.r, rwin+offset, label='window') + + return fig.show(title=title, xlabel=plotlabels.r, ylabel=ylabel, xmax=rmax, show=show) + + +def plot_chiq(dgroup, kweight=None, kmin=0, kmax=None, show_chik=False, label=None, + title=None, offset=0, show_window=False, scale_window=True, + show=True, fig=None): + """ + plot_chiq(dgroup, kweight=None, kmax=None, show_chik=False, label=None, + new=True, win=1) + + Plot Fourier filtered chi(k), optionally with k-weighted chi(k) for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after autobk() results (see Note 1) + kweight k-weighting for plot [read from last xftf(), or 0] + kmax max k to show [None, end of data] + show_chik bool whether to also plot k-weighted chi(k) [False] + show_window bool whether to also plot FT k-window [False] + scale_window bool whether to scale FT k-window to max |chi(q)| [True] + label string for label [``None`` to use 'chi'] + title string for plot title [None, may use filename if available] + offset vertical offset to use for y-array [0] + show display the BokehFig now [True] + fig BokehFigure to re-use [None] + + Notes + ----- + 1. The input data group must have the following attributes: + k, chi, kwin, filename + """ + kweight = _get_kweight(dgroup, kweight) + nk = len(dgroup.k) + chiq = dgroup.chiq_re[:nk] + + if label is None: + label = 'chi(q) (filtered)' + + title = _get_title(dgroup, title=title) + if fig is None: + fig = BokehFigure() + fig.add_plot(dgroup.k, chiq+offset, label=label) + if kmax is None: + kmax = max(dgroup.k) + + if show_chik: + chik = dgroup.chi * dgroup.k ** kweight + fig.add_plot(dgroup.k, chik+offset, label='chi(k) (unfiltered)') + + if show_window and hasattr(dgroup, 'kwin'): + kwin = dgroup.kwin + if scale_window: + kwin = kwin*max(abs(chiq)) + fig.add_plot(dgroup.k, kwin+offset, label='window') + + ylabel = set_label_weight(plotlabels.chikw, kweight) + return fig.show(title=title, xlabel=plotlabels.k, + ylabel=ylabel, xmin=kmin, xmax=kmax, show=show) + + + +def plot_chifit(dataset, kmin=0, kmax=None, kweight=None, rmax=None, + show_mag=True, show_real=False, show_imag=False, + show_bkg=False, use_rebkg=False, title=None, offset=0): + """ + plot_chifit(dataset, kmin=0, kmax=None, rmax=None, + show_mag=True, show_real=False, show_imag=False) + + Plot k-weighted chi(k) and chi(R) for fit to feffit dataset + + Arguments + ---------- + dataset feffit dataset, after running feffit() + kmin min k to show [0] + kmax max k to show [None, end of data] + kweight kweight to show [None, taken from dataset] + rmax max R to show [None, end of data] + show_mag bool whether to plot |chidr(R)| [True] + show_real bool whether to plot Re[chi(R)] [False] + show_imag bool whether to plot Im[chi(R)] [False] + title string for plot title [None, may use filename if available] + offset vertical offset to use for y-array [0] + + + """ + if kweight is None: + kweight = dataset.transform.kweight + #endif + if isinstance(kweight, (list, tuple, np.ndarray)): + kweight=kweight[0] + + title = _get_title(dataset, title=title) + + mod = dataset.model + dat = dataset.data + if use_rebkg and hasattr(dataset, 'data_rebkg'): + dat = dataset.data_rebkg + title += ' (refined bkg)' + + data_chik = dat.chi * dat.k**kweight + model_chik = mod.chi * mod.k**kweight + + # k-weighted chi(k) in first plot window + fig = BokehFigure() + fig.add_plot(dat.k, data_chik+offset, label='data') + fig.add_plot(mod.k, model_chik+offset, label='fit') + + ylabel = set_label_weight(plotlabels.chikw, kweight) + fig.show(title=title, xlabel=plotlabels.k, + ylabel=ylabel, xmin=kmin, xmax=kmax) + + # chi(R) in first plot window + rfig = BokehFigure() + + if show_mag: + rfig.add_plot(dat.r, dat.chir_mag+offset, label='|data|') + rfig.add_plot(mod.r, mod.chir_mag+offset, label='|fit|') + + if show_real: + rfig.add_plot(dat.r, dat.chir_re+offset, label='Re[data]') + rfig.add_plot(mod.r, mod.chir_re+offset, label='Re[fit]') + if show_imag: + rfig.add_plot(dat.r, dat.chir_im+offset, label='Im[data]') + rfig.add_plot(mod.r, mod.chir_im+offset, label='Im[fit]') + + ylabel = chirlab(kweight, show_mag=show_mag, show_real=show_real, show_imag=show_imag) + rfig.show(title=title, xlabel=plotlabels.r, ylabel=ylabel, xmin=0, xmax=rmax) + return fig, rfig + +def plot_path_k(dataset, ipath=0, kmin=0, kmax=None, offset=0, label=None, fig=None): + """ + plot_path_k(dataset, ipath, kmin=0, kmax=None, offset=0, label=None) + + Plot k-weighted chi(k) for a single Path of a feffit dataset + + Arguments + ---------- + dataset feffit dataset, after running feffit() + ipath index of path, starting count at 0 [0] + kmin min k to show [0] + kmax max k to show [None, end of data] + offset vertical offset to use for plot [0] + label path label ['path %d' % ipath] + fig BokehFigure for reuse + """ + kweight = dataset.transform.kweight + path = dataset.pathlist[ipath] + if label is None: + label = 'path %i' % (1+ipath) + title = _get_title(dataset, title=title) + + chi_kw = offset + path.chi * path.k**kweight + if fig is None: + fig = BokehFigure() + fig.add_plot(path.k, chi_kw, label=label) + return fig.set_style(title=title, xlabel=plotlabels.k, + yabel=set_label_weight(plotlabels.chikw, kweight), + xmin=kmin, xmax=kmax) + +def plot_path_r(dataset, ipath, rmax=None, offset=0, label=None, + show_mag=True, show_real=False, show_imag=True, fig=None): + """ + plot_path_r(dataset, ipath,rmax=None, offset=0, label=None, + show_mag=True, show_real=False, show_imag=True, fig=None) + + Plot chi(R) for a single Path of a feffit dataset + + Arguments + ---------- + dataset feffit dataset, after running feffit() + ipath index of path, starting count at 0 [0] + rmax max R to show [None, end of data] + offset vertical offset to use for plot [0] + label path label ['path %d' % ipath] + show_mag bool whether to plot |chi(R)| [True] + show_real bool whether to plot Re[chi(R)] [False] + show_imag bool whether to plot Im[chi(R)] [False] + fig BokehFigure for reuse + """ + path = dataset.pathlist[ipath] + if label is None: + label = 'path %i' % (1+ipath) + + title = _get_title(dataset, title=title) + kweight =dataset.transform.kweight + ylabel = plotlabels.chirlab(kweight, show_mag=show_mag, + show_real=show_real, show_imag=show_imag) + + if fig is None: + fig = BokehFigure() + if show_mag: + fig.add_plot(path.r, offset+path.chir_mag, label=f'|{label}|') + + if show_real: + fig.add_plot(path.r, offset+path.chir_re, label=f'Re[{label}|') + + if show_imag: + fig.add_plot(path.r, offset+path.chir_im, label=f'Im[{label}|') + + return fig.show(title=title, xlabel=plotlabels.r, ylabel=chirlab(kweight), + xmax=rmax) + + +def plot_paths_k(dataset, offset=-1, kmin=0, kmax=None, title=None, fig=None): + """ + plot_paths_k(dataset, offset=-1, kmin=0, kmax=None, fig=None) + + Plot k-weighted chi(k) for model and all paths of a feffit dataset + + Arguments + ---------- + dataset feffit dataset, after running feffit() + kmin min k to show [0] + kmax max k to show [None, end of data] + offset vertical offset to use for paths for plot [-1] + title string for plot title [None, may use filename if available] + fig BokehFigure for reuse + """ + # make k-weighted chi(k) + kweight = dataset.transform.kweight + model = dataset.model + + model_chi_kw = model.chi * model.k**kweight + + title = _get_title(dataset, title=title) + if fig is None: + fig = BokehFigure() + fig.add_plot(model.k, model_chi_kw, label='sum') + + for ipath in range(len(dataset.pathlist)): + path = dataset.pathlist[ipath] + label = 'path %i' % (1+ipath) + chi_kw = offset*(1+ipath) + path.chi * path.k**kweight + fig.add_plot(path.k, chi_kw, label=label) + + return fig.show(title=title, xlabel=plotlabels.k, + ylabel=set_label_weight(plotlabels.chikw, kweight), + xmin=kmin, xmax=kmax) + +def plot_paths_r(dataset, offset=-0.25, rmax=None, show_mag=True, + show_real=False, show_imag=False, title=None, fig=None): + """ + plot_paths_r(dataset, offset=-0.5, rmax=None, show_mag=True, show_real=False, + show_imag=False) + + Plot chi(R) for model and all paths of a feffit dataset + + Arguments + ---------- + dataset feffit dataset, after running feffit() + offset vertical offset to use for paths for plot [-0.5] + rmax max R to show [None, end of data] + show_mag bool whether to plot |chi(R)| [True] + show_real bool whether to plot Re[chi(R)] [False] + show_imag bool whether to plot Im[chi(R)] [False] + title string for plot title [None, may use filename if available] + fig BokehFigure for reuse + """ + kweight = dataset.transform.kweight + model = dataset.model + + title = _get_title(dataset, title=title) + if fig is None: + fig = BokehFigure() + + if show_mag: + fig.add_plot(model.r, model.chir_mag, label='|sum|') + + if show_real: + fig.add_plot(model.r, model.chir_re, label='Re[sum]') + + if show_imag: + fig.add_plot(model.r, model.chir_re, label='Im[sum]') + + for ipath in range(len(dataset.pathlist)): + path = dataset.pathlist[ipath] + label = 'path %i' % (1+ipath) + off = (ipath+1)*offset + if show_mag: + fig.add_plot(path.r, off+path.chir_mag, label=f'|{label}|') + + if show_real: + fig.add_plot(path.r, off+path.chir_re, label=f'Re[{label}]') + + if show_imag: + fig.add_plot(path.r, off+path.chir_im, label=f'Im[{label}]') + + return fig.show(title=title, xlabel=plotlabels.r, + ylabel=chirlab(kweight), xmax=rmax) + +def plot_prepeaks_baseline(dgroup, subtract_baseline=False, show_fitrange=True, + show_peakrange=True): + """Plot pre-edge peak baseline fit, as from `pre_edge_baseline` or XAS Viewer + + dgroup must have a 'prepeaks' attribute + """ + if not hasattr(dgroup, 'prepeaks'): + raise ValueError('Group needs prepeaks') + #endif + ppeak = dgroup.prepeaks + + px0, px1, py0, py1 = extend_plotrange(dgroup.xdat, dgroup.ydat, + xmin=ppeak.emin, xmax=ppeak.emax) + + title = "pre_edge baseline\n %s" % dgroup.filename + + fig = BokehFigure() + + ydat = dgroup.ydat + xdat = dgroup.xdat + if subtract_baseline: + fig.add_plot(ppeak.energy, ppeak.baseline, label='baseline subtracted peaks') + else: + fig.add_plot(ppeak.energy, ppeak.baseline, label='baseline') + fig.add_plot(xdat, ydat, label='data') + + if show_fitrange: + for x in (ppeak.emin, ppeak.emax): + fig.add_vline(x=x, line_width=2, line_dash="dash", line_color="#DDDDCC") + fig.add_vline(x=ppeak.centroid, line_width=2, line_dash="dash", line_color="#EECCCC") + + if show_peakrange: + for x in (ppeak.elo, ppeak.ehi): + y = ydat[index_of(xdat, x)] + fig.add_plot([x], [y], marker='o', marker_size=7) + + return fig.show(title=title, xlabel=plotlabels.energy, ylabel='mu (normalized)', + xmin=px0, xmax=px1, ymin=py0, ymax=py1) + + +def plot_prepeaks_fit(dgroup, nfit=0, show_init=False, subtract_baseline=False, + show_residual=False): + """plot pre-edge peak fit, as from Larix + + dgroup must have a 'peakfit_history' attribute + """ + if not hasattr(dgroup, 'prepeaks'): + raise ValueError('Group needs prepeaks') + #endif + if show_init: + result = pkfit = dgroup.prepeaks + else: + hist = getattr(dgroup.prepeaks, 'fit_history', None) + if nfit > len(hist): + nfit = 0 + pkfit = hist[nfit] + result = pkfit.result + #endif + + if pkfit is None: + raise ValueError('Group needs prepeaks.fit_history or init_fit') + #endif + + opts = pkfit.user_options + xeps = min(np.diff(dgroup.xdat)) / 5. + xdat = 1.0*pkfit.energy + ydat = 1.0*pkfit.norm + + xdat_full = 1.0*dgroup.xdat + ydat_full = 1.0*dgroup.ydat + + if show_init: + yfit = pkfit.init_fit + ycomps = None # pkfit.init_ycomps + ylabel = 'model' + else: + yfit = 1.0*result.best_fit + ycomps = pkfit.ycomps + ylabel = 'best fit' + + baseline = 0.*ydat + if ycomps is not None: + for label, ycomp in ycomps.items(): + if label in opts['bkg_components']: + baseline += ycomp + + fig = BokehFigure() + title ='%s:\npre-edge peak' % dgroup.filename + + + + if subtract_baseline: + ydat -= baseline + yfit -= baseline + ydat_full = 1.0*ydat + xdat_full = 1.0*xdat + plotopts['ylabel'] = '%s-baseline' % plotopts['ylabel'] + + dx0, dx1, dy0, dy1 = extend_plotrange(xdat_full, ydat_full, + xmin=opts['emin'], xmax=opts['emax']) + fx0, fx1, fy0, fy1 = extend_plotrange(xdat, yfit, + xmin=opts['emin'], xmax=opts['emax']) + + ncolor = 0 + popts = {} + plotopts.update(popts) + dymin = dymax = None + + fig.add_plot(xdat, ydat, label='data') + fig.add_plot(xday, yfit, label='fit') + + if show_residual: + dfig = BokehFigure() + dfig.add_plot(xdat, yfit-ydat, label='fit-data') + dy = yfit - ydat + dymax, dymin = dy.max(), dy.min() + dymax += 0.05 * (dymax - dymin) + dymin -= 0.05 * (dymax - dymin) + + if ycomps is not None: + ncomps = len(ycomps) + if not subtract_baseline: + fig.add_plot(xdat, baseline, label='baseline') + for icomp, label in enumerate(ycomps): + ycomp = ycomps[label] + if label in opts['bkg_components']: + continue + fig.add_plot(xdat, ycomp, label=label) + + if opts.get('show_fitrange', False): + for attr in ('emin', 'emax'): + fig.add_vline(opts[attr], line_width=2, line_dash="dash", line_color="#DDDDCC") + + if opts.get('show_centroid', False): + pcen = getattr(dgroup.prepeaks, 'centroid', None) + if hasattr(result, 'params'): + pcen = result.params.get('fit_centroid', None) + if pcen is not None: + pcen = pcen.value + if pcen is not None: + fig.add_vlinee(pcen, color='#EECCCC') + + fig.show(title=title, xlabel=plotlabels.energy, ylabel=opts['array_desc']) + dfig.show(title=tile, ylabel='fit-data', ymin=dymin, ymax=dymax) + return fig, dfig + + +def _pca_ncomps(result, min_weight=0, ncomps=None): + if ncomps is None: + if min_weight > 1.e-12: + ncomps = np.where(result.variances < min_weight)[0][0] + else: + ncomps = np.argmin(result.ind) + return ncomps + + +def plot_pca_components(result, min_weight=0, ncomps=None, min_variance=1.e-5): + """Plot components from PCA result + + result must be output of `pca_train` + """ + title = "PCA components" + + ncomps = int(result.nsig) + fig = BokehFigure() + fig.add_plot(result.x, result.mean, label='Mean') + for i, comp in enumerate(result.components): + if result.variances[i] > min_variance: + label = 'Comp# %d (%.4f)' % (i+1, result.variances[i]) + fig.add_plot(result.x, comp, label=label) + + return fig.show(title=title, xlabel=plotlabels.energy, ylabel=plotlabels.norm, + xmin=result.xmin, xmax=result.xmax) + +def plot_pca_weights(result, min_weight=0, ncomps=None): + """Plot component weights from PCA result (aka SCREE plot) + + result must be output of `pca_train` + """ + max_comps = len(result.components)-1 + + title = "PCA Variances (SCREE) and Indicator Values" + fig = BokehFigure() + + ncomps = max(1, int(result.nsig)) + + x0, x1, y0, y1 = extend_plotrange(result.variances, result.variances) + y0 = max(1.e-6, min(result.variances[:-1])) + x = 1+np.arange(ncomps) + y = result.variances[:ncomps] + fig.add_plot(x, y, label='significant', style='solid', marker='o') + + xe = 1 + np.arange(ncomps-1, max_comps) + ye = result.variances[ncomps-1:ncomps+max_comps] + + fig.add_plot(xe, ye, label='not significant', style='dashed', marker='o') + fig.set_ylog() + yi = result.ind[1:] + xi = 1 + np.arange(len(yi)) + + x0, x1, yimin, yimax = extend_plotrange(xi, yi) + + fig.add_plot(xi, result.ind[1:], y2label='Indicator Value', + style='solid', side='right') + # fig.update_yaxes(title_text='Indicator') # , secondary_y=True) + return fig.show(title=title, xlabel='Component #', ylabel='variance') + + +def plot_pca_fit(dgroup, with_components=True): + """Plot data and fit result from pca_fit, which rom PCA result + + result must be output of `pca_fit` + """ + title = "PCA fit: %s" % (dgroup.filename) + result = dgroup.pca_result + model = result.pca_model + + fig = BokehFigure() + fig.add_plot(result.x, result.ydat, label='data') + fig.add_plot(result.x, result.yfit, label='fit') + if with_components: + fig.add_plot(result.x, model.mean, label='mean') + for n in range(len(result.weights)): + cval = model.components[n]*result.weights[n] + fig.add_plot(result.x, cval, label='Comp #%d' % (n+1)) + + fig.show(title=title, xmin=model.xmin, xmax=model.xmax, + xlabel=plotlabels.energy, ylabel=plotlabels.norm) + + dfig = BokehFigure() + dfig.add_plot(result.x, result.yfit-result.ydat, label='fit-data') + dfig.show(title=title, xmin=model.xmin, xmax=model.xmax, + xlabel=plotlabels.energy, ylabel='fit-data') + return fig, dfig + +def plot_diffkk(dgroup, emin=None, emax=None, new=True, label=None, + title=None, offset=0): + """ + plot_diffkk(dgroup, norm=True, emin=None, emax=None, show_e0=False, label=None): + + Plot mu(E) and background mu0(E) for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after autobk() results (see Note 1) + norm bool whether to show normalized data [True] + emin min energy to show, absolute or relative to E0 [None, start of data] + emax max energy to show, absolute or relative to E0 [None, end of data] + show_e0 bool whether to show E0 [False] + label string for label [``None``: 'mu'] + title string for plot title [None, may use filename if available] + offset vertical offset to use for y-array [0] + + Notes + ----- + 1. The input data group must have the following attributes: + energy, mu, bkg, norm, e0, pre_edge, edge_step, filename + """ + if hasattr(dgroup, 'f2'): + f2 = dgroup.f2 + else: + raise ValueError("Data group has no array for f2") + #endif + ylabel = r'$$f \rm\,\, (e^{-})$$ ' + emin, emax = _get_erange(dgroup, emin, emax) + title = _get_title(dgroup, title=title) + + labels = {'f2': r"$$f_2(E)$$", 'fpp': r"$$f''(E)$$", 'fp': r"$$f'(E)$$", 'f1': r"$$f_1(E)$$"} + + fig = BokehFigure() + fig.add_plot(dgroup.energy, f2, label=labels['f2']) + + for attr in ('fpp', 'f1', 'fp'): + yval = getattr(dgroup, attr) + if yval is not None: + fig.add_plot(dgroup.energy, yval, label=labels[attr]) + + return fig.show(title=title, xlabel=plotlabels.energy, yaxis_label=ylabel, + xmin=emin, xmax=emax) + + +def plot_feffdat(feffpath, with_phase=True, title=None, fig=None): + """ + plot_feffdat(feffpath, with_phase=True, title=None) + + Plot Feff's magnitude and phase as a function of k for a FeffPath + + Arguments + ---------- + feffpath feff path as read by feffpath() + with_pase whether to plot phase(k) as well as magnitude [True] + title string for plot title [None, may use filename if available] + + Notes + ----- + 1. The input data group must have the following attributes: + energy, mu, bkg, norm, e0, pre_edge, edge_step, filename + """ + if hasattr(feffpath, '_feffdat'): + fdat = feffpath._feffdat + else: + raise ValueError("must pass in a Feff path as from feffpath()") + + if fig is None: + fig = BokehFigure() + fig.add_plot(result.x, result.ydat, label='data') + + + fig.add_plot(fdat.k, fdat.mag_feff, label='magnitude') + # xlabel=plotlabels.k, + # ylabel='|F(k)|', title=title, + + if with_phase: + fig.add_plot(fdat.k, fdat.pha_feff, label='phase') + # fig.fig.update_yaxis(title_text='Phase(k)') #, secondary_y=True) + return fig.show(title=title, xlabel=plotlabels.k, ylabel='|F(k)|') + +#enddef + +def plot_wavelet(dgroup, show_mag=True, show_real=False, show_imag=False, + rmax=None, kmax=None, kweight=None, title=None): + """ + plot_wavelet(dgroup, show_mag=True, show_real=False, show_imag=False, + rmax=None, kmax=None, kweight=None, title=None) + + Plot wavelet for XAFS data group + + Arguments + ---------- + dgroup group of XAFS data after xftf() results (see Note 1) + show_mag bool whether to plot wavelet magnitude [True] + show_real bool whether to plot real part of wavelet [False] + show_imag bool whether to plot imaginary part of wavelet [False] + title string for plot title [None, may use filename if available] + rmax max R to show [None, end of data] + kmax max k to show [None, end of data] + kweight k-weight to use to construct wavelet [None, take from group] + + Notes + ----- + The wavelet will be performed + """ + print("Image display not yet available with larch+bokeh") + kweight = _get_kweight(dgroup, kweight) + cauchy_wavelet(dgroup, kweight=kweight, rmax_out=rmax) + title = _get_title(dgroup, title=title) + + opts = dict(title=title, x=dgroup.k, y=dgroup.wcauchy_r, xmax=kmax, + ymax=rmax, xlabel=plotlabels.k, ylabel=plotlabels.r, + show_axis=True) + if show_mag: + _imshow(dgroup.wcauchy_mag, **opts) + elif show_real: + _imshow(dgroup.wcauchy_real, **opts) + elif show_imag: + _imshow(dgroup.wcauchy_imag, **opts) + #endif +#enddef diff --git a/larch/site_config.py b/larch/site_config.py index a990be5e9..677a9fd3a 100644 --- a/larch/site_config.py +++ b/larch/site_config.py @@ -13,8 +13,7 @@ from packaging.version import parse as version_parse -from .utils import (uname, get_homedir, unixpath, - log_warning, log_error) +from .utils import (uname, get_homedir, log_warning, log_error) from .version import __version__, __release_version__ larch_version = __version__ @@ -42,7 +41,7 @@ def update_larch(with_larix=True): user_larchdir = pjoin(home_dir, '.larch') if 'LARCHDIR' in os.environ: - user_larchdir = unixpath(os.environ['LARCHDIR']) + user_larchdir = Path(os.environ['LARCHDIR']).absolute().as_posix() # on Linux, check for HOME/.local/share, # make with mode=711 if needed @@ -55,9 +54,9 @@ def update_larch(with_larix=True): init_files = [pjoin(user_larchdir, 'init.lar')] if 'LARCHSTARTUP' in os.environ: - startup = os.environ['LARCHSTARTUP'] - if Path(startup).exists(): - init_files = [unixpath(startup)] + startup = Path(os.environ['LARCHSTARTUP']) + if startup.exists(): + init_files = [startup.as_posix()] # history file: history_file = pjoin(user_larchdir, 'history.lar') @@ -75,11 +74,12 @@ def make_user_larchdirs(): def make_dir(dname): "create directory" - if not Path(dname).exists(): + dname = Path(dname).absolute() + if not dname.exists(): try: - Path(dname).mkdir(mode=493, parents=True) + dname.mkdir(mode=493, parents=True) except PermissionError: - log_warning(f'no permission to create directory {dname}') + log_warning(f'no permission to create directory {dname.as_posix()}') except (OSError, TypeError): log_error(sys.exc_info()[1]) diff --git a/larch/utils/gformat.py b/larch/utils/gformat.py index f6588565f..c29ecf7e0 100644 --- a/larch/utils/gformat.py +++ b/larch/utils/gformat.py @@ -63,9 +63,11 @@ def gformat(val, length=11): prec -= expon def fmt(val, length, prec, form): + if prec < 0: prec = 0 out = f'{val:{length}.{prec}{form}}' if form == 'e' and 'e+0' in out or 'e-0' in out: out = f'{val:{length+1}.{prec+1}{form}}'.replace('e-0', 'e-').replace('e+0', 'e+') + return out prec += 1 diff --git a/larch/utils/paths.py b/larch/utils/paths.py index 2fe3941fe..ea40c7700 100644 --- a/larch/utils/paths.py +++ b/larch/utils/paths.py @@ -2,6 +2,7 @@ import os import platform from pathlib import Path +from charset_normalizer import from_bytes HAS_PWD = True try: @@ -9,29 +10,20 @@ except ImportError: HAS_PWD = False - def unixpath(d): + if isinstance(d, bytes): + d = str(from_bytes(d).best()) if isinstance(d, str): - return d.replace('\\', '/') - elif isinstance(d, Path): + d = Path(d).absolute() + if isinstance(d, Path): return d.as_posix() - -def winpath(d): - "ensure path uses windows delimiters" - if isinstance(d, str): - if d.startswith('//'): d = d[1:] - d = d.replace('/','\\') - return d - elif isinstance(d, Path): - return Path(d.as_posix()) + raise ValueError(f"cannot get Path name from {d}") # uname = 'win', 'linux', or 'darwin' uname = sys.platform.lower() -nativepath = unixpath if os.name == 'nt': uname = 'win' - nativepath = winpath if uname.startswith('linux'): uname = 'linux' diff --git a/larch/version.py b/larch/version.py index 20a30839f..d92c8ec43 100644 --- a/larch/version.py +++ b/larch/version.py @@ -1,8 +1,8 @@ #!/usr/bin/env python """Version information""" -__release_version__ = '0.9.80' -__date__ = '2024-July-17' +__release_version__ = '0.9.81' +__date__ = '2024-September-20' __authors__ = "M. Newville, M. Rovezzi, M. Koker, B. Ravel, and others" from ._version import __version__, __version_tuple__ diff --git a/larch/wxlib/__init__.py b/larch/wxlib/__init__.py index 8d056dfc5..f22596cb1 100644 --- a/larch/wxlib/__init__.py +++ b/larch/wxlib/__init__.py @@ -69,11 +69,17 @@ def DarwinHLine(parent, size=(700, 3)): def FileOpen(parent, message, **kws): "File Open dialog wrapper." - return Path(wxu.FileOpen(parent, message, **kws)).absolute().as_posix() + result = wxu.FileOpen(parent, message, **kws) + if result is None: + return + return Path(result).absolute().as_posix() def FileSave(parent, message, **kws): "File Save dialog" - return Path(wxu.FileSave(parent, message, **kws)).absolute().as_posix() + result = wxu.FileSave(parent, message, **kws) + if result is None: + return + return Path(result).absolute().as_posix() def SelectWorkdir(parent, **kws): "prompt for and change into a working directory " diff --git a/larch/wxlib/xrfdisplay.py b/larch/wxlib/xrfdisplay.py index 70d253d71..4ab38fcb7 100644 --- a/larch/wxlib/xrfdisplay.py +++ b/larch/wxlib/xrfdisplay.py @@ -140,6 +140,9 @@ def __init__(self, _larch=None, parent=None, filename=None, for i in range(len(statusbar_fields)): self.statusbar.SetStatusText(statusbar_fields[i], i) if filename is not None: + if isinstance(filename, Path): + filename = Path(filename).absolute().as_posix() + self.add_mca(GSEMCA_File(filename), filename=filename, plot=True) @@ -499,6 +502,8 @@ def add_mca(self, mca, filename=None, label=None, as_mca2=False, plot=True): xrfgroup = self.larch.symtable.get_group(XRFGROUP) mcaname = next_mcaname(self.larch) if filename is not None: + if isinstance(filename, Path): + filename = Path(filename).absolute().as_posix() self.larch.eval(read_mcafile.format(group=XRFGROUP, name=mcaname, filename=filename)) diff --git a/larch/wxmap/mapviewer.py b/larch/wxmap/mapviewer.py index 2d122d296..cfe6eea24 100644 --- a/larch/wxmap/mapviewer.py +++ b/larch/wxmap/mapviewer.py @@ -49,6 +49,7 @@ from larch.utils import get_cwd from larch.site_config import icondir from larch.version import check_larchversion +from larch.utils.physical_constants import PLANCK_HC from ..xrd import lambda_from_E, xrd1d, save1D, calculate_xvalues, read_poni from ..xrmmap import GSEXRM_MapFile, GSEXRM_FileStatus, h5str, ensure_subgroup, DEFAULT_XRAY_ENERGY @@ -61,7 +62,6 @@ from .maptomopanel import TomographyPanel from .mapxrfpanel import XRFAnalysisPanel -from ..wxxrd import XRD2DViewerFrame from ..wxxrd.xrd1d_display import XRD1DFrame def timestring(): @@ -1221,7 +1221,7 @@ def _getmca_area(aname): self.owner.show_XRFDisplay() mca_thread.join() - fname = Path(self.owner.current_file.filename).fname + fname = Path(self.owner.current_file.filename).name npix = area[()].sum() self._mca.filename = fname @@ -1268,9 +1268,9 @@ def onXRD(self, event=None, save=False, show=False, stem = Path(self.owner.current_file.filename).name stem = f"{stem}_{title}" + energy = 0.001*xrmfile.get_incident_energy() kwargs = dict(filename=self.owner.current_file.filename, - npixels=area[()].sum(), - energy=0.001*xrmfile.get_incident_energy(), + npixels=area[()].sum(), energy=energy, calfile=ponifile, title=title, xrd2d=False) if xrd1d and xrmfile.has_xrd1d: @@ -1310,18 +1310,19 @@ def onXRD(self, event=None, save=False, show=False, return label = f'{Path(_xrd.filename).name}: {title}' - self.owner.display_2Dxrd(_xrd.data2D, label=label, xrmfile=xrmfile) + self.owner.display_xrd2d(_xrd.data2D, label=label, xrmfile=xrmfile) + wildcards = '2D XRD file (*.tiff)|*.tif;*.tiff;*.edf|All files (*.*)|*.*' fname = xrmfile.filename + '_' + aname - dlg = wx.FileDialog(self, 'Save file as...', - defaultDir=get_cwd(), - defaultFile='%s.tiff' % fname, - wildcard=wildcards, - style=wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT) - if dlg.ShowModal() == wx.ID_OK: - filename = Path(dlg.GetPath()).absolute().as_posix() - _xrd.save_2D(file=filename, verbose=True) - dlg.Destroy() + #dlg = wx.FileDialog(self, 'Save file as...', + # defaultDir=get_cwd(), + # defaultFile='%s.tiff' % fname, + # wildcard=wildcards, + # style=wx.FD_SAVE|wx.FD_OVERWRITE_PROMPT) + #if dlg.ShowModal() == wx.ID_OK: + # filename = Path(dlg.GetPath()).absolute().as_posix() + # _xrd.save_2D(file=filename, verbose=True) + # dlg.Destroy() class MapViewerFrame(wx.Frame): @@ -1356,8 +1357,7 @@ def check_version(): self.larch = self.larch_buffer.larchshell self.subframes = {'xrfdisplay': None, - 'xrd1d': None, - 'xrd2d': None} + 'xrd1d': None} self.watch_files = False self.files_in_progress = [] @@ -1387,6 +1387,8 @@ def check_version(): self.h5convert_nrow = 0 read_workdir('gsemap.dat') + self.onFolderSelect() + self.statusbar.SetStatusText('Set Working Folder', 0) w0, h0 = self.GetSize() w1, h1 = self.GetBestSize() @@ -1399,8 +1401,14 @@ def check_version(): self.inst_name = None self.move_callback = None + + self.init_larch() + self.statusbar.SetStatusText('ready', 0) + self.Raise() + + if filename is not None: - wx.CallAfter(self.onRead, filename) + self.onRead(filename) if check_version: version_thread.join() @@ -1438,12 +1446,8 @@ def createMainPanel(self): except: pass - - self.Raise() - wx.CallAfter(self.init_larch) - def createNBPanels(self, parent): - self.title = SimpleText(parent, 'initializing...', size=(680, -1)) + self.title = SimpleText(parent, ' ', size=(680, -1)) self.SetBackgroundColour('#F0F0E8') @@ -1560,10 +1564,6 @@ def show_subframe(self, name, frameclass, **opts): def show_XRD1D(self, event=None): self.show_subframe('xrd1d', XRD1DFrame, _larch=self.larch) - def show_XRD2D(self, event=None): - self.show_subframe('xrd2d', XRD1DFrame, _larch=self.larch) - - def show_XRFDisplay(self, do_raise=True, clear=True, xrmfile=None): 'make sure XRF plot frame is enabled and visible' if xrmfile is None: @@ -1758,25 +1758,29 @@ def display_map(self, map, title='', info='', x=None, y=None, xoff=0, yoff=0, imd.Show() imd.Raise() - def display_2Dxrd(self, map, label='image 0', xrmfile=None, flip=True): + def display_xrd2d(self, map, label='image 0', xrmfile=None, flip=True): ''' displays 2D XRD pattern in diFFit viewer ''' - xrmfile = self.current_file - ponifile = bytes2str(xrmfile.xrmmap['xrd1d'].attrs.get('calfile','')) - if len(ponifile) < 2 or not Path(ponifile).exists(): - t_ponifile = Path(xrmfile.folder, 'XRD.poni') - if t_ponifile.exists(): - ponifile = t_ponifile.as_posix() - if Path(ponifile).exists(): - self.current_file.xrmmap['xrd1d'].attrs['calfile'] = ponifile - - self.show_XRD2D() + if xrmfile is None: + xrmfile = self.current_file + calfile = bytes2str(xrmfile.xrmmap['xrd1d'].attrs.get('calfile','')) + energy = xrmfile.get_incident_energy() + + if len(calfile) < 2 or not Path(calfile).exists(): + tfile = Path(xrmfile.folder, 'XRD.poni') + if tfile.exists(): + calfile = tfile.as_posix() + if Path(calfile).exists(): + self.current_file.xrmmap['xrd1d'].attrs['calfile'] = calfile + self.show_XRD1D() - self.subframes['xrd2d'].flip = 'vertical' if flip is True else False - self.subframes['xrd2d'].calfile = ponifile - self.subframes['xrd2d'].plot2Dxrd(label, map) - self.subframes['xrd2d'].Show() + self.subframes['xrd1d'].flip = 'vertical' if flip is True else False + self.subframes['xrd1d'].set_wavelength(PLANCK_HC/energy) + self.subframes['xrd1d'].calfile = calfile + self.subframes['xrd1d'].set_ponifile(calfile) + self.subframes['xrd1d'].display_xrd_image(map, label=label) + self.subframes['xrd1d'].Show() def display_xrd1d(self, counts, q, energy, label='dataset 0', xrmfile=None): ''' @@ -1807,7 +1811,6 @@ def display_xrd1d(self, counts, q, energy, label='dataset 0', xrmfile=None): self.subframes['xrd1d'].Show() def init_larch(self): - self.SetStatusText('ready') self.datagroups = self.larch.symtable if ESCAN_CRED is not None: self.move_callback = self.onMoveToPixel @@ -1821,7 +1824,6 @@ def init_larch(self): etype, emsg, tb = sys.exc_info() print('Could not connect to ScanDB: %s' % (emsg)) self.scandb = self.instdb = None - wx.CallAfter(self.onFolderSelect) def ShowFile(self, evt=None, filename=None, process_file=True, **kws): if filename is None and evt is not None: diff --git a/larch/wxxas/feffit_panel.py b/larch/wxxas/feffit_panel.py index 66ecf83a6..4a4f389ae 100644 --- a/larch/wxxas/feffit_panel.py +++ b/larch/wxxas/feffit_panel.py @@ -1198,9 +1198,9 @@ def onPlot(self, evt=None, dataset_name='_feffit_dataset', **ftargs)) self.larch_eval('\n'.join(cmds)) - self.plot_feffit_result(dataset_name, topwin=topwin, **opts) + self.plot_feffit_result(dataset_name, topwin=topwin, ftargs=ftargs, **opts) - def plot_feffit_result(self, dataset_name, topwin=None, **opts): + def plot_feffit_result(self, dataset_name, topwin=None, ftargs=None, **kws): if isValidName(dataset_name): dataset = getattr(self.larch.symtable, dataset_name, None) @@ -1217,6 +1217,8 @@ def plot_feffit_result(self, dataset_name, topwin=None, **opts): #print("plot_feffit_result/ dgroup, dataset: ", dataset_name, dgroup, dataset, has_data) + opts = self.process(dgroup) + opts.update(**kws) title = fname = opts['filename'] if title is None: title = 'Feff Sum' @@ -1228,6 +1230,12 @@ def plot_feffit_result(self, dataset_name, topwin=None, **opts): plot2 = opts['plot2_op'] plot_rmax = opts['plot_rmax'] kweight = opts['plot_kw'] + if ftargs is None: + ftargs = dict(kmin=opts['fit_kmin'], kmax=opts['fit_kmax'], dk=opts['fit_dk'], + kwindow=opts['fit_kwindow'], kweight=opts['plot_kw'], + rmin=opts['fit_rmin'], rmax=opts['fit_rmax'], + dr=opts.get('fit_dr', 0.1), rwindow='hanning') + cmds = [] for i, plot in enumerate((plot1, plot2)): if plot in Plot2_Choices: @@ -1689,9 +1697,10 @@ def onFitModel(self, event=None, dgroup=None): label = now = time.strftime("%b-%d %H:%M") - dgroup.feffit_history[0].commands = script - dgroup.feffit_history[0].timestamp = time.strftime("%Y-%b-%d %H:%M") - dgroup.feffit_history[0].label = label + if len(dgroup.feffit_history) > 0: + dgroup.feffit_history[0].commands = script + dgroup.feffit_history[0].timestamp = time.strftime("%Y-%b-%d %H:%M") + dgroup.feffit_history[0].label = label fitlabels = [fhist.label for fhist in dgroup.feffit_history[1:]] if label in fitlabels: @@ -2176,14 +2185,15 @@ def onSaveFit(self, evt=None, form='chikw'): xname = 'k' if form.startswith('chik') else 'r' yname = 'chi' if form.startswith('chik') else form + yname = 'chiq_re' if form.startswith('chiq') else form kw = 0 if form == 'chikw': kw = ds0.transform.kweight xarr = getattr(ds0.data, xname) nx = len(xarr) - ydata = getattr(ds0.data, yname) * xarr**kw - ymodel = getattr(ds0.model, yname) * xarr**kw + ydata = getattr(ds0.data, yname)[:nx] * xarr**kw + ymodel = getattr(ds0.model, yname)[:nx] * xarr**kw out = [xarr, ydata, ymodel] array_names = [xname, 'expdata', 'model'] diff --git a/larch/wxxas/xasgui.py b/larch/wxxas/xasgui.py index fc64ee938..6dbb3d1b6 100644 --- a/larch/wxxas/xasgui.py +++ b/larch/wxxas/xasgui.py @@ -1545,7 +1545,6 @@ def onReadAthenaProject_OK(self, path, namelist): jrnl = {'source_desc': f'{spath:s}: {gname:s}'} self.larch.eval(script.format(group=gid, prjgroup=gname)) - print("## ATHENA -> INSTALL GROUP ", ig, gid, label, path) dgroup = self.install_group(gid, label, process=False, source=path, journal=jrnl) groups_added.append(gid) diff --git a/larch/wxxrd/xrd1d_display.py b/larch/wxxrd/xrd1d_display.py index 50c3315dd..eb46c600c 100644 --- a/larch/wxxrd/xrd1d_display.py +++ b/larch/wxxrd/xrd1d_display.py @@ -135,8 +135,15 @@ def extract_background(x, y, smooth_width=0.1, iterations=40, cheb_order=40): return chebval(x_cheb, cheb_params) def calc_bgr(dset, qwid=0.1, nsmooth=40, cheb_order=40): - return extract_background(dset.q, dset.I, smooth_width=qwid, - iterations=nsmooth, cheb_order=cheb_order) + try: + bgr = extract_background(dset.q, dset.I, + smooth_width=qwid, + iterations=nsmooth, + cheb_order=cheb_order) + except: + bgr = 0.0*dset.I + return bgr + class WavelengthDialog(wx.Dialog): """dialog for wavelength/energy""" @@ -392,7 +399,7 @@ def onReadPONI(self, event=None): if sfile is not None: try: - self.poni.update(read_poni(sfile)) + self.set_poni(read_poni(sfile), with_pyfai=True) except: title = "Could not read PONI File" message = [f"Could not read PONI file {sfile}"] @@ -401,10 +408,11 @@ def onReadPONI(self, event=None): top, xfile = os.path.split(sfile) os.chdir(top) - try: - self.pyfai_integrator = AzimuthalIntegrator(**self.poni) - except: - self.pyfai_integrator = None + if self.pyfai_integrator is None: + try: + self.pyfai_integrator = AzimuthalIntegrator(**self.poni) + except: + self.pyfai_integrator = None self.tiff_reader.Enable(self.pyfai_integrator is not None) @@ -462,6 +470,8 @@ def onReadTIFF(self, event=None): default_dir=get_cwd(), wildcard=TIFFWcards) if sfile is not None: + top, fname = os.path.split(sfile) + if self.pyfai_integrator is None: try: self.pyfai_integrator = AzimuthalIntegrator(**self.poni) @@ -473,36 +483,37 @@ def onReadTIFF(self, event=None): return img = tifffile.imread(sfile) - img = img[::-1, :] - if self.mask is not None: - if (self.mask.shape == img.shape): - img = img*self.mask - else: - title = "Could not apply current mask" - message = [f"Could not apply current mask [shape={self.mask.shape}]", - f"to this XRD image [shape={img.shape}]"] - o = ExceptionPopup(self, title, message) - - if (img.max() > MAXVAL_INT16) and (img.max() < MAXVAL_INT16 + 64): - #probably really 16bit data - img[np.where(img>MAXVAL_INT16)] = 0 + self.display_xrd_image(img, label=fname) + + def display_xrd_image(self, img, label='Image'): + if self.mask is not None: + if (self.mask.shape == img.shape): + img = img*self.mask else: - img[np.where(img>MAXVAL)] = 0 - img[np.where(img<-1)] = -1 - # print("read tiff ", img.shape, img.min(), img.max()) + title = "Could not apply current mask" + message = [f"Could not apply current mask [shape={self.mask.shape}]", + f"to this XRD image [shape={img.shape}]"] + o = ExceptionPopup(self, title, message) - imd = self.get_imdisplay() - imd.display(img, colomap='gray', auto_contrast=True) + if (img.max() > MAXVAL_INT16) and (img.max() < MAXVAL_INT16 + 64): + #probably really 16bit data + img[np.where(img>MAXVAL_INT16)] = 0 + else: + img[np.where(img>MAXVAL)] = 0 + img[np.where(img<-1)] = -1 + img = img[::-1, :] - integrate = self.pyfai_integrator.integrate1d - q, ix = integrate(img, 2048, method='csr', unit='q_A^-1', - correctSolidAngle=True, - polarization_factor=0.999) + imd = self.get_imdisplay() + imd.display(img, colomap='gray', auto_contrast=True) + + integrate = self.pyfai_integrator.integrate1d + q, ix = integrate(img, 2048, method='csr', unit='q_A^-1', + correctSolidAngle=True, + polarization_factor=0.999) - top, fname = os.path.split(sfile) - dxrd = xrd1d(label=fname, x=q, I=ix, xtype='q', wavelength=self.wavelength) - dxrd.file = fname - self.add_data(dxrd, label=fname) + dxrd = xrd1d(label=label, x=q, I=ix, xtype='q', + wavelength=self.wavelength) + self.add_data(dxrd, label=label) def onCIFBrowse(self, event=None): @@ -759,14 +770,14 @@ def slabel(txt): self.Show() self.Raise() - def set_ponifile(self, ponifile): + def set_ponifile(self, ponifile, with_pyfai=True): "set poni from datafile" try: - self.set_poni(read_poni(ponifile)) + self.set_poni(read_poni(ponifile), with_pyfai=with_pyfai) except: pass - def set_poni(self, poni): + def set_poni(self, poni, with_pyfai=True): "set poni from dict" try: self.poni.update(poni) @@ -775,11 +786,12 @@ def set_poni(self, poni): except: pass - try: - self.pyfai_integrator = AzimuthalIntegrator(**self.poni) - except: - self.pyfai_integrator = None - + if with_pyfai: + try: + self.pyfai_integrator = AzimuthalIntegrator(**self.poni) + except: + self.pyfai_integrator = None + def set_wavelength(self, value): self.wavelength = value diff --git a/setup.cfg b/setup.cfg index 3ee74b92b..4d4881d86 100644 --- a/setup.cfg +++ b/setup.cfg @@ -63,7 +63,8 @@ install_requires = scikit-image scikit-learn psutil - pymatgen<=2024.7.18 + pymatgen<=2024.7.18; python_version <= "3.9" + pymatgen>=2024.9.10; python_version > "3.9" mp_api pycifrw fabio