{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
   Livechart API + Data Science packages\n", "
Version 1
\n", "\n", "
\n", "
\n", "
\n", "\n", "
\n", "This notebook makes use of
\n", "\n", "**Pandas** : a library for data manipulation:\n", "https://pandas.pydata.org\n", "
\n", "**Plotly** http://plotly.com : a package for interactive data visualization, \n", "here how to get it work with jupyter lab \n", "
\n", "
\n", "In short :\n", "```\n", "$ pip install pandas\n", "$ pip install plotly\n", "$ pip install ipywidgets\n", "$ jupyter labextension install jupyterlab-plotly\n", "\n", "```\n", "\n", "\n", "\n", "Write your feedback to nds.contact-point@iaea.org\n", " \n", "## API Quick how-to\n", "The service URL is nds.iaea.org/relnsd/v0/data?\n", "followed by parameters. For example:\n", "
nds.iaea.org/relnsd/v0/data?fields=decay_rads&nuclides=241am&rad_types=g.
\n", "The API v0 guide gives the detailed description, here a short summary\n", "

\n", "`fields=` specifies what is retrieved (at the bottom of this notebook there is the list of the columns for each choice)\n", "
these are the possible options

\n", "ground_states
levels
gammas
decay_rads
cumulative_fy
independent_fy
bin_beta \n", "

\n", "for ground_states, levels, gammas, decay_rads, bin_beta it is mandatory to add the nuclide parameter
\n", "`fields=levels&nuclides=135xe`\n", "

\n", "for decay_rads and bin_beta also the parameter rad_types is mandatory. \n", "
For decay_rads values are a, bp, bm, g, e, x (alpha, beta+, beta-, gamma, electron, X-ray) \n", "
For bin_beta values are bp, bm (beta+, beta-) \n", "
\n", "`fields=decay_rads&nuclides=135xe&rad_types=bm`\n", "
Be aware e aware of the fields \"p_energy\" and\n", " \"decay\". For\n", " the same nuclide there can\n", " be more than one decay mode, and, in case of metastable states, more states than the simple ground\n", " state. This might result in gamma lines having the\n", " same energy, but emitted by different decays \n", "

\n", "for _cumulative_fy, independent_fy_ one has to specify the parent and/or the product. Parents are _232th, 233u, 235u, 238u, 237np, 239pu, 241pu, 241am_ \n", "
\n", "`fields=cumulative_fy&parents=233u&product=135xe `\n", "\n", "
Examples
\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "\n", "# the service URL\n", "livechart = \"https://nds.iaea.org/relnsd/v1/data?\"\n", "\n", "# There have been cases in which the service returns an HTTP Error 403: Forbidden\n", "# use this workaround\n", "import urllib.request\n", "def lc_pd_dataframe(url):\n", " req = urllib.request.Request(url)\n", " req.add_header('User-Agent', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:77.0) Gecko/20100101 Firefox/77.0')\n", " return pd.read_csv(urllib.request.urlopen(req))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot γ energy vs intensity for Am-241 decay\n", "\n", "fields=decay_rads specifies to retrieve the decay radiations, nuclides=241am\n", "specifies the parent of the decay (241am), rad_types=g for the radiation type (gamma). \n", "
\n", "On the **plot** below, mouseover the points to see the data. Zoom an pan are enabled, and the option to save as png is is activated when the mouse is over the top-right\n", "\n", "
\n", " Important
\n", " For nuclides having metastable stable states one might want to filter the radiations coming only from the Ground State.\n", "
\n", " The energy of the parent is stored in the \"p-energy\" field. The dataframe below has the query option that makes the job\n", "
\n", " See below the full list of fields, and the API v0 guide for the full description of the fields\n", "
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "energy=%{x}
intensity=%{y}", "legendgroup": "", "marker": { "color": "#636efa", "symbol": "circle" }, "mode": "markers", "name": "", "orientation": "v", "showlegend": false, "type": "scatter", "x": [ 26.3446, 32.183, 33.196, 42.704, 43.42, 51.01, 55.56, 57.85, 59.5409, 64.83, 67.45, 69.76, 75.8, 98.97, 102.98, 106.42, 109.7, 120.36, 123.052, 125.3, 139.44, 146.55, 150.04, 154.27, 159.26, 161.54, 164.69, 165.81, 169.56, 175.07, 190.4, 191.96, 197, 201.7, 204.06, 208.01, 221.46, 232.81, 234.33, 246.73, 249, 260.8, 264.89, 267.58, 270.63, 275.77, 278.04, 291.3, 292.77, 304.21, 309.1, 316.8, 322.52, 332.35, 335.37, 337.7, 340.56, 358.25, 368.65, 370.94, 376.65, 383.81, 389, 390.62, 398.64, 401.3, 406.35, 415.88, 419.33, 426.47, 429.94, 442.81, 446.43, 452.6, 454.66, 459.68, 463.22, 468.12, 485.91, 487.3, 512.5, 514, 522.06, 529.17, 545.4, 563.05, 573.94, 582.6, 586.59, 590.28, 597.48, 619.01, 627.18, 632.93, 641.47, 653.02, 662.4, 666.5, 669.83, 676.03, 680.1, 688.72, 693.62, 696.6, 709.45, 722.01, 729.72, 731.5, 737.34, 742.9, 755.9, 759.38, 763.9, 767, 770.57, 772.4, 777.2, 780.7, 782.2, 786, 789.17, 794.92, 801.94, 806.26, 812.01, 819, 822.6, 828.5, 835.6, 841.5, 847.4, 851.6, 854.7, 860.7, 862.7, 870.7, 887.3, 898.4, 902.5, 912.4, 921.5, 928.8, 945.7, 955.7, 1014.7, 17.136, 97.078, 101.068, 114.16, 115.608, 117.47 ], "xaxis": "x", "y": [ 2.27, 0.0174, 0.126, 0.0055, 0.073, 2.6e-05, 0.0181, 0.0052, 35.9, 0.000145, 0.00042, 0.0029, 0.00059, 0.0203, 0.0195, 1.5e-05, 4.9e-06, 4.5e-06, 0.001, 0.00408, 5.3e-06, 0.000461, 7.4e-05, 5.4e-07, 1.4e-06, 1.5e-06, 6.67e-05, 2.32e-05, 0.000173, 1.82e-05, 2.2e-06, 2.16e-05, 4.9e-07, 8e-07, 2.9e-06, 0.000791, 4.24e-05, 4.64e-06, 6.6e-07, 2.42e-06, 5.4e-07, 1.21e-06, 9e-06, 2.63e-05, 6.4e-07, 6.6e-06, 4.4e-07, 3.1e-06, 1.42e-05, 1.01e-06, 1.4e-06, 5e-08, 0.0001518, 0.000149, 0.000496, 4.29e-06, 4.3e-06, 1.2e-06, 0.000217, 5.23e-05, 0.0001383, 2.82e-05, 4.9e-07, 5.9e-06, 2e-06, 4.9e-07, 1.45e-06, 3.1e-06, 2.87e-05, 2.46e-05, 1.15e-06, 3.5e-06, 4.9e-07, 2.4e-06, 9.7e-06, 3.63e-06, 1e-06, 2.88e-06, 1e-06, 4.4e-07, 1.15e-06, 2.58e-06, 9.5e-07, 4.6e-07, 7.4e-07, 7.4e-07, 1.25e-06, 2.3e-07, 1.31e-06, 2.86e-06, 7.41e-06, 5.94e-05, 5.6e-07, 1.26e-06, 7.1e-06, 3.77e-05, 0.000364, 4.9e-07, 3.8e-07, 6.4e-07, 3.13e-06, 3.25e-05, 3.68e-06, 5.34e-06, 6.41e-06, 0.000196, 1.33e-06, 4.7e-07, 8e-06, 3.5e-07, 7.6e-06, 1.67e-06, 2e-07, 5e-06, 4.74e-06, 2.66e-06, 6.1e-08, 2.5e-07, 1.5e-07, 6.2e-07, 3.9e-07, 9.4e-07, 1.36e-06, 3.1e-07, 6.1e-07, 4e-07, 2.2e-07, 2.4e-07, 2.1e-07, 4e-08, 2.7e-07, 3.8e-07, 2e-07, 8.2e-08, 5.3e-07, 4.6e-07, 2.2e-07, 7.2e-08, 3e-07, 2.5e-07, 1.9e-07, 5.5e-08, 5.6e-08, 5.8e-07, 6.4e-08, 36.63949694703623, 0.0011632926626854, 0.001851787110292, 0.000675252240823, 0.0009075390116662, 0.0002322867708431 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -41.98581334045775, 1073.8218133404578 ], "title": { "text": "energy" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -8.091839590227172, 2.2578490838155205 ], "title": { "text": "intensity" }, "type": "log" } } }, "image/png": "iVBORw0KGgoAAAANSUhEUgAABYgAAAFoCAYAAAD93h8mAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAAFiKADAAQAAAABAAABaAAAAACZnHW1AABAAElEQVR4AezdB3xc1Znw/+fekSzbsiVZkns37sa9YBtsbMD0FgIsJKEFQgspkM3uZpO8bLLZ7CbvG/6b7CYhCSSQEEggEELovRrj3jsuuMqyXCQXtZn5n2fEFE2xyhTNnfs7fMyM7ty595zvvdOee85zLL8pQkEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwnYDtuhbTYAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGAAAFiTgQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIECAmHMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwqQABYpceeJqNAAIIIIAAAggggAACCCCAAAIIIIAAAggQIOYcQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEXCpAgNilB55mI4AAAggggAACCCCAAAIIIIAAAggggAACBIg5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZcKECB26YGn2QgggAACCCCAAAIIIIAAAggggAACCCCAAAFizgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMClAgSIXXrgaTYCCCCAAAIIIIAAAggggAACCCCAAAIIIJDnJgKfzy/6n8eOjYt7vV7ZX3lY+vQqjfv4seMn5cTJOulVXtKMbG/VyWZ/O+WPsqICKci3paq6TuoafE6pNvVEIGcEPLYl5cUFUnG4NmfaREMQcJpA37Iuss+hn+NOs6a+CMQT6FXSWQ7V1Emj1x/vYZYhgECaBfQ34bGTDfweTLMzm0cgkUBJYb7Um8/AE7WNiVZheRsE+pnv9pT2C7gmQOz3++Vf//M3Aan/+vbtzcSefuFd+Y+fPSYN9Q3SqSBf7r/vZrn8/NmBderMMn3eK28vEcssGTygt/z8P+8N3DbbCH8ggAACCCCAAAIIIIAAAggggAACCCCAAAIOE4jtSuuwBrSmus+9ulDmXPlV+ftrC2NWr6w6It/7ySPyrXs+Jytee0juu/1a+e6PH5bDR2sC6z5jgscfLd8gf3/0h7LohV9Kvz495Qc//UPMdliAAAIIIIAAAggggAACCCCAAAIIIIAAAgg4TcAVAeJzz5oif/7V/bLg7Gkxx+fND1ZIcVGhXHPZPMnL88j1V54rXToXyNsLVwbWffXdpXLBvOkydFBfKezaWW669nxZtHSd1JiUExQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDJAq4IEGtgt3+fcins0jnmWFVUHpKB/XqFltsmL+jAfj1l/4FDgWX6uKaVCJZB/XuLz6SrOGh6HlMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEnC7gmB3Gig1Rdc1wKCjo1e7hTp/xQD+GaYyeaPV7QqWndarNci05w48Si+ZS1lJqJCSgIIJB5geBr0KnvIZkXY48IpF5AX4e8BlPvyhYRaK2AvgZ1ojqmqGutGOshkFoBfQ0W5BfwGkwtK1tDoNUC+hrsav4Vm8nqKAh0tIDrA8RF3QulvqGh2XGoq2uQom76MhXpbm51orpgqauvD9wNPu7U2c91xtqCfFsOVdcxa23w4HKLQAYFPGa0QnlxgVQcrs3gXtkVAghECmhw2Kmf45Ht4D4CThXQ4PChmjppNDO4UxBAIPMC+pvw2MkGfg9mnp49IhAQKDGB4XrzGXiithGRFAj0c2gHzhQ0PSWbcEWKiVNJ9SrvIZ/sORBaxefzy669B0SXa+nds1Q+2b0/9PjO3RViWyawU1YSWsYdBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAiQKuCBB7fT7TS7hRvF6f+ecN3NdAsJZzzpwiR48ek6f+/nbgsSeefUNq6+pl3uxJgccXzJ0qL7+1RLbv2i8nTtbKo0+9IjOnjpXuhc5MLRFoFP9DAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSMgCtSTPzxmdflR//7eOiAv/jmR/Kdr90g13/mXNNTuES+8/Ub5Ic/e0y+/8Cjkp+fJ//2jZultKR7YP2rLp4ri1dskMtu/JZofpgBZkK7X/zXvaFtcQcBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHCqgOU3xamVT2W9Gxu9srfioPTrXS55eZ6YTetkdceOn5S+vcuaPba36mSzv53yRzAHcRU5iJ1yyKhnjgmQgzjHDijNcaQAOYgdediodA4JkIM4hw4mTXGkADmIHXnYqHQOCZCDOLUHkxzEyXm6ogdxa4g0KDyof++Eq+pkdfqPEitgsnfIwYOWlJX6pVOn2MedsMRkHxGPKxKuOOFoUEcEEEAAAQQQQAABBBBAAAEEEEAAgUwJECDOlHSO7mflaltefNmW2lox6TlEzp3vk9kzTbTVIWX9BkveX2hLxQFLJk/0y1lneqWk2CGVp5oIIIAAAggggAACCCCAAAIIIIAAAggkKUCfySQB3fz0Q4ctefa5puCwOjQ0iLz8qi27dmu25uwvW7Za8qenPLJ7jxWo++Klljz2uEfMnIYUBBBAAAEEEEAAAQQQQAABBBBAAAEEXCFAgNgVhzk9jdQAa7xg6uYtzggQa+/n6HKg0pK9+5xR/+i68zcCCCCAAAIIIIAAAggggAACCCCAAAJtFYiNkLV1C6zvWoGi7vHnNyzq7gwST+xchIGKx5mj0BkNopYIIIAAAggggAACCCCAAAIIIIAAAgi0UYAAcRvBWD0sMHKEX/r0aR4k1vy9E8Y7I0fD9Kk+saI6Cw8e5JfevZu3Kdxi7iGAAAIIIIAAAggggAACCCCAAAIIIJBbAkxSl1vHM6Ot0R64t97klfUbbdF0E0MH+2XcWJ8UFGS0Gu3e2cABfrnjNq8sXmrL3r0ikyf5zb/YoHG7d8ATEUAAAQQQQAABBBBAAAEEEEAAAQQQyHIBAsRZfoCyvXoaDJ480Wf+ZXtN49evX1+/XHmZN/6DLEUAAQQQQAABBBBAAAEEEEAAAQQQQCDHBUgxkeMHmOYhgAACCCCAAAIIIIAAAggggAACCCCAAAKJBAgQJ5JhOQIIIIAAAggggAACCCCAAAIIIIAAAgggkOMCBIhz/ADTPAQQQAABBBBAAAEEEEAAAQQQQAABBBBAIJEAAeJEMixHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyXIAAcY4fYJqHAAIIIIAAAggggAACCCCAAAIIIIAAAggkEiBAnEiG5QgggAACCCCAAAIIIIAAAggggAACCCCAQI4LECDO8QNM8xBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUQCBIgTybAcAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIcQECxDl+gGkeAggggAACCCCAAAIIIIAAAggggAACCCCQSIAAcSIZliOAAAIIIIAAAggggAACCCCAAAIIIIAAAjkuQIA4xw8wzUMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBIJECBOJMNyBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgxwUIEOf4AaZ5CCCAAAIIIIAAAggggAACCCCAAAIIIIBAIgECxIlkWI4AAggggAACCCCAAAIIIIAAAggggAACCOS4AAHiHD/ANA8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEgkQIA4kQzLEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBHBcgQJzjB5jmIYAAAggggAACCCCAAAIIIIAAAggggAACiQQIECeSYTkCCCCAAAIIIIAAAggggAACCCCAAAIIIJDjAnk53j6al0UCVVWWbNpiSadOIiNH+KSoexZVjqoggAACCCCAAAIIIIAAAggggAACCCDgQgECxC486B3R5DXrLHnqaU9o1x6PLbfd4pX+/fyhZdxBAAEEEEAAAQQQQAABBBBAAAEEEEAAgcwKkGIis96u3JuGgF97PRwcVgSvV+SNtzj9XHlC0GgEEEAAAQQQQAABBBBAAAEEEEAAgawRIEKXNYcidytSWyty5Ghs+/ZXWLELWYIAAggggAACCCCAAAIIIIAAAggggAACGRMgQJwxavfuqEtnkd69YlNJDB4Uu8y9SrQcAQQQQAABBBBAAAEEEEAAAQQQQACBzAsQIM68uSv3eMlFPsnPDze9W6HIgnN84QXcQwABBBBAAAEEEEAAAQQQQAABBBBAAIGMCzBJXRvIGxoaTZATsjaQhVYdMtgv/3hvo+zYYQUCxYNM7+FOEQHj0IrcQQABBBBAAAEEEEAAAQQQQAABBBBAAIGMCdCDuJXUb36wXKZfdGcr12a1eAKaamLMaL8MP43gcDwfliGAAAIIIIAAAggggAACCCCAAAIIIJBpAbrDtiB+6HC1XH/3v8vufZWSnwdXC1w8jAACCCCAAAIIIIAAAggggAACCCCAAAIOEqAHcQsHq6S4uzz8wD/JD/751hbW5GEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBZAnSJbeF42bYlA/r2lG0798Vds3tXZybSzfNYgfZ0KcgzuYD9cdvGQgQQSJ+AeWsR27LEqe8h6ZNhywhkTkA/CXkNZs6bPSEQLaDfsws754mPr6LRNPyNQEYE9DdhV34PZsSanSAQTyA/zxbbI+LRH4cUBDpYgABxsgfA79BvtKFqmztObUOyx47nI9CBAn6xxG/+4/XXgQeBXbteIPBRyGeg688DADpQIPj6C952YFXYNQJuFNCXHt9H3XjkaXNWCQReiIFvpVlVLSrjPgECxEke85qTjUluoWOe3infIx5zxfhknVfqGnwdUwn2ioCLBfQqcZcCjzj1PcTFh46m55BANzMKiNdgDh1QmuI4AR3Jdry2URq9/DB23MGjwjkhoL8J+T2YE4eSRjhUQH8T1pvPwBPms5CSvAAjA5MzJAdxcn48GwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcKwAAeJWHLr6hkZpaGy6oqP3Gxu9rXgWqyCAAAIIIIAAAggggAACCCCAAAIIIIAAAtktQIqJFo7PgYNHZP7VXw+tNXnBbTJ94mh55Kf/ElrGHQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwIkCBIhbOGq9yktk3duPtLAWDyOAAAIIIIAAAggggAACCCCAAAIIIIAAAs4TIMWE844ZNUYAAQQQQAABBBBAAAEEEEAAAQQQQAABBFIiQIA4JYxsBAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcJ4AAWLnHTNqjAACCCCAAAIIIIAAAggggAACCCCAAAIIpESAAHFKGNkIAggggAACCCCAAAIIIIAAAggggAACCCDgPAECxM47ZtQYAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIiQAB4pQwshEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMB5AgSInXfMqDECCCCAAAIIIIAAAggggAACCCCAAAIIIJASAQLEKWFkIwgggAACCCCAAAIIIIAAAggggAACCCCAgPMECBA775hRYwQQQAABBBBAAAEEEEAAAQQQQAABBBBAICUCBIhTwshGEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB5wnkOa/K1DgXBerqRVavtmXVGkuGDPbL9Kk+KS7OxZbSJgQQQAABBBBAAAEEEEAAAQQQQAABBLJHgABx9hwL19bE6xP53aMe2bvPChh8ssuSRYtt+fKdjdKjxLUsNBwBBBBAAAEEEEAAAQQQQAABBBBAAIG0C5BiIu3E7KAlgZ07rVBwOLhuvelRvGw5p2fQg1sEEEAAAQQQQAABBBBAAAEEEEAAAQTSIUAELh2qbLNNAnV18VdPtDz+2ixFAAEEEEAAAQQQQAABBBBAAAEEEEAAgbYKECBuqxjrp1xg2FC/FBY236xlsk2MP93ffCF/IYAAAggggAACCCCAAAIIIIAAAggggEBKBchBnFJONtYegYICkS9c75XX37Tl422WlJf7Zf7ZPhk0MHGAeN9+y6SgsKS6xpKpk30yYrhfbC53tIef5yCAAAIIIIAAAggggAACCCCAAAIIuFiAALGLD342Nb1/P7/c9AWv1NaKFHQWaZquLn4NN2+x5LEnPKEHN27yyMwZPrn4QjPbHQUBBBBAAAEEEEAAAQQQQAABBBBAAAEEWi1An8tWU7FiJgQ6txAc1jp88GHsabtkmS31DZmoIftAAAEEEEAAAQQQQAABBBBAAAEEEEAgdwRiI2250zZakqMCJ0/GNszrFalPMNld7NosQQABBBBAAAEEEEAAAQQQQAABBBBAAAEVIEDMeeA4gXFjY3MTDx7kl27dHNcUKowAAggggAACCCCAAAIIIIAAAggggECHCpCDuEP52Xl7BM6c7ZP6epFFi21pbBQZM9ovFy4wXYgpCCCAAAIIIIAAAggggAACCCCAAAIIINAmAQLEbeJi5WwQyDPz0y041yfz5vqkweQd7to1G2pFHRBAAAEEEEAAAQQQQAABBBBAAAEEEHCeAAFi5x0zavypQH6+iP6jIIAAAggggAACCCCAAAIIIIAAAggggED7BMhB3D43noUAAggggAACCCCAAAIIIIAAAggggAACCDhegACx4w8hDUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNonQIC4fW48CwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcLxAUgHi6prjcu/9P5elqzY5HoIGIOAkAb+TKktdEUAAAQQQQAABBBBAAAEEEEAAAQSyViCpSeps25ZV6z+WV99ZIiNPGyifv+o8ufS8WdK5oFPWNpiKIeBkgSNHRT5cZMvKVbYMHOiX2TN9Ulrqlw0bbamuFhk9yi+DzHLLcnIrqTsCCCCAAAIIIIAAAggggAACCCCAQKYELL8pyezM6/PJW++vkCeefUMWLV8vxUWF8tmL58r1nzlP+vUuS2bTjnju3qqTjqhndCXLigqkIN+Wquo6qWvwRT+c0b8bGkQ+3m7Jrl1WILg5bJhf8pO6dBG/+nV1IitMYHXZckvKzKk5Y5pPhg1N6vSPv6M0LW30ivz8wTypqgrvQAPB+s+8DENl3lyfnDMvYkHoEe5kk4DHtqS8uEAqDtdmU7WoCwKuEuhb1kX2OfRz3FUHisbmrECvks5yqKZOGr3O+T6WsweDhrlSQH8THjvZ0OG/B12JT6MRMAIlhflSbz4DT9Q24pECgX7muz2l/QJJh+E8phfxeXOnBv5t/2RfIFD8x2del0f+/LLMP3Oy6VW8QM6YMqb9NeSZOS3gNXHM3/3eI7v3hLu8ag/YL97sFRM/S2l56hmPbN7StNGKAyLrN3jkc//gDfS6TemO0rQxDaBHBod1N3p5J/oSz/sLbTlrtk860ZE/TUeCzSKAAAIIIIAAAggggAACCCCAAAK5I5BUDuJIBu2IvLfioPlXJQ0NjWJ7bFm7aYd88b4fyRU3f1s+XLYucnXuIxAQ2LjJahYc1oWfmEDo5s2pjQ4fNekXgsHhSPoly1L2EojcbFrutzZtRKO5+Hi0OrV+aWkQG0UAAQQQQAABBBBAAAEEEEAAAQQQ6HCBpHsQH60+Ln996T3583NvySd7KqRXeYncffOVcs1l86S0pEje/nCFPPrkK7Jo2XqZNXVcxhrs9Xplf+Vh6dOrVLSXc0ulNetr4Ds/HbkPWqpcDj9eWRk/kHnALNd8uqkq/gQZF6J736Zqf+nYjuYc7t3LLxUHwmYaNI5uQ0mJSJnJS0xBAAEEEEAAAQQQQAABBBBAAAEEEECgJYGkAsTVNcdl/jX3Sl1dvUyfNFruvf1qOfesKeLxeEL7PefMKaL/amvrQ8vSfefpF96V//jZY9JQ3yCdCvLl/vtulsvPn51wt61Z/80Plst99/9CVr7+UMLt8EDbBYYMjh/IHDok/vK276HpGRo01W1u3xEOruojkycmiBy3d0dpfJ7plC833+CVpcvDk9Rp/d94yw70utZdd+ks8pnLTXqOlq+JpLGmbBoBBBBAAAEEEEAAAQQQQAABBBBAwCkCSQWILROFuuqis+S6K8+V4UP6n7LNnTtnJiFqZdUR+d5PHpHv3nujfOaiOYGezd/98cMy54zx0qO4e0wdW1r/0OFquf7uf5fd+yrNxGlJccXsmwUiGiDWyeIWLw1HNGfO8MnAAakNEKv1tVd75aPFtiw1aSXKyvwy8wyfjBuT+v2k87gWFoqcPccX+Bfcz623eKXS9Co+fkJkQH8zwV9+8BFuEUAAAQQQQAABBBBAAAEEEEAAAQQQOLVAOCp36vUSPrrp493SVbstRpWHn3hRfvyLP0UtTf+fb36wQoqLCgMpLvLyPHK9CV536Vwgby9cGXfnLa1fYoLKDz/wT/KDf7417vNZmLzApRf75Otf8cp113jlXnN78YXp6dVb2FXknHk++eY3GuVWMwme04LDiaS1T3Qvk3pCe0gTHE6kxHIEEEAAAQQQQAABBBBAAAEEEEAAgXgCSQWINSfv8jWbAykmojfu8/nk3UWrohen/e+KykMysF+v0H5s2zJ/95T9Bw6FlkXeaWl9ff6Avj2lrEdx5NO4n2KB0h5+GWt68/Ywt+kuzZNMpHtvbB8BBBBAAAEEEEAAAQQQQAABBBBAAIHsFWhXzgQNDFeZ1AuHj9YEWqZpGrSXrha/mTHrqMlN/JbpyTt0YJ/Askz+T/MiFxQ0T2fRqVO+1Bw/GbcabV0/eiP9yrpEL3LU32VFTcfNUZWmsgjkkIDT30Ny6FDQFJcK8Bp06YGn2Vkj0KskdiRi1lSOiiDgAoGCfH4PuuAw08QsFjADnaWkkDyRWXyIXFO1dgWIF6/cILd/8ychpFvu/VHofvBOUfdCueeWzwT/zNit7re+oaHZ/urqGqSom77sYktb14/ewt6q+IHn6PWy7W8NDBfk21JVXSd1DelJ6ZBtbaY+CGSTgMeMTigvLpCKw7XZVC3qgoCrBPqai7z7HPo57qoDRWNzVkCDw4dq6qTRm/4RZDmLSMMQSEJAfxMeO9nA78EkDHkqAskIaGC43nwGnqhtTGYzPPdTATp+JHcqtCtAPGb4YHng375seuWekPv/7+/kn+/5nPQu7xGqSXlpsUw6fbh4zCR2mS69TD0+2XMgtFufzy+79h4QXR6vtHX9eNtgGQIIIIAAAggggAACCCCAAAIIIIAAAggg4ESBdgWIS3sUyQXzpovX5BkeM2KwDB/SXwpMGodsKOecOUV++NPH5Km/vy1XXTxH/vS3t6S2rl7mzZ4UqN7ajdvlOz96WO7/xk0y+fQR0tL6+qR6k1KjobHpio7ety1LdAI8SscI7NlryUdLbNm1y5JJE30ybYpPCgvbVhdz6opup7LS5Kge6Jee5fRcaZsgayOAAAIIIIAAAggggAACCCCAAAII5IJAuwLEK9ZuCQRdZ0waLcdNbt9V67bGtSgp7i4jhw2I+1i6FvYqL5HvfP0G+eHPHpPvP/Co5Ofnyb9942YpLeke2OXJujrZsn23HD/RNKy7pfUPHDwi86/+eqi6kxfcJtMnjpZHfvovoWXcyZzA3n2W/Oa3HtEAr5Y33rJl7TpL7rzda3qsNy1r6f9er8gjf/DIzk/C09WdOcsnFywg1UZLdjyOAAIIIIAAAggggAACCCCAAAIIIJBbApaZVK7NXSe//K//LZUmcPrgj78hc678SkKROWdMkAd/dF/Cx9P5QGOjV/ZWHJR+vctb1du3resH604O4qBEZm5feMkO9B6O3tsXb/LKkMGtO5WXr7Tl2eeaR5NNp3D56t1eKStr3Tai9++Gv01HfKmptgJG6kVJToAcxMn58WwEUiFADuJUKLINBNovQA7i9tvxTARSIUAO4lQosg0E2i9ADuL228V7JjmI46m0flm7ehB/7dbPSm19vRQXFcrTD30/4d66de2S8LF0P6ApIAb1793q3bR1/VZvmBVTKhDsORy90bZc5ti1Oza6qc/fvacp+Bm9bbf/rTZvvm3LBx/aoplWyspErri09QF5t/vRfgQQQAABBBBAAAEEEEAAAQQQQCCbBdoVIB552sBQm0YPHxS6H7xTc+yEdO/WNfgntwikTGDSRL8sXS4SGRDWgKXmEW5t6dtH140NEvcJLG/tVtyz3hqTwuOd98I9rquqRJ78i0e+/tVGyZLU4+45GLQUAQQQQAABBBBAAAEEEEAAAQQQSLFAOOrTzg1/7bv/I98zuX61HDx0VK7+0v0y89K75Zrb75dDh6vbuVWehkB8gYED/HLTF7wycoRfOncWmXWGz/zdKG2ZM3DyJJ/07tU8oDxlkj9mWfwaSCj/caLHc235ho2xbxPHjktgksBcayvtQQABBBBAAAEEEEAAAQQQQAABBNwm0K4exEGkioOH5fX3lsnPfvDVwKKHn3hRPt6xR+688XJ55sV35fnXP5Qbr7kguDq3CKREYNhQvwwb6pX4/YBb3oWZt1Buv80rO3ZYUnHACuQu7t+vecA43lZqjol8sNCWZSts6dHDL2fO9MuECb44fZHjPdu5y7p3iy/dvWneR+c2jJojgAACCCCAAAIIIIAAAggggAACCEhSAeI9+yoDhNMmjArcvrdolZw7Z6p85YtXSb6Jwr23eA0BYk6ytAnEJolo/a40SDxiuD/wrzXP0hDpn5/yyCe7mva6f78lTz9riWWZIPH4loPLrdlHtq4zc4bfBMVFGhrCNRw10i+9euZ2u8Ot5R4CCCCAAAIIIIAAAggggAACCCCQuwJJBYiDk9BpzuGTtXWyY3eFfP6zCwJaXQo6ya49B3JXjpa5SuDwISsUHI5s+MrVtgkQeyMX5dz90lK/fOWuRlm1xpZ9JjB++lifjB5FcDjnDjQNQgABBBBAAAEEEEAAAQQQQAABVwokFSAeMqiv9Covka9856emx3C+dDIzVp1z1pQA5NJVm6R/n3JXotLo3BPweOIHRD2e3GtrvBaVlIicPccX7yGWIYAAAggggAACCCCAAAIIIIAAAgg4WCB29qk2NKaTGaf/4+/caYaeN8qhI9XyrXs+J73Le8imj3fJWwtXytyZE9qwNVZFIHsFiotFNK1CdJkxrWOCpn5TlS1bLfnjnzzy+z96ZOMmS3yx1YuuLn8jgAACCCCAAAIIIIAAAggggAACCCDQTMDym9JsSQr+0E36zD+PnVT8OQU1Sf8m9ladTP9O0rCHsqICKci3paq6TuoaOibImYZmpXWT9SYH7yqTUmLZckvKy0RmTPfJoIEpf/m0qg0ffGjLK681f33Nm+uTc+ZxLFsFmAUreWxzHhUXSMXh2iyoDVVAwJ0Cfcu6yD6Hfo6784jR6lwT6FXSWQ7V1Emjt2O+T+WaJ+1BoK0C+pvw2MkGfg+2FY71EUiRQElhvtSbz8ATtY0p2qK7N9PPfLentF8gqRQTkbvdvbdSjp9sHujoVtiFNBORSNx3tIDJoCLTp/rMv45vxkdLmgeHtUa6bL4JECczeV/Ht4waIIAAAggggAACCCCAAAIIIIAAAghkUiDpAPGfn3tLHn3yZdlpJqiLLnPOmCAP/ui+6MX8jQACSQo0xrnAGG9Zkrvh6QgggAACCCCAAAIIIIAAAggggAACOS6QVID4SPUx+fEvnpCJY0+Tr912tRQXFZrei+H+iyXF3XKcj+YhkBkBk+ZbPt5myY4dlgw0aS1OH+uTRYub9yKeMN4f8erLTL3YCwIIIIAAAggggAACCCCAAAIIIICAswWSChBv2LJTamvr5dtfvUFOG9LP2RLUHoEsFdDJ537/mEd2fvLpxZdFIr16+uWc+T5ZbNJK+Eza4TNMPuTZs1Kff1gzlFcetKRboV+6ds1SIKqFAAIIIIAAAggggAACCCCAAAIIINBugaQCxKOGDRTLsmTfgSoCxO0+BDwRgVMLfPyxFQ4Of7rqgUpLehT75R/vbRQN4nqadyY+9QYjHj1yVOSgCQD362cCwFH53HfvseSZZz1ysErM61xkmsm/fPGFvnbvK2K33EUAAQQQQAABBBBAAAEEEEAAAQQQyBKBpALEpT2K5Ly5U+XXj/1dxo4YHNOkfDOrV3czUR0FAQTaL6A9eOOVyoMitj4U/+F4TwktMzFlee7vHlm2ounJtgkwa/B3xrSmXshec/Pk0x45cqTpKRqEXrLUlvIykVlnpL6ncqhi3IkR8HrNBQBPzGIWIIAAAggggAACCCCAAAIIIIAAAikRSCpAfOhIjbz2ztJAReZ85qsxFWKSuhgSFiDQZoFhQzScG1uGJlgeu2bskg0brFBwWB/VNBUvvWLLqBE+KS4WqaiwQsHhyGdv2myZAHHkEu6nS0BzTr/3gS07dloydoxfzj7LJ717xz8X0lUHtosAAggggAACCCCAAAIIIIAAArkvkFSAWHsH/9e3b0+o1Lu8R8LHeAABBFon0KePCQ7O8ck774XzSEw3PX1PG9a+YKH2Bl69JrytYC20p+r2nbZMmuCT7t3NhHemc7GuG1m6d4/8i/vpEqg4YMkfHvcEAve6j7XrLNm+3SNfu6dROndO117ZLgIIIIAAAggggAACCCCAAAIIuFEgqQBxfn6eXLZgthvdaDMCMQLHjons2WuZCeREevSIiqzGrN22BeeaCemmmxzAe/db0ruXX3qUtO35kWu/9oYt6zfGz0tR+mm9u3cTmTzRL8tXhtfTNAdnpWEivMi6cb9JYM1aKxQcDpocPyGyxeSjHj8utedWcPvcIoAAAggggAACCCCAAAIIIICAOwWSChAr2fETtfLbJ16UxSs3StXho/LcIz+UXfsq5cc/f1xuuvZCmTllrDtlabWrBN5faIsGXoM9bqdM9ssVl3nbkx44oVtRkUhRUXLBwRMmyPjhR7G9h3WnI4b7ZeDA8PYvu8Qro0dbpveqLWWlfpk43i+l5paSfoG8BO/MeeQiTj8+e0AAAQQQQAABBBBAAAEEEEDAZQLxI0VtQPiRCQT/5vHnpb6hQXburhCfiZANHdhHDh46Kk+/8G4btsSqCDhToKrKahYc1lYsN5O/bTb5erOtVB2yRFNJRJc+Jrft9dc2D2hrj+HRI/1y9We8Mv9sX9qDwxp6bmiMrpk7/5400SedOjVve2mpyHATxKcg0JKATjK50bz//OWvHnnrHVsOmdc9BQEEEEAAAQQQQAABBBBAAIFEAgn6qSVavfnymmMn5IU3Fsn/+z93yfjRw+S8f/hGaIVZ006X9xatCv3NHQScLKA9b0+ctKSszOTmjWrIdjOJWLDncORD27ZbMsoEWLOp6CRnXUwO25O1zWulk6Al6rXafM3U/6VCy5bbor2wq6vF5EDWnMvewGR5qd+bM7ZYYiYKvPt2ryxeaolOVjf+dL9MnewTk9WHgkCLAn9/3hORHsaSd98XufM2L5MctijHCggggAACCCCAAAIIIICAOwWSCjes2bhdamvr5czp46W65ngzweJuXaWB7oDNTPjDeQKNprftiy95ZJnpEaxBYO1pe9UVPtGJ44JF0y/EK+Xl8ZZ27LJO+SKaOuLpZz2hnsQD+vtl1kzT5bCDyurVtjz3fHgww9Lllsm17JE7bjU9mqOj8R1Ux47YrabzuPD8+OdWR9SHfTpDoMbkQl+xqvkLR0cNvP+hLZ+9Ms7wAWc0i1oigAACCCCAAAIIIIAAAgikUSCpALGmktDy4dJ1Mm7UkMB9/Z/fRNLeXLhChgzqG1rGHQScKPDhIls0YBks+ysseeoZW758l1fsTxcPGeKX04b5Az09g+uVmXQAEyd0XNA1WI94t6ebSc6GDW2UT3ZZ0r27SN++/lBb4q2f7mWr1oR9g/vaayb7O2hSd/QsJ0AaNOEWgdYI1NTEH9FQU9OaZ7MOAggggEBHCgRSBJmJhBcvtaWwUGTGNJ8MGcx3oY48JuwbAQQQQAABtwgkFSDu27tM5p85Wb73wCNywfwZAbOX31osL7z+oaxcu1Ue+sk33eJIO3NUYOOm2OBl5UFLDpngZfmnwUtd4/PXeQMBYk0roT1yR4zwi/bWzdbStavJLzwqO35w5Cdwys/Pjvpl6zGkXgjEE9A0MiUlIkeONH8029LdNK8dfyGAAAIIqMBLL3sC6aWCGmvXeczoD1/WdjoI1pNbBBBAAAEEEHC+QHhcdzvb8sN/uU3mzJwof3r2zcAWvvXDX8vKdVvle/94i8yaOq6dW+VpCGSHgPawjS62edUUdmsevNT8vRqAuegCXyBfbOeC6GfxdyKBM6b7YlJJaPBa8/BSEECgbQIe8/507We9Ul7W9DxN0zLd9ECbYV5nFAQQQACB7BVoNBP1akqz6LJoceyy6HX4GwEEEEAAAQQQSFYgqR7EuvOi7oWiQeLvfv0G+WTPAelc0EkG9OspHo2iURBwuMBZs32ycZNHfBGxlWlTmiZ6c3jTsqb6w4b65Q4zgdYSM5yy8qCYydj8cvrpEeBZU1MqgoAzBHQUw1fubjSvJ0u6FfpFRwxQEEAAAQSyW0DTS2jO+OjS0BC9hL8RQAABBBBAAIHUCyQVID5afVwuvelb8tj/fFsGD+gto04bGKrhA796Uj7Ze0D++3v3hJZxBwGnCWig5Z47vaJ5cg8fsWSCCVwOP61572GntSm6vidPiqxdb4umx9BcyuPG+qRL5+i10vt3P5MH+YrL4vwqSu9u2ToCOSugPYd79cy+9yq92FZfL9I5w+8xOXugaRgCCOSMgOljE0j/FZ3ebML47Hsvzxl0GoIAAggggAACIYGkAsRe80vv0OFq07sytrdft8IusnXb7tCOuIOAUwU01/C583Pzy3m96ZXyq4fz5NChpqOzbr0lCxdZctftXslP6t3BqUebeiOAQDoEzNy18uFHtry/0Jbjx0XGjPbLgnN9Ulaam++t6TBkmwggkPsCV5qL5e/20AmSbTMqU2TWTJ/MPCP2d1buS4RbePxE0/1CRsOEUbiHAAIIIIBAGgTaFQKqrjku67fsFL3VojmHKw4eDtz3m1+B2rP4b698IBPGDAss438IIJCdAutMz+FgcDhYw4NmWPqGDaa3dA73WNFglfawpCCAQGYEVq625eVXw6mn1pv3mKoqW+66wys2r8XMHAT2ggACWS+gKYEuPN8n55kLaJqtz83vj3ox8QUzaZ92XtAyfpxfLr7QS9qkrD+LqSACCCCAgFMF2hUgXrNxm9z+zZ+E2vydHz0cuq93LBN5GTtisHzhswuaLecPBBDILoGamvj1qa7RL+O51bPPZ5qzapVtejFacvyEJTPMxF06QR5D3eOfAyxFIJUCa9fFRoErDlhSaf717p1b7zWpdGNbCCDgToE8jzvbHdnql17xSORnx+q1ltgej1x1BSnJIp24jwACCCCAQKoE2hUgnjJ+pDzz8L+bnsLH5JZ7fyQ/+8FXZUDfnqE6lfUokvLS4tDf3EHAaQKaNWXPXsv0khcZPMhvJnpyWgtaV99RI/zyxlsmFBwRn9Getbo818pHi2156ZVwD8Y33rJl335LrruGHxq5dqxzpT2HD1uyfKUle/dpj36fjB3jT5j65YTJJb5pk57TEsiTPszkE8+mAEOBGSodrxR0zr33mnjtZBkCCCCAQOsF9JNh/cbYC4sbdNkVrd8OayKAAAIIIIBA6wXaFSDuYpJiBSekW/zig2ZCqwIzDCr2Q7z11WBNBLJHoK5O5OFHPbLfBA+16BA/7a2QiykXtOfeZZf45JXXbNF2a29aHdrYMwsnt0r2DFm5KvY9Sn9o1JkJs3RiGAoC2SSgqV5+8WuPNDY21WrLVo+sWu2XGz7vlegz+WStyC8ezAtc0NK1Fy02F3lG+uXz12XPxY+ZM3yyfoPHzFkQVtaAd0nUteTtOyxZvNSWA5WWTJ3skymT6OUfFuMeAggg4A4B/ZzTnMNHq5u3V1NwUBBAAAEEEEAgPQLtChBHVqWwa2fZubtCPliyxgzbNr9SI8qg/r3lgnnTI5ak/25DQ6PkJ5hdS/MkdzeT53XtEjt9utfrlf2Vh6VPr1LxaESQ4lqBhYvsUHBYETSg8fxLHhk9ulE65ecey7QpPpk4wWfygVpSVpa4h6LTWx7vZa29pbm25fQjm5v1X2R6vAeDw8EWbv3YkooKS/pEpWRYuswOBYeD627abAVGQfTvlx09dAcN9MuXTb7hJcusUPB3jAkQR5YtWy35w+PhcdWas3jzFktuuiE2KB75PO4jgAACCOSewJmzffLiy81/k51lllEQQAABBBBAID0CSQeI//H7v5SX3vwoULv8vLxmEz+dOWN8RgPEb36wXO67/xey8vWHmmlt27lXvvyvP5Xdew8Ell983kz5wT/dGgokP/3Cu/IfP3tMGuobpFNBvtx/381y+fmzm22DP9wjsGNndP88kVpz7UMDMwMHNA9o5IqKXlOJDjrlStuC7Zg+zS97nmt+bCdOMAHxHAz6B9vMrXMFtFdwvHLSpJKILodMKop4RSeg7N8v3iMds0xHJlx8YeL30KXLmwcCtJbbtlty+JAlpaWJn9cxrWGvCCCAAALpFDjDjDzR76YrzPwRekF/8kSfDDJp3ygIIIAAAgggkB6BpALE2z/ZJy+/tVjuvOFyue3zlwRSTaSnmqfe6qHD1XL93f8uu/dVmvyMsU36/gO/l2GD+8rTD31P9uw/KDd+9T/lb69+IFdfcrZUVh2R7/3kEfnuvTfKZy6aI39+7i357o8fljlnjJcexd1PvWMezUmB3r38osOcI4uZEyPQuzZyGfedJTDZDFXvVug3PRhtqTkmMmOqX8abvK4UBLJRYNwYn6xZG+5Nq3UsKhIZEOci1WlDfbJsefN19T1r8GBn/ZCOzIUeeUwi01JELuc+AggggEDuCug38SHmc2zI4OxJl5S72rQMAQQQQAABM7o6GYQjZpI6v/lFd+mCWR0WHNb6l5hA7sMP/JP84J9vjWnO4aM1snT1JrnpmgsCqSVGDB0g582ZIq+9szSw7psfrJDiokK55rJ5kmdm9Ln+ynMDbXl74crA49fcfr+s27Q9tN2HHn9BHvjVk6G/uZN7Ajp8LToLyZmzfOb8yb22uqlF+kNjpJl8T/Oy3nmbV6aY/KYJstG4iYW2ZqmApl+4YIF5L/r0fUdHL1x/rTfuOTturF8mmd7wwaLB4Usu8kmRw65xau+w6KKpKcrKw22Lfpy/EUAAAQQQQAABBBBAAAEEkheI7W7bhm2OGT7Y9MjrIivWbpWhg/q24ZmpXVUnyBvQt6ds27kvZsMHDh4JBLEHD+gdemzwgD6yZkNT0Lei8pAM7Ncr9Jhua2C/nrL/gBmba8qmrbua5Vbeb9Y/fKQmtH63LkkRhraT6TueTxOvdinwmIBDUtcJMl31tO+vmwnI/Os/imzYJHLosMjokWJSS+jwNpzSjh+xAx1ir6k9epRELMyhu7YZL6lDJp36HpJDhyJrm3LBOSLnzhXzGSTmQqZe4mjeSziy4jdeL3LphSKVB837VX+Rrl31/cpZ71nTJ5uJMgtE3vtAZH+FyMwZInNmW9I9zZ+zvAYjzyTuI5BZAf06Wtg5T7w+LgRlVp69IdAkoL8J+T3I2YBAxwloLMa2/WZeHGfGlTpOjj2nQyCps9DjsWXCmGHyv799xpzU+uO1eends4fMmjqu+cIM/1Vdczywx4KCTqE9F5iZxmr0F7cp+njkY7qsU+DxOIke9cGoYmmEx4ElXO2mIJUDm5DWKnfrJjJ9alp3wcYTCOjEXH97QWThR/7ABIGnDRP5h6ss6Vme4AkJFpt5J6XCpB0vLW0KOiVYrcMW62vQvPpMkNiZ7yEdBueyHWuO7JLi1jW6zJzr+s/JZYL5yqD/NN1EJl4a+urjNejkM4a6O17g0xc6r0PHH0ka4FCB8PdRhzaAaiOQCwLmhcjnYC4cSOe3IakAcc3xk7Jw6bqAwrf/q/nEcLpwzhkTOjxAXNS9MFC/OjMBXbDo/e6FXQN/6uP1DeHHdGFdXYMUdWt6PPicRLc1J5o/N9F62ba8k7lSpVeMT9Y1Sl1D7LDebKsv9XGPwNvv2vL+h+Gejx9vE3n0cZ/cbtJCtDaUunmLJX973iM1prO/piWfc6ZP5p+dXee5vv46dzJ1dOh7iHvOSFqaywLae5jXYC4fYdqW7QJdzOfg8dpGafSmrgdxbZ0ELjCTGizbjz71ywYB/U14gt+D2XAoqINLBTyF+VJvPgNPmM9CSvIC6R55mHwNs3sLSQWIS0u6y8Lnfp6whZrTt6NLr/KSwNWYT3ZXSO/yHoHq7Ni1X7R3s5ZeZtkne0w3w0+Lzwxx27X3QGB5cBm3CCCQOYH1G2PDwHv2WlJT3TRJV0s1OWE6/z/1jMdc6GlaU3skv/WOLf36+mXUyNT9AG2pHjyOAAIIIIAAApkT0MDwq695ZPlKKzAKYfzpfrnwfK9Jh5e5OrAnBBBAAAEEEEDAqQLhbnrtbIFO8JboX2HXzu3catufVt/QKA0aCTJF7zc2Ns1428NMYDd1wkh59KlX5KT55rh1xx55/b1lsmDutMC655w5RY4ePSZP/f1t8Zox6U88+4bU1tXLvNmTAo/r/55+8d3A85as2igfLF4bWs4dBBBIvUC8H3I66VbnVr6dbN9hhYLDkbXbuDnpt7vIzXEfAQTaKaC5xd9faMtP/zdP/vC4RzZtjr0o1M5N8zQEEHCxwOtv2LJ0uRXoPaxpalavseSFlzq+s4qLDwlNRwABBBBAAAEHCbSrB/Ef/vKq1Bw7IV+87mL539/9NWFzhw3uJ1ddPCfh46l6QCeim3/110Obm7zgNpk+cbQ88tN/CSz77r03ype/9d8y46I7RfsPXjR/hlx+wZmBx7SH8Xe+foP88GePyfcfeFTy8/Pk375xs2jv6GDZtnOvXHHzt6VrlwLp17uc/DBBGG4RSIPAWbN98vE2T6D3T3DzM6b5TG7w4F+nvi0Kv3Sbrdi9m/t6D3tNVg2TKp6CQFYJPPm0R7Z+3BQUrjpkyZatHrn2s145fZz7XqNZdWCoDAIOF1i3PvYDb4MZlaTz38WZKsXhraX6CCCAAAIIIIBAagXaFSBetnqT7K88LJ+/aoE8//qHCWt0xpQxGQkQa5B33duPJKzH8CH95ZUn/q/sraiS7ia3cPfCLs3Wvfby+aaec83jBwMB4OjUGN+86zoZPXyQdDHTq2sAmYIAAukTGDbUL3ff7pUVqywziaQlkyb4ZPhprQ8cDRjgF93Gtu3hXoldzEt+ugkyu6Xs22/Jex/Yoj+MTxvmD+RgHjyo9YZucaKdmReoNnnBg8HhyL0vX2mbAHHTyJ/I5dxHAAEEWivQpatfjp8If/br87qY0UeZmPCytXVkPQQQQAABBBBAIFsF2hXt/O/vfyXUnref/u/Q/Wy/0693WcIqalB4UP/eCR8PTnaXcAUeQACBlAn07q15A9sX0NSfhp+/zisbN1myYZMt/fv5ZcLpPnNxKGXVy+oNHT8h8sjvPSalTlM1dcK+bds9cs+dXiktbZ9pVjeYyiGAAAIIIGAEzprll2f/3jxAPHuWr9UT3KYTUedS+GiJLbt2mQvfE30ybYpPCsmNnE5yto0AAggggAACbRRoV4C4jftw9OqafmLIwL6ObgOVR8BtAvn5Ijo5zfjT3dcjcZPJtRwMDgePu6ZnX7POkrPnECAOmnDbMQKaAmbEcL9JK9E8iDNlknt6+HeMPHtFIPcFpkz2SXm5X5avsM28JCJTzd9DzYiiji5791nym996ArmRtS5vvGXLWvOZfKcZLZUtaaD0e4KOvPrEBLAH6kgsM/qIQZMdfeawfwQQQAABBDIrQIC4BW9NP0FBAAEEnCKQ54n/YziPd3unHMKcr+c1V3nNRFK2LDP/tFe75hgfNTL+eRsPQ9NUHD1qSd8+fsm289rMdRsIsBw5YsmQwX7p0aP17YrXVpYhgEDbBAYN9Muggdl1cXjFyqaJ8yJbUnHACvQm1veJji6ao/nRxzyyrYqfmAAAQABJREFU85PwhbsB/f1y2y1esWPTOnd0ddk/AggggAACCKRJgJBBmmDZbMcK6NftfabHhvaG6Gl6k2gPkk6mVykFgVwX0EBbUZFIdXW4pSZ9eiDNRniJs+7V1Ytoqgwdmjt0iD+Qk1p7iVOcKdDZ5ATVySj1X1uKz6z+t+c9osEWLV1NbvGrrvTKyBEdH2DR+uh5+puHPXKgMhxkufB8n8yeGdtOrXF4LX02BQEEclVA37viFX92vHUFPl8jg8Na1917rECqrrFjsqSS8QBZhgACCCCAAAIpFSBAnFJONpYtAi+/YsuHH4W7PZT2ELnr9kYpMIEyinsEtKfhx9tsKejUNHGdBqZyveg5fvutjbJ0mS3rNtgycrjPTNDnl+5maL8Tiw57jQy6LVospneYX754s+nZRITNiYe03XXWieyCwWHdyImTIk8/65H7vmbe2zu1e7Mpe+Ii85kTGRzWDb/+ph3IN6rBbC06QZ9+Nu3ebclkk1ZjlgkeF5sLOhQEEMhdgUkT/WbUhEhkQLjMTIsy0HyWZUM5YHozxyv6fkaAOJ4MyxBAAAEEEMhNAQLEuXlcXd2qI0dFFi0OB4cV49BhCUwOMvesBN04XC2Wm43ftNmSx//sCf0g0160t9/mlfKy9P4g0/NvhQlk7TIBIM2DPG6sL+O91zXP6znzfIF/Tj+6a9fFBt00R+JWk8M2W3qOOt3YKfWPzlus9T5pgsR7TE+3YVmQZ1Rf89FFL3DoaJbTTD7PHTst+cPj4fekhYvsQC7mu80EktmShzS6/vyNAALJC2hO35u+4BV9zevn12QzSZ1eHDLzY2dFSZTmItHyrKg0lUAAAQQQQACBlAsQIE45KRvsaIH9FVYoKBhZl337Y3+8Rz7O/dwR0F46L74cDsRoy2rrmnrzXXdN/NyEOrHbUZM3tGcvf7uDNYcPW/LzX3mk3gw116K9BbXH4y03esXi9GtCaeP/D1bFf0LVIQVNb7A//p5Z2lECJcV6vGNfSCXFHVWj5vvVnMiaCiWyaP7O3r2bztOVq+2Yz6bKg1agN/HgQZzLkW7cRyDXBPQi1rCh3sCnVvN3iY5vqY7KmWFGGi1eGq7ZtCn+QB71jq8dNUAAAQQQQACBTAkQIM6UNPvJmED/vv7ApBrROd+0BwfFHQInTogcPhLbVu1pGF30rHjtdTvQs0fPGU3FcOVlXhkxvO3ny5JlVig4HNyP9hrUXH6cf0GRtt1qz8t332/+HA22DzO5iCnuEtAed5pmos5c7AkW7aWvE91lQ9H6rVxly9GI/N+aZ7lbYVPtEqVEYRKobDh61AGBzAjEfgvJzH5b2sulF3tNvnRL9u4XMwGoSFmWvK+2VG8eRwABBBBAAIHUCRAgTp0lW8oSAQ3wzT/bJ2+8FU4z0c8EjadPdUd6iVrTE1Yn5/OajrLDTHCtsGuWHJgMVqOraXNJiciRqCBxv36xgaR16y15f2H4XKkxeYufesbkNf1qo7Q1Z/GJk/F/+mnAmtI+AZ2UbtYZvlBOcQ0O6+s72CuzfVvlWU4U0J7CX7mrUdaut6XS5MYcPcoXmLAwW9qieYbvubtRtm2zTFojk1bC9BjsbXoVB8uUyT5ZtqL5yAbtddw/zvtS8DncIoAAApkS0IttpaWZ2hv7QQABBBBAAIFsEyBAnG1HhPqkRODsOT4ZP87kfPzECvSCGGB6D7shx2OFmWjkod95Qj3sPCa/nea9c1seOQ0iXnS+V/70VDgY08lMYnXeObEXCTZtCgeHgyefBtl1Ru9RI8PBneBjp7odN8b0cDQBoMiiAfoh9HaNJGnz/Ysu8MnsWT7RiXQ0oNatW5s30aYnaIqQVSYdgPYI1x/LM6b5siLHbZsakaMrF5kJ3WabnrrZWnSyvDGj9X0j9r1jQH+/fOmL3kA+fB1VoHlIp07xBUa8ZGt7qBcCCCCAAAIIIIAAAgi4Q4AAsTuOsytb2dQTIvZHei5jvGpSJUQOv9ZexC++bMvdd8TPu5vLFhqk0V7AWz62pXOBPzBJVBfTwy+6FBXpORLb81cDUW0tmpbikot88vY7thw3vYa1x/LFJripQSNKcgLF5ngUB45VcttpzbOfftYjGzY2nRP7K0TWb/CI5q5mNvfW6LHOqQQ0SDygv/vej09lwmMIIIAAAggggAACCCDQ8QIEiDv+GFADBFImsGt3bKBTJ+1raBTJd+GrvdgMSZ9meuidqsyY7pMlS+3AJHbB9UaO8EufiKHhweWtuT3DbG+aSWdy/JhIe4LMrdkH66RP4NhxCQWHI/eyZJltAsQE9iJNuI8AAggggAACCCCAAAIIIJAbAi4MGeXGgaMVCMQT0FzLmn84svQs97syOBxpcKr72jP1HpPXdM06W/bus2TsaJ+MNKklmiueaguxj2k6E4LDsS5OWOJLEAOOnvTSCW2hjghEC9SZ9Ck7d1rSaC4aDjU5krt0jl6DvxFAAAEEEEAAAQQQQMCNAgSI3XjUaXPOCiw41xfIQaypJbRoLt4Lzz91D9qmNd39fw3mnmly3FKyR0BTdKw1QXvNBa09useYwH0mUnXouXCamdzxYzPRWGSZPNFd6Woi28793BCoPNiUo/7kyab25Oc35agfNJBzOzeOMK1AAAEEEEAAAQQQQKD9AgSI22/HMxHIOoH+JuftN77WKFtN3t1GEyQecZpPNM0CBQEnCegkgb/8dZ5UVzfVeu06y+RztuR2M8GXHTunYMqbds1VXlm02ExSZ1KPlJT4ZZaZFG386QTRUg7NBjMq8MprtgSDw7rjhoamHPV3filBt/mM1o6dIYAAAukRqK7R1FG2HD4sgcmHBw/2i938GnC7dlxbJ6KT2hZ1b9fTeRICCCCAAAJZJ0CAOOsOCRVCIDmBbt1EJk2kN2xyijy7IwVWrrZDweFgPfbutcyFj6bexMFl6brt2lXknHk+mX+2L9ALP137YbsIZFLgk12xERFNq+PWHPWZtGdfTQIaTNP396XLLCktFZkxzSe9JjtPR1O1bDOjTLRX/jCTqkUvzuuILUr2CRw+bMnPf+UJBHK1dgsXicyc4ZOLL2z/92R9z3zVXHBbutwWHbE3/DS/XHaxT3r04EJy9p0B1AgBBBBAoC0CBIjbosW6CCCAAAJpFwj2HI7eUXWN/gKP/wNMAw+r19qycpUlAwf4ZfpUvwlAxF83eruJ/uYHfyIZljtRoE9vv+ww+YcjS1mZOycwjTTgfuYE/vJXj2zc1HQO7q8QWb/BI53z/DJoSObqkOyetNfoL3+VJ4ePhLekge5LTYCQEl9AP4n3mYtReuwLC0XGjPJlbJ6G9xdaoeBwsHaLzeigs+f4AnUJLmvL7Xvv2/LRkvBwJr14/fSzttx2C6Mx2uLIuggggAAC2ScQ/nTLvrpRIwQQQAABFwqMMpMERhePR3vpxP8B7jer//6PHnnueVu0l+QHH9ryvw965EBl82BY9Db5O7cE9KzR3NF//ovH/Fj3BPJX51YLk2uN5qiPTtFywXkENJJT5dmtFagxw/yDweHI57z7Yfz39ch1sum+ph6KDA5r3ZYs0/QFfN4kOk4LzWfyg7/xyNvv2vLCS7b8f/+TJxUHMuNVdSh2Pzrp7KEkjtf6DbHb1O8ex44nEmA5AggggAACzhCgB7EzjhO1RCApgRNmUqLOBRITHEhqo214cqUJ1C0xQ0oPVlkyZZLPTDjmFw34URCIJzB4kF/OP88nb71jB/KkdukicslFXilJkE97j0k/ET18vtEMAV2y1DLPiw02x9sny5wvoL26Xn8zfN171WqPXH6pT6ZNcVYAKl1HQnvW3/fVRtm81eSoN6+PUWbyR4ZEp0ub7UYLaFAuXvE57BrF/orY4KBepNQe0T16xGth7izT73D6ubp9hxXIyz91sk80JdOpis6HoYHhyKJpGfTz/bpr0n/wddLZbdubH7NCU+c+fdr/3UB7QUtlZItE8swv6gLzPftURXufa0/qMjO6SSfEpSCAAAIIIJBtAgSIs+2IUB8EUiiggdlXXrdl8xZLupkvtHPO8snMM0xe1VPsQ7/A7t5tSbH58lre0+TVO8W6rXlIA3cPP+IR/QGlZevHHjl9nF+u/Wz6fxg07ZH/t0ZALyJs3mybXrdN+fSG6CQuzX/TtWYzKVvnrNk+mW6G7R4yvX96lvsDP74SbVzTS8Qr9Q3Jnr3xtsqybBTQtxftpRZddBkB4rCKBiXwCHtwL3MCOmGu5uuNDtbNmh77us1crdq+p0ED/bJmbfPPFr3gPaB/+wOOba9F5p+hn8W//LUncNFW966Bck3pdNcdXsk7xQX/mmpL6sz3yuhy8GD0kvT8rfmG9ZzT0SVaNIh75eVeyU/iF7B+P9m+o3mjz5juO+U2V6y05e8vNl2c03pMmWRSq5hzae36prRY2nmCSaVVhoIAAggg0JECSXw8dmS12TcCCLQk4DW9dR5/0iNVVU1r6tC3l16xTc41v0w4Pf4PGR02p0OzdXZ7Ldrz4nPXJfdF+sOP7FBwuGmrImvXWXLhAjPzMz0ogiQdenv8hMjPf5kXGh75/kIz0eEEv1x1ZccG8Qs6ifRtRS+fgeZHlv6wOnq0OeP4cQm6rDVfLaf/0mHP2ntffyBr6g69UKPDvIcO8ZsJovxSVhb/vcBpKNounTgqupysjV7C3wgg0FEC15gLw4vMd4KlJiWD9l6fZS5Yz5hq8vke66gatX2/2mt29RpLdpkL6cGik5p27x78Kzdvl680E1p++t0w2EKdpG+7+WwZMTzx50hxiY5UEJOCI/isplv9DMpEyc8XufELXjlo6qppTjSQ38l8t0imaHvvvt0ranLsuGW+L/lk+CkMdL+RwWHdtz5X/2nRHMYLF9ny5TsapaQksIj/IYAAAggg0CECBIg7hJ2dIpB+AR3GFgwOR+5t7TrbBIhjA3/aC/Nvz4eDw/oc7XGx2EzEceas9gfaahMEaGprLRMgzswPhMj2cz9W4KPFdig4HHx05WpLzp7bNBQyuCxbb7Un0A3Xe+WNt+1A8LO0VGSemYBGZxZ3c9E8mZGzt+817wnBor2/li4X+fKdXinNgZnXbdO0sWNie/adPrb9711Bq2y+1TM8fFTbX1MdQbDLjPbQQE7PFIwcaX9NeGYuC+jQ/nPn+0QDqsFJQIO3Tmm3phK49WavaGoj7VU7aJBPeuRwUO+EuYD8sQkC79sf/51G09Wcquh78xWXeuXxP3tCk8X1Mu8x887O3Huz1lxHIvUsP1VN2/aYpqi4+MLWfcfQyUFbctJe1prLWnPFUxBAAAEEEOgoAQLEHSXPfhFIs0CXzvF3oLmI4xX98n/SBAmiiwaJz5wVvbT1f2s6ieDQvuCz+vT2B9JXBP/mtmMFNK9gvKJDQMtMsNUJpVcvv1x/rTcwlLWTOcfjt8gJLUldHXVIa6L0G7oX7Q22wvRgOnd+637kpq5m6dnSxRd6pVO+R1aYYc+aHkVTKWgwKhdLdbWIjs5Ybo5x/35NPTFP1YvvVAYrV9nm4qAtmhdUi15Y0ZEjpxo23rQm/0egfQJOCwpHt1LfXzSnt/7L5aIXFTVFWHTP4WCbu3czaUPMSLOWiqYW+ea9jYG5AnROgX59OzaFVUv1TfXjrc1NfeIk31xSbc/2EEAAAQTaJkCAuG1erI2AYwR06LgOKd+0OfyFU3/UaA7ieKXUTJqhP9qCuYKD65QnOQRd86pp4Pn9hXbgdvSopgnItFcJJSyg7gfMrN56jFKR+zm85Zbv6VBPTfsRWXRY5gAH/vhtaZKYyDbGu6/DhhebWeo1XYUGGMeONfmPm6cajPe0rFyW6Ed9ZGVb6tUUuW6239feiVdc5pULzjcTcprTOdlhxK1pb2QKj/EmdY++32kAJJ1FJ/v6w+MeqTDvF1p0eLLmdr/5Bm8gx2tb9q3vzc+/FA4OB7e3bLktmlMzumhP4xMnzMgC87nQ/B0jek3+drNAtRlSr5OXOfW9M5eOnY7iWmpez/qa1u95M0xuf/1u2JryymtNE8VGrqvvq/q5cbr5bJxrRupoKqjWFP1sbu9FrNZsP5vX6W/SWow0k4LqfCDBEu/79rgxse+5wfW5RQABBBBAIBMCBIgzocw+EOgggX+42iuaV1h7mWmv3WlTTc/dBAFf7Qky3Ty+2MxQHSz6hT6Z9BK6Hf0SrBN6zJrpk3ozhC7dwZNg3Z10q8NUn3jSDgV8NEee9obNVE7DKSanol5ICP540SD1JRf6RANubiobNupxCEeDd+z0yMSP/fLZDs7F3N5joAHLhYtiL/oEt6fHecL41gUKgs9xwm2iURKprnu8FB76XnvX7Y1pDYxpr75gcDiyTStMT+BhQ2PTB0WuE31/9x4rbi9zzVl9xvTw2o1msy+9bHLHmiCTXszSz5PPXOFrVY7w8Fay656e+Rrg79zZL13THNTPrpanrzaaduFFc57ohTb9rJ9rJsadbVJUhb9VpG/fbDm+wJNPewIXkfTRKvNdY8tWT2CSYB3d1VLR4xlddFTKd7/VKHoRmdI6AVW87hqvbDUj8rZstUwPapHu3fzypkmLpe/nOuLvtE9TYjWY4HsyE+i1rkashQACCCCAQHwBAsTxXViKQE4IaK48DQBNGN+6oIEO0R431goEC8vKRMaM8kk3EzhORfGYYBTB4fiSOnlJZMBHgzavvOaRq69q3XGLv9XWL9Vj83mTw/dgpcmpaCaS0WGz2vurLUUngNlgJj/TCwLaSzzRhYi2bDPT635k8m1HF52M6OILzbmbIGVL9PrZ9LcO4/3cP3jlnffsQL5M7SnuMfFvTfmis6dr8KY1kwBmU5uyqS7xUnhUVopsM77aWyxdRV9j8YoG/OMVrcl+k0JIj7vmm9bJR4M97ctNXs54Pdk0X2dk0YnFND9msGgO66dM4OmeuxsDvbWDy51yW2ne6/76nC36XqvtnzJZ84kmNyGrU9qernpqYEsvsGn6Ey3aO117oPYwk5RpfnBK5gW0J7eOMIgueiHr9HEtf7/QzxDNnxtZNPcuweFIkdbd1+/jo03Pbf0XLCNGeOVV8xr54EM7MIpr7TpPIK3XHV9qlExd6AzWhVsEEEAAAQRUgAAx5wECCIQENMCgQST9R2mbgPaq0QDp5i22DB7kN4H21vXA9ZoRhdpbL7poL5NMFt2bTk7Vs2fb9xrd8/bV10W+YALO6QyStb2WLT8jXkoG7S3p5DQMOpR41EhvIIdk8Ee95prVQDElOYF454tuUQNl6Sx9TdBG8w5H9u7TIOdUMxIgXnnjTVvefT8c3NWRCXff0RgYIaCTa02Z5JdlK8LvNzpyIDoVUWSqouA+DlaZHonmwpC+bzipaG3/8lc7NOmWvsaXLbekuMiWeXPjGzqpfR1V192m13AwOBxZh3UbbBMgbjkYGfkc7meHwPnn+eS3j3pCn4H6HfHCBbxGUnV0jh8XWWQmCY4sVYfMMnNBjveiSBXuI4AAAghkSoAAcaak2Q8CCOSsgOYEfeQPnkBvNG3kmrWWvGt6bd5zl+kF0kLPU/3BpQGb6B/WxabXlVPKG281/4Gj9X7dLBtpesc4qWhvex0aHVl0ch1Nv+L0EgwOazvcHBzWV9V2c0FGUyVoUPUCM0Ff9x7tm9QwXgqPwkIzVLgVkzYlcz5pfuUbPu8N5BTVXsz9TLBY8wXHmzBLAxDaOy2y1JhehQvNsgXnNgV6LrvEa0aZNA197lku5oKCL2YEQbyRJOpXaIZJO60cPSKh4HBk3TeaC3zz5kYu4X5bBBKNEHLi6Iu2tDub1y0y3y0072/0BWfNld6aoumuvvG1xkBqBJ/PkuHDfBlLfdWa+jl9HR3ZEZwcNLItkRf/IpdzHwEEEEAAgXQLECBOtzDbRwCBnBfQHsA6VDmy6NBOzQk6K8GkgMF19VnaU+S555sHceaZyV+cUDQ8dLCqedu13ppywmllupm8R3sTavDs+AntWekLTMLjtHZQ38QCemx12HuwrFnbaFIL2DJzRttfb9EpPDS1ynzzWs7E0GDNmTvnTF/gX7At8W4PmFQK8QIQmiIiWPQiVUsjRzSP/IaNHtGLYcGikzg6MXdvF9NDWi+SRLvEC4IH28ptywK9TV7q4SaPamRKAx1W357XVst7Y43WClxjUlW1d5I63Yde9JoYyFXvvItBrTVK1Xr6vcFnrou3dv4ITdcR771IR4hQEEAAAQQQ6AgBAsQdoc4+EUAgLQInzJdzHfZdXJyWzSfc6NHqcLAlcqXoXsGRj0Xen2oCLTrp09p1lugPap0dXH84OKFoy4cM9sekyXBimhLtmanBDO2NqcEjPRaU3BJYuCgcHA62TIPG7Q1ixUvhEdxuNtwG84VGp8OI19v4VPXVgMU9d3ll1WorMLHb+NN9Mtz0THRiKegkMsNcDPrQDOMOFu0NrUFwSvsF9LPgepPzXCfGXb3G9Gw3qVA0fUkPk/ea0nECOopJz23O7/Qdg5O1Ii++5JHVZvSYXmTWzwUdmaE9uE9Vupng+9mmM4BOVhcsZaWxKX6Cj3GLAAIIIIBAugX4+ZtuYbaPAAJpE9Agnk6gov+0B6/25NUv55oW4IpLfRn7YarDLj1mprfoHmmtzcGrP6x1KKf+c2K5+EJfIMXGsWNNtdfeMxed79xgiwaLUhUc1osWW7baoj3Kdaiv9rLT403JvIC+uurqYvdba37cR5Y6k0981WpbVq5qmsxv+jS/mTjo1K/NyBQekdvq6Ps6vF/ziL7wUjgA0buXv8WRDfHqrRNPnmtScuRCucDkUdXernqci4r8MmmiX3o5LJdyNh6HfPOrQnubTmzlxLjZ2Abq5EwBnQci31z86YjP19dNnvdVZkLbYNGc7ZblCUwSG1yW6FZHkI0zkzhu22EFPmcGmwvu+jrKdNHRIftMygudtFjzyuv3IAoCCCCAgPsELL8p7mt2/BZ7TXTHNmMtrTZ8Ku6tMtM0O7CUFRVIQb4tVdV1Utfg3ECOA+mpcooE9J3r93/0yMfb4n+L1R6st9yYuRy4q82Pg+dNDxINNmlwUb/0zz0r8WvLY7qrlhcXSMXhqOhUinwyvRmdmEsnKdKivRPbGmDVD6JDJlVF5y7+wORZgQ05/H9HTK7TX/wqT2ojgpKa91VTA+RK0eOmFwZ0eH78V2J2tfTZ5zyyfGXzmk6b4pfLL216r/CZBj3023A+ca29nst3fcnruMnYIuWPHNWLabaUFPsDr08356GOdOF+k0C9GXmjubmrDlmin53a87z5qyS+1FFzXi1ZZsu69Zpz3md6ZpuLKeZCQltKr5LOcqimThq9bXteW/bR2nX19a+fY5omQ9sx0lzUS5RbubXbZL30Chw4YMmb79gmBY4VuMhztvnupaOwMll+/JM8OWbyvUcW/Sn5f77dGAi4Ri7Pxvve+gJ58LdeqTjQVDvNa/85MxqgpR7Q2dgW6oSAEwVKCvOl3nwGnqhN8yzHTsRpR537lZk8bJR2C3TANcp21zWtTzxSfUyuvOU78m/fuFnmzZ6U1n2xcQQQSF5AA8OJgsO69e2mN4YGa1uaJC75mjRtQSc4GzOmUSpNzs9S09swE3lIU1X3VGxHe7y0N61EhfmB98yzdqD3iv6ommyGJV9ykbdDetGkwiK4jXffNxcMIoLDuvwdM3mhprDoZHo6Ob3oxEevvm6bH5XmYkeZ9lT1iubhzeZywfnmvDIXR5etaOpRO3e2SS8xy3Q9+7RocCg6n3ij+b6+ZJllchWnvm0akNOLS3o7flxT2gbtwZXqUmLS7kyakDsXJpLx0bzMal5dY8kEkypjmJlUUNPLuLXoe9SDv86TQ4fDApqOQHuen6ro83792zzRSQ+1aMqO5StFvmImZy0qalrmtP+/9LItHy0JvwA1/+49dzYG8vA6rS1uqK+mzvn9457QJL/6WfTkXzxSaDoHtPf7SHvcCgv9JkDc/E1ELyxofncnlD8/7QsFh7W+e/daJle/RzR/NQUBBBBAwF0CBIjN8f7mvz8ob7y3TOpMFwo6VLvrBUBrnSugPwROVTQAp8MNM1k0SKp5FymtF9Ce4H95pinIqM/Sv5evsExPRzvQC7v1W+rYNbXeOvO4pjnRofinmeHr+yti66TDYA8dtgI5p2Mfdc4S7R39xJMe0eCploNVIn96yhPIU6vtz9aiKRcuucgn2pNbu0gO7tNF9pm6B0t0rt7w8lO/3wTXa8utzmD/q4c9odQ0q9d4ZKrpzXzFp72Z27KtVK9bZXrz7/ikacizjghIR49jHXWwc6dlAozaa9UnJSWpbkXs9jQd0e9+7wm8z+ijK1d5ArlZWwqGxm4pd5Z8tNhuFhzWlmmubs3LfapArw6jDwaHgxqawmX1WtuR+W6PVossXto8onfc9ArVwPd555w6WB5sP7eZFdDXc7y5HtaYc3DokMwFN88yo4Ke/quZ+TKi6EWW1H9qROwgRXe95tTetDX2M1svAFMQQAABBNwnQIDYHPN//vL18rXbPiuX3/ztmDPgmtvvD/QqHjdqaOCxhx5/wfQ6OS733XFtzLosQACBzAkMGhj7hTZy77NnmrzAzX/rRT7M/SwROHzECvRAja7ORhN8mDc3emn2/v3SK7YsMoGWYNGJEkeN9MX0Ru3aVQIB5OB6Tr3duNkOBYeDbdAchjpB1dyzTv3aDK7fkbeJenAPGuQPTHKpQ+cji07KluryvpkcLzpvuV4cOXe+Sdlhei52VHn7XbvZpEmabuBLt2jP69TVSHNzP/hQnuiFhqZimzQfPplmJuxMZ3nvAzsUHA7uR1+38842vfqj2qcXC1aaHMVLllqBNAM6sd1YkyvUyrG4ieYdjS7BfKSanzlR8flin6fr6nOdWHT0j17oiy7a45ySnQKJ3sc7dYpzINPYBM27XVbqlRUmb31DvWVGQfkCk/emcZdt3rQG0/UCiF6k1vcynRNB38u0l7OOMDkcei9u2nSPkswatrlBPAEBBBBAIC0CBIgNa3mp+WQ0Jd6X/k1bd8nxE+EcofsrD5kP0U/H0wWexf8QQKAjBLRXm+YOXbo8/OOtzAxzH2Byp02aaIYNm4nqsr3oD2ntdapBEg146+RubiuFXc3wbvMDJTqo0N3ktHVK0WBi5LBkrbcuKzA92PWcrPq0h6q281KTOqOt+Zmz0SFR6han5+vUUQA3fM4bCJBqTsue5X7RnJbpeD+J7n2px1kDVMdMj9puZshyRxStkwaII4v2dNY8s3rRLVVFA7Xh4HDTVl95zZYJ42MDtanap24nnrn2gj9pAtadmr4Khnb3wssmX7UJ2AfL9h0euegCX7sm+AtuIxtvdXJUvbATWbTHuOYhPVXRC2BduthyMmIqDn1vGz/u1M871TY78rH+pr16ESR6FMGQwc5sT0daZmrfA833Jj1u+j0qWPQc1O+GmS7ZPNHw0uW2PPd8+H198xZPYI4M7RmvchctsOXxp5q/v+s8GhQEEEAAAfcJECBO8pj3LXVmEuxgMLy0e0GSAjwdgY4TuONGkcsv8MvOXWbipf6W9OujM0d3XH3asmf9Efr//v/27gTOifp8/PiTZGFZ7vsSOURAEBFQFDkUREU8EG/R1qNWxWrr1V/t+e+vrf607b+H1dZbW+VXr3qVioqAigoiCIiCggqIist9X7ub5Pc8E2Y3197JJpN8vi8x2Uky8533ZJLJM88833vK5PPVkcsSrd8XnxeQMSMrDuJrMz8vP/fE0UGZ+WbFjxGzmDShkXRp28A1QuqIuLE4rIG9A7UWouaxe1eB3PbTgJadCMv2nTrgUW+/tGoZl6YY9Xwv3W17nMict8q0tETFD/HWrXxy0qhCaeqhr0X7uIj/Hu/SVuTIfiJ2ubxlqKXrM2WYZpmtXhN7GXSHdloXt1+TtJR0qMn7a8O6sJ6sSXwvb9pQoE6xl1DXZH6VPWf9eltGxXvHnmfe4f1NpEun9H2IH3VkUF6aUfFZY8vt3s0n/XoWxWznMt0sHyzVD+m4tnRpQM6ZkPrjJtuP3v8grKUewjJkkA741ttXr/qlX3+j81sSlpJSPZE62C89Dq78u3HiKZr5v7xMvtJt77YzTw1I3+7Vf1bd+oOwzHg9JAuX6MBg/f0yfqxfDulZ/evc5dit7V8ddKC6uLdD9FMa7P7kc0Py+FPB8kziQ3v5ZOLJhSnNnm+wlcmTBd36A5F3F4Zk3gKt4a7by46hOnao3Xsw16kWLkz8vF2g2cTfOrfQ2f+6jBTp1d0vCxeHpEA/5o8e4tdkCwxz/X3B+mWPgH0P6gWG0qop+132bJX87QkB4npu++KtUakT9ZxXQ77cAsOFOlDP1l37ZX9p7I+lhuwHy0KgvgI+DeD07B2Zy/q4S+TqO+90vv5drWv4+eqKYLBlDj79QlC699wvVoYgU22T1h61zEnL6O1/WNgZFTzdfRl9vGaraZDmgw8tgBqWIweFpXnrMinemnzJVrvUBhMr1DiNXf6e6QGmmuqATIFAQUK5gI6dNIC6o0Raahax/durQae9laxT8jXN7qlXXWk1XP3yyQqf9O2jgS0NeO7YV6r/srvf0b3rrCd5q/we18zSdLUBA/WH+JcBeV+vgrD937KVz5kUlI07EgOT6epD/HybaOa+z1dQHiBzH2/TzvbHisH83Ol1vW3f3i+frar4/LP5OMH4wn2V7vd1XVb064YerZ8d3wS0Tm7EvIt+fkw8Myjrt8WaW4A4HE48RC4tC1X9foleWA3vF6/3yX0PVtSinv1WSEZrTdO61kW22sBTn6gI5r86O6R1rbV8x1GVH+t99zsia7/0yRYdLLGHZsy2d7Z39SsQ0LjuhAkip55acSKlss/tyubWoVUT51i0TEdwz3TroyeGfnSz1sZWizat9eSRvj8274p9b2S6j9Ut3xStXIZly3fTK63sSpZcb/0P1+MV/WfN3uW1fQ86L/Tw/5w9R/9nQaZkbfeexM+y/fpxvm7LXqcUm/0mbNW6VEaNrviMyDfDZG5MQ6ChBCwwXKrfgXv2J56gb6g+5NJy4hM/cmndGmJdEr8xGmKpObQM+1Hn5Wb99/o6eNmfvuevwOovEo/kLav4q699Tm24TMjYoCSP64jgbps5W+SCc4MyMM2XDNuPmkN1ULdDe1dkU1b2uWQ+/6vBj90HAnedO4Xl0m8FM1qztZkG9O1STbtE3m122eswDchUth7u87x8awO+HXesXXJfsRZeXN9M9dlKjkw8PSgnjRXZvVsHN9QAse0LmeqPbUUblMy2qQ1S5ra2mlFtNStT2a/Ro0Ly4TK/rre7FJGxB+oAp3I5FXOP3LMSIueeHZTxJ4vs26eD8Jm5PhS/TKtfP0jrii7RmqLRbegQu1ogekr97895O7EWtfmbUZM6JCvPfrNi27m9e13LhgzV+s6xa+M+GqlDaqUU3HIKdVnHurzG7YG9tj6vd+dTm1tbno5NnRBAbab1vwfoyVG3NXS/3OXW5dbWx74fV6+JbGk76XL+OUGth1+xPnWZL6/JTgErj2PlfyzhIKSb2AaWtM/q+HrxVrrHyvpEt8MHRE6uu+9ve4e496Ofl+z+xk1adkhrs1tpjyGDw2kvDZSsD0xDwE5yrNHPOkto6dkzLHZM6uVWm33Qy+tJ37NfgACxbqMyTRUJHfhWLNVv2xJNT2tsvyIOtGenz5G2bVrK1u075Z33PpIBfXu4D3GLAAINIGC7p/3gsQGDippEMky7drGvUu+2Th0S6z5acKijTs9Um/VG7A8I68es1wMaIK75Ge19epn4Rxr4sWBzLz1gs8G9LICaimYyL07ToFJUVqdl31kfzzqjIricimXVdh4jjwvJ4f1D8sVav7RpExarR2gBQBoC1QnYFQNNtRZ3trTxp4ScLP5VqyM1mG0/jg841LevVmP8huvLZNUqn2zf4ZNDDwlLhwb87Guuy2/evGrzM04LStcufh3YyeeUTTlmmJZRSMPJsq1JriqwwQt3qkuTOphs25oYBt6xQ5wrHOzy8XxvttUX6uX1FjDboVm2AzVQduLYkB7nV/1+8ILbPD2x4AaHrb82INkL0wJyyw1lOVH73gvboCH7OFuP2d6eW3GgYSeprbb76RMqMoGtP1ZP2GozWyDZro6wwUDrWmPYBrW8/6FA+bgRX+rVXEs/9MkVlwUl8ZOnITVSuyw7lv1Mj2PXb/A5YxD00AFsOaZLrXF95maDhz78aED2HrhizU6GXabJIjY+DQ0BBOonUBEFrd98PP3qy2+8UxZ/9KmzDjf98q/O7Vsv3C1tW0dGjFr1xTo56/Kf6Q+EQunaqb1m+OTSV6CnNx2dzxOBN9/yO4NGRVbXJ/Pma73eC4NyWD9vHAhYL+0st40gbcFuy/AYMTwsi5boYGb6w91tNr1V3EBJ7mMNcbtJs0Li2xYNXtjZ+ZocGNsPDztgswNqa1aqwn68XD+lTCob1Cx+eVX9bdmG7ryjn2dBpmxorfWS5NatY3+YZUO/8qEP676J7F8WCLPMbcuYq+49a/vlxyvC8vLsgFPz115nWSjZ8W6q3VazgKINNGaDEdlJpmFH1708jK2/XVpv/9LZLDt2QH9bRnqXU9d1aKylAC0jb/gxlc/BPhvtJJUF0J0M8MqfWukjdvWEXRkR3ay8Qbt2dXOx+VkJjehmQX6CwxGRD9Vm2vSKoJpZrd/gl2uvCWa8XFH0Nou+bydFFy2OlPOx456hQ5KfeLWTOvHNvjftPWonLXO52dpt0GOPNV/o1Ri671hALxcGhK1qm1mJp/hmiRSnaYA4+p1gn092hcbxelWCZRpH5UDFv7zav+041j73opuZr9fAsZX8yoVmweG/3Vcg23SwYWtvvqXjEugVJXb1SXzbuUuc0jz2fWlBSlrDCNjJEDc4bEu0k2EvveKXKd9N3EYN0yOWgkDuCBAg1m059Z6fVblF/+vai+SwQ7tr5qINVAFZlVg8iECKBexA9J15iQfBlv1zWD9vHAhYVs8rMyrWYfnHAb3EOST/fWuBzH1/vw705ZPemkFn9QIz2Q7pFXbqyUb3oTZZEyu19mV8ANcCdlZb+FjNvqtvs8HP7N+euNLv8YEU64Nd/mgZBkfpJeGHD4hkz9R3+bw+OwXsRMQTT1ekRn72eUADpCE587Sq33Nv6aX9M2dbdnzkp/RHywIyYbyVzKj6ddmo8MxzASdAbH2zDML3Fop8RzO6bP/N5mYnlZYvjwS2rQazBbatbIwXmn2+PPFUQDZvifTWMpcm64nL5lqioDZt1IiQEyD+7PPI+9Bef/ZZGqys+MqozezklJOD+p0SEDtpYq1dW6l2X6jVAjz+5KX6fRTf7DvDgovZ+N7bpxly995f4GQ7W78tY3P+e3ri9drEE6+2rVeviV07ex/lQnZ07Fol/jVzViQr3H3ETpRdrcEiO9GTq81f8bVXvopWHic6OFz+gN4J6POTvCT6KdXet0BcsmblTXKl2QB+bnDYXacPNEv6+FF6Vc2BqzrsW2r6y3aFSSTxw4LDE/VKtkEDvfH95a6XV2+tZn58W6clT+yYgpOh8TL8jUDtBIh21tCrZYtaHvHXcL48DQEEqhawurw2un1827kr8eAg/jnZ8rcFiOPbXA16n3OaaAkGO5jMjgPKUzVoXVwcKD8wtsuw4y9VjF+P6L93aR3VZG2XZlikotkP3ROOD8nLr1Z42gUdlhnjti/W+uSRfwTKa+lZZssyzay0jHNabgrM08tm49v7mkk7/qRQlRk9yV5n07wWILarECx7OLrZlQq2Lj26Z/f7/tnnA7JMA8TWbF9duEjk8m8HxTJes739+z/+8uCw9dUCd7M0G/0sHfSuNs0CC5deEtR5+ZyazF21fnl9fuC21IvfrtHAmAU87ceyZbbVNdhcm/XwynNtcNNkLVuz/z5aHimFEd1nK43xmr7XLFO2t57Y7dgxsr+M0sENl34UcLLp3OdbFnwmB751+5HO2+2a6RldasGWZSdwLNBn5Z9ytdlVZzNnx37/HXxwWJ58JuCcHLQSX7U9YVWd1SCd54f6HotudmLCxl2wZv//WL+P5i+wIKvPuaLHSlqk4ioyZwF1/J8NwPmpnoRrWqT7jF5lYckGlTV77yRrGzbqYLIdIo/YiU1bR7dZ4PxFLedySK+ylJu7y+C2QsBO5tnxfnSzk8z1+e6Mnhf3EchnAQLE1Wz9n9/4bel5cJdqnsXDCCCQLgH7MWfBgui6eras/v28c9BvAynEt2TT4p/T0H+3bRt26oLa5c6WuW0ZcbW5RLPvoSHNUIkdcMkCuKksBTJcszu76w8gyxot0gP8/jqYUHR21LuaWXWgpHw53ycrfE5dPisBQcs9gWQnkOz9ayeXKgv62I/YZJlQJUlORmW7WMn+2B9Jbn+TrZ/7WDbcWpDLDQ67/bF91/bhXj1rF2R1X99Qt/aeS5bBZLXX69ra6eevBVpS0exzt5NHMrFTsb61mYddzWJXC0R/T1hJmujvkdrML93PrWw/tqtk3FzRcVpD+YTRkTrKN1xXpqVz/GK1rW29umf5VQSp8LNs+ejt6c7TzaJ3/46/3atXI1kA/nMtU2VXcQ3Uq43suMIrzU4I2EkCG6jOTgbt1JOFK/RKLmt20vBtvdLOMs1TuU72njpnUkjmaOk3K0Fmg93ZSXrLTrZmZS+ef7EicDpjpt8J5F1yUeY+061Pz0X1yX5XTLkqqJ+3kaB2pOcV/+/VIywfLI39LLf1s+C72z79vGId3Wl2zGEnOq2uOS29AiePCznJIHas57ZTNCmAhgAC9RcgQFyN4QUTx1bzDB5GAIF0C0w6MyTP/9vvHHjZD18bKGjsGO8cCFjGhV2GFt2y9TI0Owiu62XpFoC1Gm3TXwmIZQ1bxogdxLmZJdHrX9f7dshu86tsnpX9mC4psVdy0F5X92x+nWXh28A50c3KpTSr4sIfe/YR+jmyaEns6yzjymvNat9a7cfiOANbv2xukX0ysYeV7cOJz8zclEaa9WuDb0YPmGm9ad06u80zJ5Y9S7YTjNdpvWH7TraMwKFahsgCg9na+h8WyRLVMbQrbTZOg2WTWiCwhWaQ2/18ajZosR0bxgeJqxrM2IJ5DzxcUH4VgAVU7cqu711TlvJBOdO1Lfy6zlbHfUD/oDMw8OP/jM3stfq4izU4OmJ47d8PZmlXRdgJ9pYtI0kZ7hgZgwfZQKahyEnYuBIeixbHfqfaulvQeo/W0c5EJntQV/1VDVJHNzvBZwP8nX9O8qD1kUfqSSTNEHbL/th7y45l7eoMt7VxPusT19Xqx2eq2TZbr/XGrQybJXikenDZTK1XsuXa5/jNPyiTlZ9FklL6Hhp2BohO9lymIYBA7QQIENfOi2cjgEAGBNro6OJWT9NGZy7Qg9FUXzKX7lWyA0vLZHSDxDYY1okn5GYgwTIn+h9WJlu0rrJtt9pkIKdiO1jgPT6Lzy5Fc+vGpWIZzCO7BI7TH7+WRTJXS7nYD78jB4XlxDHJf/hF9/zUUzSDqHVjeeMdHdBHf+dZUMVKmHitWd8nn69BpNf9TkauBcatru1g/ZGbzc2ytyyAE5/lFym7k80910E71fx4zdiMLndjPR7jwfdPdkunp3dWkuGM06r/jEjP0ms3Vws4WfblLN2/7eoey36Mv2rCgsd2WXxdT+7WrkfZ92wLXNpnno1N4bbIYJ2VfwYu+zi2RIy9zuqJ23QLgHqt7dyZGKy0ddipV2rUpb0xxy+vv1nhaZnAlnVrrtZsacnqO9v3UdJW2fSkT67fRAsKWz1aq9/dqlXYKd0TP8dvDtRoj59uf1sd529r2R8Lttp7woKt0cFhe87RehxvV7vYIJBu66NBSisRlIlmQeHHplbUnreTRZMvCErPHpnpT0MY2IkLK19CQwCB1AoQIE6tJ3NDAIE0Cni1RID9oLNLn07UrGc7VLOxLgMWYcjRZgfXmQrIDtIfdla+w34o2g8jyzYfp+6V/mjJ0W2QT6tl7zcbnX2kBggsUFKoJ2Nq0izD/YKzAzJ8RInzY7ehT2bUpI81fY6djLFsqImn6+eLrr8XPl5sn7zo/KBmcgW0bqpPrOa5BXmGZHlg290mVu6m20FhJyhvJwDts8YNnrjP4RaBVAhY+YPehwRlrwa8LMvVMoajm73/8r2kyEl6In7QEWHnSjMru2DB8qo+0ysLnFY2Pdo7E/c36gkAG0/BvuMsazg+O/rQ3jYYr5aaiMs0r0uJL7uKI76ms83X3neVZd26JjYwsJVZiG5WCqyymr92crdYA7E6DrxYmbP6Nrt67ZHHArJpU6QPdvxtmcuWwRzdqhsU2l5tyQWdO0W/quK+XUFy/ZQyLeXhl3V69Y6VWLMAceyaVzw/3ffe1IB+9MlWK59iNZF/oCVnkh3/mpOVotmwIVKKppdedWXHUjQEEECAADHvAQQQQKCBBKr6sdJAXcj5xdjBuWWCWpZ2VTVocx4iD1fQftwEahgcjuaxEza50uzHsJeanfQ7Z1JQszkjV4d4IbDt+tpnjWWW2b98aLaWa9b4nCthLIBkn7N9+ugAeJmKiOQDetw6FulJrRE66NqHy7T+q2Y2us2uUrIAWz43ext20sxw+1eTZrV0Z86OLUthgTSbnm3NroqKLh8x520RK702dEhF9qRlU56nJb7+M11LfGlWq500sKSEumSVb9/hc46f4h02b46fkvi3lZ5o0iTsfE7YVX9HHxXWck4hWfmpT3ZolrMNqmgnNK1ZQPMpHVBvqz7Pmp0IuVBPGsa/ly2I7NPv95p81NgJRzc4bPO0bHsru2Lb1kowWLOrbGpylVHk2ZX/3+YzLEXlXCwDeJXWwg4GrR62Di6oJ0xr06yOdnyz7OdtOoBjfNkLOwlyz70Fzgkne838BZErr87V72IaAgggkEM/i9iYCCCAAAIIRATsx4D9QKIhgED2C7CvZv82WqgDYU17SaM0B9qnnwWczP2TTqwIUrmPcZs+AQsSW+biF2t9YoE8G1DLDbilb6m5N2fL9rcg6ysz/E6gzFxPPSWU0qsAdjiDxmkZAs1etcCz1YpPDONVbxtd6sF9tk0bogHi6PlZZnG/vmWy+UCJr7rWoLXyP621bIcFF6Ob1favSbN17dc3Emy0YPV9DxRocLjilWeeHnJKNNhgdm5w2B61IKdlwo4/OfKZYlm/72jGvH32NNcgr9VSHjq46ivCVn8RLRJZpgVEr7mqTDZu9EuRBq+76Haw/Wfh+5HBCS2I7q/4aKvoaAPdszEUHno0UB6Ut8GeL9USF710gO6atnbtrMxM7LPthHGLJIHmefMj7/noZ9ugfGOP91WaxW3bokizphN1o+fCfQQQyAUBAsS5sBVZBwQQQAABBBBAAAEE0iTw7vzE0MD8BX4ZN7bqgE2aupPXs7WrkSzbUnMi89qhvis/RIONNjDpli2RwFgqr/Ky+rX3PRTQjNBIL2e/ITJhfEiO09I0tW2btX/xbbsGn23eBbHj0okNNGw1tuvTLFg6aWJQnnw6IPs0A9eaXSlhpZxq29562x8THLbXWz3jXj1Dsn5D4nrZoGNugPjZFwLlY0pYeZUXp0UGJKtqEMYOOmirBcijm9XjtVIRB3UJOXWJ/3p/gWw/EPy2bGwbu+K8Sgasi55Puu6bh13x5jbbrlbf/ns6mGZNm9W/X7Ey4IzH4L5m9EgrO+L+VXG7ZWusj/vIlq1W5sP9K3K7eo1PXpsVqX9u2fl2QjAbs+xje81fCCBQH4EkHxv1mR2vRQABBBBAAAEEEEAAgVwSKClNDCpYUCOksahA4kO5tOqsSw4LWACtvgHVZDyvayasGxx2H39Ds34tuGlB3No0Oxnw0bLYncwGH4sPDtdmntU917KFf3hzmXz1lc+p4WvBQbsyq7bNBlaMb1b2Ye9en3OVl5WriW5tWkeC25Z1HT/gsD1vyQc+NYx+Rez9sSeE9HUVgXl71IKnbn3dBZqN7AaH3VdaDfzjR/tSmj3uzrsmt8mMrC5zWZITAJXNz7Kib7xeayJ/Ehk4z2pPV1Zn2d5Py7WedXSzbGOrqR/dLOt76hMVmc0W0H9CTxpcNyUoFoinIYBAbgpk8IKK3ARlrRBAAAEEEEAAAQQQyCWBQZppGd8GDmBgo3gT/kbABOIv97dplgW7UwcHq20bf3IwprayBZgtoJcssFjbeVf1/MaNRCxQbIO11SU4bPM+qGtiINH6boMqjtR62tHNluFmKds6JltmsozY6Hm4gdLTJ4TEgsVTrgqKDSjqNhvsL1nbGFeeIdlz0jUtmZEF5Gt7AsBq+lsZDqtJbhnfyddUnNrVVo7EbVbiadKZWvtZy6xEtxUrYzOb7TGrB71seWVzjn419xFAwKsCZBB7dcvRbwQQQAABBBBAAAEEGkBgjAZbrKbpu+9FMiOPHhrSrLuKwEsDdIFFIOAZAasfGz1YmnW8bRuRVjqYXG2bvWaKlht4UEtW2MBulpm8YqVPB34LyBWXBsWyibO1jdayFB8t98uuqMC4lSmw4LNl9pqTBRxtwLeBh4elfbvIuthAdUdo6YelH8YGI6sqL+EatNL6yccOS/7Z1FOXt0Tr7UY3C0YffHDmDC2g+/DfA1JWFumVlfiwetjpapZNfZEOBmilJmwgQQtQW9A+vhUWJjeJDyTHv46/EUDA2wIEiL29/eg9AggggAACCCCAAAJpFWikvxgsoGMBn7DGLqrL5EtrZ5g5AlkuYNmrq1f7ZdPmSEctS3PiGcGkWbE1WZXNm3xOcDj6uWGN39kAbj171LxWbfTrG+K+DZJ2g5Y+WL3ap7WIdVA4zUhudyAIbBnCFtyuLMB9lma19u7llwU6mJwFfS04XJuB25Kt35GDQk55hZWfRoLE1odTTgpJSx0EL1PNArS33FDmDNIXDPrk0N4hadEA/WnbJuyctKhsva1MhZ3UsNrEbmuugfxkV5O4j3OLAALeFyBA7P1tyBoggAACCCCAAAIIIJB2AaeWJwXq0u7MArwtYIG0664tk6+1Bu8+LS3RvXtYLCu2ri2+nrE7n2D6Ek3dRdT7tlCD4xZsrO2ginZSygYSHDK43l0on4F9fl0yOSgbtZ6uBe8tc9iC2JlulkE96IjaG6Wz35blfc13y2TxB34nqN63T9gpT9G0aTqXyrwRQCDTAgSIM70FWD4CCCCAAAIIIIAAAggggEDOCFgwsnuKShdYzV6rr/tNcSTz1UUarBmxtNoJmKANTNixY+1el4/PLiqK1DUeMTwf1551RiA/BcgByM/tzlojgAACCCCAAAIIIIAAAghkuYBfo5qXXBSU43TANasXa4FnqyPbr2/yOrFZvjpZ2z3LyF77pU8++9wnJSVZ2006hgACCKRNgAzitNEyYwQQQAABBBBAAAEEEEAAAQTqJ9BSB6ubMD4k43UAMwsY01IrsGOHyCOPFciWLZH52mBs39JyFKnKAk9tb7Vgh54b2L5dxN4XNrAdDQEEEEiFAAHiVCgyDwQQQAABBBBAAAEEEEAAAQTSKEBwOD24M2cHyoPDtgSrHT3tJb98b4oOLpieRdZ5ritW+uTlV7W/OoCcBYhPOSmog8eRTV5n0Bx4YWmpSCOtG01DoL4CBIjrK8jrEUAAAQQQQAABBBBAAAEEEEiBwJatPvn4E5/s3SvS/7CwdO0azrogZQpWM6tmsfqLxDDweh3Mbs8ekWZZNDDbNs0afupfASkri/BZ5vOzzwekU4egWK1qWn4JfL7KJ2/M8TulUQ7tHZaxJ4Sk20G8D/LrXZDatSVAnFpP5oYAAggggAACCCCAAAIIIIBArQW+/MonDz0acEoI2IvnvB0pLWH1h2npE+jQPqwlG2KDxM2bidhAbdnUVqz0lweH3X5ZuYnlekKhoQPEIX1Lrl5jJzP80rZtWGWh2zQAAB7PSURBVA4fEJJWmtFclxYMinNSZNESv7RvF5ZhR4WlQwcCnVVZbtrsk6lPBMTsrH36mU8DxQG54foysfcuDYG6CBAgrosar0EAAQQQQCAFAnZwbYOhLP7ALy1bhGXI4LB0JgMkBbLMAgEEEEAAAe8JvP6mvzw47Pb+Tc0QPHaY1h6m1qxLkvLbE8eEZNXqgNhxmdvG6rRsK+nRrGnyoGnTDGQ5T3/FL+8tdN+UPpn1upbkuCYo7TRYXNv27AsB+WhZJEBvx8XzF4hceXn21oCu7fql4/nm5QaH3fnv3y+yUk8iDB0S9UZ2H+QWgRoIECCuARJPQQABBBBAIB0CM2b6Ze67FQfX8+aLXHpJUOwyMRoCCCCAAAII5JfAZs0KjG97tNSE/SMrMF4mdX/bZfk3aublxyv8YkG2w/pl5wn7ftqv9u1ENm2uWPeWLUQGHdGwAcFdu0UWvO8ev0b6YnVw35nrl4lnHEhprehilfesTIYbHHafaFnR8+b7dZDA2s3LfX0+3FZWc7hRI35D5MP2T9c6EiBOlyzzRQABBBBAoAoBO5CevyD24Nqe/rYeXB/amwPiKuh4CAEEEEAAgZwUOOSQsLy/KDZI3KljWJpxyXj59rbazMs+9mvGr096q9fh/UPSpEn5w3W+07q1SLaX8mik0ZurriyTpR/6xQars/UfPCgkRSlY/9rAbdvmS8h0t9fbwHm1bfv2x77f3dfbQIG0ygWO1JMCc972O7XK3WfZe7hfXwLErge3tRcgQFx7M16BAAIIIIBAvQX2aYZK/KVhNlMbEIWGAAIIIIAAAvknMG5sUL7+OiDF6yNBMxsg7awzQwxSd+CtYCfXH3i4QDZviUywzNO583xy7dVBKciTyIYFg63kyLHDMrd/WDm05s1Fdu2K7UNdroBrr/WfbX7ue96d48DDCXS6Fsluzf9715TJQs3ktvrDA/qH5SgtLdG4cbJnMw2BmgnkycdozTB4FgIIIIAAAg0l0EIP7A7uFhYbkCa62YjlNAQQQAABBBDIPwErIzFFg53FxT6n1EE3PU6wrFFaRGDZcn95cNg12bjJ5wzSNmggx0+uSbpvLRh/zllB+ddzAaf8iS3PynIMP6b2pS6szvOF54fEyq59ssLnDAw4akRIhg6u/bzSvd7ZNn8bFHDc2JD+y7ae0R+vCvB149UtR78RQAABBDwvcO6kkEyb7pfPV/kkEBA5emhIRo3kgNjzG5YVQAABBBBAoI4CFjDr2oVgZzK+HTuTTRXZscNOtmOWXCc9Uy1b+JabymTdOp8OtCzSpk3d/W1gu8kXBJ1yCY0LRQKJFdjSsxLMFQEEYgQIEMdw8AcCCCCAAAINJ9BWD4gv+1ZQ7AePXRLWRA+KaQgggAACCCCAAAKJAn37hGXW6xoKjopF+jQ2bNNpDS9g2e09uqfOvqio4deBJSKAQIUA52YqLLiHAAIIIIBARgQs84LgcEboWSgCCCCAAAIIeETAatWeMaGizmqhnlg/8/SQdOyQuiClRyjoJgIIIJByATKIU07KDBFAAAEEEEAAAQQQQAABBBBAINUCw44OyZFHhmTzZp+0b6c1mhulegnMDwEEEMhPAQLE+bndWWsEEEAAAQQQQAABBBBAAAEEPCfQWIPCXTqTNey5DUeHEUAgqwUoMZHVm4fOIYAAAggggAACCCCAAAIIIIAAAggggAAC6RMgQJw+W+aMAAIIIIAAAggggAACCCCAAAIIIIAAAghktQAB4qzePHQOAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIn0BeBYhDobAEQ6GkmsFgUL4u3lTp40lfxEQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDDAnkzSF04HJaf3vGgs6nu/NnVMZvs2ZfmyO1/mSqlJaXSuLCR/PLmy2XiKSNinsMfCCCAAAIIIIAAAggggAACCCCAAAIIIIBArgnkRQbxv2fMldGTfiDTXpubsP02bt4mv/rD3+Un118si197SG6++gL5xe8elq3bdyY8lwkIIIAAAggggAACCCCAAAIIIIAAAggggEAuCeRFBvG4UUPlqEF95ff3Ppmw7Wa/s1hatWwm5585xnls8qRxcvcjz8kbc5fI2RNGyx8feEa2abB489YdsmDJJzL0iL5y41XnyWGHdk+YFxMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEvCeRFgLhZ0ybi/CtqIsFgbA3i9Ru3yMFdO5ZvM7/fp393kOINW5xp32hd4tlzF8t3LjpNLj57nDw49SV5+J8vye//37XO482LvEkY0PW0VlQYkEYFeZFI7qwv/0MgWwR8Pp/of+LVz5BscaQfCNRXgH2wvoK8HoG6C9jhaNPCAglpKTgaAgg0vID9JixqzO/BhpdniQhEBAo0FuPzh8Xv82Zcie2YWwJ5/y7csXO3FBY2jtmqjRs3kp2795ZPmzR+lFx3+STn75279srtdz1e/pjfIjwebG63NUSlH0YeXAG6jIDHBWy/i+x/7IAe35R038MCtvd59Xvcw+x0HYEKAT0g9VueQojvwgoU7iHQcAL2m9CnB6Uan6IhgEAGBNxjUY5HM4DPIhME8j5A3LJFMykpLY2B2b+/VFo2bxozzf2jebMi2bNvv/un7NgT+9ryB7L8jmUN2xnjPfvLZH9pbFZ1lned7iGQEwK2/xVqxoZXP0NyYiOwEnkv0EyvAmIfzPu3AQAZFGii34O79pZJWZDoVAY3A4vOYwH7TbhnH78H8/gtwKpnWKB1s0ZSot+Bth/S6i/AlYH1M8z7AHHH9m1k7dcbyhVDobB8uW6D2PSatK7timrytKx9TruWhVnbNzqGQD4IeP0zJB+2EeuY2wLsg7m9fVm77Bfo2LpJ9neSHiKQwwKFjfg9mMObl1XzgIClJlqgmIZApgXyovhsMBTSLOEyp/5wMBh07lsg2NqJI4fK9u275Jlpb+jjQXnihVmyb3+JjBkxONPbhuUjgAACCCCAAAIIIIAAAggggAACCCCAAAJpFciLDOL/fW6m/Paef5ZDTp89X35+w7dlsg4617F9a/n5jd+W//nLVPn1H/8hjRoVyH/fcrm0bd3Ceb7VZIpu1IaJ1uA+AggggAACCCCAAAIIIIAAAggggAACCHhZwBfW5uUVSFXfy8qCsm79Junaqb0UFARSNdusnc8uHYRvz979ToA8aztJxxDwsMCWbTultKxMOlVSrqa6fXD9pq3SQmueNy3i0lsPvw3oehYL2FVDxRu3SueObbUmf+IFVdXto1m8anQNgawSsIGft+/Y5Rxj+6MSL9gHs2oz0ZkcFdiwaZsztk6TJrGDstvqsg/m6EZntTIuUKpXr1viYbJW1W889slkYkxrSAECxA2pnQXL2l9SKj+940F59Y0FYrnRPbp1kr/ecZNzmwXdowsIeF7g8zXr5Ls//J3YAbm1vod0k2u+PVFOHXuM83d1++CqL9bJdT+9S77SWujWTjtpuNz2oysrPchwnsT/EEAgqYDtb1fceKfs1dJRzz/8m/LnPPvSHLldrxwq1ccbFzaSX958uUw8ZYTzeHX7aPlMuIMAAlUKzJzzvvz+vif1+2yj87xnHviVDOjbw7nPPlglHQ8iUG+B199ZLPf+40X5ZsNmKSkpk6GD+sjvfj5FWhwYiJ19sN7EzACBpAKz31kkN//yb7Jk5kMxj1f3G499MoaLPzIkkJgyk6GOsNiGEXhOfxTPX/SxTPvH/8i7L90rXTt3kNvuerxhFs5SEMgDAat5fvHZJ8nsf/1J3nrhbhk0oLf8/t4nnSwNW/3q9sFf//ExOaRHF5k//V557pHfyJx3l8qLM97JAzlWEYHUCtgFUj+78yFZtnKNSNTFUhs3b5Nf/eHv8pPrL5bFrz0kN199gfzidw/L1u07nQ5Ut4+mtpfMDYHcFJjx5gK55dd/kxNHDJEX/367vPX8X6RX987OyrIP5uY2Z62yR8CujP2v39wnI4YNlDm678165o+y5sv1MvXZ19gHs2cz0ZMcE9iydYeMn/xf8v2f/SXpmlX1G4/vxaRkTMyAAAHiDKBncpEz5iyU8WOG6UF6F2nWtIlcdsEp8u7CZWKX/9EQQKD+ApYxfNUlZzilJayW+aRTR0nxhi1imcXWqtoHLUC1cOkKuez88U5piT69uslJo4fKa28urH/HmAMCeSZwz6PPy6ervpJrvnVmzJrP1qyqVi2byflnjnFKSk2eNE6KmhTKG3OXOM+rah+NmRF/IIBAUgE7OWP737hRQ+VWPRFzaM+DpG2bls5+Zi9gH0zKxkQEUiZgV8KUlJY6JZR8Pp8015Jllr2/5qtiZxnsgymjZkYIlAu0btVCHv7jj+S2W68sn+beqe43HvukK8VtpgUIEGd6CzTw8tdv3BJTTqL7QZ0kpAfymzSjioYAAqkXmKcnYJoWFcrBB3V0Zl7VPmhlKeyHtZV+cVuPbp1lvdZJpSGAQM0F/vPaPHl62hvytztvkuYHLqd1X2374MFdI/ujTbOaqAd37eCcyLG/q9pH7XEaAghULbBZs6jspOjuPfvkipt+K5Ov/Y388f6nZd++EueF7INV+/EoAvUVsCQgO/n5P3dNda4UfW76W7LwgxVyyTknObNmH6yvMK9HIFHAjie7dekg7dq0Sniwut947JMJZEzIkEDyytkZ6gyLTb/Azl17pLCwYpCCwsaR+zt0Og0BBFIrsGDJJ/LA1P/I9688pzxzqqp9sEQzPqzF7qONNMOf/TO1W4a55bLA4o8+ld/86TG573c3y0Gd2yes6o6du2P2MXtC48a2n0WupKlqH02YGRMQQCBB4Jv1m51pdhXNyGFHyHbd5/769+fFLqG946dXC/tgAhkTEEi5wKhjjpDps+fLF1+tl6demC1HDepXfnKUfTDl3MwQgSoFbJ+zVtlvPPbJKvl4sAEFCBA3IHY2LMoGJrDLjty2vySSzdEyLsPKfZxbBBCom8Ann62V7//8LzJpwii5cvJp5TOpah8s0RFvrcXuo6XSolnT8tdzBwEEqhawQT7atW0plkVs/z75fK0Uaxa+BY1v+O650rJFM+fS2+i57N9f6ozybtOq2kejX8N9BBCoWuCWKRdK+7aRTCq/Xub+pwefca6SYR+s2o1HEaivgF3Oft1P/uxcRWOBYhsc6we/uFvr7T8i99x+A9+D9QXm9QjUUsC+96xV9huP78VagvL0tAlQYiJttNk5404d2sraA/WnrId2VtkO2tu3a52dHaZXCHhQ4P2lK+WyG+5w6g//8ubLYtagqn2wY/vWYrXi1up+6bY1XxZLpw5t3D+5RQCBagTGjhwiE8YeK61bNnf+NdX6wgUBv3Pf7/dLx/ZtZO3XG8rnEgqF5ct1G5zpNrGqfbT8RdxBAIFKBdySSnaM6bbSYFBs4CwbL5J90FXhFoH0CNiVNDZo8oA+PZwFHNKjq5w1fqTY8ak19kGHgf8h0GAC1f3GY59ssE3BgqoRIEBcDVCuPXzy8UfJK68vkNUadNqzd5/845lXZfhRAzRDsSjXVpX1QSAjAu8uWi5X3HinnDrmGLn47HFO4Gnt1+vFsjmsVbUPttHBDY4a1NfZL/fu2y+frflaZr71vr7m6IysCwtFwIsCNjCWlXVx/40efqSTxWh/20A9J44cKtu375JntEZxUINWT7wwS/btL5ExIwY7q1vVPupFD/qMQEML2MmZY4b0l7sfeU52aekWO+Z88ZW3ZfjQAU7Nb/bBht4iLC/fBGxgyICeEH30qZf1ipky2b5jt3M86QaM2Qfz7R3B+jaUgO1vpWWRK0Ltvp0YtVbdbzz2yYbaQiynOgGfDoik5/Jp+SJgP4J/fPv9epCwSHy60t10oB4bxKfXwZ3zhYD1RCCtAo88+bL84b6nEpYxedKJ8vMbL3UCUVXtgxYUtssC1xVvEvtwnjD2GLn9J1dJ40ZUBEpAZQICNRB47F8z5Pnpc+T5R24rf/bT/35d7rj7f50D90a6b/3ipkvl7Amjncf5nixn4g4CdRaw7OEb9JL2VWvX6YmYkAwZ2Ef+8N/fk06awW+NfbDOtLwQgRoJ/HvGXJn67Gt6tWixc3XaaC01cePV55fX5mcfrBEjT0KgxgI2EN3Y826Mef6wIw+Tv9/1Y2dadb/x2Cdj6PgjQwIEiDMEn+nF2iA8ltXRpVO7THeF5SOQlwLV7YPrdJAfq4VKdn9evj1Y6QYQsKyOdes3SddO7aWgIJCwxOr20YQXMAEBBBIEijdscfYvtxZx9BPYB6M1uI9AegTWb9wirTSrv0nUIOXuktgHXQluEWg4gap+47FPNtx2YEnJBQgQJ3dhKgIIIIAAAggggAACCCCAAAIIIIAAAgggkPMC1CDO+U3MCiKAAAIIIIAAAggggAACCCCAAAIIIIAAAskFCBAnd2EqAggggAACCCCAAAIIIIAAAggggAACCCCQ8wIEiHN+E7OCCCCAAAIIIIAAAggggAACCCCAAAIIIIBAcgECxMldmIoAAggggAACCCCAAAIIIIAAAggggAACCOS8AAHinN/ErCACCCCAAAIIIIAAAggggAACCCCAAAIIIJBcgABxchemIoAAAggggAACCCCAAAIIIIAAAggggAACOS9AgDjnNzEriAACCCCAAAIIIIAAAggggAACCCCAAAIIJBcgQJzchakIIIAAAggggAACCCCAAAIIIIAAAggggEDOCxAgzvlNzAoigAACCCCAAAIIIIAAAggggAACCCCAAALJBQgQJ3dhKgIIIIAAAggggAACCCCAAAIIIIAAAgggkPMCBIhzfhOzgggggAACCCCAAAIIIIAAAggggAACCCCAQHIBAsTJXZiKAAIIIIAAAggggAACCCCAAAIIIIAAAgjkvAAB4pzfxKwgAggggAACCCCAAAIIIIAAAggggAACCCCQXIAAcXIXpiKAAAIIIIAAAggggAACCCCAAAIIIIAAAjkvQIA45zcxK4gAAggggAACCCCAAAIIIIAAAggggAACCCQXIECc3IWpCCCAAAIIIIAAAggggAACCCCAAAIIIIBAzgsQIM75TcwKIoAAAggggAACCCCAAAIIIIAAAggggAACyQUIECd3YSoCCCCAAAIIIIAAAggggAACCCCAAAIIIJDzAgU5v4asIAIIIIAAAggggIDnBTZt2S6/veef8t6ST2Tvvv0yqH9v+cn3L5HePbs663br7fdL29YtJRQKyWtz3peCgF/OOf14ueLCCVLYuJHznJrMo2e3ztLnkG7yn9fmykZd5j233yDNmxbJPY8+L9Nemyebt26XgYf1koDfL6eOPUYuPvskue6nf5ZuXTo4/XGhd+7eK1N+9Ac5e8JoOe+ME9zJ3CKAAAIIIIAAAgggkHUCZBBn3SahQwgggAACCCCAAALRAvtLSuWS626T5Z9+Id+5aILcdNX5Urxxi0y59Q9ij1lbueoreeyZV2XJss9k8qQTZfhRA+Tuh5+TdxZ86Dxe03nc//g0+fHtD8juPfukRbOmEg6L3HbX4/LwE9OlX+9u8subL5ehA/vI0uWr5Jv1m5159+nVTZ54fpas1z657cVX3pYPln8uxww5zJ3ELQIIIIAAAggggAACWSlABnFWbhY6hQACCCCAAAIIIOAKPDd9jnxdvElefPT28ozhgw/qKNdohu6iD1fKcUcd7jz1hOFHyl/vuFF8Pp/z96KlK+WNuUvkxJFDpabz6HPIQXLvnTdL+7atnHls3LxNnn1pjmYCj5Lf/OhKt0vy4qvvlN+/cOJYeUQDyE9Pe0O+/51zNKgclidfmCWjjz1Cuh/Uqfx53EEAAQQQQAABBBBAIBsFCBBn41ahTwgggAACCCCAAALlAp98tta5f8uv/lY+rbSszLm/rjiSxWt/tG7VvDw4bH937thO3MdrOg/LBnaDwzaPz9Z87QR8TzhusP2ZtHXp1E7GjBgs//rPm3LtpWc5ZTBWf1ksP9YSGDQEEEAAAQQQQAABBLJdgABxtm8h+ocAAggggAACCOS5wL79JdJGg7/Xf+fsBIn+fXokTHMnBLQOcTAYcv6s6zxKSiOB6KImjd3Zlt+6mco2YfLZ42TW24tk5lvvy0sz50nPgzvLyGEDy5/LHQQQQAABBBBAAAEEslWAAHG2bhn6hQACCCCAAAIIIOAI9OreRV6ePV8G9O0pXTVbN7pZOYeatLrOwy0R8d7iTzTge4SzqH37SqS0LOhkFrvLHj50gPTSoLANZrf2q/Vy6/UXx2Qzu8/jFgEEEEAAAQQQQACBbBNgkLps2yL0BwEEEEAAAQQQQCBG4JwJx0tRk0L53o//JG+++4FT9uGV19+TK276rSxY8knMcyv7o67zsKCvBX8f+udL8t1bfi+3/fkxOe1bt8qOnbtjFmXZxBdNGier134jTTTbeNKpo2Ie5w8EEEAAAQQQQAABBLJVgABxtm4Z+oUAAggggAACCCDgCHRs31oe/P8/lEaNAk6Q+KzLfyZWj3j3nn3SqUNb5zk+0YHpDgxO57JZ0NadVNd52Lz+/Ovr5bzTT5DijVtkxedfyZUXny4tmjeVZk2L3EU5t+PHDHNuzxo/Sh9rEvMYfyCAAAIIIIAAAgggkK0CPr0sr2bX5WXrGtAvBBBAAAEEEEAAgbwRsKDwhs3bpJMGjZsW1S0IW995fF28SU656Ifyu59PkdNPGl5u/8QLs+T2u6bKtMfucMpNlD/AHQQQQAABBBBAAAEEsliAGsRZvHHoGgIIIIAAAggggECsgGXm9mraOXZiLf+q7TwemDpNln/6hfTp1U1COujdv2e842Qujxs1tHzJoVBYHv/XDBlx9ECCw+Uq3EEAAQQQQAABBBDwggABYi9sJfqIAAIIIIAAAgggkDEBG6juzXkfyMcrv5DmzYrkWK1JfN3lk5xaw26nPv50jTQpLJTLLzzVncQtAggggAACCCCAAAKeEKDEhCc2E51EAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRSL8Agdak3ZY4IIIAAAggggAACCCCAAAIIIIAAAggggIAnBAgQe2Iz0UkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCD1AgSIU2/KHBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAU8IECD2xGaikwgggAACCCCAAAIIIIAAAggggAACCCCAQOoFCBCn3pQ5IoAAAggggAACCCCAAAIIIIAAAggggAACnhAgQOyJzUQnEUAAAQQQQAABBBBAAAEEEEAAAQQQQACB1AsQIE69KXNEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQ8IUCA2BObiU4igAACCCCAAAIIIIAAAggggAACCCCAAAKpFyBAnHpT5ogAAggggAACCCCAAAIIIIAAAggggAACCHhCgACxJzYTnUQAAQQQQAABBBBAAAEEEEAAAQQQQAABBFIvQIA49abMEQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8IQAAWJPbCY6iQACCCCAAAIIIIAAAggggAACCCCAAAIIpF6AAHHqTZkjAggggAACCCCAAAIIIIAAAggggAACCCDgCQECxJ7YTHQSAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIvQAB4tSbMkcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMATAgSIPbGZ6CQCCCCAAAIIIIAAAggggAACCCCAAAIIIJB6AQLEqTdljggggAACCCCAAAIIIIAAAggggAACCCCAgCcECBB7YjPRSQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIPUCBIhTb8ocEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABTwgQIPbEZqKTCCCAAAIIIIAAAggggAACCCCAAAIIIIBA6gUIEKfelDkigAACCCCAAAIIIIAAAggggAACCCCAAAKeECBA7InNRCcRQAABBBBAAAEEEEAAAQQQQAABBBBAAIHUCxAgTr0pc0QAAQQQQAABBBBAAAEEEEAAAQQQQAABBDwhQIDYE5uJTiKAAAIIIIAAAggggAACCCCAAAIIIIAAAqkXIECcelPmiAACCCCAAAIIIIAAAggggAACCCCAAAIIeEKAALEnNhOdRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEUi9AgDj1pswRAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwhAABYk9sJjqJAAIIIIAAAggggAACCCCAAAIIIIAAAgikXoAAcepNmSMCCCCAAAIIIIAAAggggAACCCCAAAIIIOAJAQLEnthMdBIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEi9AAHi1JsyRwQQQAABBBBAAAEEEEAAAQQQQAABBBBAwBMCBIg9sZnoJAIIIIAAAggggAACCCCAAAIIIIAAAgggkHoBAsSpN2WOCCCAAAIIIIAAAggggAACCCCAAAIIIICAJwQIEHtiM9FJBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAg9QIEiFNvyhwRQAABBBBAAAEEEEAAAQQQQAABBBBAAAFPCPwfQ3KoT+DGqOYAAAAASUVORK5CYII=", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load data into a dataframe \n", "df = lc_pd_dataframe(livechart + \"fields=decay_rads&nuclides=241am&rad_types=g\").query(\"p_energy==0\")\n", "\n", "df = df[pd.to_numeric(df['intensity'],errors='coerce').notna()] # remove blanks (unknown intensities)\n", "df.intensity = df['intensity'].astype(float) # convert to numeric. Note how one can specify the field by attribute or by string \n", "\n", "\n", "fig = px.scatter(df, x=\"energy\", y=\"intensity\", log_y=True) # plot in log scale\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Log FT vs Transition type for Nb-100 decay\n", "The fields to be retieved are decay_rads, the nuclide is 100-Nb, and the radiation type is bm\n", "
\n", "In this case, only the relevant fields are loaded into the dataset; then data are grouped by transition, binned, and plotted\n", "\n", "The transition types in the plot legend are not decoded. A stands for Allowed, 1NU for 1st non unique, ..." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "shape": "spline" }, "mode": "markers", "name": "1NU", "type": "scatter", "x": [ 5.9945, 5.9955, 5.997, 5.9985, 5.999499999999999, 6.000500000000001, 6.0015, 6.003, 6.0045, 6.0055 ], "y": [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ] }, { "line": { "shape": "spline" }, "mode": "markers", "name": "1U", "type": "scatter", "x": [ 7.993, 7.9945, 7.996, 7.9975000000000005, 7.9990000000000006, 8.001000000000001, 8.002500000000001, 8.004000000000001, 8.005500000000001, 8.007 ], "y": [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ] }, { "line": { "shape": "spline" }, "mode": "markers", "name": "2NU", "type": "scatter", "x": [ 6.3945, 6.3955, 6.3965, 6.398, 6.3995, 6.4005, 6.401999999999999, 6.403499999999999, 6.4045000000000005, 6.4055 ], "y": [ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 ] }, { "line": { "shape": "spline" }, "mode": "markers", "name": "2U", "type": "scatter", "x": [ 5.3395, 5.42, 5.5, 5.58, 5.66, 5.74, 5.82, 5.9, 5.98, 6.06 ], "y": [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 1 ] }, { "line": { "shape": "spline" }, "mode": "markers", "name": "A", "type": "scatter", "x": [ 5.1645, 5.295, 5.425000000000001, 5.555, 5.6850000000000005, 5.8149999999999995, 5.945, 6.074999999999999, 6.205, 6.335 ], "y": [ 3, 0, 3, 0, 2, 2, 4, 2, 0, 2 ] } ], "layout": { "autosize": true, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "autorange": true, "range": [ 4.99579372779448, 8.17570627220552 ], "type": "linear" }, "yaxis": { "autorange": true, "range": [ -0.3300970873786408, 4.330097087378641 ], "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "url = livechart+\"fields=decay_rads&nuclides=100nb&rad_types=bm\"\n", "\n", "df=lc_pd_dataframe(url)[['transition_type','log_ft']] # optional:load only transition_type and log_ft \n", "\n", "df = df[pd.to_numeric(df.log_ft,errors='coerce').notna()] # remove unknowns and convert to numeric values\n", "df.log_ft = df.log_ft.astype(float)\n", "\n", "ans = [pd.DataFrame(y) for x, y in df.groupby('transition_type', as_index=False)] # group log ft values by transition\n", "fig = go.Figure()\n", "\n", "for a in ans:\n", " ct = pd.cut(a.log_ft, bins=10) # bin each grouping \n", " tst = ct.apply(lambda x: x.mid).value_counts(sort=False) # assign the mid value to each bin\n", " fig.add_trace(go.Scatter(x=tst.index.values, y = tst, mode=\"markers\", line_shape='spline', name=a.transition_type.iloc[0]))\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the total beta- and anti-nu spectra from the decay of Xe-135, metastable @526 keV\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "bin_en=%{x}
dn_de=%{y}", "legendgroup": "", "marker": { "color": "#636efa", "symbol": "circle" }, "mode": "markers", "name": "", "orientation": "v", "showlegend": false, "type": "scatter", "x": [ 0, 0.3, 0.6, 0.9, 1.2, 1.7, 2.2, 2.7, 3.2, 3.7, 4.2, 4.7, 5.2, 5.7, 6.2, 6.7, 7.2, 7.7, 8.2, 8.7, 9.2, 9.7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 103, 106, 109, 112, 115, 118, 121, 124, 127, 130, 133, 136, 139, 142, 145, 148, 151, 154, 157, 160, 163, 166, 169, 172, 175, 178, 181, 184, 187, 190, 193, 196, 199, 202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 241, 244, 247, 250, 253, 256, 259, 262, 265, 268, 271, 274, 277, 280, 283, 286, 289, 292, 295, 298, 301, 304, 307, 310, 313, 316, 319, 322, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 430, 433, 436, 439, 442, 445, 448, 451, 454, 457, 460, 463, 466, 469, 472, 475, 478, 481, 484, 487, 490, 493, 496, 499, 502, 505, 508, 511, 514, 517, 520, 523, 526, 529, 532, 535, 538, 541, 544, 547, 550, 553, 556, 559, 562, 565, 568, 571, 574, 577, 580, 583, 586, 589, 592, 595, 598, 601, 604, 607, 610, 613, 616, 619, 622, 625, 628, 631, 634, 637, 640, 643, 646, 649, 652, 655, 658, 661, 664, 667, 670, 673, 676, 679, 682, 685, 688, 691, 694, 697, 700, 703, 706, 709, 712, 715, 718, 721, 724, 727, 730, 733, 736, 739, 742, 745, 748, 751, 754, 757, 760, 763, 766, 769, 772, 775, 778, 781, 784, 787, 790, 793, 796, 799, 802, 805, 808, 811, 814, 817, 820, 823, 826, 829, 832, 835, 838, 841, 844, 847, 850, 853, 856, 859, 862, 865, 868, 871, 874, 877, 880, 883, 886, 889, 892, 895, 898, 901, 904, 907, 910, 913, 915.2 ], "xaxis": "x", "y": [ 0.00171012, 0.00170191, 0.00169701, 0.00169542, 0.00169483, 0.00169491, 0.00169571, 0.00169697, 0.00169846, 0.00169977, 0.00170104, 0.00170224, 0.00170338, 0.00170445, 0.00170547, 0.00170642, 0.00170732, 0.00170815, 0.00170893, 0.00170966, 0.00171037, 0.0017111, 0.00171152, 0.00171289, 0.00171415, 0.00171542, 0.00171665, 0.00171783, 0.001719, 0.00172013, 0.00172124, 0.00172233, 0.00172341, 0.00172446, 0.00172549, 0.00172651, 0.00172751, 0.0017285, 0.00172947, 0.00173043, 0.00173139, 0.00173233, 0.00173326, 0.00173418, 0.00173509, 0.00173599, 0.00173689, 0.00173778, 0.00173866, 0.00173954, 0.00174041, 0.00174127, 0.00174213, 0.00174298, 0.00174383, 0.00174467, 0.00174552, 0.00174635, 0.00174719, 0.00174801, 0.00174883, 0.00174966, 0.00175047, 0.00175129, 0.0017521, 0.00175291, 0.00175372, 0.00175452, 0.00175532, 0.00175611, 0.00175691, 0.0017577, 0.00175849, 0.00175927, 0.00176005, 0.00176083, 0.00176161, 0.00176238, 0.00176315, 0.00176392, 0.00176469, 0.00176545, 0.00176621, 0.00176696, 0.00176771, 0.00176847, 0.00176921, 0.00176996, 0.0017707, 0.00177144, 0.00177218, 0.00177291, 0.00177365, 0.00177438, 0.00177511, 0.00177583, 0.00177655, 0.00177726, 0.00177798, 0.00177869, 0.0017794, 0.00178011, 0.00178082, 0.00178152, 0.00178222, 0.00178291, 0.00178361, 0.0017843, 0.00178498, 0.00178567, 0.00178635, 0.00178703, 0.00178771, 0.00178973, 0.00179167, 0.00179352, 0.00179525, 0.00179689, 0.00179843, 0.00179985, 0.00180118, 0.00180239, 0.0018035, 0.0018045, 0.00180539, 0.00180616, 0.00180682, 0.00180738, 0.00180782, 0.00180815, 0.00180837, 0.00180848, 0.00180848, 0.00180836, 0.00180812, 0.00180777, 0.00180731, 0.00180674, 0.00180605, 0.00180525, 0.00180434, 0.0018033, 0.00180215, 0.00180087, 0.00179947, 0.00179795, 0.0017963, 0.00179454, 0.00179264, 0.00179062, 0.00178849, 0.00178623, 0.00178385, 0.00178134, 0.00177872, 0.00177597, 0.0017731, 0.00177012, 0.00176701, 0.00176379, 0.00176044, 0.00175697, 0.00175338, 0.00174968, 0.00174586, 0.00174192, 0.00173787, 0.00173369, 0.00172941, 0.001725, 0.00172048, 0.00171585, 0.00171111, 0.00170625, 0.00170128, 0.0016962, 0.001691, 0.0016857, 0.00168029, 0.00167476, 0.00166914, 0.0016634, 0.00165756, 0.00165161, 0.00164556, 0.00163939, 0.00163314, 0.00162677, 0.00162031, 0.00161375, 0.00160708, 0.00160031, 0.00159345, 0.00158649, 0.00157943, 0.00157228, 0.00156504, 0.0015577, 0.00155027, 0.00154275, 0.00153513, 0.00152744, 0.00151965, 0.00151177, 0.00150381, 0.00149577, 0.00148763, 0.00147943, 0.00147113, 0.00146275, 0.0014543, 0.00144577, 0.00143717, 0.00142848, 0.00141973, 0.00141089, 0.00140199, 0.00139301, 0.00138397, 0.00137486, 0.00136569, 0.00135645, 0.00134714, 0.00133777, 0.00132834, 0.00131885, 0.0013093, 0.0012997, 0.00129004, 0.00128032, 0.00127055, 0.00126073, 0.00125085, 0.00124094, 0.00123097, 0.00122096, 0.0012109, 0.0012008, 0.00119066, 0.00118048, 0.00117026, 0.00116001, 0.00114972, 0.00113938, 0.00112903, 0.00111865, 0.00110823, 0.00109779, 0.00108732, 0.00107683, 0.00106632, 0.00105578, 0.00104523, 0.00103466, 0.00102407, 0.00101348, 0.00100287, 0.000992248, 0.00098162, 0.000970983, 0.000960343, 0.000949699, 0.000939054, 0.000928409, 0.000917766, 0.000907121, 0.000896459, 0.00088578, 0.000875086, 0.000864378, 0.000853657, 0.000842927, 0.000832187, 0.000821441, 0.000810688, 0.00079993, 0.00078917, 0.000778408, 0.000767647, 0.000756888, 0.000746133, 0.000735382, 0.000724639, 0.000713904, 0.000703178, 0.000692464, 0.000681765, 0.00067108, 0.000660411, 0.000649762, 0.000639133, 0.000628524, 0.00061794, 0.000607381, 0.00059685, 0.000586346, 0.000575875, 0.000565435, 0.000555028, 0.000544658, 0.000534326, 0.000524033, 0.00051378, 0.000503572, 0.000493409, 0.000483292, 0.000473223, 0.000463206, 0.000453241, 0.00044333, 0.000433474, 0.000423677, 0.00041394, 0.000404264, 0.000394653, 0.000385106, 0.000375627, 0.000366219, 0.000356882, 0.000347618, 0.000338429, 0.000329317, 0.000320286, 0.000311335, 0.000302468, 0.000293687, 0.000284992, 0.000276387, 0.000267874, 0.000259454, 0.00025113, 0.000242903, 0.000234775, 0.000226746, 0.00021882, 0.000210997, 0.000203281, 0.000195671, 0.000188173, 0.000180785, 0.000173513, 0.000166357, 0.000159319, 0.000152402, 0.000145608, 0.000138939, 0.000132397, 0.000125984, 0.000119703, 0.000113555, 0.000107543, 0.000101669, 9.59352e-05, 9.03438e-05, 8.48971e-05, 7.95972e-05, 7.44465e-05, 6.94471e-05, 6.46013e-05, 5.99113e-05, 5.53795e-05, 5.10079e-05, 4.67991e-05, 4.27551e-05, 3.88784e-05, 3.51711e-05, 3.16356e-05, 2.82742e-05, 2.50893e-05, 2.2083e-05, 1.92577e-05, 1.66159e-05, 1.41596e-05, 1.18914e-05, 9.81347e-06, 7.92818e-06, 6.23786e-06, 4.74482e-06, 3.45136e-06, 2.35979e-06, 1.47238e-06, 7.91377e-07, 3.18962e-07, 5.71762e-08, 0 ], "yaxis": "y" }, { "type": "scatter", "x": [ 0, 0.3, 0.6, 0.9, 1.2, 1.7, 2.2, 2.7, 3.2, 3.7, 4.2, 4.7, 5.2, 5.7, 6.2, 6.7, 7.2, 7.7, 8.2, 8.7, 9.2, 9.7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 103, 106, 109, 112, 115, 118, 121, 124, 127, 130, 133, 136, 139, 142, 145, 148, 151, 154, 157, 160, 163, 166, 169, 172, 175, 178, 181, 184, 187, 190, 193, 196, 199, 202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 241, 244, 247, 250, 253, 256, 259, 262, 265, 268, 271, 274, 277, 280, 283, 286, 289, 292, 295, 298, 301, 304, 307, 310, 313, 316, 319, 322, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 430, 433, 436, 439, 442, 445, 448, 451, 454, 457, 460, 463, 466, 469, 472, 475, 478, 481, 484, 487, 490, 493, 496, 499, 502, 505, 508, 511, 514, 517, 520, 523, 526, 529, 532, 535, 538, 541, 544, 547, 550, 553, 556, 559, 562, 565, 568, 571, 574, 577, 580, 583, 586, 589, 592, 595, 598, 601, 604, 607, 610, 613, 616, 619, 622, 625, 628, 631, 634, 637, 640, 643, 646, 649, 652, 655, 658, 661, 664, 667, 670, 673, 676, 679, 682, 685, 688, 691, 694, 697, 700, 703, 706, 709, 712, 715, 718, 721, 724, 727, 730, 733, 736, 739, 742, 745, 748, 751, 754, 757, 760, 763, 766, 769, 772, 775, 778, 781, 784, 787, 790, 793, 796, 799, 802, 805, 808, 811, 814, 817, 820, 823, 826, 829, 832, 835, 838, 841, 844, 847, 850, 853, 856, 859, 862, 865, 868, 871, 874, 877, 880, 883, 886, 889, 892, 895, 898, 901, 904, 907, 910, 913, 915.2 ], "y": [ 0, 1.72034e-09, 6.70883e-09, 1.49634e-08, 2.6482e-08, 5.29261e-08, 8.84181e-08, 1.32944e-07, 1.86493e-07, 2.49052e-07, 3.2061e-07, 4.01151e-07, 4.90667e-07, 5.89144e-07, 6.96481e-07, 8.12571e-07, 9.37518e-07, 1.07131e-06, 1.21393e-06, 1.36537e-06, 1.52552e-06, 1.69429e-06, 1.79974e-06, 2.17385e-06, 2.58268e-06, 3.0256e-06, 3.50278e-06, 4.01413e-06, 4.55901e-06, 5.13758e-06, 5.74976e-06, 6.39492e-06, 7.07323e-06, 7.78459e-06, 8.52837e-06, 9.30476e-06, 1.01137e-05, 1.09544e-05, 1.18272e-05, 1.2732e-05, 1.36682e-05, 1.46358e-05, 1.56348e-05, 1.66647e-05, 1.77255e-05, 1.88172e-05, 1.99392e-05, 2.10916e-05, 2.22744e-05, 2.34869e-05, 2.47294e-05, 2.60016e-05, 2.73031e-05, 2.86339e-05, 2.99941e-05, 3.13829e-05, 3.28007e-05, 3.42472e-05, 3.57219e-05, 3.7225e-05, 3.87563e-05, 4.03153e-05, 4.19022e-05, 4.35168e-05, 4.51586e-05, 4.68276e-05, 4.8524e-05, 5.0247e-05, 5.19968e-05, 5.37734e-05, 5.55762e-05, 5.74053e-05, 5.92606e-05, 6.11416e-05, 6.30483e-05, 6.49809e-05, 6.69386e-05, 6.89217e-05, 7.093e-05, 7.2963e-05, 7.50208e-05, 7.71033e-05, 7.92101e-05, 8.13411e-05, 8.34964e-05, 8.56753e-05, 8.78781e-05, 9.01046e-05, 9.23543e-05, 9.46272e-05, 9.69234e-05, 9.92423e-05, 0.000101584, 0.000103948, 0.000106335, 0.000108743, 0.000111174, 0.000113626, 0.0001161, 0.000118596, 0.000121112, 0.00012365, 0.000126208, 0.000128787, 0.000131386, 0.000134005, 0.000136643, 0.0001393, 0.000141975, 0.000144665, 0.00014737, 0.000150088, 0.000152832, 0.000128604, 0.000135595, 0.000142737, 0.000150026, 0.000157459, 0.000165033, 0.000172747, 0.000180597, 0.000188581, 0.000196696, 0.00020494, 0.00021331, 0.000221804, 0.000230417, 0.00023915, 0.000247998, 0.00025696, 0.000266032, 0.000275213, 0.000284498, 0.000293887, 0.000303376, 0.000312962, 0.000322643, 0.000332415, 0.000342273, 0.000352208, 0.000351928, 0.000361821, 0.000371796, 0.000381851, 0.000391983, 0.000402192, 0.000412473, 0.000422825, 0.000433247, 0.000443735, 0.000454288, 0.000464903, 0.000475578, 0.000486312, 0.000497101, 0.000507945, 0.00051884, 0.000529784, 0.000540777, 0.000551815, 0.000562896, 0.000574019, 0.000585181, 0.00059638, 0.000607616, 0.000618885, 0.000630184, 0.000641513, 0.00065287, 0.000664251, 0.000675657, 0.000687085, 0.000698532, 0.000709996, 0.000721477, 0.000732971, 0.000744478, 0.000755995, 0.00076752, 0.000779052, 0.000790588, 0.000802128, 0.000813669, 0.000825209, 0.000836746, 0.00084828, 0.000859807, 0.000871327, 0.000882838, 0.000894337, 0.000905824, 0.000917295, 0.000928751, 0.000940189, 0.000951607, 0.000963004, 0.000974378, 0.000985728, 0.000997052, 0.00100835, 0.00101962, 0.00103085, 0.00104205, 0.00105322, 0.00106436, 0.00107546, 0.00108652, 0.00109753, 0.00110851, 0.00111945, 0.00113034, 0.00114118, 0.00115198, 0.00116273, 0.00117343, 0.00118408, 0.00119468, 0.00120521, 0.0012157, 0.00122613, 0.0012365, 0.00124681, 0.00125706, 0.00126725, 0.00127738, 0.00128744, 0.00129744, 0.00130737, 0.00131723, 0.00132702, 0.00133674, 0.00134639, 0.00135598, 0.00136548, 0.00137491, 0.00138427, 0.00139355, 0.00140275, 0.00141188, 0.00142092, 0.00142988, 0.00143876, 0.00144756, 0.00145627, 0.00146491, 0.00147345, 0.00148191, 0.00149028, 0.00149857, 0.00150676, 0.00151487, 0.00152288, 0.00153079, 0.00153863, 0.00154635, 0.00155398, 0.00156153, 0.00156895, 0.00157629, 0.00158351, 0.00159062, 0.00159761, 0.00160447, 0.0016111, 0.00162128, 0.00151855, 0.00152501, 0.00153136, 0.00153761, 0.00154377, 0.00154983, 0.0015558, 0.00156166, 0.00156743, 0.00157309, 0.00157865, 0.00158412, 0.00158949, 0.00159474, 0.00159989, 0.00160495, 0.00160989, 0.00161474, 0.00161947, 0.0016241, 0.00162863, 0.00163305, 0.00163736, 0.00164157, 0.00164566, 0.00164964, 0.00165352, 0.00165729, 0.00166095, 0.00166449, 0.00166793, 0.00167126, 0.00167448, 0.00167757, 0.00168057, 0.00168345, 0.00168621, 0.00168886, 0.00169141, 0.00169384, 0.00169615, 0.00169835, 0.00170044, 0.00170242, 0.00170427, 0.00170602, 0.00170765, 0.00170917, 0.00171058, 0.00171187, 0.00171304, 0.0017141, 0.00171505, 0.00171589, 0.0017166, 0.00171721, 0.0017177, 0.00171808, 0.00171834, 0.00171849, 0.00171853, 0.00171845, 0.00171826, 0.00171795, 0.00171753, 0.00171698, 0.0017168, 0.00170293, 0.0017021, 0.00170117, 0.00170013, 0.00169897, 0.00169772, 0.00169635, 0.00169489, 0.00169333, 0.00169165, 0.00168987, 0.00168799, 0.00168601, 0.00168394, 0.00168176, 0.00167949, 0.00167712, 0.00167466, 0.00167211, 0.00166946, 0.00166672, 0.0016639, 0.00166099, 0.00165799, 0.00165492, 0.00165176, 0.00164852, 0.00164522, 0.00164182, 0.00163837, 0.00163483, 0.00163123, 0.00162756, 0.00162383, 0.00162003, 0.00161616, 0.00161223, 0.00160824, 0.00160418, 0.00160004, 0.00159583, 0.00159154, 0.00158715, 0.00158266, 0.00157803, 0.00157327, 0.00156834, 0.00156318, 0.00155775, 0.00155185, 0.00154486, 0.00164617, 0.00207539 ] } ], "layout": { "autosize": true, "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ -54.36455275546938, 969.5645527554694 ], "title": { "text": "bin_en" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.00015759074646074645, 0.0021929153024453025 ], "title": { "text": "dn_de" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "url=livechart+\"fields=bin_beta&nuclides=135xe&rad_types=bm&metastable_seqno=1\"\n", "\n", "df=lc_pd_dataframe(url)\n", "fig = px.scatter(df, x=\"bin_en\", y=\"dn_de\")\n", "fig.add_trace(go.Scatter(x=df[\"bin_en\"], y=df[\"dn_de_nu\"]))\n", "\n", "fig.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply further filters once the data are loaded\n", "The pandas query function acts on the loaded dataset.\n", "
\n", "To plot the Half-life of Ac isotopes, apply the *query* function to the column *symbol* to extract the *Ac* isotopes\n", "```python\n", ".query('symbol==\"Ac\"')\n", "```\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "n=%{x}
half_life_sec=%{y}", "legendgroup": "", "marker": { "color": "#636efa", "symbol": "circle" }, "mode": "markers", "name": "", "orientation": "v", "showlegend": false, "type": "scatter", "x": [ 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147 ], "xaxis": "x", "y": [ 0.02, 0.022, 0.027, 0.095, 0.087, 0.35, 0.21, 0.88, 0.738, 8.2, 0.17, 0.00044, 6.9e-08, 1.03e-06, 1.18e-05, 0.0264, 0.052, 5, 126, 10008, 857088, 105732, 687057392.318817, 22140, 3762, 122, 450, 119, 143, 44, 62, 72 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "tracegroupgap": 0 }, "margin": { "t": 60 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 114.16274824116128, 148.83725175883873 ], "title": { "text": "n" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -8.39985234185191, 10.07569444930853 ], "title": { "text": "half_life_sec" }, "type": "log" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "url=livechart+\"fields=ground_states&nuclides=all\"\n", "\n", "df=lc_pd_dataframe(url)[['symbol','n','half_life_sec']].query('symbol==\"Ac\"') # further filter\n", "\n", "fig = px.scatter(df, x=\"n\", y=\"half_life_sec\", log_y=True)\n", "fig.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D and 2D plots of fission yields\n", "Use the mouse to rotate and zoom" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": [ 2.6891e-05, 8.2182e-06, 0.000108, 0.000108, 0.0017, 7.3602e-12, 1.9996e-11, 4.1079e-11, 4.0473e-11, 2.852e-11, 1.0302e-11, 2.485e-12, 1.0523e-11, 4.9592e-11, 1.8948e-10, 5.8481e-10, 8.5494e-10, 4.86e-10, 1.6621e-09, 6.2365e-10, 6.2614e-10, 6.7629e-10, 1.8044e-10, 3.1693e-11, 2.6088e-12, 1.0523e-11, 5.1847e-11, 2.3151e-10, 9.6078e-10, 3.6578e-09, 6.5085e-09, 4.863e-09, 2.9138e-08, 4.3646e-08, 5.0028e-08, 2.7733e-08, 1.0709e-08, 2.1473e-09, 2.8974e-10, 1.8116e-11, 5.1847e-11, 2.3151e-10, 9.729e-10, 3.8549e-09, 1.6337e-10, 1.4487e-08, 1.155e-08, 2.0179e-08, 3.0598e-08, 1.5863e-07, 3.8525e-07, 7.5021e-07, 8.1929e-07, 7.1733e-07, 1.5434e-07, 1.5434e-07, 9.117e-08, 1.2676e-08, 1.1339e-09, 4.0232e-11, 2.3151e-10, 9.729e-10, 3.8811e-09, 1.4528e-08, 3.3942e-11, 5.1872e-08, 1.6141e-07, 1.3804e-08, 5.5863e-07, 1.2923e-06, 4.6342e-07, 7.0932e-07, 4.3438e-06, 8.6195e-06, 1.383e-05, 1.1182e-05, 1.3845e-06, 7.4968e-06, 2.2493e-06, 4.1192e-07, 3.81e-08, 2.2541e-09, 4.3937e-11, 1.4528e-08, 1.753e-07, 5.6132e-07, 1.8855e-08, 1.7033e-06, 3.7612e-06, 4.6314e-06, 1.322e-05, 3.2632e-05, 6.9665e-05, 0.00010531, 0.00012615, 7.4207e-05, 3.3569e-05, 6.9822e-06, 7.0862e-07, 4.1544e-08, 1.1735e-09, 1.8075e-11, 5.6132e-07, 1.7034e-06, 1.6783e-06, 4.9242e-06, 1.3341e-05, 6.3541e-07, 3.4591e-05, 2.7203e-05, 7.1479e-05, 0.00019808, 0.00017052, 0.00025813, 0.00078247, 0.00087169, 0.00018654, 0.0009957, 0.00030963, 8.3514e-05, 8.1734e-06, 6.5806e-07, 2.2237e-08, 5.7353e-10, 5.2795e-12, 1.3341e-05, 2.6244e-10, 4.7098e-09, 8.5265e-05, 0.00020099, 0.0004543, 0.00098806, 0.001937, 0.0014829, 0.0016975, 0.0037412, 0.0016748, 0.0016748, 0.0015095, 0.00039824, 6.7195e-05, 6.1875e-06, 3.0595e-07, 7.7925e-09, 1.1871e-10, 4.7098e-09, 8.5265e-05, 4.0407e-11, 0.00020099, 0.00045421, 0.00044356, 0.00099264, 0.0020033, 0.00012805, 0.0037963, 0.0030575, 0.0027407, 0.010229, 0.010044, 0.010545, 0.0055175, 0.0023601, 0.00040647, 5.128e-05, 2.7978e-06, 1.0424e-07, 1.5399e-09, 2.0123e-11, 2.4841e-07, 1.4016e-11, 1.8113e-09, 1.4074e-09, 0.0020035, 4.3317e-06, 1.1702e-06, 0.0058674, 0.010549, 0.00031991, 0.013085, 0.019282, 0.022793, 0.022874, 0.015813, 0.0060784, 0.0015697, 0.00020594, 1.6075e-05, 7.4049e-07, 2.1937e-08, 3.1667e-10, 3.3428e-12, 1.661e-09, 6.3009e-12, 4.3609e-06, 0.0058674, 0.0058622, 0.010872, 0.0028554, 0.013071, 0.020416, 0.02685, 0.037181, 0.041465, 0.043146, 0.029297, 0.016396, 0.0041763, 0.00090763, 9.3037e-05, 6.643e-06, 2.4127e-07, 7.0608e-09, 8.4521e-11, 1.2352e-12, 2.086e-10, 1.4126e-10, 0.013129, 7.6218e-07, 5.1615e-07, 0.026871, 0.037466, 0.043832, 0.038661, 0.013781, 0.052733, 0.051914, 0.041833, 0.024406, 0.010645, 0.0017647, 0.0011765, 0.00039748, 1.9434e-05, 1.9441e-05, 2.2813e-06, 7.8677e-08, 1.5185e-09, 1.4772e-11, 6.6752e-12, 7.6218e-07, 3.4103e-09, 5.7185e-10, 0.037466, 0.043839, 0.052208, 0.053739, 0.058614, 0.060261, 0.059203, 0.053257, 0.040486, 0.018733, 0.0078265, 0.0014502, 0.00023638, 1.5369e-05, 7.3727e-07, 1.4245e-08, 1.9892e-10, 3.2772e-12, 1.539e-12, 0.043839, 4.2261e-06, 0.052208, 1.9896e-08, 0.05374, 0.031618, 0.058641, 0.060616, 0.015393, 0.061459, 0.063395, 0.047523, 0.014757, 0.02227, 0.031587, 0.0077634, 0.014442, 0.032071, 0.029067, 0.0067782, 0.0065437, 0.0038237, 0.00029664, 0.00029664, 5.5509e-05, 2.6647e-06, 6.6371e-08, 1.7871e-11, 0.052208, 0.05374, 0.058641, 0.060616, 0.061465, 0.063488, 0.063171, 0.060319, 0.061809, 0.056738, 0.058903, 0.036395, 0.020146, 0.0055774, 0.001223, 0.00010271, 3.8059e-08, 1.3288e-08, 2.3031e-07, 1.6845e-09, 4.958e-12, 2.6678e-12, 2.9023e-10, 1.0881e-10, 0.063449, 0.00068589, 6.878e-07, 0.060334, 0.057334, 0.061848, 0.00013844, 0.037368, 0.021099, 0.059904, 0.0048561, 0.05208, 0.031611, 0.011465, 0.027237, 0.007072, 0.0058475, 0.0041048, 0.00033832, 0.00014064, 5.0784e-05, 2.8642e-06, 5.5514e-08, 6.5748e-10, 2.8043e-12, 0.063488, 6.8782e-07, 0.060334, 0.061987, 0.058047, 0.064795, 0.052388, 0.044811, 0.032439, 0.022611, 0.011704, 0.0042764, 0.0016994, 0.00052793, 0.00013423, 1.9737e-05, 1.4249e-06, 4.224e-08, 1.0328e-09, 1.0028e-11, 0.058045, 0.051116, 4.8581e-09, 0.052389, 0.044813, 1.8619e-06, 0.032481, 0.022917, 0.012817, 0.0046896, 0.0018438, 0.00078576, 0.00042528, 0.00023691, 8.6107e-05, 1.4162e-05, 1.9154e-06, 9.814e-08, 2.8591e-09, 1.8e-10, 5.5028e-12, 1.8913e-06, 4.8581e-09, 0.052389, 0.044815, 0.032481, 6.5916e-09, 0.022918, 0.01282, 0.0046896, 0.0018438, 0.00079127, 0.00045652, 0.0003603, 0.00029945, 0.00017131, 7.5712e-05, 4.9286e-05, 2.3845e-05, 3.1726e-06, 6.6381e-07, 7.0361e-08, 5.0616e-09, 1.5249e-11, 0.032481, 0.032092, 4.9871e-12, 3.8789e-12, 0.01282, 0.0036392, 0.0046896, 0.0018438, 0.00079127, 2.3787e-09, 0.00045664, 7.3193e-08, 0.00032837, 0.00021698, 4.5677e-05, 0.00031968, 0.00012366, 9.9805e-05, 0.0001055, 1.7937e-05, 4.7099e-05, 2.2011e-05, 5.2546e-06, 1.4357e-07, 7.9489e-09, 1.9121e-10, 3.3995e-12, 4.9697e-12, 0.01282, 0.0046896, 0.0018438, 0.00079127, 0.00045664, 0.00022832, 0.00036348, 0.00032812, 1.4091e-06, 0.00026507, 0.00034582, 1.9258e-05, 0.00031288, 0.0001475, 0.00012016, 0.00031124, 0.00025207, 0.00015802, 0.00016478, 3.3652e-05, 7.0619e-06, 7.4712e-07, 5.4108e-08, 2.439e-09, 5.4418e-11, 0.00045664, 0.00045642, 0.00032687, 0.00032608, 0.00026508, 0.00022668, 0.00033124, 0.00031462, 1.445e-06, 0.00014281, 0.00016076, 0.00032729, 3.7613e-05, 0.00015511, 0.00023742, 0.00026615, 0.0002106, 0.00025759, 0.00011783, 0.00012027, 7.7228e-05, 1.0053e-05, 1.0781e-05, 3.618e-06, 2.5947e-07, 1.8172e-07, 1.9934e-08, 6.3123e-10, 8.5425e-12, 0.0003285, 0.00026508, 0.00034201, 3.919e-06, 0.00031462, 0.00025789, 1.195e-05, 0.00036306, 0.0002974, 8.4012e-05, 0.00040685, 0.00031324, 0.00011175, 0.0004269, 0.00014974, 0.00023354, 0.00028188, 4.2837e-05, 0.00011223, 5.7258e-05, 3.7441e-06, 1.0458e-05, 2.0998e-06, 1.9181e-08, 3.0369e-08, 7.4668e-07, 2.0889e-06, 3.4493e-07, 1.2139e-08, 2.0843e-10, 3.9135e-06, 0.00025695, 0.00025789, 1.8703e-11, 6.5168e-11, 3.4966e-11, 0.00023656, 0.00027382, 0.00040685, 2.9839e-08, 1.6132e-08, 0.00015969, 0.0002834, 0.00043185, 4.9512e-06, 4.9512e-06, 0.000341, 0.00011053, 0.00034517, 5.2921e-05, 5.2921e-05, 0.00035867, 7.8161e-05, 0.00021693, 0.00017215, 0.00025415, 5.0657e-05, 8.6079e-05, 9.0541e-05, 0.00016065, 3.2366e-05, 0.00013626, 3.8362e-05, 0.00014687, 0.00038854, 7.8561e-05, 6.4885e-05, 9.3991e-05, 0.00013093, 1.6449e-05, 1.6272e-05, 1.6272e-05, 3.9239e-06, 1.3791e-07, 2.3114e-08, 1.6991e-09, 8.6021e-12, 1.2895e-05, 8.3871e-11, 0.00038141, 8.0598e-07, 0.00040688, 0.00042722, 0.00014426, 0.00044178, 0.00044202, 3.9044e-05, 0.00045611, 4.822e-05, 0.00041694, 0.00050337, 0.00029762, 0.0003529, 0.00086136, 0.0019778, 0.00081468, 0.0045176, 0.0032448, 0.002225, 0.0050753, 0.0025423, 0.0065596, 0.0013481, 0.0037007, 0.0017335, 0.0002922, 1.8744e-05, 4.7727e-07, 9.4984e-09, 1.0636e-10, 0.00045076, 4.0386e-09, 2.3125e-09, 0.00046526, 1.1207e-06, 9.1368e-07, 5.624e-07, 0.00066774, 5.7847e-05, 6.7669e-05, 3.7454e-05, 0.0030064, 0.00465, 0.00017627, 0.0070085, 0.00378, 0.011768, 0.0092253, 0.02844, 0.014011, 0.01087, 0.016909, 0.0012473, 0.0033573, 0.00060676, 4.9443e-05, 2.6897e-06, 7.7594e-08, 1.4633e-09, 3.9336e-09, 7.622e-12, 6.0298e-12, 1.3497e-06, 0.00066776, 0.00014944, 0.00011704, 0.0029945, 0.00049545, 0.00482, 0.0086667, 0.0043378, 0.021858, 0.028799, 0.006081, 0.046388, 0.031528, 0.03491, 0.055331, 0.022149, 0.0085595, 0.0016032, 0.00023507, 1.597e-05, 7.3998e-07, 1.7748e-08, 2.919e-10, 1.0669e-12, 1.5343e-10, 0.0030064, 0.010272, 1.4546e-06, 4.0794e-07, 0.033648, 0.046953, 0.00028347, 0.066091, 0.0024508, 0.076924, 0.010251, 0.060085, 0.019241, 0.029075, 0.031882, 0.013819, 0.0043412, 0.0006706, 7.8353e-05, 4.5242e-06, 1.6903e-07, 2.6124e-09, 6.7049e-11, 1.52e-06, 0.033648, 0.00036549, 0.046993, 4.2331e-07, 0.066118, 0.001901, 0.077711, 0.00063863, 0.06321, 0.012276, 0.064075, 0.057664, 0.059348, 0.044799, 0.027062, 0.0092454, 0.0024356, 0.0003118, 2.4956e-05, 8.4771e-07, 2.1712e-08, 2.0608e-10, 1.4635e-11, 0.066118, 2.7926e-07, 1.3114e-07, 0.063293, 3.9171e-06, 0.00010836, 5.7074e-05, 0.058889, 0.064429, 0.0036085, 0.063237, 0.056815, 0.048508, 0.031725, 0.016558, 0.0031587, 0.0020897, 0.00068824, 5.999e-05, 3.0114e-06, 7.7478e-08, 1.165e-09, 8.4722e-12, 2.7926e-07, 6.4127e-10, 4.7242e-10, 0.00013695, 3.8013e-08, 0.05889, 0.055592, 0.065154, 0.06365, 0.059594, 0.05786, 0.056488, 0.051126, 0.03946, 0.017333, 0.0063841, 0.0010686, 0.00013792, 7.3289e-06, 2.7697e-07, 4.5364e-09, 6.88e-11, 5.323e-11, 4.5806e-09, 0.06365, 0.059599, 0.05795, 0.057208, 0.05531, 0.050724, 0.036826, 0.012414, 0.012651, 0.013823, 0.005094, 0.0011999, 0.00014967, 1.2214e-05, 6.4186e-07, 1.7174e-08, 2.9535e-10, 1.9655e-12, 3.4019e-12, 2.5062e-12, 0.059599, 0.05795, 0.057209, 0.055327, 0.050943, 0.037963, 0.029241, 0.021267, 0.016104, 0.0085932, 0.0041144, 0.00097724, 0.00022305, 1.8851e-05, 1.5865e-06, 3.6849e-08, 6.5625e-10, 9.1316e-12, 0.05795, 1.7001e-11, 1.2438e-11, 0.055327, 0.050943, 0.00070305, 0.037965, 0.029267, 0.021475, 0.00071196, 0.010609, 0.0068757, 0.0039879, 0.0024356, 0.00072177, 0.0001655, 1.6755e-05, 9.7747e-07, 6.5505e-08, 1.9233e-09, 3.6893e-11, 1.6998e-11, 0.055327, 0.050943, 0.037965, 0.029267, 0.021476, 0.016966, 0.010637, 0.0070243, 0.0043102, 0.0030361, 0.0014462, 0.00094397, 0.00025452, 5.9046e-05, 1.1865e-05, 1.5826e-06, 9.8543e-08, 2.8652e-09, 4.1101e-11, 0.021476, 7.4244e-11, 1.781e-10, 0.010637, 2.0057e-07, 0.0043127, 0.0030391, 6.4128e-06, 8.3767e-06, 0.0015119, 8.2605e-05, 0.0004292, 0.00016377, 8.8877e-05, 2.9251e-05, 7.1916e-06, 6.2975e-07, 4.1109e-08, 1.4982e-09, 3.57e-11, 0.021476, 2.4344e-10, 0.010637, 2.0058e-07, 0.0043127, 0.0030539, 0.0015121, 1.2134e-07, 0.0011111, 0.00043528, 0.00017825, 0.00011602, 6.3285e-05, 2.8241e-05, 7.3949e-06, 1.2452e-06, 1.856e-07, 1.3551e-08, 7.1141e-10, 1.9129e-11, 0.0043127, 0.0015121, 3.978e-10, 2.0638e-10, 0.00043529, 0.00017829, 0.00011642, 6.4725e-05, 3.1635e-05, 1.01e-05, 2.9044e-06, 8.0301e-07, 1.8151e-07, 2.5224e-08, 2.8294e-09, 1.5733e-10, 3.9772e-10, 0.00043529, 0.00017829, 0.00011642, 6.4728e-05, 3.1655e-05, 1.0168e-05, 3.0212e-06, 9.6535e-07, 2.8951e-07, 7.9505e-08, 1.8166e-08, 3.4032e-09, 3.7779e-10, 3.2008e-11, 3.1655e-05, 3.3378e-11, 3.0215e-06, 9.6649e-07, 2.9271e-07, 8.3915e-08, 2.2732e-08, 5.7409e-09, 1.3019e-09, 2.3135e-10, 3.3016e-11, 3.3378e-11, 3.0215e-06, 9.6649e-07, 2.9272e-07, 8.3932e-08, 2.2787e-08, 6.4423e-12, 5.8573e-09, 1.4254e-09, 3.2724e-10, 6.9646e-11, 1.3182e-11, 2.2787e-08, 5.8573e-09, 1.4254e-09, 3.286e-10, 7.1771e-11, 1.3614e-11, 5.8573e-09, 1.4254e-09, 1.7033e-10, 3.2861e-10, 7.1771e-11, 1.4792e-11, 1.5296e-12, 1.5296e-12, 1.5296e-12, 1.5296e-12, 1.3313e-12, 1.3258e-12, 2.9067e-10, 6.3651e-12, 1.3189e-12, 2.6254e-12, 1.7604e-12, 1.1777e-12, 1.7604e-12, 1.7604e-12, 7.1771e-11, 1.7604e-12 ], "colorscale": [ [ 0, "#440154" ], [ 0.1111111111111111, "#482878" ], [ 0.2222222222222222, "#3e4989" ], [ 0.3333333333333333, "#31688e" ], [ 0.4444444444444444, "#26828e" ], [ 0.5555555555555556, "#1f9e89" ], [ 0.6666666666666666, "#35b779" ], [ 0.7777777777777778, "#6ece58" ], [ 0.8888888888888888, "#b5de2b" ], [ 1, "#fde725" ] ], "size": 7 }, "mode": "markers", "text": [ "1H", "2H", "3H", "3He", "4He", "64Fe", "65Fe", "66Fe", "67Fe", "68Fe", "69Fe", "70Fe", "64Co", "65Co", "66Co", "67Co", "68Co", "68Co", "69Co", "70Co", "70Co", "71Co", "72Co", "73Co", "74Co", "64Ni", "65Ni", "66Ni", "67Ni", "68Ni", "69Ni", "69Ni", "70Ni", "71Ni", "72Ni", "73Ni", "74Ni", "75Ni", "76Ni", "77Ni", "65Cu", "66Cu", "67Cu", "68Cu", "68Cu", "69Cu", "70Cu", "70Cu", "70Cu", "71Cu", "72Cu", "73Cu", "74Cu", "75Cu", "76Cu", "76Cu", "77Cu", "78Cu", "79Cu", "80Cu", "66Zn", "67Zn", "68Zn", "69Zn", "69Zn", "70Zn", "71Zn", "71Zn", "72Zn", "73Zn", "73Zn", "73Zn", "74Zn", "75Zn", "76Zn", "77Zn", "77Zn", "78Zn", "79Zn", "80Zn", "81Zn", "82Zn", "83Zn", "69Ga", "71Ga", "72Ga", "72Ga", "73Ga", "74Ga", "74Ga", "75Ga", "76Ga", "77Ga", "78Ga", "79Ga", "80Ga", "81Ga", "82Ga", "83Ga", "84Ga", "85Ga", "86Ga", "72Ge", "73Ge", "73Ge", "74Ge", "75Ge", "75Ge", "76Ge", "77Ge", "77Ge", "78Ge", "79Ge", "79Ge", "80Ge", "81Ge", "81Ge", "82Ge", "83Ge", "84Ge", "85Ge", "86Ge", "87Ge", "88Ge", "89Ge", "75As", "75As", "76As", "77As", "78As", "79As", "80As", "81As", "82As", "82As", "83As", "84As", "84As", "85As", "86As", "87As", "88As", "89As", "90As", "91As", "76Se", "77Se", "77Se", "78Se", "79Se", "79Se", "80Se", "81Se", "81Se", "82Se", "83Se", "83Se", "84Se", "85Se", "86Se", "87Se", "88Se", "89Se", "90Se", "91Se", "92Se", "93Se", "94Se", "79Br", "79Br", "80Br", "80Br", "81Br", "82Br", "82Br", "83Br", "84Br", "84Br", "85Br", "86Br", "87Br", "88Br", "89Br", "90Br", "91Br", "92Br", "93Br", "94Br", "95Br", "96Br", "97Br", "80Kr", "81Kr", "82Kr", "83Kr", "83Kr", "84Kr", "85Kr", "85Kr", "86Kr", "87Kr", "88Kr", "89Kr", "90Kr", "91Kr", "92Kr", "93Kr", "94Kr", "95Kr", "96Kr", "97Kr", "98Kr", "99Kr", "83Rb", "84Rb", "84Rb", "85Rb", "86Rb", "86Rb", "87Rb", "88Rb", "89Rb", "90Rb", "90Rb", "91Rb", "92Rb", "93Rb", "94Rb", "95Rb", "96Rb", "96Rb", "97Rb", "98Rb", "98Rb", "99Rb", "100Rb", "101Rb", "102Rb", "84Sr", "86Sr", "87Sr", "87Sr", "88Sr", "89Sr", "90Sr", "91Sr", "92Sr", "93Sr", "94Sr", "95Sr", "96Sr", "97Sr", "98Sr", "99Sr", "100Sr", "101Sr", "102Sr", "103Sr", "104Sr", "88Y", "88Y", "89Y", "89Y", "90Y", "90Y", "91Y", "91Y", "92Y", "93Y", "93Y", "94Y", "95Y", "96Y", "96Y", "97Y", "97Y", "97Y", "98Y", "98Y", "99Y", "100Y", "100Y", "101Y", "102Y", "102Y", "103Y", "104Y", "105Y", "108Y", "90Zr", "91Zr", "92Zr", "93Zr", "94Zr", "95Zr", "96Zr", "97Zr", "98Zr", "99Zr", "100Zr", "101Zr", "102Zr", "103Zr", "104Zr", "105Zr", "106Zr", "107Zr", "108Zr", "109Zr", "110Zr", "93Nb", "94Nb", "94Nb", "95Nb", "95Nb", "96Nb", "97Nb", "97Nb", "98Nb", "98Nb", "99Nb", "99Nb", "100Nb", "100Nb", "101Nb", "102Nb", "102Nb", "103Nb", "104Nb", "104Nb", "105Nb", "106Nb", "107Nb", "108Nb", "109Nb", "110Nb", "111Nb", "112Nb", "95Mo", "96Mo", "97Mo", "98Mo", "99Mo", "100Mo", "101Mo", "102Mo", "103Mo", "104Mo", "105Mo", "106Mo", "107Mo", "108Mo", "109Mo", "110Mo", "111Mo", "112Mo", "113Mo", "114Mo", "99Tc", "99Tc", "100Tc", "101Tc", "102Tc", "102Tc", "103Tc", "104Tc", "105Tc", "106Tc", "107Tc", "108Tc", "109Tc", "110Tc", "111Tc", "112Tc", "113Tc", "114Tc", "115Tc", "116Tc", "117Tc", "99Ru", "100Ru", "101Ru", "102Ru", "103Ru", "103Ru", "104Ru", "105Ru", "106Ru", "107Ru", "108Ru", "109Ru", "110Ru", "111Ru", "112Ru", "113Ru", "113Ru", "114Ru", "115Ru", "116Ru", "117Ru", "118Ru", "119Ru", "103Rh", "103Rh", "104Rh", "104Rh", "105Rh", "105Rh", "106Rh", "107Rh", "108Rh", "108Rh", "109Rh", "110Rh", "111Rh", "112Rh", "112Rh", "113Rh", "114Rh", "114Rh", "115Rh", "116Rh", "116Rh", "117Rh", "118Rh", "119Rh", "120Rh", "121Rh", "122Rh", "104Pd", "105Pd", "106Pd", "107Pd", "108Pd", "109Pd", "109Pd", "110Pd", "111Pd", "111Pd", "112Pd", "113Pd", "113Pd", "114Pd", "115Pd", "115Pd", "116Pd", "117Pd", "117Pd", "118Pd", "119Pd", "120Pd", "121Pd", "122Pd", "123Pd", "124Pd", "109Ag", "109Ag", "111Ag", "111Ag", "112Ag", "113Ag", "113Ag", "114Ag", "114Ag", "115Ag", "115Ag", "116Ag", "116Ag", "117Ag", "117Ag", "118Ag", "118Ag", "119Ag", "120Ag", "120Ag", "121Ag", "122Ag", "122Ag", "123Ag", "124Ag", "124Ag", "125Ag", "126Ag", "130Ag", "111Cd", "112Cd", "113Cd", "113Cd", "114Cd", "115Cd", "115Cd", "116Cd", "117Cd", "117Cd", "118Cd", "119Cd", "119Cd", "120Cd", "121Cd", "121Cd", "122Cd", "123Cd", "123Cd", "124Cd", "125Cd", "125Cd", "126Cd", "127Cd", "128Cd", "129Cd", "129Cd", "130Cd", "131Cd", "132Cd", "113In", "115In", "115In", "116In", "116In", "116In", "117In", "117In", "118In", "118In", "118In", "119In", "119In", "120In", "120In", "120In", "121In", "121In", "122In", "122In", "122In", "123In", "123In", "124In", "124In", "125In", "125In", "126In", "126In", "127In", "127In", "128In", "128In", "128In", "129In", "129In", "130In", "130In", "130In", "131In", "131In", "131In", "132In", "133In", "133In", "134In", "135In", "115Sn", "116Sn", "117Sn", "117Sn", "118Sn", "119Sn", "119Sn", "120Sn", "121Sn", "121Sn", "122Sn", "123Sn", "123Sn", "124Sn", "125Sn", "125Sn", "126Sn", "127Sn", "127Sn", "128Sn", "128Sn", "129Sn", "129Sn", "130Sn", "130Sn", "131Sn", "131Sn", "132Sn", "133Sn", "134Sn", "135Sn", "136Sn", "137Sn", "121Sb", "122Sb", "122Sb", "123Sb", "124Sb", "124Sb", "124Sb", "125Sb", "126Sb", "126Sb", "126Sb", "127Sb", "128Sb", "128Sb", "129Sb", "129Sb", "130Sb", "130Sb", "131Sb", "132Sb", "132Sb", "133Sb", "134Sb", "134Sb", "135Sb", "136Sb", "137Sb", "138Sb", "139Sb", "122Te", "123Te", "123Te", "124Te", "125Te", "125Te", "126Te", "127Te", "127Te", "128Te", "129Te", "129Te", "130Te", "131Te", "131Te", "132Te", "133Te", "133Te", "134Te", "135Te", "136Te", "137Te", "138Te", "139Te", "140Te", "141Te", "142Te", "125I", "126I", "127I", "129I", "130I", "130I", "131I", "132I", "132I", "133I", "133I", "134I", "134I", "135I", "136I", "136I", "137I", "138I", "139I", "140I", "141I", "142I", "143I", "144I", "126Xe", "130Xe", "131Xe", "131Xe", "132Xe", "132Xe", "133Xe", "133Xe", "134Xe", "134Xe", "135Xe", "135Xe", "136Xe", "137Xe", "138Xe", "139Xe", "140Xe", "141Xe", "142Xe", "143Xe", "144Xe", "145Xe", "146Xe", "147Xe", "132Cs", "133Cs", "134Cs", "134Cs", "135Cs", "135Cs", "136Cs", "136Cs", "137Cs", "138Cs", "138Cs", "139Cs", "140Cs", "141Cs", "142Cs", "143Cs", "144Cs", "144Cs", "145Cs", "146Cs", "147Cs", "148Cs", "149Cs", "150Cs", "134Ba", "135Ba", "135Ba", "136Ba", "136Ba", "137Ba", "137Ba", "138Ba", "139Ba", "140Ba", "141Ba", "142Ba", "143Ba", "144Ba", "145Ba", "146Ba", "147Ba", "148Ba", "149Ba", "150Ba", "151Ba", "152Ba", "137La", "138La", "139La", "140La", "141La", "142La", "143La", "144La", "145La", "146La", "146La", "147La", "148La", "149La", "150La", "151La", "152La", "153La", "154La", "155La", "139Ce", "139Ce", "140Ce", "141Ce", "142Ce", "143Ce", "144Ce", "145Ce", "146Ce", "147Ce", "148Ce", "149Ce", "150Ce", "151Ce", "152Ce", "153Ce", "154Ce", "155Ce", "156Ce", "157Ce", "141Pr", "142Pr", "142Pr", "143Pr", "144Pr", "144Pr", "145Pr", "146Pr", "147Pr", "148Pr", "149Pr", "150Pr", "151Pr", "152Pr", "153Pr", "154Pr", "155Pr", "156Pr", "157Pr", "158Pr", "159Pr", "142Nd", "143Nd", "144Nd", "145Nd", "146Nd", "147Nd", "148Nd", "149Nd", "150Nd", "151Nd", "152Nd", "153Nd", "154Nd", "155Nd", "156Nd", "157Nd", "158Nd", "159Nd", "160Nd", "161Nd", "147Pm", "148Pm", "148Pm", "149Pm", "150Pm", "151Pm", "152Pm", "152Pm", "152Pm", "153Pm", "154Pm", "155Pm", "156Pm", "157Pm", "158Pm", "159Pm", "160Pm", "161Pm", "162Pm", "163Pm", "147Sm", "148Sm", "149Sm", "150Sm", "151Sm", "152Sm", "153Sm", "153Sm", "154Sm", "155Sm", "156Sm", "157Sm", "158Sm", "159Sm", "160Sm", "161Sm", "162Sm", "163Sm", "164Sm", "165Sm", "151Eu", "153Eu", "154Eu", "154Eu", "155Eu", "156Eu", "157Eu", "158Eu", "159Eu", "160Eu", "161Eu", "162Eu", "163Eu", "164Eu", "165Eu", "166Eu", "154Gd", "155Gd", "156Gd", "157Gd", "158Gd", "159Gd", "160Gd", "161Gd", "162Gd", "163Gd", "164Gd", "165Gd", "166Gd", "167Gd", "168Gd", "159Tb", "160Tb", "161Tb", "162Tb", "163Tb", "164Tb", "165Tb", "166Tb", "167Tb", "168Tb", "169Tb", "160Dy", "161Dy", "162Dy", "163Dy", "164Dy", "165Dy", "165Dy", "166Dy", "167Dy", "168Dy", "169Dy", "170Dy", "165Ho", "166Ho", "167Ho", "168Ho", "169Ho", "170Ho", "166Er", "167Er", "167Er", "168Er", "169Er", "170Er", "63Fe", "63Co", "63Ni", "63Cu", "70Ga", "70Ge", "129Ag", "167Eu", "169Gd", "170Tb", "171Dy", "170Ho", "171Ho", "171Er", "169Tm", "171Tm" ], "type": "scatter3d", "x": [ 1, 1, 1, 2, 2, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 68, 26, 27, 28, 29, 31, 32, 47, 63, 64, 65, 66, 67, 67, 68, 69, 69 ], "y": [ 0, 1, 2, 1, 2, 38, 39, 40, 41, 42, 43, 44, 37, 38, 39, 40, 41, 41, 42, 43, 43, 44, 45, 46, 47, 36, 37, 38, 39, 40, 41, 41, 42, 43, 44, 45, 46, 47, 48, 49, 36, 37, 38, 39, 39, 40, 41, 41, 41, 42, 43, 44, 45, 46, 47, 47, 48, 49, 50, 51, 36, 37, 38, 39, 39, 40, 41, 41, 42, 43, 43, 43, 44, 45, 46, 47, 47, 48, 49, 50, 51, 52, 53, 38, 40, 41, 41, 42, 43, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 40, 41, 41, 42, 43, 43, 44, 45, 45, 46, 47, 47, 48, 49, 49, 50, 51, 52, 53, 54, 55, 56, 57, 42, 42, 43, 44, 45, 46, 47, 48, 49, 49, 50, 51, 51, 52, 53, 54, 55, 56, 57, 58, 42, 43, 43, 44, 45, 45, 46, 47, 47, 48, 49, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 44, 44, 45, 45, 46, 47, 47, 48, 49, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 44, 45, 46, 47, 47, 48, 49, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 46, 47, 47, 48, 49, 49, 50, 51, 52, 53, 53, 54, 55, 56, 57, 58, 59, 59, 60, 61, 61, 62, 63, 64, 65, 46, 48, 49, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 49, 49, 50, 50, 51, 51, 52, 52, 53, 54, 54, 55, 56, 57, 57, 58, 58, 58, 59, 59, 60, 61, 61, 62, 63, 63, 64, 65, 66, 69, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 52, 53, 53, 54, 54, 55, 56, 56, 57, 57, 58, 58, 59, 59, 60, 61, 61, 62, 63, 63, 64, 65, 66, 67, 68, 69, 70, 71, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 56, 56, 57, 58, 59, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 55, 56, 57, 58, 59, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 69, 70, 71, 72, 73, 74, 75, 58, 58, 59, 59, 60, 60, 61, 62, 63, 63, 64, 65, 66, 67, 67, 68, 69, 69, 70, 71, 71, 72, 73, 74, 75, 76, 77, 58, 59, 60, 61, 62, 63, 63, 64, 65, 65, 66, 67, 67, 68, 69, 69, 70, 71, 71, 72, 73, 74, 75, 76, 77, 78, 62, 62, 64, 64, 65, 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, 73, 73, 74, 75, 75, 76, 77, 77, 78, 79, 83, 63, 64, 65, 65, 66, 67, 67, 68, 69, 69, 70, 71, 71, 72, 73, 73, 74, 75, 75, 76, 77, 77, 78, 79, 80, 81, 81, 82, 83, 84, 64, 66, 66, 67, 67, 67, 68, 68, 69, 69, 69, 70, 70, 71, 71, 71, 72, 72, 73, 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, 79, 79, 79, 80, 80, 81, 81, 81, 82, 82, 82, 83, 84, 84, 85, 86, 65, 66, 67, 67, 68, 69, 69, 70, 71, 71, 72, 73, 73, 74, 75, 75, 76, 77, 77, 78, 78, 79, 79, 80, 80, 81, 81, 82, 83, 84, 85, 86, 87, 70, 71, 71, 72, 73, 73, 73, 74, 75, 75, 75, 76, 77, 77, 78, 78, 79, 79, 80, 81, 81, 82, 83, 83, 84, 85, 86, 87, 88, 70, 71, 71, 72, 73, 73, 74, 75, 75, 76, 77, 77, 78, 79, 79, 80, 81, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 72, 73, 74, 76, 77, 77, 78, 79, 79, 80, 80, 81, 81, 82, 83, 83, 84, 85, 86, 87, 88, 89, 90, 91, 72, 76, 77, 77, 78, 78, 79, 79, 80, 80, 81, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 77, 78, 79, 79, 80, 80, 81, 81, 82, 83, 83, 84, 85, 86, 87, 88, 89, 89, 90, 91, 92, 93, 94, 95, 78, 79, 79, 80, 80, 81, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 81, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 82, 83, 83, 84, 85, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 86, 87, 87, 88, 89, 90, 91, 91, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 85, 86, 87, 88, 89, 90, 91, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 88, 90, 91, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 94, 95, 96, 97, 98, 99, 99, 100, 101, 102, 103, 104, 98, 99, 100, 101, 102, 103, 98, 99, 99, 100, 101, 102, 37, 36, 35, 34, 39, 38, 82, 104, 105, 105, 105, 103, 104, 103, 100, 102 ], "z": [ 2.6891e-05, 8.2182e-06, 0.000108, 0.000108, 0.0017, 7.3602e-12, 1.9996e-11, 4.1079e-11, 4.0473e-11, 2.852e-11, 1.0302e-11, 2.485e-12, 1.0523e-11, 4.9592e-11, 1.8948e-10, 5.8481e-10, 8.5494e-10, 4.86e-10, 1.6621e-09, 6.2365e-10, 6.2614e-10, 6.7629e-10, 1.8044e-10, 3.1693e-11, 2.6088e-12, 1.0523e-11, 5.1847e-11, 2.3151e-10, 9.6078e-10, 3.6578e-09, 6.5085e-09, 4.863e-09, 2.9138e-08, 4.3646e-08, 5.0028e-08, 2.7733e-08, 1.0709e-08, 2.1473e-09, 2.8974e-10, 1.8116e-11, 5.1847e-11, 2.3151e-10, 9.729e-10, 3.8549e-09, 1.6337e-10, 1.4487e-08, 1.155e-08, 2.0179e-08, 3.0598e-08, 1.5863e-07, 3.8525e-07, 7.5021e-07, 8.1929e-07, 7.1733e-07, 1.5434e-07, 1.5434e-07, 9.117e-08, 1.2676e-08, 1.1339e-09, 4.0232e-11, 2.3151e-10, 9.729e-10, 3.8811e-09, 1.4528e-08, 3.3942e-11, 5.1872e-08, 1.6141e-07, 1.3804e-08, 5.5863e-07, 1.2923e-06, 4.6342e-07, 7.0932e-07, 4.3438e-06, 8.6195e-06, 1.383e-05, 1.1182e-05, 1.3845e-06, 7.4968e-06, 2.2493e-06, 4.1192e-07, 3.81e-08, 2.2541e-09, 4.3937e-11, 1.4528e-08, 1.753e-07, 5.6132e-07, 1.8855e-08, 1.7033e-06, 3.7612e-06, 4.6314e-06, 1.322e-05, 3.2632e-05, 6.9665e-05, 0.00010531, 0.00012615, 7.4207e-05, 3.3569e-05, 6.9822e-06, 7.0862e-07, 4.1544e-08, 1.1735e-09, 1.8075e-11, 5.6132e-07, 1.7034e-06, 1.6783e-06, 4.9242e-06, 1.3341e-05, 6.3541e-07, 3.4591e-05, 2.7203e-05, 7.1479e-05, 0.00019808, 0.00017052, 0.00025813, 0.00078247, 0.00087169, 0.00018654, 0.0009957, 0.00030963, 8.3514e-05, 8.1734e-06, 6.5806e-07, 2.2237e-08, 5.7353e-10, 5.2795e-12, 1.3341e-05, 2.6244e-10, 4.7098e-09, 8.5265e-05, 0.00020099, 0.0004543, 0.00098806, 0.001937, 0.0014829, 0.0016975, 0.0037412, 0.0016748, 0.0016748, 0.0015095, 0.00039824, 6.7195e-05, 6.1875e-06, 3.0595e-07, 7.7925e-09, 1.1871e-10, 4.7098e-09, 8.5265e-05, 4.0407e-11, 0.00020099, 0.00045421, 0.00044356, 0.00099264, 0.0020033, 0.00012805, 0.0037963, 0.0030575, 0.0027407, 0.010229, 0.010044, 0.010545, 0.0055175, 0.0023601, 0.00040647, 5.128e-05, 2.7978e-06, 1.0424e-07, 1.5399e-09, 2.0123e-11, 2.4841e-07, 1.4016e-11, 1.8113e-09, 1.4074e-09, 0.0020035, 4.3317e-06, 1.1702e-06, 0.0058674, 0.010549, 0.00031991, 0.013085, 0.019282, 0.022793, 0.022874, 0.015813, 0.0060784, 0.0015697, 0.00020594, 1.6075e-05, 7.4049e-07, 2.1937e-08, 3.1667e-10, 3.3428e-12, 1.661e-09, 6.3009e-12, 4.3609e-06, 0.0058674, 0.0058622, 0.010872, 0.0028554, 0.013071, 0.020416, 0.02685, 0.037181, 0.041465, 0.043146, 0.029297, 0.016396, 0.0041763, 0.00090763, 9.3037e-05, 6.643e-06, 2.4127e-07, 7.0608e-09, 8.4521e-11, 1.2352e-12, 2.086e-10, 1.4126e-10, 0.013129, 7.6218e-07, 5.1615e-07, 0.026871, 0.037466, 0.043832, 0.038661, 0.013781, 0.052733, 0.051914, 0.041833, 0.024406, 0.010645, 0.0017647, 0.0011765, 0.00039748, 1.9434e-05, 1.9441e-05, 2.2813e-06, 7.8677e-08, 1.5185e-09, 1.4772e-11, 6.6752e-12, 7.6218e-07, 3.4103e-09, 5.7185e-10, 0.037466, 0.043839, 0.052208, 0.053739, 0.058614, 0.060261, 0.059203, 0.053257, 0.040486, 0.018733, 0.0078265, 0.0014502, 0.00023638, 1.5369e-05, 7.3727e-07, 1.4245e-08, 1.9892e-10, 3.2772e-12, 1.539e-12, 0.043839, 4.2261e-06, 0.052208, 1.9896e-08, 0.05374, 0.031618, 0.058641, 0.060616, 0.015393, 0.061459, 0.063395, 0.047523, 0.014757, 0.02227, 0.031587, 0.0077634, 0.014442, 0.032071, 0.029067, 0.0067782, 0.0065437, 0.0038237, 0.00029664, 0.00029664, 5.5509e-05, 2.6647e-06, 6.6371e-08, 1.7871e-11, 0.052208, 0.05374, 0.058641, 0.060616, 0.061465, 0.063488, 0.063171, 0.060319, 0.061809, 0.056738, 0.058903, 0.036395, 0.020146, 0.0055774, 0.001223, 0.00010271, 3.8059e-08, 1.3288e-08, 2.3031e-07, 1.6845e-09, 4.958e-12, 2.6678e-12, 2.9023e-10, 1.0881e-10, 0.063449, 0.00068589, 6.878e-07, 0.060334, 0.057334, 0.061848, 0.00013844, 0.037368, 0.021099, 0.059904, 0.0048561, 0.05208, 0.031611, 0.011465, 0.027237, 0.007072, 0.0058475, 0.0041048, 0.00033832, 0.00014064, 5.0784e-05, 2.8642e-06, 5.5514e-08, 6.5748e-10, 2.8043e-12, 0.063488, 6.8782e-07, 0.060334, 0.061987, 0.058047, 0.064795, 0.052388, 0.044811, 0.032439, 0.022611, 0.011704, 0.0042764, 0.0016994, 0.00052793, 0.00013423, 1.9737e-05, 1.4249e-06, 4.224e-08, 1.0328e-09, 1.0028e-11, 0.058045, 0.051116, 4.8581e-09, 0.052389, 0.044813, 1.8619e-06, 0.032481, 0.022917, 0.012817, 0.0046896, 0.0018438, 0.00078576, 0.00042528, 0.00023691, 8.6107e-05, 1.4162e-05, 1.9154e-06, 9.814e-08, 2.8591e-09, 1.8e-10, 5.5028e-12, 1.8913e-06, 4.8581e-09, 0.052389, 0.044815, 0.032481, 6.5916e-09, 0.022918, 0.01282, 0.0046896, 0.0018438, 0.00079127, 0.00045652, 0.0003603, 0.00029945, 0.00017131, 7.5712e-05, 4.9286e-05, 2.3845e-05, 3.1726e-06, 6.6381e-07, 7.0361e-08, 5.0616e-09, 1.5249e-11, 0.032481, 0.032092, 4.9871e-12, 3.8789e-12, 0.01282, 0.0036392, 0.0046896, 0.0018438, 0.00079127, 2.3787e-09, 0.00045664, 7.3193e-08, 0.00032837, 0.00021698, 4.5677e-05, 0.00031968, 0.00012366, 9.9805e-05, 0.0001055, 1.7937e-05, 4.7099e-05, 2.2011e-05, 5.2546e-06, 1.4357e-07, 7.9489e-09, 1.9121e-10, 3.3995e-12, 4.9697e-12, 0.01282, 0.0046896, 0.0018438, 0.00079127, 0.00045664, 0.00022832, 0.00036348, 0.00032812, 1.4091e-06, 0.00026507, 0.00034582, 1.9258e-05, 0.00031288, 0.0001475, 0.00012016, 0.00031124, 0.00025207, 0.00015802, 0.00016478, 3.3652e-05, 7.0619e-06, 7.4712e-07, 5.4108e-08, 2.439e-09, 5.4418e-11, 0.00045664, 0.00045642, 0.00032687, 0.00032608, 0.00026508, 0.00022668, 0.00033124, 0.00031462, 1.445e-06, 0.00014281, 0.00016076, 0.00032729, 3.7613e-05, 0.00015511, 0.00023742, 0.00026615, 0.0002106, 0.00025759, 0.00011783, 0.00012027, 7.7228e-05, 1.0053e-05, 1.0781e-05, 3.618e-06, 2.5947e-07, 1.8172e-07, 1.9934e-08, 6.3123e-10, 8.5425e-12, 0.0003285, 0.00026508, 0.00034201, 3.919e-06, 0.00031462, 0.00025789, 1.195e-05, 0.00036306, 0.0002974, 8.4012e-05, 0.00040685, 0.00031324, 0.00011175, 0.0004269, 0.00014974, 0.00023354, 0.00028188, 4.2837e-05, 0.00011223, 5.7258e-05, 3.7441e-06, 1.0458e-05, 2.0998e-06, 1.9181e-08, 3.0369e-08, 7.4668e-07, 2.0889e-06, 3.4493e-07, 1.2139e-08, 2.0843e-10, 3.9135e-06, 0.00025695, 0.00025789, 1.8703e-11, 6.5168e-11, 3.4966e-11, 0.00023656, 0.00027382, 0.00040685, 2.9839e-08, 1.6132e-08, 0.00015969, 0.0002834, 0.00043185, 4.9512e-06, 4.9512e-06, 0.000341, 0.00011053, 0.00034517, 5.2921e-05, 5.2921e-05, 0.00035867, 7.8161e-05, 0.00021693, 0.00017215, 0.00025415, 5.0657e-05, 8.6079e-05, 9.0541e-05, 0.00016065, 3.2366e-05, 0.00013626, 3.8362e-05, 0.00014687, 0.00038854, 7.8561e-05, 6.4885e-05, 9.3991e-05, 0.00013093, 1.6449e-05, 1.6272e-05, 1.6272e-05, 3.9239e-06, 1.3791e-07, 2.3114e-08, 1.6991e-09, 8.6021e-12, 1.2895e-05, 8.3871e-11, 0.00038141, 8.0598e-07, 0.00040688, 0.00042722, 0.00014426, 0.00044178, 0.00044202, 3.9044e-05, 0.00045611, 4.822e-05, 0.00041694, 0.00050337, 0.00029762, 0.0003529, 0.00086136, 0.0019778, 0.00081468, 0.0045176, 0.0032448, 0.002225, 0.0050753, 0.0025423, 0.0065596, 0.0013481, 0.0037007, 0.0017335, 0.0002922, 1.8744e-05, 4.7727e-07, 9.4984e-09, 1.0636e-10, 0.00045076, 4.0386e-09, 2.3125e-09, 0.00046526, 1.1207e-06, 9.1368e-07, 5.624e-07, 0.00066774, 5.7847e-05, 6.7669e-05, 3.7454e-05, 0.0030064, 0.00465, 0.00017627, 0.0070085, 0.00378, 0.011768, 0.0092253, 0.02844, 0.014011, 0.01087, 0.016909, 0.0012473, 0.0033573, 0.00060676, 4.9443e-05, 2.6897e-06, 7.7594e-08, 1.4633e-09, 3.9336e-09, 7.622e-12, 6.0298e-12, 1.3497e-06, 0.00066776, 0.00014944, 0.00011704, 0.0029945, 0.00049545, 0.00482, 0.0086667, 0.0043378, 0.021858, 0.028799, 0.006081, 0.046388, 0.031528, 0.03491, 0.055331, 0.022149, 0.0085595, 0.0016032, 0.00023507, 1.597e-05, 7.3998e-07, 1.7748e-08, 2.919e-10, 1.0669e-12, 1.5343e-10, 0.0030064, 0.010272, 1.4546e-06, 4.0794e-07, 0.033648, 0.046953, 0.00028347, 0.066091, 0.0024508, 0.076924, 0.010251, 0.060085, 0.019241, 0.029075, 0.031882, 0.013819, 0.0043412, 0.0006706, 7.8353e-05, 4.5242e-06, 1.6903e-07, 2.6124e-09, 6.7049e-11, 1.52e-06, 0.033648, 0.00036549, 0.046993, 4.2331e-07, 0.066118, 0.001901, 0.077711, 0.00063863, 0.06321, 0.012276, 0.064075, 0.057664, 0.059348, 0.044799, 0.027062, 0.0092454, 0.0024356, 0.0003118, 2.4956e-05, 8.4771e-07, 2.1712e-08, 2.0608e-10, 1.4635e-11, 0.066118, 2.7926e-07, 1.3114e-07, 0.063293, 3.9171e-06, 0.00010836, 5.7074e-05, 0.058889, 0.064429, 0.0036085, 0.063237, 0.056815, 0.048508, 0.031725, 0.016558, 0.0031587, 0.0020897, 0.00068824, 5.999e-05, 3.0114e-06, 7.7478e-08, 1.165e-09, 8.4722e-12, 2.7926e-07, 6.4127e-10, 4.7242e-10, 0.00013695, 3.8013e-08, 0.05889, 0.055592, 0.065154, 0.06365, 0.059594, 0.05786, 0.056488, 0.051126, 0.03946, 0.017333, 0.0063841, 0.0010686, 0.00013792, 7.3289e-06, 2.7697e-07, 4.5364e-09, 6.88e-11, 5.323e-11, 4.5806e-09, 0.06365, 0.059599, 0.05795, 0.057208, 0.05531, 0.050724, 0.036826, 0.012414, 0.012651, 0.013823, 0.005094, 0.0011999, 0.00014967, 1.2214e-05, 6.4186e-07, 1.7174e-08, 2.9535e-10, 1.9655e-12, 3.4019e-12, 2.5062e-12, 0.059599, 0.05795, 0.057209, 0.055327, 0.050943, 0.037963, 0.029241, 0.021267, 0.016104, 0.0085932, 0.0041144, 0.00097724, 0.00022305, 1.8851e-05, 1.5865e-06, 3.6849e-08, 6.5625e-10, 9.1316e-12, 0.05795, 1.7001e-11, 1.2438e-11, 0.055327, 0.050943, 0.00070305, 0.037965, 0.029267, 0.021475, 0.00071196, 0.010609, 0.0068757, 0.0039879, 0.0024356, 0.00072177, 0.0001655, 1.6755e-05, 9.7747e-07, 6.5505e-08, 1.9233e-09, 3.6893e-11, 1.6998e-11, 0.055327, 0.050943, 0.037965, 0.029267, 0.021476, 0.016966, 0.010637, 0.0070243, 0.0043102, 0.0030361, 0.0014462, 0.00094397, 0.00025452, 5.9046e-05, 1.1865e-05, 1.5826e-06, 9.8543e-08, 2.8652e-09, 4.1101e-11, 0.021476, 7.4244e-11, 1.781e-10, 0.010637, 2.0057e-07, 0.0043127, 0.0030391, 6.4128e-06, 8.3767e-06, 0.0015119, 8.2605e-05, 0.0004292, 0.00016377, 8.8877e-05, 2.9251e-05, 7.1916e-06, 6.2975e-07, 4.1109e-08, 1.4982e-09, 3.57e-11, 0.021476, 2.4344e-10, 0.010637, 2.0058e-07, 0.0043127, 0.0030539, 0.0015121, 1.2134e-07, 0.0011111, 0.00043528, 0.00017825, 0.00011602, 6.3285e-05, 2.8241e-05, 7.3949e-06, 1.2452e-06, 1.856e-07, 1.3551e-08, 7.1141e-10, 1.9129e-11, 0.0043127, 0.0015121, 3.978e-10, 2.0638e-10, 0.00043529, 0.00017829, 0.00011642, 6.4725e-05, 3.1635e-05, 1.01e-05, 2.9044e-06, 8.0301e-07, 1.8151e-07, 2.5224e-08, 2.8294e-09, 1.5733e-10, 3.9772e-10, 0.00043529, 0.00017829, 0.00011642, 6.4728e-05, 3.1655e-05, 1.0168e-05, 3.0212e-06, 9.6535e-07, 2.8951e-07, 7.9505e-08, 1.8166e-08, 3.4032e-09, 3.7779e-10, 3.2008e-11, 3.1655e-05, 3.3378e-11, 3.0215e-06, 9.6649e-07, 2.9271e-07, 8.3915e-08, 2.2732e-08, 5.7409e-09, 1.3019e-09, 2.3135e-10, 3.3016e-11, 3.3378e-11, 3.0215e-06, 9.6649e-07, 2.9272e-07, 8.3932e-08, 2.2787e-08, 6.4423e-12, 5.8573e-09, 1.4254e-09, 3.2724e-10, 6.9646e-11, 1.3182e-11, 2.2787e-08, 5.8573e-09, 1.4254e-09, 3.286e-10, 7.1771e-11, 1.3614e-11, 5.8573e-09, 1.4254e-09, 1.7033e-10, 3.2861e-10, 7.1771e-11, 1.4792e-11, 1.5296e-12, 1.5296e-12, 1.5296e-12, 1.5296e-12, 1.3313e-12, 1.3258e-12, 2.9067e-10, 6.3651e-12, 1.3189e-12, 2.6254e-12, 1.7604e-12, 1.1777e-12, 1.7604e-12, 1.7604e-12, 7.1771e-11, 1.7604e-12 ] } ], "layout": { "autosize": true, "margin": { "b": 0, "l": 0, "r": 0, "t": 0 }, "scene": { "aspectmode": "auto", "aspectratio": { "x": 1, "y": 1, "z": 1 }, "camera": { "center": { "x": 0, "y": 0, "z": 0 }, "eye": { "x": 0.1, "y": 2, "z": -1.5 }, "up": { "x": 0, "y": 0, "z": 1 } } }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "markers", "text": [ "1H", "2H", "3H", "3He", "4He", "64Fe", "65Fe", "66Fe", "67Fe", "68Fe", "69Fe", "70Fe", "64Co", "65Co", "66Co", "67Co", "68Co", "68Co", "69Co", "70Co", "70Co", "71Co", "72Co", "73Co", "74Co", "64Ni", "65Ni", "66Ni", "67Ni", "68Ni", "69Ni", "69Ni", "70Ni", "71Ni", "72Ni", "73Ni", "74Ni", "75Ni", "76Ni", "77Ni", "65Cu", "66Cu", "67Cu", "68Cu", "68Cu", "69Cu", "70Cu", "70Cu", "70Cu", "71Cu", "72Cu", "73Cu", "74Cu", "75Cu", "76Cu", "76Cu", "77Cu", "78Cu", "79Cu", "80Cu", "66Zn", "67Zn", "68Zn", "69Zn", "69Zn", "70Zn", "71Zn", "71Zn", "72Zn", "73Zn", "73Zn", "73Zn", "74Zn", "75Zn", "76Zn", "77Zn", "77Zn", "78Zn", "79Zn", "80Zn", "81Zn", "82Zn", "83Zn", "69Ga", "71Ga", "72Ga", "72Ga", "73Ga", "74Ga", "74Ga", "75Ga", "76Ga", "77Ga", "78Ga", "79Ga", "80Ga", "81Ga", "82Ga", "83Ga", "84Ga", "85Ga", "86Ga", "72Ge", "73Ge", "73Ge", "74Ge", "75Ge", "75Ge", "76Ge", "77Ge", "77Ge", "78Ge", "79Ge", "79Ge", "80Ge", "81Ge", "81Ge", "82Ge", "83Ge", "84Ge", "85Ge", "86Ge", "87Ge", "88Ge", "89Ge", "75As", "75As", "76As", "77As", "78As", "79As", "80As", "81As", "82As", "82As", "83As", "84As", "84As", "85As", "86As", "87As", "88As", "89As", "90As", "91As", "76Se", "77Se", "77Se", "78Se", "79Se", "79Se", "80Se", "81Se", "81Se", "82Se", "83Se", "83Se", "84Se", "85Se", "86Se", "87Se", "88Se", "89Se", "90Se", "91Se", "92Se", "93Se", "94Se", "79Br", "79Br", "80Br", "80Br", "81Br", "82Br", "82Br", "83Br", "84Br", "84Br", "85Br", "86Br", "87Br", "88Br", "89Br", "90Br", "91Br", "92Br", "93Br", "94Br", "95Br", "96Br", "97Br", "80Kr", "81Kr", "82Kr", "83Kr", "83Kr", "84Kr", "85Kr", "85Kr", "86Kr", "87Kr", "88Kr", "89Kr", "90Kr", "91Kr", "92Kr", "93Kr", "94Kr", "95Kr", "96Kr", "97Kr", "98Kr", "99Kr", "83Rb", "84Rb", "84Rb", "85Rb", "86Rb", "86Rb", "87Rb", "88Rb", "89Rb", "90Rb", "90Rb", "91Rb", "92Rb", "93Rb", "94Rb", "95Rb", "96Rb", "96Rb", "97Rb", "98Rb", "98Rb", "99Rb", "100Rb", "101Rb", "102Rb", "84Sr", "86Sr", "87Sr", "87Sr", "88Sr", "89Sr", "90Sr", "91Sr", "92Sr", "93Sr", "94Sr", "95Sr", "96Sr", "97Sr", "98Sr", "99Sr", "100Sr", "101Sr", "102Sr", "103Sr", "104Sr", "88Y", "88Y", "89Y", "89Y", "90Y", "90Y", "91Y", "91Y", "92Y", "93Y", "93Y", "94Y", "95Y", "96Y", "96Y", "97Y", "97Y", "97Y", "98Y", "98Y", "99Y", "100Y", "100Y", "101Y", "102Y", "102Y", "103Y", "104Y", "105Y", "108Y", "90Zr", "91Zr", "92Zr", "93Zr", "94Zr", "95Zr", "96Zr", "97Zr", "98Zr", "99Zr", "100Zr", "101Zr", "102Zr", "103Zr", "104Zr", "105Zr", "106Zr", "107Zr", "108Zr", "109Zr", "110Zr", "93Nb", "94Nb", "94Nb", "95Nb", "95Nb", "96Nb", "97Nb", "97Nb", "98Nb", "98Nb", "99Nb", "99Nb", "100Nb", "100Nb", "101Nb", "102Nb", "102Nb", "103Nb", "104Nb", "104Nb", "105Nb", "106Nb", "107Nb", "108Nb", "109Nb", "110Nb", "111Nb", "112Nb", "95Mo", "96Mo", "97Mo", "98Mo", "99Mo", "100Mo", "101Mo", "102Mo", "103Mo", "104Mo", "105Mo", "106Mo", "107Mo", "108Mo", "109Mo", "110Mo", "111Mo", "112Mo", "113Mo", "114Mo", "99Tc", "99Tc", "100Tc", "101Tc", "102Tc", "102Tc", "103Tc", "104Tc", "105Tc", "106Tc", "107Tc", "108Tc", "109Tc", "110Tc", "111Tc", "112Tc", "113Tc", "114Tc", "115Tc", "116Tc", "117Tc", "99Ru", "100Ru", "101Ru", "102Ru", "103Ru", "103Ru", "104Ru", "105Ru", "106Ru", "107Ru", "108Ru", "109Ru", "110Ru", "111Ru", "112Ru", "113Ru", "113Ru", "114Ru", "115Ru", "116Ru", "117Ru", "118Ru", "119Ru", "103Rh", "103Rh", "104Rh", "104Rh", "105Rh", "105Rh", "106Rh", "107Rh", "108Rh", "108Rh", "109Rh", "110Rh", "111Rh", "112Rh", "112Rh", "113Rh", "114Rh", "114Rh", "115Rh", "116Rh", "116Rh", "117Rh", "118Rh", "119Rh", "120Rh", "121Rh", "122Rh", "104Pd", "105Pd", "106Pd", "107Pd", "108Pd", "109Pd", "109Pd", "110Pd", "111Pd", "111Pd", "112Pd", "113Pd", "113Pd", "114Pd", "115Pd", "115Pd", "116Pd", "117Pd", "117Pd", "118Pd", "119Pd", "120Pd", "121Pd", "122Pd", "123Pd", "124Pd", "109Ag", "109Ag", "111Ag", "111Ag", "112Ag", "113Ag", "113Ag", "114Ag", "114Ag", "115Ag", "115Ag", "116Ag", "116Ag", "117Ag", "117Ag", "118Ag", "118Ag", "119Ag", "120Ag", "120Ag", "121Ag", "122Ag", "122Ag", "123Ag", "124Ag", "124Ag", "125Ag", "126Ag", "130Ag", "111Cd", "112Cd", "113Cd", "113Cd", "114Cd", "115Cd", "115Cd", "116Cd", "117Cd", "117Cd", "118Cd", "119Cd", "119Cd", "120Cd", "121Cd", "121Cd", "122Cd", "123Cd", "123Cd", "124Cd", "125Cd", "125Cd", "126Cd", "127Cd", "128Cd", "129Cd", "129Cd", "130Cd", "131Cd", "132Cd", "113In", "115In", "115In", "116In", "116In", "116In", "117In", "117In", "118In", "118In", "118In", "119In", "119In", "120In", "120In", "120In", "121In", "121In", "122In", "122In", "122In", "123In", "123In", "124In", "124In", "125In", "125In", "126In", "126In", "127In", "127In", "128In", "128In", "128In", "129In", "129In", "130In", "130In", "130In", "131In", "131In", "131In", "132In", "133In", "133In", "134In", "135In", "115Sn", "116Sn", "117Sn", "117Sn", "118Sn", "119Sn", "119Sn", "120Sn", "121Sn", "121Sn", "122Sn", "123Sn", "123Sn", "124Sn", "125Sn", "125Sn", "126Sn", "127Sn", "127Sn", "128Sn", "128Sn", "129Sn", "129Sn", "130Sn", "130Sn", "131Sn", "131Sn", "132Sn", "133Sn", "134Sn", "135Sn", "136Sn", "137Sn", "121Sb", "122Sb", "122Sb", "123Sb", "124Sb", "124Sb", "124Sb", "125Sb", "126Sb", "126Sb", "126Sb", "127Sb", "128Sb", "128Sb", "129Sb", "129Sb", "130Sb", "130Sb", "131Sb", "132Sb", "132Sb", "133Sb", "134Sb", "134Sb", "135Sb", "136Sb", "137Sb", "138Sb", "139Sb", "122Te", "123Te", "123Te", "124Te", "125Te", "125Te", "126Te", "127Te", "127Te", "128Te", "129Te", "129Te", "130Te", "131Te", "131Te", "132Te", "133Te", "133Te", "134Te", "135Te", "136Te", "137Te", "138Te", "139Te", "140Te", "141Te", "142Te", "125I", "126I", "127I", "129I", "130I", "130I", "131I", "132I", "132I", "133I", "133I", "134I", "134I", "135I", "136I", "136I", "137I", "138I", "139I", "140I", "141I", "142I", "143I", "144I", "126Xe", "130Xe", "131Xe", "131Xe", "132Xe", "132Xe", "133Xe", "133Xe", "134Xe", "134Xe", "135Xe", "135Xe", "136Xe", "137Xe", "138Xe", "139Xe", "140Xe", "141Xe", "142Xe", "143Xe", "144Xe", "145Xe", "146Xe", "147Xe", "132Cs", "133Cs", "134Cs", "134Cs", "135Cs", "135Cs", "136Cs", "136Cs", "137Cs", "138Cs", "138Cs", "139Cs", "140Cs", "141Cs", "142Cs", "143Cs", "144Cs", "144Cs", "145Cs", "146Cs", "147Cs", "148Cs", "149Cs", "150Cs", "134Ba", "135Ba", "135Ba", "136Ba", "136Ba", "137Ba", "137Ba", "138Ba", "139Ba", "140Ba", "141Ba", "142Ba", "143Ba", "144Ba", "145Ba", "146Ba", "147Ba", "148Ba", "149Ba", "150Ba", "151Ba", "152Ba", "137La", "138La", "139La", "140La", "141La", "142La", "143La", "144La", "145La", "146La", "146La", "147La", "148La", "149La", "150La", "151La", "152La", "153La", "154La", "155La", "139Ce", "139Ce", "140Ce", "141Ce", "142Ce", "143Ce", "144Ce", "145Ce", "146Ce", "147Ce", "148Ce", "149Ce", "150Ce", "151Ce", "152Ce", "153Ce", "154Ce", "155Ce", "156Ce", "157Ce", "141Pr", "142Pr", "142Pr", "143Pr", "144Pr", "144Pr", "145Pr", "146Pr", "147Pr", "148Pr", "149Pr", "150Pr", "151Pr", "152Pr", "153Pr", "154Pr", "155Pr", "156Pr", "157Pr", "158Pr", "159Pr", "142Nd", "143Nd", "144Nd", "145Nd", "146Nd", "147Nd", "148Nd", "149Nd", "150Nd", "151Nd", "152Nd", "153Nd", "154Nd", "155Nd", "156Nd", "157Nd", "158Nd", "159Nd", "160Nd", "161Nd", "147Pm", "148Pm", "148Pm", "149Pm", "150Pm", "151Pm", "152Pm", "152Pm", "152Pm", "153Pm", "154Pm", "155Pm", "156Pm", "157Pm", "158Pm", "159Pm", "160Pm", "161Pm", "162Pm", "163Pm", "147Sm", "148Sm", "149Sm", "150Sm", "151Sm", "152Sm", "153Sm", "153Sm", "154Sm", "155Sm", "156Sm", "157Sm", "158Sm", "159Sm", "160Sm", "161Sm", "162Sm", "163Sm", "164Sm", "165Sm", "151Eu", "153Eu", "154Eu", "154Eu", "155Eu", "156Eu", "157Eu", "158Eu", "159Eu", "160Eu", "161Eu", "162Eu", "163Eu", "164Eu", "165Eu", "166Eu", "154Gd", "155Gd", "156Gd", "157Gd", "158Gd", "159Gd", "160Gd", "161Gd", "162Gd", "163Gd", "164Gd", "165Gd", "166Gd", "167Gd", "168Gd", "159Tb", "160Tb", "161Tb", "162Tb", "163Tb", "164Tb", "165Tb", "166Tb", "167Tb", "168Tb", "169Tb", "160Dy", "161Dy", "162Dy", "163Dy", "164Dy", "165Dy", "165Dy", "166Dy", "167Dy", "168Dy", "169Dy", "170Dy", "165Ho", "166Ho", "167Ho", "168Ho", "169Ho", "170Ho", "166Er", "167Er", "167Er", "168Er", "169Er", "170Er", "63Fe", "63Co", "63Ni", "63Cu", "70Ga", "70Ge", "129Ag", "167Eu", "169Gd", "170Tb", "171Dy", "170Ho", "171Ho", "171Er", "169Tm", "171Tm" ], "type": "scatter", "x": [ 1, 2, 3, 3, 4, 64, 65, 66, 67, 68, 69, 70, 64, 65, 66, 67, 68, 68, 69, 70, 70, 71, 72, 73, 74, 64, 65, 66, 67, 68, 69, 69, 70, 71, 72, 73, 74, 75, 76, 77, 65, 66, 67, 68, 68, 69, 70, 70, 70, 71, 72, 73, 74, 75, 76, 76, 77, 78, 79, 80, 66, 67, 68, 69, 69, 70, 71, 71, 72, 73, 73, 73, 74, 75, 76, 77, 77, 78, 79, 80, 81, 82, 83, 69, 71, 72, 72, 73, 74, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 72, 73, 73, 74, 75, 75, 76, 77, 77, 78, 79, 79, 80, 81, 81, 82, 83, 84, 85, 86, 87, 88, 89, 75, 75, 76, 77, 78, 79, 80, 81, 82, 82, 83, 84, 84, 85, 86, 87, 88, 89, 90, 91, 76, 77, 77, 78, 79, 79, 80, 81, 81, 82, 83, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 79, 79, 80, 80, 81, 82, 82, 83, 84, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 80, 81, 82, 83, 83, 84, 85, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 83, 84, 84, 85, 86, 86, 87, 88, 89, 90, 90, 91, 92, 93, 94, 95, 96, 96, 97, 98, 98, 99, 100, 101, 102, 84, 86, 87, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 88, 88, 89, 89, 90, 90, 91, 91, 92, 93, 93, 94, 95, 96, 96, 97, 97, 97, 98, 98, 99, 100, 100, 101, 102, 102, 103, 104, 105, 108, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 93, 94, 94, 95, 95, 96, 97, 97, 98, 98, 99, 99, 100, 100, 101, 102, 102, 103, 104, 104, 105, 106, 107, 108, 109, 110, 111, 112, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 99, 99, 100, 101, 102, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 99, 100, 101, 102, 103, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 113, 114, 115, 116, 117, 118, 119, 103, 103, 104, 104, 105, 105, 106, 107, 108, 108, 109, 110, 111, 112, 112, 113, 114, 114, 115, 116, 116, 117, 118, 119, 120, 121, 122, 104, 105, 106, 107, 108, 109, 109, 110, 111, 111, 112, 113, 113, 114, 115, 115, 116, 117, 117, 118, 119, 120, 121, 122, 123, 124, 109, 109, 111, 111, 112, 113, 113, 114, 114, 115, 115, 116, 116, 117, 117, 118, 118, 119, 120, 120, 121, 122, 122, 123, 124, 124, 125, 126, 130, 111, 112, 113, 113, 114, 115, 115, 116, 117, 117, 118, 119, 119, 120, 121, 121, 122, 123, 123, 124, 125, 125, 126, 127, 128, 129, 129, 130, 131, 132, 113, 115, 115, 116, 116, 116, 117, 117, 118, 118, 118, 119, 119, 120, 120, 120, 121, 121, 122, 122, 122, 123, 123, 124, 124, 125, 125, 126, 126, 127, 127, 128, 128, 128, 129, 129, 130, 130, 130, 131, 131, 131, 132, 133, 133, 134, 135, 115, 116, 117, 117, 118, 119, 119, 120, 121, 121, 122, 123, 123, 124, 125, 125, 126, 127, 127, 128, 128, 129, 129, 130, 130, 131, 131, 132, 133, 134, 135, 136, 137, 121, 122, 122, 123, 124, 124, 124, 125, 126, 126, 126, 127, 128, 128, 129, 129, 130, 130, 131, 132, 132, 133, 134, 134, 135, 136, 137, 138, 139, 122, 123, 123, 124, 125, 125, 126, 127, 127, 128, 129, 129, 130, 131, 131, 132, 133, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 125, 126, 127, 129, 130, 130, 131, 132, 132, 133, 133, 134, 134, 135, 136, 136, 137, 138, 139, 140, 141, 142, 143, 144, 126, 130, 131, 131, 132, 132, 133, 133, 134, 134, 135, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 132, 133, 134, 134, 135, 135, 136, 136, 137, 138, 138, 139, 140, 141, 142, 143, 144, 144, 145, 146, 147, 148, 149, 150, 134, 135, 135, 136, 136, 137, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 139, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 141, 142, 142, 143, 144, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 147, 148, 148, 149, 150, 151, 152, 152, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 147, 148, 149, 150, 151, 152, 153, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 151, 153, 154, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 160, 161, 162, 163, 164, 165, 165, 166, 167, 168, 169, 170, 165, 166, 167, 168, 169, 170, 166, 167, 167, 168, 169, 170, 63, 63, 63, 63, 70, 70, 129, 167, 169, 170, 171, 170, 171, 171, 169, 171 ], "y": [ 2.6891e-05, 8.2182e-06, 0.000108, 0.000108, 0.0017, 7.3602e-12, 1.9996e-11, 4.1079e-11, 4.0473e-11, 2.852e-11, 1.0302e-11, 2.485e-12, 1.0523e-11, 4.9592e-11, 1.8948e-10, 5.8481e-10, 8.5494e-10, 4.86e-10, 1.6621e-09, 6.2365e-10, 6.2614e-10, 6.7629e-10, 1.8044e-10, 3.1693e-11, 2.6088e-12, 1.0523e-11, 5.1847e-11, 2.3151e-10, 9.6078e-10, 3.6578e-09, 6.5085e-09, 4.863e-09, 2.9138e-08, 4.3646e-08, 5.0028e-08, 2.7733e-08, 1.0709e-08, 2.1473e-09, 2.8974e-10, 1.8116e-11, 5.1847e-11, 2.3151e-10, 9.729e-10, 3.8549e-09, 1.6337e-10, 1.4487e-08, 1.155e-08, 2.0179e-08, 3.0598e-08, 1.5863e-07, 3.8525e-07, 7.5021e-07, 8.1929e-07, 7.1733e-07, 1.5434e-07, 1.5434e-07, 9.117e-08, 1.2676e-08, 1.1339e-09, 4.0232e-11, 2.3151e-10, 9.729e-10, 3.8811e-09, 1.4528e-08, 3.3942e-11, 5.1872e-08, 1.6141e-07, 1.3804e-08, 5.5863e-07, 1.2923e-06, 4.6342e-07, 7.0932e-07, 4.3438e-06, 8.6195e-06, 1.383e-05, 1.1182e-05, 1.3845e-06, 7.4968e-06, 2.2493e-06, 4.1192e-07, 3.81e-08, 2.2541e-09, 4.3937e-11, 1.4528e-08, 1.753e-07, 5.6132e-07, 1.8855e-08, 1.7033e-06, 3.7612e-06, 4.6314e-06, 1.322e-05, 3.2632e-05, 6.9665e-05, 0.00010531, 0.00012615, 7.4207e-05, 3.3569e-05, 6.9822e-06, 7.0862e-07, 4.1544e-08, 1.1735e-09, 1.8075e-11, 5.6132e-07, 1.7034e-06, 1.6783e-06, 4.9242e-06, 1.3341e-05, 6.3541e-07, 3.4591e-05, 2.7203e-05, 7.1479e-05, 0.00019808, 0.00017052, 0.00025813, 0.00078247, 0.00087169, 0.00018654, 0.0009957, 0.00030963, 8.3514e-05, 8.1734e-06, 6.5806e-07, 2.2237e-08, 5.7353e-10, 5.2795e-12, 1.3341e-05, 2.6244e-10, 4.7098e-09, 8.5265e-05, 0.00020099, 0.0004543, 0.00098806, 0.001937, 0.0014829, 0.0016975, 0.0037412, 0.0016748, 0.0016748, 0.0015095, 0.00039824, 6.7195e-05, 6.1875e-06, 3.0595e-07, 7.7925e-09, 1.1871e-10, 4.7098e-09, 8.5265e-05, 4.0407e-11, 0.00020099, 0.00045421, 0.00044356, 0.00099264, 0.0020033, 0.00012805, 0.0037963, 0.0030575, 0.0027407, 0.010229, 0.010044, 0.010545, 0.0055175, 0.0023601, 0.00040647, 5.128e-05, 2.7978e-06, 1.0424e-07, 1.5399e-09, 2.0123e-11, 2.4841e-07, 1.4016e-11, 1.8113e-09, 1.4074e-09, 0.0020035, 4.3317e-06, 1.1702e-06, 0.0058674, 0.010549, 0.00031991, 0.013085, 0.019282, 0.022793, 0.022874, 0.015813, 0.0060784, 0.0015697, 0.00020594, 1.6075e-05, 7.4049e-07, 2.1937e-08, 3.1667e-10, 3.3428e-12, 1.661e-09, 6.3009e-12, 4.3609e-06, 0.0058674, 0.0058622, 0.010872, 0.0028554, 0.013071, 0.020416, 0.02685, 0.037181, 0.041465, 0.043146, 0.029297, 0.016396, 0.0041763, 0.00090763, 9.3037e-05, 6.643e-06, 2.4127e-07, 7.0608e-09, 8.4521e-11, 1.2352e-12, 2.086e-10, 1.4126e-10, 0.013129, 7.6218e-07, 5.1615e-07, 0.026871, 0.037466, 0.043832, 0.038661, 0.013781, 0.052733, 0.051914, 0.041833, 0.024406, 0.010645, 0.0017647, 0.0011765, 0.00039748, 1.9434e-05, 1.9441e-05, 2.2813e-06, 7.8677e-08, 1.5185e-09, 1.4772e-11, 6.6752e-12, 7.6218e-07, 3.4103e-09, 5.7185e-10, 0.037466, 0.043839, 0.052208, 0.053739, 0.058614, 0.060261, 0.059203, 0.053257, 0.040486, 0.018733, 0.0078265, 0.0014502, 0.00023638, 1.5369e-05, 7.3727e-07, 1.4245e-08, 1.9892e-10, 3.2772e-12, 1.539e-12, 0.043839, 4.2261e-06, 0.052208, 1.9896e-08, 0.05374, 0.031618, 0.058641, 0.060616, 0.015393, 0.061459, 0.063395, 0.047523, 0.014757, 0.02227, 0.031587, 0.0077634, 0.014442, 0.032071, 0.029067, 0.0067782, 0.0065437, 0.0038237, 0.00029664, 0.00029664, 5.5509e-05, 2.6647e-06, 6.6371e-08, 1.7871e-11, 0.052208, 0.05374, 0.058641, 0.060616, 0.061465, 0.063488, 0.063171, 0.060319, 0.061809, 0.056738, 0.058903, 0.036395, 0.020146, 0.0055774, 0.001223, 0.00010271, 3.8059e-08, 1.3288e-08, 2.3031e-07, 1.6845e-09, 4.958e-12, 2.6678e-12, 2.9023e-10, 1.0881e-10, 0.063449, 0.00068589, 6.878e-07, 0.060334, 0.057334, 0.061848, 0.00013844, 0.037368, 0.021099, 0.059904, 0.0048561, 0.05208, 0.031611, 0.011465, 0.027237, 0.007072, 0.0058475, 0.0041048, 0.00033832, 0.00014064, 5.0784e-05, 2.8642e-06, 5.5514e-08, 6.5748e-10, 2.8043e-12, 0.063488, 6.8782e-07, 0.060334, 0.061987, 0.058047, 0.064795, 0.052388, 0.044811, 0.032439, 0.022611, 0.011704, 0.0042764, 0.0016994, 0.00052793, 0.00013423, 1.9737e-05, 1.4249e-06, 4.224e-08, 1.0328e-09, 1.0028e-11, 0.058045, 0.051116, 4.8581e-09, 0.052389, 0.044813, 1.8619e-06, 0.032481, 0.022917, 0.012817, 0.0046896, 0.0018438, 0.00078576, 0.00042528, 0.00023691, 8.6107e-05, 1.4162e-05, 1.9154e-06, 9.814e-08, 2.8591e-09, 1.8e-10, 5.5028e-12, 1.8913e-06, 4.8581e-09, 0.052389, 0.044815, 0.032481, 6.5916e-09, 0.022918, 0.01282, 0.0046896, 0.0018438, 0.00079127, 0.00045652, 0.0003603, 0.00029945, 0.00017131, 7.5712e-05, 4.9286e-05, 2.3845e-05, 3.1726e-06, 6.6381e-07, 7.0361e-08, 5.0616e-09, 1.5249e-11, 0.032481, 0.032092, 4.9871e-12, 3.8789e-12, 0.01282, 0.0036392, 0.0046896, 0.0018438, 0.00079127, 2.3787e-09, 0.00045664, 7.3193e-08, 0.00032837, 0.00021698, 4.5677e-05, 0.00031968, 0.00012366, 9.9805e-05, 0.0001055, 1.7937e-05, 4.7099e-05, 2.2011e-05, 5.2546e-06, 1.4357e-07, 7.9489e-09, 1.9121e-10, 3.3995e-12, 4.9697e-12, 0.01282, 0.0046896, 0.0018438, 0.00079127, 0.00045664, 0.00022832, 0.00036348, 0.00032812, 1.4091e-06, 0.00026507, 0.00034582, 1.9258e-05, 0.00031288, 0.0001475, 0.00012016, 0.00031124, 0.00025207, 0.00015802, 0.00016478, 3.3652e-05, 7.0619e-06, 7.4712e-07, 5.4108e-08, 2.439e-09, 5.4418e-11, 0.00045664, 0.00045642, 0.00032687, 0.00032608, 0.00026508, 0.00022668, 0.00033124, 0.00031462, 1.445e-06, 0.00014281, 0.00016076, 0.00032729, 3.7613e-05, 0.00015511, 0.00023742, 0.00026615, 0.0002106, 0.00025759, 0.00011783, 0.00012027, 7.7228e-05, 1.0053e-05, 1.0781e-05, 3.618e-06, 2.5947e-07, 1.8172e-07, 1.9934e-08, 6.3123e-10, 8.5425e-12, 0.0003285, 0.00026508, 0.00034201, 3.919e-06, 0.00031462, 0.00025789, 1.195e-05, 0.00036306, 0.0002974, 8.4012e-05, 0.00040685, 0.00031324, 0.00011175, 0.0004269, 0.00014974, 0.00023354, 0.00028188, 4.2837e-05, 0.00011223, 5.7258e-05, 3.7441e-06, 1.0458e-05, 2.0998e-06, 1.9181e-08, 3.0369e-08, 7.4668e-07, 2.0889e-06, 3.4493e-07, 1.2139e-08, 2.0843e-10, 3.9135e-06, 0.00025695, 0.00025789, 1.8703e-11, 6.5168e-11, 3.4966e-11, 0.00023656, 0.00027382, 0.00040685, 2.9839e-08, 1.6132e-08, 0.00015969, 0.0002834, 0.00043185, 4.9512e-06, 4.9512e-06, 0.000341, 0.00011053, 0.00034517, 5.2921e-05, 5.2921e-05, 0.00035867, 7.8161e-05, 0.00021693, 0.00017215, 0.00025415, 5.0657e-05, 8.6079e-05, 9.0541e-05, 0.00016065, 3.2366e-05, 0.00013626, 3.8362e-05, 0.00014687, 0.00038854, 7.8561e-05, 6.4885e-05, 9.3991e-05, 0.00013093, 1.6449e-05, 1.6272e-05, 1.6272e-05, 3.9239e-06, 1.3791e-07, 2.3114e-08, 1.6991e-09, 8.6021e-12, 1.2895e-05, 8.3871e-11, 0.00038141, 8.0598e-07, 0.00040688, 0.00042722, 0.00014426, 0.00044178, 0.00044202, 3.9044e-05, 0.00045611, 4.822e-05, 0.00041694, 0.00050337, 0.00029762, 0.0003529, 0.00086136, 0.0019778, 0.00081468, 0.0045176, 0.0032448, 0.002225, 0.0050753, 0.0025423, 0.0065596, 0.0013481, 0.0037007, 0.0017335, 0.0002922, 1.8744e-05, 4.7727e-07, 9.4984e-09, 1.0636e-10, 0.00045076, 4.0386e-09, 2.3125e-09, 0.00046526, 1.1207e-06, 9.1368e-07, 5.624e-07, 0.00066774, 5.7847e-05, 6.7669e-05, 3.7454e-05, 0.0030064, 0.00465, 0.00017627, 0.0070085, 0.00378, 0.011768, 0.0092253, 0.02844, 0.014011, 0.01087, 0.016909, 0.0012473, 0.0033573, 0.00060676, 4.9443e-05, 2.6897e-06, 7.7594e-08, 1.4633e-09, 3.9336e-09, 7.622e-12, 6.0298e-12, 1.3497e-06, 0.00066776, 0.00014944, 0.00011704, 0.0029945, 0.00049545, 0.00482, 0.0086667, 0.0043378, 0.021858, 0.028799, 0.006081, 0.046388, 0.031528, 0.03491, 0.055331, 0.022149, 0.0085595, 0.0016032, 0.00023507, 1.597e-05, 7.3998e-07, 1.7748e-08, 2.919e-10, 1.0669e-12, 1.5343e-10, 0.0030064, 0.010272, 1.4546e-06, 4.0794e-07, 0.033648, 0.046953, 0.00028347, 0.066091, 0.0024508, 0.076924, 0.010251, 0.060085, 0.019241, 0.029075, 0.031882, 0.013819, 0.0043412, 0.0006706, 7.8353e-05, 4.5242e-06, 1.6903e-07, 2.6124e-09, 6.7049e-11, 1.52e-06, 0.033648, 0.00036549, 0.046993, 4.2331e-07, 0.066118, 0.001901, 0.077711, 0.00063863, 0.06321, 0.012276, 0.064075, 0.057664, 0.059348, 0.044799, 0.027062, 0.0092454, 0.0024356, 0.0003118, 2.4956e-05, 8.4771e-07, 2.1712e-08, 2.0608e-10, 1.4635e-11, 0.066118, 2.7926e-07, 1.3114e-07, 0.063293, 3.9171e-06, 0.00010836, 5.7074e-05, 0.058889, 0.064429, 0.0036085, 0.063237, 0.056815, 0.048508, 0.031725, 0.016558, 0.0031587, 0.0020897, 0.00068824, 5.999e-05, 3.0114e-06, 7.7478e-08, 1.165e-09, 8.4722e-12, 2.7926e-07, 6.4127e-10, 4.7242e-10, 0.00013695, 3.8013e-08, 0.05889, 0.055592, 0.065154, 0.06365, 0.059594, 0.05786, 0.056488, 0.051126, 0.03946, 0.017333, 0.0063841, 0.0010686, 0.00013792, 7.3289e-06, 2.7697e-07, 4.5364e-09, 6.88e-11, 5.323e-11, 4.5806e-09, 0.06365, 0.059599, 0.05795, 0.057208, 0.05531, 0.050724, 0.036826, 0.012414, 0.012651, 0.013823, 0.005094, 0.0011999, 0.00014967, 1.2214e-05, 6.4186e-07, 1.7174e-08, 2.9535e-10, 1.9655e-12, 3.4019e-12, 2.5062e-12, 0.059599, 0.05795, 0.057209, 0.055327, 0.050943, 0.037963, 0.029241, 0.021267, 0.016104, 0.0085932, 0.0041144, 0.00097724, 0.00022305, 1.8851e-05, 1.5865e-06, 3.6849e-08, 6.5625e-10, 9.1316e-12, 0.05795, 1.7001e-11, 1.2438e-11, 0.055327, 0.050943, 0.00070305, 0.037965, 0.029267, 0.021475, 0.00071196, 0.010609, 0.0068757, 0.0039879, 0.0024356, 0.00072177, 0.0001655, 1.6755e-05, 9.7747e-07, 6.5505e-08, 1.9233e-09, 3.6893e-11, 1.6998e-11, 0.055327, 0.050943, 0.037965, 0.029267, 0.021476, 0.016966, 0.010637, 0.0070243, 0.0043102, 0.0030361, 0.0014462, 0.00094397, 0.00025452, 5.9046e-05, 1.1865e-05, 1.5826e-06, 9.8543e-08, 2.8652e-09, 4.1101e-11, 0.021476, 7.4244e-11, 1.781e-10, 0.010637, 2.0057e-07, 0.0043127, 0.0030391, 6.4128e-06, 8.3767e-06, 0.0015119, 8.2605e-05, 0.0004292, 0.00016377, 8.8877e-05, 2.9251e-05, 7.1916e-06, 6.2975e-07, 4.1109e-08, 1.4982e-09, 3.57e-11, 0.021476, 2.4344e-10, 0.010637, 2.0058e-07, 0.0043127, 0.0030539, 0.0015121, 1.2134e-07, 0.0011111, 0.00043528, 0.00017825, 0.00011602, 6.3285e-05, 2.8241e-05, 7.3949e-06, 1.2452e-06, 1.856e-07, 1.3551e-08, 7.1141e-10, 1.9129e-11, 0.0043127, 0.0015121, 3.978e-10, 2.0638e-10, 0.00043529, 0.00017829, 0.00011642, 6.4725e-05, 3.1635e-05, 1.01e-05, 2.9044e-06, 8.0301e-07, 1.8151e-07, 2.5224e-08, 2.8294e-09, 1.5733e-10, 3.9772e-10, 0.00043529, 0.00017829, 0.00011642, 6.4728e-05, 3.1655e-05, 1.0168e-05, 3.0212e-06, 9.6535e-07, 2.8951e-07, 7.9505e-08, 1.8166e-08, 3.4032e-09, 3.7779e-10, 3.2008e-11, 3.1655e-05, 3.3378e-11, 3.0215e-06, 9.6649e-07, 2.9271e-07, 8.3915e-08, 2.2732e-08, 5.7409e-09, 1.3019e-09, 2.3135e-10, 3.3016e-11, 3.3378e-11, 3.0215e-06, 9.6649e-07, 2.9272e-07, 8.3932e-08, 2.2787e-08, 6.4423e-12, 5.8573e-09, 1.4254e-09, 3.2724e-10, 6.9646e-11, 1.3182e-11, 2.2787e-08, 5.8573e-09, 1.4254e-09, 3.286e-10, 7.1771e-11, 1.3614e-11, 5.8573e-09, 1.4254e-09, 1.7033e-10, 3.2861e-10, 7.1771e-11, 1.4792e-11, 1.5296e-12, 1.5296e-12, 1.5296e-12, 1.5296e-12, 1.3313e-12, 1.3258e-12, 2.9067e-10, 6.3651e-12, 1.3189e-12, 2.6254e-12, 1.7604e-12, 1.1777e-12, 1.7604e-12, 1.7604e-12, 7.1771e-11, 1.7604e-12 ] } ], "layout": { "autosize": true, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "autorange": true, "range": [ -9.075251580728471, 181.07525158072846 ], "title": { "text": "A" }, "type": "linear" }, "yaxis": { "autorange": true, "range": [ -0.005191220646635109, 0.08290222064770202 ], "title": { "text": "Yield " }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "url=livechart+\"fields=cumulative_fy&parents=235u\"\n", "df = lc_pd_dataframe(url) [['z_daughter','a_daughter',\"element_daughter\",'cumulative_fast_fy']]\n", "df = df[pd.to_numeric(df.cumulative_fast_fy,errors='coerce').notna()]\n", "df.cumulative_fast_fy = df.cumulative_fast_fy.astype(float)\n", " \n", "\n", "fig = go.Figure(data=[go.Scatter3d(\n", " x=df[\"z_daughter\"],\n", " y=df[\"a_daughter\"]-df[\"z_daughter\"],\n", " z=df[\"cumulative_fast_fy\"],\n", " mode='markers',\n", " text = df['a_daughter'].apply(str) + df[\"element_daughter\"],\n", " marker=dict(\n", " size=7\n", " ,color=df[\"cumulative_fast_fy\"] # array/list of values setting the color\n", " ,colorscale='Viridis' # colorscale\n", " )\n", " \n", "\n", ")])\n", "\n", "camera = dict( # initial spacial settings \n", " up=dict(x=0, y=0, z=1),\n", " center=dict(x=0, y=0, z=0),\n", " eye=dict(x=0.1, y=2, z=-1.5) # view it from the bottom\n", ")\n", "\n", "fig.update_layout(scene_camera=camera, margin=dict(l=0, r=0, b=0, t=0), height=900)\n", "fig.show()\n", "\n", "# 2D plot\n", "fig2 = go.Figure()\n", "fig2.add_trace(go.Scatter( x=df[df.columns[1]], y=df['cumulative_fast_fy'], mode=\"markers\", text= df['a_daughter'].apply(str) + df[\"element_daughter\"]))\n", "fig2.update_layout( height=600, xaxis_title=\"A\", yaxis_title=\"Yield \") \n", "fig2.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Column titles and samples of the available sets\n", "\n", "** Ground state**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
znsymbolradiusunc_rabundanceunc_aenergy_shiftenergyunc_eripl_shiftjphalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hlsdecay_1decay_1_%unc_1decay_2decay_2_%unc_2decay_3decay_3_%unc_3isospinmagnetic_dipoleunc_mdelectric_quadrupoleunc_eqqbmunc_qbqbm_nunc_qbmnqaunc_qaqecunc_qecsnunc_snspunc_spbindingunc_baatomic_massunc_ammassexcessunc_meme_systematicsdiscoveryENSDFpublicationcut-offENSDFauthorsExtraction_date
093143NpNaNNaNNaNNaNNaN0NaNNaN6(-)155000.0NaN1Y4.891324e+123.155693e+10EC87.80.2B-120.1A0.160.04NaNNaNNaNNaNNaN476.585450.3887-6875.570854.41395006.62950.9333933.51250.4335736.268750.41964829.659850.41397579.21480.21362.360466e+0854.12943378.09450.421N19491-Apr-2022Shaofei Zhu2024-03-06
\n", "
" ], "text/plain": [ " z n symbol radius unc_r abundance unc_a energy_shift energy \\\n", "0 93 143 Np NaN NaN NaN NaN NaN 0 \n", "\n", " unc_e ripl_shift jp half_life operator_hl unc_hl unit_hl \\\n", "0 NaN NaN 6(-) 155000.0 NaN 1 Y \n", "\n", " half_life_sec unc_hls decay_1 decay_1_% unc_1 decay_2 decay_2_% \\\n", "0 4.891324e+12 3.155693e+10 EC 87.8 0.2 B- 12 \n", "\n", " unc_2 decay_3 decay_3_% unc_3 isospin magnetic_dipole unc_md \\\n", "0 0.1 A 0.16 0.04 NaN NaN NaN \n", "\n", " electric_quadrupole unc_eq qbm unc_qb qbm_n unc_qbmn \\\n", "0 NaN NaN 476.5854 50.3887 -6875.5708 54.4139 \n", "\n", " qa unc_qa qec unc_qec sn unc_sn sp \\\n", "0 5006.629 50.9333 933.512 50.433 5736.2687 50.4196 4829.6598 \n", "\n", " unc_sp binding unc_ba atomic_mass unc_am massexcess unc_me \\\n", "0 50.4139 7579.2148 0.2136 2.360466e+08 54.129 43378.094 50.421 \n", "\n", " me_systematics discovery ENSDFpublicationcut-off ENSDFauthors \\\n", "0 N 1949 1-Apr-2022 Shaofei Zhu \n", "\n", " Extraction_date \n", "0 2024-03-06 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.set_option('display.max_columns', None)\n", "pd.set_option(\"display.max_rows\", 3)\n", "\n", "# GS\n", "df=lc_pd_dataframe(livechart+\"fields=ground_states&nuclides=236np\")\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Level**\n", "
\n", "Note the \"X\" in the energy_shift field for the 2nd level, and the value in the \"ripl_shift\" column (see the API guide for the meaning of the shift)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
znsymbolidxenergy_shiftenergyunc_eripl_shiftjpjp_orderhalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hl.1decay_1decay_1_%unc_1decay_2decay_2_%unc_2decay_3decay_3_%unc_3isospinmagnetic_dipoleunc_mnelectric_quadrupoleunc_eqENSDF_publication_cut-offENSDF_authorsExtraction_date
093143Np0NaN0NaNNaN6(-)1155000.0NaN1.0Y4.891324e+123.155693e+10EC87.80.2B-12.00.1A0.160.04NaNNaNNaNNaNNaN1-Apr-2022Shaofei Zhu2024-03-06
......................................................................................................
493143Np4NaN33450.0NaN(5-)1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1-Apr-2022Shaofei Zhu2024-03-06
\n", "

5 rows × 33 columns

\n", "
" ], "text/plain": [ " z n symbol idx energy_shift energy unc_e ripl_shift jp \\\n", "0 93 143 Np 0 NaN 0 NaN NaN 6(-) \n", ".. .. ... ... ... ... ... ... ... ... \n", "4 93 143 Np 4 NaN 334 50.0 NaN (5-) \n", "\n", " jp_order half_life operator_hl unc_hl unit_hl half_life_sec \\\n", "0 1 155000.0 NaN 1.0 Y 4.891324e+12 \n", ".. ... ... ... ... ... ... \n", "4 1 NaN NaN NaN NaN NaN \n", "\n", " unc_hl.1 decay_1 decay_1_% unc_1 decay_2 decay_2_% unc_2 decay_3 \\\n", "0 3.155693e+10 EC 87.8 0.2 B- 12.0 0.1 A \n", ".. ... ... ... ... ... ... ... ... \n", "4 NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " decay_3_% unc_3 isospin magnetic_dipole unc_mn electric_quadrupole \\\n", "0 0.16 0.04 NaN NaN NaN NaN \n", ".. ... ... ... ... ... ... \n", "4 NaN NaN NaN NaN NaN NaN \n", "\n", " unc_eq ENSDF_publication_cut-off ENSDF_authors Extraction_date \n", "0 NaN 1-Apr-2022 Shaofei Zhu 2024-03-06 \n", ".. ... ... ... ... \n", "4 NaN 1-Apr-2022 Shaofei Zhu 2024-03-06 \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# LEVEL\n", "df=lc_pd_dataframe(livechart+\"fields=levels&nuclides=236np\")\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Gamma**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
znsymbolstart_level_idxstart_level_energyunc_slestart_level_jpend_level_idxend_level_energyunc_eleend_level_jpgamma_idxenergyunc_enrelative_intensityunc_rimultipolaritymixing_ratiounc_mrb_e1unc_be1b_e2unc_be2b_m1unc_bm1b_m2unc_bm2tot_conv_coefftce_uncensdf_publication_cut-offensdf_authorsExtraction_date
05481Xe1288.4550.0151/2+00.0NaN3/2+0288.4510.016100.0NaN[M1 E2]NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.04630.001422-Jan-2008BALRAJ SINGH and ALEXANDER A. RODIONOV and YU...2024-03-06
...................................................................................................
965481Xe373169.9000.900NaN262356.50.8NaN1813.4000.90075.020.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN22-Jan-2008BALRAJ SINGH and ALEXANDER A. RODIONOV and YU...2024-03-06
\n", "

97 rows × 32 columns

\n", "
" ], "text/plain": [ " z n symbol start_level_idx start_level_energy unc_sle \\\n", "0 54 81 Xe 1 288.455 0.015 \n", ".. .. .. ... ... ... ... \n", "96 54 81 Xe 37 3169.900 0.900 \n", "\n", " start_level_jp end_level_idx end_level_energy unc_ele end_level_jp \\\n", "0 1/2+ 0 0.0 NaN 3/2+ \n", ".. ... ... ... ... ... \n", "96 NaN 26 2356.5 0.8 NaN \n", "\n", " gamma_idx energy unc_en relative_intensity unc_ri multipolarity \\\n", "0 0 288.451 0.016 100.0 NaN [M1 E2] \n", ".. ... ... ... ... ... ... \n", "96 1 813.400 0.900 75.0 20.0 NaN \n", "\n", " mixing_ratio unc_mr b_e1 unc_be1 b_e2 unc_be2 b_m1 unc_bm1 b_m2 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", ".. ... ... ... ... ... ... ... ... ... \n", "96 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " unc_bm2 tot_conv_coeff tce_unc ensdf_publication_cut-off \\\n", "0 NaN 0.0463 0.0014 22-Jan-2008 \n", ".. ... ... ... ... \n", "96 NaN NaN NaN 22-Jan-2008 \n", "\n", " ensdf_authors Extraction_date \n", "0 BALRAJ SINGH and ALEXANDER A. RODIONOV and YU... 2024-03-06 \n", ".. ... ... \n", "96 BALRAJ SINGH and ALEXANDER A. RODIONOV and YU... 2024-03-06 \n", "\n", "[97 rows x 32 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# GAMMA transitions\n", "df=lc_pd_dataframe(livechart+\"fields=gammas&nuclides=135xe\")\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Cumulative fission**" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
z_daughtera_daughterelement_daughterz_parentz_parent.1element_parentdaughter_level_idxcumulative_thermal_fyunc_ctcumulative_fast_fyunc_cfcumulative_14mev_fyunc_c14Extraction_date
054135Xe92235U00.0661400.0022490.0632100.0018330.0642420.0181172024-03-06
.............................................
1554135Xe95241Am20.0202040.0061540.0209250.006764NaNNaN2024-03-06
\n", "

16 rows × 14 columns

\n", "
" ], "text/plain": [ " z_daughter a_daughter element_daughter z_parent z_parent.1 \\\n", "0 54 135 Xe 92 235 \n", ".. ... ... ... ... ... \n", "15 54 135 Xe 95 241 \n", "\n", " element_parent daughter_level_idx cumulative_thermal_fy unc_ct \\\n", "0 U 0 0.066140 0.002249 \n", ".. ... ... ... ... \n", "15 Am 2 0.020204 0.006154 \n", "\n", " cumulative_fast_fy unc_cf cumulative_14mev_fy unc_c14 \\\n", "0 0.063210 0.001833 0.064242 0.018117 \n", ".. ... ... ... ... \n", "15 0.020925 0.006764 NaN NaN \n", "\n", " Extraction_date \n", "0 2024-03-06 \n", ".. ... \n", "15 2024-03-06 \n", "\n", "[16 rows x 14 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# CUMULATIVE_FISSION\n", "df=lc_pd_dataframe(livechart+\"fields=cumulative_fy&products=135xe\")\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Independent fission**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
z_daughtera_daughterelement_daughterz_parenta_parentelement_parentdaughter_level_idxindependent_thermal_fyunc_itindependent_fast_fyunc_ifindependent_14mev_fyunc_i14Extraction_date
011H92235U00.0000170.0000030.0000270.0000090.0000260.0000092024-03-06
.............................................
122672179Hf92235U0NaNNaNNaNNaN0.0000000.0000002024-03-06
\n", "

1227 rows × 14 columns

\n", "
" ], "text/plain": [ " z_daughter a_daughter element_daughter z_parent a_parent \\\n", "0 1 1 H 92 235 \n", "... ... ... ... ... ... \n", "1226 72 179 Hf 92 235 \n", "\n", " element_parent daughter_level_idx independent_thermal_fy unc_it \\\n", "0 U 0 0.000017 0.000003 \n", "... ... ... ... ... \n", "1226 U 0 NaN NaN \n", "\n", " independent_fast_fy unc_if independent_14mev_fy unc_i14 \\\n", "0 0.000027 0.000009 0.000026 0.000009 \n", "... ... ... ... ... \n", "1226 NaN NaN 0.000000 0.000000 \n", "\n", " Extraction_date \n", "0 2024-03-06 \n", "... ... \n", "1226 2024-03-06 \n", "\n", "[1227 rows x 14 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# INDEPENDENT FISSION\n", "df=lc_pd_dataframe(livechart+\"fields=independent_fy&parents=235u\")\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Decay radiation**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
3
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [3]\n", "Index: []" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DECAY_RADIATION, type alpha\n", "df=lc_pd_dataframe(livechart+\"fields=decay_radiations&nuclides=238u&rad_types=a\")\n", "df" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
energyunc_enintensityunc_istart_level_energyend_level_energymultipolaritymixing_ratiounc_mrconversion_coeffunc_ccp_zp_np_symbolp_energy_shiftp_energyunc_pejphalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hlsdecaydecay_%unc_dqunc_qd_zd_nd_symbolensdf_publication_cut-offensdf_authorsExtraction_date
044.915NaNNaNNaN44.9160.0E2NaNNaNNaNNaN92146UNaN2557.90.50+280.0NaN6ns2.800000e-076.000000e-09IT97.40.4NaNNaN92146U1-Jun-2014E. BROWNE and J. K. TULI2024-03-06
............................................................................................................
19114.446NaN0.007980.00162NaNNaNNaNNaNNaNNaNNaN92146UNaN2557.90.50+280.0NaN6ns2.800000e-076.000000e-09IT97.40.4NaNNaN92146U1-Jun-2014E. BROWNE and J. K. TULI2024-03-06
\n", "

20 rows × 35 columns

\n", "
" ], "text/plain": [ " energy unc_en intensity unc_i start_level_energy end_level_energy \\\n", "0 44.915 NaN NaN NaN 44.916 0.0 \n", ".. ... ... ... ... ... ... \n", "19 114.446 NaN 0.00798 0.00162 NaN NaN \n", "\n", " multipolarity mixing_ratio unc_mr conversion_coeff unc_cc p_z p_n \\\n", "0 E2 NaN NaN NaN NaN 92 146 \n", ".. ... ... ... ... ... ... ... \n", "19 NaN NaN NaN NaN NaN 92 146 \n", "\n", " p_symbol p_energy_shift p_energy unc_pe jp half_life operator_hl \\\n", "0 U NaN 2557.9 0.5 0+ 280.0 NaN \n", ".. ... ... ... ... .. ... ... \n", "19 U NaN 2557.9 0.5 0+ 280.0 NaN \n", "\n", " unc_hl unit_hl half_life_sec unc_hls decay decay_% unc_d q \\\n", "0 6 ns 2.800000e-07 6.000000e-09 IT 97.4 0.4 NaN \n", ".. ... ... ... ... ... ... ... .. \n", "19 6 ns 2.800000e-07 6.000000e-09 IT 97.4 0.4 NaN \n", "\n", " unc_q d_z d_n d_symbol ensdf_publication_cut-off \\\n", "0 NaN 92 146 U 1-Jun-2014 \n", ".. ... ... ... ... ... \n", "19 NaN 92 146 U 1-Jun-2014 \n", "\n", " ensdf_authors Extraction_date \n", "0 E. BROWNE and J. K. TULI 2024-03-06 \n", ".. ... ... \n", "19 E. BROWNE and J. K. TULI 2024-03-06 \n", "\n", "[20 rows x 35 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DECAY_RADIATION, type gamma\n", "df=lc_pd_dataframe(livechart+\"fields=decay_rads&nuclides=238u&rad_types=g\")\n", "df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
energyunc_enintensityunc_itypeshellp_zp_np_symbolp_energy_shiftp_energyunc_pejphalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hlsdecaydecay_%unc_dqunc_qd_zd_nd_symbolensdf_publication_cut-offensdf_authorsExtraction_date
015.784NaN7.322620.958957XL92146UNaN0.0NaN0+4.468000e+09NaN6Y1.409963e+171.893416e+14A100.0NaN4269.921.090144Th1-Jun-2006E. BROWNE and J. K. TULI2024-03-06
.............................................................................................
11114.446NaN0.007980.001620XKpB292146UNaN2557.90.50+2.800000e+02NaN6ns2.800000e-076.000000e-09IT97.40.4NaNNaN92146U1-Jun-2014E. BROWNE and J. K. TULI2024-03-06
\n", "

12 rows × 30 columns

\n", "
" ], "text/plain": [ " energy unc_en intensity unc_i type shell p_z p_n p_symbol \\\n", "0 15.784 NaN 7.32262 0.958957 X L 92 146 U \n", ".. ... ... ... ... ... ... ... ... ... \n", "11 114.446 NaN 0.00798 0.001620 X KpB2 92 146 U \n", "\n", " p_energy_shift p_energy unc_pe jp half_life operator_hl unc_hl \\\n", "0 NaN 0.0 NaN 0+ 4.468000e+09 NaN 6 \n", ".. ... ... ... .. ... ... ... \n", "11 NaN 2557.9 0.5 0+ 2.800000e+02 NaN 6 \n", "\n", " unit_hl half_life_sec unc_hls decay decay_% unc_d q unc_q \\\n", "0 Y 1.409963e+17 1.893416e+14 A 100.0 NaN 4269.9 21.0 \n", ".. ... ... ... ... ... ... ... ... \n", "11 ns 2.800000e-07 6.000000e-09 IT 97.4 0.4 NaN NaN \n", "\n", " d_z d_n d_symbol ensdf_publication_cut-off ensdf_authors \\\n", "0 90 144 Th 1-Jun-2006 E. BROWNE and J. K. TULI \n", ".. ... ... ... ... ... \n", "11 92 146 U 1-Jun-2014 E. BROWNE and J. K. TULI \n", "\n", " Extraction_date \n", "0 2024-03-06 \n", ".. ... \n", "11 2024-03-06 \n", "\n", "[12 rows x 30 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DECAY_RADIATION, type x-ray\n", "df=lc_pd_dataframe(livechart+\"fields=decay_rads&nuclides=238u&rad_types=x\")\n", "df" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
mean_energyunc_meintensity_betaunc_ibdaughter_level_energymax_energyunc_me.1log_ftunc_lftransition_typeanti_nu_mean_energyunc_amep_zp_np_symbolp_energy_shiftp_energyunc_pejphalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hlsdecaydecay_%unc_dqunc_qd_zd_nd_symbolensdf_publication_cut-offensdf_authorsExtraction_date
026.91.10.1230.0061062.4210345.710.06NaNNaNNaN5481XeNaN0.000NaN3/2+9.14NaN2h32904.072B-100.0NaN116945580Cs22-Jan-2008BALRAJ SINGH and ALEXANDER A. RODIONOV and YU...2024-03-06
...............................................................................................................
9641.41.70.6000.6000.00169248.50NaN1UNaNNaN5481XeNaN526.5510.01311/2-15.29NaN5m917.43B-0.6NaN116945580Cs22-Jan-2008BALRAJ SINGH and ALEXANDER A. RODIONOV and YU...2024-03-06
\n", "

10 rows × 36 columns

\n", "
" ], "text/plain": [ " mean_energy unc_me intensity_beta unc_ib daughter_level_energy \\\n", "0 26.9 1.1 0.123 0.006 1062.42 \n", ".. ... ... ... ... ... \n", "9 641.4 1.7 0.600 0.600 0.00 \n", "\n", " max_energy unc_me.1 log_ft unc_lf transition_type anti_nu_mean_energy \\\n", "0 103 4 5.71 0.06 NaN NaN \n", ".. ... ... ... ... ... ... \n", "9 1692 4 8.50 NaN 1U NaN \n", "\n", " unc_ame p_z p_n p_symbol p_energy_shift p_energy unc_pe jp \\\n", "0 NaN 54 81 Xe NaN 0.000 NaN 3/2+ \n", ".. ... ... ... ... ... ... ... ... \n", "9 NaN 54 81 Xe NaN 526.551 0.013 11/2- \n", "\n", " half_life operator_hl unc_hl unit_hl half_life_sec unc_hls decay \\\n", "0 9.14 NaN 2 h 32904.0 72 B- \n", ".. ... ... ... ... ... ... ... \n", "9 15.29 NaN 5 m 917.4 3 B- \n", "\n", " decay_% unc_d q unc_q d_z d_n d_symbol ensdf_publication_cut-off \\\n", "0 100.0 NaN 1169 4 55 80 Cs 22-Jan-2008 \n", ".. ... ... ... ... ... ... ... ... \n", "9 0.6 NaN 1169 4 55 80 Cs 22-Jan-2008 \n", "\n", " ensdf_authors Extraction_date \n", "0 BALRAJ SINGH and ALEXANDER A. RODIONOV and YU... 2024-03-06 \n", ".. ... ... \n", "9 BALRAJ SINGH and ALEXANDER A. RODIONOV and YU... 2024-03-06 \n", "\n", "[10 rows x 36 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DECAY_RADIATION, type beta-\n", "df=lc_pd_dataframe(livechart+\"fields=decay_rads&nuclides=135xe&rad_types=bm\")\n", "df" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
energyunc_enintensityunc_itypeshellp_zp_np_symbolp_energy_shiftp_energyunc_pejphalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hlsdecaydecay_%unc_dqunc_qd_zd_nd_symbolensdf_publication_cut-offensdf_authorsExtraction_date
013.113NaN8.0610351.047936AUL92146UNaN0.0NaN0+4.468000e+09NaN6Y1.409963e+171.893416e+14A100.0NaN4269.921.090144Th1-Jun-2006E. BROWNE and J. K. TULI2024-03-06
.............................................................................................
292512.371NaN0.0003010.000074CEO92146UNaN2557.90.50+2.800000e+02NaN6ns2.800000e-076.000000e-09IT97.40.4NaNNaN92146U1-Jun-2014E. BROWNE and J. K. TULI2024-03-06
\n", "

30 rows × 30 columns

\n", "
" ], "text/plain": [ " energy unc_en intensity unc_i type shell p_z p_n p_symbol \\\n", "0 13.113 NaN 8.061035 1.047936 AU L 92 146 U \n", ".. ... ... ... ... ... ... ... ... ... \n", "29 2512.371 NaN 0.000301 0.000074 CE O 92 146 U \n", "\n", " p_energy_shift p_energy unc_pe jp half_life operator_hl unc_hl \\\n", "0 NaN 0.0 NaN 0+ 4.468000e+09 NaN 6 \n", ".. ... ... ... .. ... ... ... \n", "29 NaN 2557.9 0.5 0+ 2.800000e+02 NaN 6 \n", "\n", " unit_hl half_life_sec unc_hls decay decay_% unc_d q unc_q \\\n", "0 Y 1.409963e+17 1.893416e+14 A 100.0 NaN 4269.9 21.0 \n", ".. ... ... ... ... ... ... ... ... \n", "29 ns 2.800000e-07 6.000000e-09 IT 97.4 0.4 NaN NaN \n", "\n", " d_z d_n d_symbol ensdf_publication_cut-off ensdf_authors \\\n", "0 90 144 Th 1-Jun-2006 E. BROWNE and J. K. TULI \n", ".. ... ... ... ... ... \n", "29 92 146 U 1-Jun-2014 E. BROWNE and J. K. TULI \n", "\n", " Extraction_date \n", "0 2024-03-06 \n", ".. ... \n", "29 2024-03-06 \n", "\n", "[30 rows x 30 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DECAY_RADIATION, type electron\n", "df=lc_pd_dataframe(livechart+\"fields=decay_rads&nuclides=238u&rad_types=e\")\n", "df" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
mean_energyunc_meintensity_betaunc_ibdaughter_level_energyenergy_ecunc_eecintensity_ecunc_ielog_ftunc_lftransition_typenu_mean_energyunc_amep_zp_np_symbolp_energy_shiftp_energyunc_pejphalf_lifeoperator_hlunc_hlunit_hlhalf_life_secunc_hlsdecaydecay_%unc_dqunc_qd_zd_nd_symbolensdf_publication_cut-offensdf_authorsExtraction_date
0NaNNaNNaNNaN309.7886205087.04.014.10.101NUNaNNaN93143NpNaN0NaN6(-)155000.0NaN1Y4.891324e+123.155693e+10EC87.80.29345092144U1-Apr-2022Shaofei Zhu2024-03-06
.....................................................................................................................
4NaNNaNNaNNaN0.0009877140.07.07.10.111NUNaNNaN93143NpNaN5751.01(-)22.5NaN4h8.100000e+041.440000e+03EC51.01.09345092144U1-Apr-2022Shaofei Zhu2024-03-06
\n", "

5 rows × 38 columns

\n", "
" ], "text/plain": [ " mean_energy unc_me intensity_beta unc_ib daughter_level_energy \\\n", "0 NaN NaN NaN NaN 309.788 \n", ".. ... ... ... ... ... \n", "4 NaN NaN NaN NaN 0.000 \n", "\n", " energy_ec unc_eec intensity_ec unc_ie log_ft unc_lf transition_type \\\n", "0 620 50 87.0 4.0 14.1 0.10 1NU \n", ".. ... ... ... ... ... ... ... \n", "4 987 71 40.0 7.0 7.1 0.11 1NU \n", "\n", " nu_mean_energy unc_ame p_z p_n p_symbol p_energy_shift p_energy \\\n", "0 NaN NaN 93 143 Np NaN 0 \n", ".. ... ... ... ... ... ... ... \n", "4 NaN NaN 93 143 Np NaN 57 \n", "\n", " unc_pe jp half_life operator_hl unc_hl unit_hl half_life_sec \\\n", "0 NaN 6(-) 155000.0 NaN 1 Y 4.891324e+12 \n", ".. ... ... ... ... ... ... ... \n", "4 51.0 1(-) 22.5 NaN 4 h 8.100000e+04 \n", "\n", " unc_hls decay decay_% unc_d q unc_q d_z d_n d_symbol \\\n", "0 3.155693e+10 EC 87.8 0.2 934 50 92 144 U \n", ".. ... ... ... ... ... ... ... ... ... \n", "4 1.440000e+03 EC 51.0 1.0 934 50 92 144 U \n", "\n", " ensdf_publication_cut-off ensdf_authors Extraction_date \n", "0 1-Apr-2022 Shaofei Zhu 2024-03-06 \n", ".. ... ... ... \n", "4 1-Apr-2022 Shaofei Zhu 2024-03-06 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DECAY_RADIATION, type beta+/electron capture\n", "df=lc_pd_dataframe(livechart+\"fields=decay_rads&nuclides=236np&rad_types=bp\")\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Beta and Neutrino spectra**" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
p_zp_np_symbolp_energyd_zd_nd_symbolbin_endn_deunc_dn_dedn_de_nuunc_dn_de_nuextraction_date
05481Xe526.5515580Cs0.00.001710.0000620.0000000.0000002024-03-06
..........................................
3845481Xe526.5515580Cs915.20.000000.0000000.0020750.0005252024-03-06
\n", "

385 rows × 13 columns

\n", "
" ], "text/plain": [ " p_z p_n p_symbol p_energy d_z d_n d_symbol bin_en dn_de \\\n", "0 54 81 Xe 526.551 55 80 Cs 0.0 0.00171 \n", ".. ... ... ... ... ... ... ... ... ... \n", "384 54 81 Xe 526.551 55 80 Cs 915.2 0.00000 \n", "\n", " unc_dn_de dn_de_nu unc_dn_de_nu extraction_date \n", "0 0.000062 0.000000 0.000000 2024-03-06 \n", ".. ... ... ... ... \n", "384 0.000000 0.002075 0.000525 2024-03-06 \n", "\n", "[385 rows x 13 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "df=lc_pd_dataframe(livechart+\"fields=bin_beta&nuclides=135xe&rad_types=bm&metastable_seqno=1\")\n", "df" ] }, { "cell_type": "code", "execution_count": null, "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }