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andersy005/trait-space-joint-pdf.ipynb

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trait-space-joint-pdf.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "b3e43153",
"metadata": {},
"source": [
"# Defining trait space"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fe7678c2",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9db287d9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mclong/miniconda3/envs/metabolic/lib/python3.7/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.\n",
" from distributed.utils import tmpfile\n"
]
}
],
"source": [
"import os\n",
"\n",
"import cmocean\n",
"import constants\n",
"import matplotlib.pyplot as plt\n",
"import metabolic as mi\n",
"import numpy as np\n",
"import util\n",
"import xarray as xr"
]
},
{
"cell_type": "markdown",
"id": "81c87ff3",
"metadata": {},
"source": [
"## Load traits database\n",
"\n",
"A subset of the trait data from {cite:t}`Deutsch-Penn-etal-2020`, including only the marine organisms for which temerature-dependent hypoxia metabolic traits have been determined. \n",
"\n",
"The `open_traits_df` function is defined in the [metabolic](https://github.com/matt-long/aerobic-safety-margins/blob/main/notebooks/metabolic.py) module and makes the trait data available via a [pandas](https://pandas.pydata.org/) `DataFrame`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "969bfed8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Species</th>\n",
" <th>Phylum</th>\n",
" <th>alphaD_log10</th>\n",
" <th>Emet</th>\n",
" <th>Eo</th>\n",
" <th>Ao</th>\n",
" <th>Ac</th>\n",
" <th>Phi_crit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Acanthephyra acutifrons</td>\n",
" <td>Crustacea</td>\n",
" <td>0.479565</td>\n",
" <td>0.040200</td>\n",
" <td>-0.027836</td>\n",
" <td>0.264715</td>\n",
" <td>0.131959</td>\n",
" <td>2.006040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Acanthephyra curtirostris</td>\n",
" <td>Crustacea</td>\n",
" <td>0.377714</td>\n",
" <td>0.043938</td>\n",
" <td>0.038027</td>\n",
" <td>0.252578</td>\n",
" <td>0.141861</td>\n",
" <td>1.780456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Acanthephyra purpurea</td>\n",
" <td>Crustacea</td>\n",
" <td>1.035748</td>\n",
" <td>0.840722</td>\n",
" <td>0.131972</td>\n",
" <td>0.230068</td>\n",
" <td>0.073638</td>\n",
" <td>3.124330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Acanthephyra smithi</td>\n",
" <td>Crustacea</td>\n",
" <td>1.082577</td>\n",
" <td>0.313173</td>\n",
" <td>-0.030067</td>\n",
" <td>0.286307</td>\n",
" <td>0.174423</td>\n",
" <td>1.641453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Acipenser brevirostrum</td>\n",
" <td>Chordata</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.362569</td>\n",
" <td>0.218524</td>\n",
" <td>0.048415</td>\n",
" <td>4.513525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>Styela plicata</td>\n",
" <td>Tunicata</td>\n",
" <td>0.433791</td>\n",
" <td>0.346635</td>\n",
" <td>-0.190998</td>\n",
" <td>0.061336</td>\n",
" <td>0.043505</td>\n",
" <td>1.409863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>Systellaspis debilis</td>\n",
" <td>Crustacea</td>\n",
" <td>0.779626</td>\n",
" <td>0.418652</td>\n",
" <td>0.226970</td>\n",
" <td>0.219289</td>\n",
" <td>0.096003</td>\n",
" <td>2.284196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>Tarletonbeania crenularis</td>\n",
" <td>Chordata</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.213307</td>\n",
" <td>0.203696</td>\n",
" <td>0.051780</td>\n",
" <td>3.933876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>Tautogolabrus adspersus</td>\n",
" <td>Chordata</td>\n",
" <td>0.801974</td>\n",
" <td>0.327012</td>\n",
" <td>0.244520</td>\n",
" <td>0.170886</td>\n",
" <td>0.051204</td>\n",
" <td>3.337395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>Zoarces viviparus</td>\n",
" <td>Chordata</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.789434</td>\n",
" <td>0.177870</td>\n",
" <td>0.062111</td>\n",
" <td>2.863717</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>61 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Species Phylum alphaD_log10 Emet Eo \\\n",
"0 Acanthephyra acutifrons Crustacea 0.479565 0.040200 -0.027836 \n",
"1 Acanthephyra curtirostris Crustacea 0.377714 0.043938 0.038027 \n",
"2 Acanthephyra purpurea Crustacea 1.035748 0.840722 0.131972 \n",
"3 Acanthephyra smithi Crustacea 1.082577 0.313173 -0.030067 \n",
"4 Acipenser brevirostrum Chordata NaN NaN 0.362569 \n",
".. ... ... ... ... ... \n",
"56 Styela plicata Tunicata 0.433791 0.346635 -0.190998 \n",
"57 Systellaspis debilis Crustacea 0.779626 0.418652 0.226970 \n",
"58 Tarletonbeania crenularis Chordata NaN NaN 0.213307 \n",
"59 Tautogolabrus adspersus Chordata 0.801974 0.327012 0.244520 \n",
"60 Zoarces viviparus Chordata NaN NaN 0.789434 \n",
"\n",
" Ao Ac Phi_crit \n",
"0 0.264715 0.131959 2.006040 \n",
"1 0.252578 0.141861 1.780456 \n",
"2 0.230068 0.073638 3.124330 \n",
"3 0.286307 0.174423 1.641453 \n",
"4 0.218524 0.048415 4.513525 \n",
".. ... ... ... \n",
"56 0.061336 0.043505 1.409863 \n",
"57 0.219289 0.096003 2.284196 \n",
"58 0.203696 0.051780 3.933876 \n",
"59 0.170886 0.051204 3.337395 \n",
"60 0.177870 0.062111 2.863717 \n",
"\n",
"[61 rows x 8 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = mi.open_traits_df()\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "b6d90a34",
"metadata": {},
"source": [
"## Illustrating trait distributions\n",
"\n",
"Recall the definition of the [Metabolic Index](./metabolic-index-defining.ipynb):\n",
"\n",
"$$\n",
" \\Phi = A_o \\frac{P_{\\mathrm{O}_2}}\n",
" {\\mathrm{exp}\\left[\n",
" \\frac{-E_o}{k_B}\\left(\n",
" \\frac{1}{T} - \\frac{1}{T_{ref}}\n",
" \\right)\n",
" \\right]\n",
" }\n",
"$$\n",
"\n",
"\n",
"Parameters relevant to defining trait space:\n",
"- $E_o$: Temperature sensitivity of metabolic rates.\n",
"- $A_o$: Hypoxic tolerance for resting metabolism.\n",
"- $\\Phi_{crit}$: lowest value of Metabolic Index at which *active* metabolism can be sustained.\n",
"\n",
"Since active metabolism requires $\\Phi >= \\Phi_{crit}$, we can directly compute values of the Metabolic Index relevant for active metabolic rates, defining a new parameter:\n",
"- $A_c$: Hypoxic tolerance for active metabolism (i.e., $A_o / \\Phi_{crit}$)\n",
"\n",
"\n",
"The hidden code below defines a function for plotting trait histograms."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7ea6c70c",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"def plot_trait_hist(df, trait):\n",
" fig, ax = plt.subplots()\n",
"\n",
" pdf = mi.trait_pdf(df, trait, 30)\n",
" beta = pdf.beta\n",
" trait_median = pdf.median()\n",
"\n",
" trait_values = df[trait].values\n",
" if trait in pdf.normal_traits:\n",
" n, bins, h = ax.hist(trait_values, 30, density=True)\n",
" else:\n",
" log10_values = np.log10(trait_values)\n",
" n, bins, h = ax.hist(\n",
" trait_values, np.logspace(log10_values.min(), log10_values.max(), 30), density=True\n",
" )\n",
"\n",
" ax.plot(pdf.coord, pdf.fitted())\n",
" ax.set_title(f'{trait} distribution')\n",
" ax.set_xlabel(f\"df[trait].attrs['long_name'] {df[trait].attrs['units']}\")\n",
"\n",
" ax.axvline(trait_median, color='black', label=f'median ({trait_median:0.3f})')\n",
"\n",
" ax.legend()\n",
" return trait_median, beta"
]
},
{
"cell_type": "markdown",
"id": "446e5328",
"metadata": {},
"source": [
"The following plots illustrate trait distributions. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "db34e33c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GmNlsM5u9Zs2aKkYVEZFYxVzczawx8BxwqbuvKz27jFV8qxfcH3T3bu7erVWrVlVLKiIiMYupuJtZOkFhn+Duz5exSDbQocTz9sDKbY8nIiLVEcvZMgY8DCxx9zvKWWwKMDA8a+YQIMfdV8Uxp4iIVEEsZ8v8HjgLWGRm88PXrgY6Arj7/cArQF9gGbARGBz3pCIiErNKi7u7v0fZ+9RLLuPAn+MVSkREto2uUBURSUIq7iIiSUjFXUQkCam4i4gkIRV3EZEkpOIuIpKEVNxFRJKQiruISBJScRcRSUIq7iIiSUjFXUQkCam4i4gkIRV3EZEkpOIuIpKEVNxFRJKQiruISBJScRcRSUIq7iIiSUjFXUQkCam4i4gkIRV3EZEkpOIuIpKEVNxFRJJQWtQBJDZZw1+OrM3lo46L+7ZFpGap5y4ikoRU3EVEkpCKu4hIEqq0uJvZI2a22swWlzO/l5nlmNn88HFd/GOKiEhVxHJA9VHgHuDxCpaZ4e7HxyWRiIhss0p77u7+LvBDLWQREZE4idc+90PNbIGZvWpmnctbyMyGmNlsM5u9Zs2aOG1aRERKi0dxnwvs7O5dgbuBF8tb0N0fdPdu7t6tVatWcdi0iIiUZZuLu7uvc/f14fQrQLqZtdzmZCIiUm3bXNzNbEczs3D6oLDNtdvaroiIVF+lZ8uY2USgF9DSzLKB64F0AHe/H+gPXGhmBcAmYIC7e40lFhGRSlVa3N399Erm30NwqqRIbNyhKB9WLYQNq6FJW2jVCVJ0TZ1IvGjgMIm7HVhHj5SFtLEfaWnraGU/0ZIcWlkOLS2H/itWBQs+0OOXlRpuDx1/B1m/h51/DzvuAymp0fwCIklAxV3ipqstY2Da6xyf8iENLB+ATZ7BGm/GGpqzwtswp2gPaD4fUtPhtNtgu1bw43JY8R4snwmfhyNVNmgKHQ8JCn1Wd2jbNVhHRGKi4i7bJn8TLH4ePh7H5AbzWO+ZTCrsxdOFh7Pc27CBTMB+tUpW89uCid+eGPzc+VDYN9z7t25lUORXhI8vXg9eT98uKPI9h0GHA2vndxOpw1TcpXp+XA4fPwzzxsOmH6HVXlybP5gXCruzgYbVb7fpTtDl1OABsH51UOSXz4RPJ8PDR0Lnk+HI62H7rHj8JiJJScVdYldUBF++DR89GPSoLQX2Og4OGgJZ3Xnib6/Ef5uNW0PnPwSPI0fC+2Nh5lj4bCocfD70uCLYXy8iv6LiLrH53yJ4/nxY/Qls1zrYPXLAIGjWrvYyNGgMh18dbPftm+D9e2DeE3DYcOh2DqRl1F4WkQSnc8+kQkYRzPwnPHg4bPweTh4Hl30Cva+p3cJeUtOdoN+9cP67sGMXeO0q+NfBsOSl4DRLEVFxl/K1ZS1Ppt8Mb1wHexwDF34AXU5LnB5y2y4wcDL83zOQmgFPnQn/7gvZc6JOJhI5FXcp0wkp7zOtwVXsk/IVnHQv/PEJ2K5F1LG2ZgZ7HA0XzITj74S1X8BDveHlK6EgL+p0IpFRcZdfacoG7ky/l7sz7uELb0ffvFtgvzODIprIUtOC/e5/mQeHXAQfj4Px/WC9hpaW+kkHVGWLg20Jt2fcx478wO35/flX4UkUkkrW8Jejjha7Bk2gzy3Q7gCY/GcYdzgMmBBcBCVSj6jnLqRTwFVpE5mYcSP5nsopeSO5u/BkCqnDl//v0x/OeQ28CB4+BhY9G3UikVql4l7PdbTveDFjBBemvcSkwsM5Lu8WFvhuUceKj532gyHTYad94blz4c2RUFQYcSiR2qHdMvVYJ1vB4xmjSKOQ8/Ku4M2iA6KOFH+NW8PAKfDqX+G9O+G7T+CUhyCzWdTJRGqUeu711IH2GU9l/IN8Uumfd31yFvZiaRlwwl1w3B3BFbbjjoDvv4g6lUiNUnGvh45ImcP4jFtY483ov3kkX3pEFyPVtgPPDXrxm36Ecb1h6etRJxKpMSru9cwpKe/yQPqdfOYd6J93PSupZ7e7zfp9sB9++yx48rRgV42uapUkpOJej5yb+jK3Z9zPrKJOnJF3DT/SNOpI0WjeAc6ZBnufHBxkffN6FXhJOjqgWi84f017iovSpvBy4UFclv9n8qjnN77IaASnPAwNdwjGzikqhKNvTPyLtURipOKe5FIo4qa0hzk97R0mFBzBiILBFOkPtoAZ9L0tGLr4g3uC3vsxN6nAS1JQcU9iDcjjrvR7OTb1Y8YW9OOOglMpfVekes8Mjr01KPCz7g0ueupziwq81Hkq7kmqMRt5MP0Ofpf6KTfkn8W/C4+NOlLiMgsLenGBL4RjR6vAS52m4p6EmrCR8Rk309lWcGneRbxY1D3qSInPLNglk5IC798d9OCPvS14LlIHqbgnmYbk8nDGbXS2FVyYf2lyX5wUb2Zw1D+CHvzMfwYFvu/tKvBSJ6m4J5EM8nkg/U4OsKX8Jf8SFfbqMIMjbwBLhffuCM6iOf4uFXipc1Tck0QaBdyTPpaeqYu4Mv98Xi46JOpIdZcZHHFd0IOfMSbowZ8wVgVe6hQV9ySQQhFj0u/n6NQ5jMgfxLOFh0Udqe4zg97XBgX+3dHBaZInjoWUOjwMstQrlXZFzOwRM1ttZovLmW9mNtbMlpnZQjPbP/4xpXzOTWkP0y/1fUblD2B84dFRB0oeZsGNwHv9DeY/AS8N1ZWsUmfE8nfmo0CfCuYfC+wePoYA9217LImNMyLtCU5Pe4e7C/pxf+GJUQdKTr2GQ89hMG98MFSBSB1Q6W4Zd3/XzLIqWOQk4HF3d2CWmTU3s7buvipeIaVsl6U9y7lpr/JIQR9uLzg16jjJ7fBrgtEkZ/4zGLKg+6VRJxKpUDz2ubcDvi3xPDt8bavibmZDCHr3dOzYMQ6brr/OT32JoWkvMKmgF38vOAtdeVrDzILz3jf9GPTeG24PB5wddSqRcsXj8H9ZVaXMHZPu/qC7d3P3bq1atYrDpuunM1Pf4G/pE5lSeChXF5yHCnstSUmBfvfDbkfC1Evh0ylRJxIpVzyKezbQocTz9sDKOLQrZTgl5V1uTP83bxQewOX5F2oQsNqWlgGnPQ7tugX3Zf1qetSJRMoUj90yU4CLzWwScDCQk8z727OGvxzTcstHHRf3bR+T8hGj0x9gRuHeXJx/CQVJfiZrvN/rWNuLRTPO46mMf7DXpDPg7CnQTheMSWKJ5VTIicAHwJ5mlm1m55rZBWZ2QbjIK8BXwDJgHHBRjaWtxw5N+YSx6fcw33djSP7lbCYj6kj1Wg6NGZg3HBq1gCf6w5rPo44k8iuxnC1zeiXzHfhz3BLJ1lYt4MH0O1jhbTgnbxibyIw6kQCr2R7OegEe6QPj/xDc3al5h8pXFKkF2mGb6H74Cp7ozzoaMTBvODk0jjqRlNRiVzjredi8PijwG76POpEIoOKe2H7+LigYRfkMzBvO/2gRdSIpy477wP89BTnfwhOnQO66qBOJqLgnrNwcmHAKrF8NZzzLl94u6kRSkZ0PDc6i+d8imPR/kJ8bdSKp51TcE1F+Lkw6A1YvgT+Oh/bdok4ksdjjGOh3HyyfEZwmWVQYdSKpx1TcE01RITx/XlAg+t0XXDAjdUfXP0KfUfDZ1OBCJw00JhFJ7hOl6xp3ePlyWPISHHMLdDkt6kRSHYdcCBvWwIzbYbtWwdjwIrVMxT2RvHMzzHkUul8Gh+pygTqt94jgzJkZt0Ojlvr3lFqn4p4oPnwwuCnEfmfCERpWts4zg+PvhE0/wLS/BRc7df1j1KmkHtE+90Sw+Hl49a+wZ184/p9BYZC6LyUVTn4IsnrA5Itg6etRJ5J6RMU9al+8Cc8PgY6HQP9HIFV/TCWV9EwY8CS06QxPD4RvPow6kdQTKu5RWvEBPHUmtO4UXAST3jDqRFITMpvCGc9B07bw5Knw3adRJ5J6QMU9Kivnw5OnQbP2cObzkNks6kRSkxq3grNehLSG8MTJ8OOKqBNJklNxj8KapcEHPLMZDHwx+OBL8tt+52AcmvyNwbAS69dEnUiSmIp7bftxBTx+ElgqDJwc9Nyl/mjTGf7vaVi3Eib0h80/R51IkpSKe236+TsY3w/yNwRDxbbYNepEEoWOh8BpjwXj0Dw5API2Rp1IkpCKe23Z9GPwp/jP/4MznoUd9446kURpj2Pg5AdhxUwNNCY1QsW9NmxeDxNOhbVfwIAJ0OGgqBNJItinP5x0L3z1TnCaZEFe1Ikkiai417T83KBn9t+5wXnsu/aOOpEkkv3OCK5k/WIaPDsYCvOjTiRJQsW9JhUWBEO/fv2foIfW6YSoE0ki6nYO9Lk1GEnyhfM1VLDEhS6HrClFRTDl4uADe+xo2LfCW9FKfXfIBVC4Gd64DlIz4KR/QYr6XlJ9Ku41wCiCqUNhwUQ4/Fo4+PyoIyWUWV+tBSBr+MtxaS9e7US/7d1Yfuw18M5NQYE/ofxxhqL8nZePOi6ybUvsVNzjLIUibkt/AObOgB5XQs8ro44kdUnPYVCQGwwVnNYg+KtPA8lJNai4x1EqhdyRfh8npb4Ph18Dh/016khS15gFY8EXbIYP7gl68EffqAIvVabiHidpFDA2/R76pn7EqPwBDFdhl+oyCwp6cYFPbwi9r406ldQxKu5xkEE+96aP5ajUOfwj/0weLuzL8KhDSd1mFuySKdwM794GqQ3gsGFRp5I6RMV9GzUgj/vT7+Tw1AVcmz+YJwqPijqSJIuUlODmLYX58M6NwbAVR1yvXTQSExX3bZDJZsal387vUz7hqvw/8VTh4VFHkmSTkhJcI5GWCe/dCRvXwnF3Rp1K6oCYTqQ1sz5m9rmZLTOzrfY4mFkvM8sxs/nhI+lv996IXP6dfhu/S/mEYfnnq7BLzUlJDa5i7TkM5j4Oz5xNAzRUgVSs0p67maUC9wJHAdnAx2Y2xd1L305mhrsfXwMZE05jNvLvjNHsZ8u4LP8iphT9PupIkuzMgoOqjVrCa1fx7/SvGJJ/OetpFHUySVCx9NwPApa5+1fungdMAk6q2ViJqykbGJ8xin3tSy7Jv0SFXWrXIRfAyeM4MOVzJmbcSAtyok4kCSqW4t4O+LbE8+zwtdIONbMFZvaqmXUuqyEzG2Jms81s9po1dfAuNOtWMjHjRjrb11yUP5RXiw6OOpHUR11O40/5l7ObreSZjBtoRx38LEmNi6W4l3Vo3ks9nwvs7O5dgbuBF8tqyN0fdPdu7t6tVas6dmu5lfNgXG862mr+lH8lbxR1izqR1GPTi/bjzLy/0cLW8VyDkexu2VFHkgQTS3HPBjqUeN4eWFlyAXdf5+7rw+lXgHQzaxm3lFH7dAo8ciykpNM/73r+U9Q16kQizPE9OS3vOgznmYwb2N+WRh1JEkgsxf1jYHcz28XMMoABwJSSC5jZjmbBybdmdlDY7tp4h6117jDjDnj6rODOSX96i8+9Y9SpRLb43DtySt5IfvTGPJFxC4elLIg6kiSISou7uxcAFwPTgCXA0+7+iZldYGYXhIv1Bxab2QJgLDDA3UvvuqlbCvJg8p/hrRtg71Pg7JegceuoU4lsJdtbc2reSL7ytjyUPob+qf+JOpIkgJguYgp3tbxS6rX7S0zfA9wT32gR2rA26K2vmAm9/gaHXaWrAiWhfU8zTs+7lvvS72RM+gPsa8v4e8FA8kiPOppERHcDKG3NUnjoCMieDac8DL2Gq7BLnfAzjTg7fzj3FZzAmWlv8XTGDezE91HHkoiouJf05Tvw0JGQtx4GTQ1uYCxShxSSyq0FpzMk7zJ+Y6uY2uBqeqQsjDqWREDFvdjsR+CJU6BZO/jT29DhoKgTiVTb60UHclLejaz27Xks/VYuTn0huEOY1Bsq7rk58MKFMPUy2LU3nDMNmuuMGKn7vva2/CHvBiYX/Y4r059hXPrtNGV91LGkltTv4r58JtzXHRZOgp5/hdMnQWbTqFOJxM0mMrks/yJG5A+iZ8pCXsq4lt/a8qhjSS2on8W9ILzL/KPHQWoanPM69L4mmBZJOsb4wqMZkDeCDCvg+YzrdbpkPVD/ivt3n8C43jDzn3DA2XD+DOhwYNSpRGrcXN+D4zffxJyiPRiT/gA3p41jOzZFHUtqSJ3sqmYNf7nK6xhFnJv6KsPSnmIdjbgq/0renrk/zAx6MMtHHRdJxnhvV6Qia2nGwPzhXOHPcEHqSxyWupDr8wfxZtEBUUdLCon0ua8XPfed+J4n02/m2vQJTC/al2M2j+btov2jjiUSiUJSGV0wgP5517POG/FQxu3cn34nOybBiCHyiyQv7k6/lPd4rcFV7JPyFcPyh3B+/mX8gA6aisz1PTgh7yZG5Q/gsJQFvNHgr5ydOo0UnTKZFJK2uO9s/+OB9Du5K+NffO4dODbvFp4p7EXZIxiL1E8FpHF/4YkcnXcrc4t254b0x3g+4zqdUZMEkq6478A6RqY9ypsZw+iesohR+QP4Y951fOttoo4mkrC+9TacnX8Vl+RdTDtby5SMa7k6bQKNyI06mlRTnTygWpZMNnNO6qtcmPYSDdnMxMLe/LPgFL6nWdTRROoI46Wi3/GfzV0YnjaJIWkv0zf1Q67LH6RjVHVQnS/uKRTRP/U/XJ72LDvaj0wr7Mbogj/ypZd1J0ARqcw6GnN1wXk8V9iDW9If4pGMMfynsAtjC/7AHN8z6ngSozpc3J3DU+YzPG0ie6ZkM7doNy7Ou4TZvlfUwUSSwhzfk+PybmFw6qucnzaV5xrcwKyiTvBlI/hNL42WmuDqZHHfx77i6rQnOTT1U74uasMFeZfyWtGB6GCpSHzlk8aDhScwvvAoTk99hyFpU2F8P2h3APQcBnv0UZFPUHWvuC+YxEsNruV7b8qI/EFMLOxNQR38NUTqkk1k8kjhsTxReCRL+/8A790JEwdAm32gx+Xw25MgJTXqmFJC3auKux/NXQUn81BBX9bTKOo0IvVKHunQ7RzY7yxY9Cy8dwc8Oxha7A49rgjugZCquz8lgrp3KmSjHbiroL8Ku0iUUtNh39Phollw6qOQlgkvXgBj94O3/g6rP4s6Yb1X93ruIpI4UlKh8x/gt/1g6TT46MFgl82M22HHfWCf04LefNOdok5a76i4i8i2M4M9+wSP9ath8fOw6Gl4Y0QwvHZWd+hyGnQ6ERo2jzptvVD3dsuISGJr3BoOuSC4XeUlc4ObzK9bCVMugTF7wFNnwqdTIG9j1EmTmnruIlJzWuwaFPfDroKVc2HhM7D4OVjyEqSkQ/tuQa8+qzt0OBjSG0adOGmouItIzTMLzo1vdwAcfSMsnwFfvQPL3wv2z797G6RmQLtusEuPoNi3P1DFfhuouItI7UpNg10PDx4Auevgm1lBwV8+Iyj0/7kVUhsEBX7nQ6FNZ2j9W9jhNzrVMkYq7iISrcymsMfRwQMgNyco9l+/+0vP3sMx5lPSoeUe0LoTtN4rKPitO0HzLEjRIcSSVNxFJLFkNoM9jgkeAPmb4PulsHrJL4/sj2Dxs7+sk9YQWu0J22dBs/bBqZdNd4Km4XSTHevdFbQxFXcz6wP8E0gFHnL3UaXmWzi/L7ARGOTuc+OcVUTqo/SG0LZr8Chp88+w5vNfCv6aJfDd4uB8+4JSN/621KDAN90JmraDJm2h0Q6Q2Rwabl/iET7PbFbnvwwqLe5mlgrcCxwFZAMfm9kUd/+0xGLHAruHj4OB+8KfIiI1o0GT4Gyb9t1+/bo75P4EOf8NTsFc99/wsRJysuG7T2DZW5D3c8XtZzYLin+DJpDeCDIaBT/LnN4O0jPpn/oZeZ5GPr888kj71Wt5pMGGtbBdi5p6Z4DYeu4HAcvc/SsAM5sEnASULO4nAY+7uwOzzKy5mbV191VxTywiUhGzX3riO+5d/nKF+cH+/U0/ho+fSkz/GHxBbPoRNq+H/A3B7qENa4PpvI3B8/wNUFSwpckxsR7r/WAlHDlyG37JyllQjytYwKw/0MfdzwufnwUc7O4Xl1hmKjDK3d8Ln78FXOXus0u1NQQYEj7dE/i8GplbAt9XY73akMjZILHzJXI2SOx8iZwNEjtfImeDsvPt7O6tKlsxlp57WYM1l/5GiGUZ3P1B4MEYtll+GLPZ7t6t8iVrXyJng8TOl8jZILHzJXI2SOx8iZwNti1fLOcOZQMdSjxvD6ysxjIiIlJLYinuHwO7m9kuZpYBDACmlFpmCjDQAocAOdrfLiISnUp3y7h7gZldDEwjOBXyEXf/xMwuCOffD7xCcBrkMoJTIQfXXORt261TwxI5GyR2vkTOBomdL5GzQWLnS+RssA35Kj2gKiIidY+u1xURSUIq7iIiSSjhi7uZ7WBmb5jZF+HP7ctYpoOZvWNmS8zsEzMbWsOZ+pjZ52a2zMyGlzHfzGxsOH+hme1fk3mqmO2MMNNCM3vfzLqW1U5U+Uosd6CZFYbXWSRMNjPrZWbzw/9n/6mtbLHkM7NmZvaSmS0I89Xksa/S237EzFab2eJy5kf2mYgxX2Sfi8qylViuap8Jd0/oBzAaGB5ODwduLWOZtsD+4XQTYCnw2xrKkwp8CfwGyAAWlN4WwcHlVwnO/z8E+LCW3qtYsv0O2D6cPra2ssWar8RybxMcqO+fKNmA5gRXZncMn7dOpPcOuLr48wG0An4AMmopX09gf2BxOfMj+UxUIV+Un4sKs5X496/SZyLhe+4EQxs8Fk4/BvQrvYC7r/JwoDJ3/xlYArSroTxbhmNw9zygeDiG0pkf98AsoLmZta2hPFXK5u7vu/uP4dNZBNck1JZY3juAS4DngNUJlu3/gOfd/RsAd0+0fA40CQfya0xQ3AuoBe7+bri98kT1mQAqzxfl5yKG9w6q8ZmoC8W9jYfnzIc/W1e0sJllAfsBH9ZQnnbAtyWeZ7P1F0ksy9SEqm73XILeVG2pNJ+ZtQP+ANxfi7kgtvduD2B7M5tuZnPMbGCtpYst3z1AJ4ILCBcBQ92LB0KPXFSfieqo7c9Fhar7mUiI8dzN7E1gxzJmXVPFdhoTfLtd6u7r4pGtrM2U8Vq1hmOoATFv18wOJ/hP3L1GE5XabBmvlc53F8G4RIVBB7TWxJItDTgAOAJoCHxgZrPcfWlNhyO2fMcA84HewK7AG2Y2owY/C1UR1WeiSiL6XFTmLqrxmUiI4u7uR5Y3z8y+Kx5hMvwzrsw/S8wsnaCwT3D352soKiT2cAwxbdfMugAPAce6+9payFUslnzdgEnhf+KWQF8zK3D3FxMgWzbwvbtvADaY2btAV4JjPDUtlnyDCQbwc2CZmX0N7AV8VAv5KpPwQ5RE+LmoTPU+E7V5UKOaBxtu49cHVEeXsYwBjwN31UKeNOArYBd+ObDVudQyx/Hrg0cf1dJ7FUu2jgRXEv8ugn/LSvOVWv5Rau+AaizvXSfgrXDZRsBiYO8EyncfMDKcbgP8F2hZi/++WZR/wDKSz0QV8kX2uagsW6nlYv5MJETPvRKjgKfN7FzgG+BUADPbieCuUH2B3wNnAYvMbH643tXu/kq8w3jiDcdQ1WzXAS2Af4U9gQKvpVHxYswXiViyufsSM3sNWAgUEfz/q/D0tdrMB/wDeNTMFhEU0avcvVaGszWziUAvoKWZZQPXA+klskXymahCvsg+FzFkq1674beBiIgkkbpwtoyIiFSRiruISBJScRcRSUIq7iIiSUjFXUQkCam4i4gkIRX3eszMRprZleH0XuFQtvPMbFcz21R8zYCZNTezi6q5jVfC9X/VRriN+Wa2vortXV1iulq5zGy5mS0ys27h8+nhmETF81pWtc26wMyWhz+r/N6H79ExpV671Mz+FeeYEicq7lKsHzDZ3fcDCoEv3X3fcF5zoMwiamapFTXq7n3d/afSbbh7yfar4uoS09XOBRzu7rOrsf06r5rv/URgQKnXBoSvSwJSca9nzOya8IYPbwJ7hq/1BS4FzjOzd8pYbRRQ3Nu7zYIbVrxjZk8SjD6Imb0YjpT4iZkNKbG94p7wr9qIIedW7ZnZKKBh2MaEynKZ2XZm9rIFN69YbGZ/LGdzPxB8oZXOcHm43mIzuzR8LcuCm8KMC7O9bmYNw3kHWnCzhw/CPOVevWpmg8zseTN7zYIb0YwuMe8+M5sdtn9Dqffy5rD92Wa2v5lNM7Mvi69UDZcbZmYfh1luKLHZNZW97xWs/yxwvJk1KH4fgJ2A92JpUyIQxTgKekTzIBjRcBHBuChNCS4FvzKcN7LEdBYlxrko43kvYAOwS4nXdgh/NiQYc6VF+Hw5wWBHv2qjxHrry8laXnvrY80FnAKMKzG/WclM5Wy3OG/xe7UdwdjonxAMJZ1FMEb6vuHyTwNnhtOLCccmIfjiqejmC4MIxoppBmQCK4AOpX73VGA60KVEtgvD6TsJhkFoQnBjjtXh60cDDxIMP5ACTAV6lpNhq/e+ovWBl4GTwunhwG1R/5/Wo/yHeu71Sw/gBXff6MEwsFO2oa2P3P3rEs//YmYLCG500AHYfRva3pb2SuZaBBxpZreaWQ93z6nC9rsTvFcb3H098DzB+wfwtbvPD6fnAFlm1hxo4u7vh68/GcM23nL3HHfPJbjD087h66eZ2VxgHtAZ+G2JdYr/zRYR3C3oZ3dfA+SGGY4OH/OAuQSjQlbl36Ki9UvumtEumQRXFwYOk/iK12BCG4onzKwXcCRwqLtvNLPpBL3RatnG9rbkcvelZnYAwYBVt5jZ6+7+91hjVDBvc4npQoK/Lqoz+HzpdtLMbBfgSuBAd//RzB7l17978TpFpdYvIvg8G3CLuz9QjTxUsv6LwB0W3P+0oYd3P5PEpJ57/fIu8Acza2hmTYATYlzvZ4I//8vTDPgxLMR7EQzpWtU2Ym0v34Kx+ytt04KRQze6+xPAGIL7VMbqXaCfmTUys+0I7oQzo7yFPbhF289mVpy19MHHWDUl+ILKMbM2BPfzrIppwDkW3LgGM2tnZhXevSzW9cO/YKYDj6Bee8JTz70ecfe5ZvYUwd16VlBBsSq13lozmxkeIHyVYN9rSa8BF5jZQuBzgl0pFbbh7sNKzrdfD+FcUXsPAgvNbK67n1FJrn2A28ysCMgHLozl9w3zzg17zcU3unjI3eeFBxLLcy4wzsw2EBTBquwGKt7uAjObR7CP/ytgZhXXf93MOhHcJQpgPXAmMd57M4b1JxLsoqrul5fUEg35K1sJC9hUd9+7Fra13t0b1/R2Sm1zOdDN4zzWuZk1Dnu3mNlwoK27D43nNuIpivdeao92y0hZCoFm9suNT+LOwgtpgO9qahsVWAO8ZeFFTHF0XHha5mKCg683xrn9uIj4vZdaop67SA2y4KrOW0u9/LW7/yGKPFJ/qLiLiCQh7ZYREUlCKu4iIklIxV1EJAmpuIuIJKH/B7Fk8JxQlVSoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Eo_med, Eo_beta = plot_trait_hist(df, 'Eo')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "44696206",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Ao_med, Ao_beta = plot_trait_hist(df, 'Ao')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c52ca622",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Phi_crit_med, Phi_crit_beta = plot_trait_hist(df, 'Phi_crit')"
]
},
{
"cell_type": "markdown",
"id": "dba13b64",
"metadata": {},
"source": [
"These individual traits can be combined into a joint probability distribution defining a \"metabolic trait space.\" Here we illustrate an idealized version of this trait space, fitting a normal distribution to $E_o$ and a lognormal distribution to $A_c = A_o/\\Phi_{crit}$."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "82b3bc71",
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"markerorder = [\"o\", \"v\", \"^\", \"<\", \">\", \"s\", \"p\", \"P\"]\n",
"\n",
"Phyla = list(df.Phylum.unique())\n",
"\n",
"Eo_fit = mi.trait_pdf(df, 'Eo', 8, [df.Eo.min(), df.Eo.max()])\n",
"Ac_fit = mi.trait_pdf(df, 'Ac', 8, [df.Ac.min(), df.Ac.max()])\n",
"\n",
"# Eo_bins = np.linspace(-1.0, 2.0, 30)\n",
"# Ac_bins = np.logspace(-3, np.log10(df.Ac.max()), 50)\n",
"\n",
"X, Y = np.meshgrid(Eo_fit.coord, Ac_fit.coord)\n",
"Z1, Z2 = np.meshgrid(Eo_fit.fitted(), Ac_fit.fitted())\n",
"\n",
"fig, ax = plt.subplots() # figsize=(6, 6))\n",
"\n",
"mx = np.max(Z1 * Z2)\n",
"cf = ax.contourf(\n",
" X,\n",
" Y,\n",
" Z1 * Z2 / mx,\n",
" cmap=cmocean.cm.tempo,\n",
" levels=np.arange(0.001, 1.02, 0.01),\n",
")\n",
"\n",
"\n",
"for i, phylum in enumerate(Phyla):\n",
" ndx = df.Phylum == phylum\n",
" ax.plot(\n",
" df.Eo.loc[ndx],\n",
" df.Ac.loc[ndx],\n",
" linestyle='none',\n",
" marker=markerorder[i],\n",
" color='k',\n",
" markerfacecolor='none',\n",
" label=phylum,\n",
" )\n",
"\n",
"ax.set_xlabel(util.attrs_label(df.Eo.attrs))\n",
"ax.set_ylabel(util.attrs_label(df.Ac.attrs))\n",
"\n",
"ax.set_xlim((-0.25, 1))\n",
"# ax.set_ylim((0, 0.4));\n",
"\n",
"ax.legend(ncol=2)\n",
"plt.savefig(f'figures/misc/trait-space-idealized.png', dpi=300)"
]
},
{
"cell_type": "markdown",
"id": "a309a716",
"metadata": {},
"source": [
"## Define the trait space for analysis\n",
"\n",
"Trait space can be thought of something like an integration kernel, which enables computing the net capacity of the ocean to support aerobic metabolism, inclusive of the trait density distribution for ecothermic organisms. \n",
"\n",
"In the sequence of operations below, we build two versions of the idealized trait space distribution:\n",
"- a low resolution (in trait space) version enabling analysis with the Earth system model;\n",
"- a high resolution version for more refined assessments of habitat and trait variability.\n",
"\n",
"### Define trait-coordinates "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a02bed02",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<defs>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Ac&#x27; (Ac: 8)&gt;\n",
"array([0.0258, 0.0399, 0.0619, 0.0959, 0.1486, 0.2303, 0.3568, 0.5528])\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0399 0.0619 0.0959 ... 0.2303 0.3568 0.5528\n",
"Attributes:\n",
" long_name: Hypoxic tolerance (normalized by critical MI)\n",
" units: 1/kPa</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Ac'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Ac</span>: 8</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3e549a53-960c-4c9e-adbc-6efdcb6280bb' class='xr-array-in' type='checkbox' checked><label for='section-3e549a53-960c-4c9e-adbc-6efdcb6280bb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0258 0.0399 0.0619 0.0959 0.1486 0.2303 0.3568 0.5528</span></div><div class='xr-array-data'><pre>array([0.0258, 0.0399, 0.0619, 0.0959, 0.1486, 0.2303, 0.3568, 0.5528])</pre></div></div></li><li class='xr-section-item'><input id='section-847a66d4-623f-46d6-a9a3-7651d454b9f4' class='xr-section-summary-in' type='checkbox' checked><label for='section-847a66d4-623f-46d6-a9a3-7651d454b9f4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ac</span></div><div class='xr-var-dims'>(Ac)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0258 0.0399 ... 0.3568 0.5528</div><input id='attrs-c00bf945-0b8c-47ac-9c04-353c108a878b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c00bf945-0b8c-47ac-9c04-353c108a878b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f80c45e-fd10-422e-9042-66e309d7adbf' class='xr-var-data-in' type='checkbox'><label for='data-1f80c45e-fd10-422e-9042-66e309d7adbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0258, 0.0399, 0.0619, 0.0959, 0.1486, 0.2303, 0.3568, 0.5528])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a307978f-2e06-431f-a654-f5c3963801c9' class='xr-section-summary-in' type='checkbox' checked><label for='section-a307978f-2e06-431f-a654-f5c3963801c9' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance (normalized by critical MI)</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'Ac' (Ac: 8)>\n",
"array([0.0258, 0.0399, 0.0619, 0.0959, 0.1486, 0.2303, 0.3568, 0.5528])\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0399 0.0619 0.0959 ... 0.2303 0.3568 0.5528\n",
"Attributes:\n",
" long_name: Hypoxic tolerance (normalized by critical MI)\n",
" units: 1/kPa"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
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{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"obj_Ac_pdf = mi.trait_pdf(df, 'Ac', 8, [df.Ac.min(), df.Ac.max()])\n",
"plt.plot(obj_Ac_pdf.coord, obj_Ac_pdf.fitted(), '.-')\n",
"plt.title('Trait PDF: Ac')\n",
"plt.xlabel(util.attrs_label(df.Ac.attrs))\n",
"obj_Ac_pdf.coord"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "242ac408",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
"<defs>\n",
"<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
"<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Ao&#x27; (Ao: 8)&gt;\n",
"array([0.0613, 0.0962, 0.1508, 0.2364, 0.3707, 0.5813, 0.9114, 1.4291])\n",
"Coordinates:\n",
" * Ao (Ao) float64 0.0613 0.0962 0.1508 0.2364 0.3707 0.5813 0.9114 1.429\n",
"Attributes:\n",
" long_name: Hypoxic tolerance\n",
" units: 1/kPa</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Ao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Ao</span>: 8</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c8025b6c-a15c-450b-b9fb-3bfac072ae0c' class='xr-array-in' type='checkbox' checked><label for='section-c8025b6c-a15c-450b-b9fb-3bfac072ae0c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0613 0.0962 0.1508 0.2364 0.3707 0.5813 0.9114 1.429</span></div><div class='xr-array-data'><pre>array([0.0613, 0.0962, 0.1508, 0.2364, 0.3707, 0.5813, 0.9114, 1.4291])</pre></div></div></li><li class='xr-section-item'><input id='section-5c505615-9789-4536-b511-d6f33112c884' class='xr-section-summary-in' type='checkbox' checked><label for='section-5c505615-9789-4536-b511-d6f33112c884' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ao</span></div><div class='xr-var-dims'>(Ao)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0613 0.0962 ... 0.9114 1.429</div><input id='attrs-f2bcc97f-6748-415f-b15b-b9fbcd533c5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2bcc97f-6748-415f-b15b-b9fbcd533c5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0a26f92-dc58-4c3a-9b72-56bc555b3914' class='xr-var-data-in' type='checkbox'><label for='data-d0a26f92-dc58-4c3a-9b72-56bc555b3914' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0613, 0.0962, 0.1508, 0.2364, 0.3707, 0.5813, 0.9114, 1.4291])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5cb540a7-6763-4c00-9de9-17e01cbd194b' class='xr-section-summary-in' type='checkbox' checked><label for='section-5cb540a7-6763-4c00-9de9-17e01cbd194b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'Ao' (Ao: 8)>\n",
"array([0.0613, 0.0962, 0.1508, 0.2364, 0.3707, 0.5813, 0.9114, 1.4291])\n",
"Coordinates:\n",
" * Ao (Ao) float64 0.0613 0.0962 0.1508 0.2364 0.3707 0.5813 0.9114 1.429\n",
"Attributes:\n",
" long_name: Hypoxic tolerance\n",
" units: 1/kPa"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"obj_Ao_pdf = mi.trait_pdf(df, 'Ao', 8, [df.Ao.min(), df.Ao.max()])\n",
"plt.plot(obj_Ao_pdf.coord, obj_Ao_pdf.fitted(), '.-')\n",
"plt.title('Trait PDF: Ao')\n",
"plt.xlabel(util.attrs_label(df.Ao.attrs))\n",
"obj_Ao_pdf.coord"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "082b6a1f",
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Eo&#x27; (Eo: 7)&gt;\n",
"array([-0.2, -0. , 0.2, 0.4, 0.6, 0.8, 1. ])\n",
"Coordinates:\n",
" * Eo (Eo) float64 -0.2 -0.0 0.2 0.4 0.6 0.8 1.0\n",
"Attributes:\n",
" long_name: Temperature sensitivity of MI\n",
" units: eV</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Eo'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Eo</span>: 7</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a53c283b-2d54-417d-875f-232e4947cf95' class='xr-array-in' type='checkbox' checked><label for='section-a53c283b-2d54-417d-875f-232e4947cf95' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.2 -0.0 0.2 0.4 0.6 0.8 1.0</span></div><div class='xr-array-data'><pre>array([-0.2, -0. , 0.2, 0.4, 0.6, 0.8, 1. ])</pre></div></div></li><li class='xr-section-item'><input id='section-46e35289-1909-4640-ade2-e0982e3b0b2a' class='xr-section-summary-in' type='checkbox' checked><label for='section-46e35289-1909-4640-ade2-e0982e3b0b2a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Eo</span></div><div class='xr-var-dims'>(Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2 -0.0 0.2 0.4 0.6 0.8 1.0</div><input id='attrs-685ccb34-86dc-4531-98da-f857b4d481d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-685ccb34-86dc-4531-98da-f857b4d481d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e6ad83-c8fd-4ab0-8b20-cb8ae9df83ed' class='xr-var-data-in' type='checkbox'><label for='data-35e6ad83-c8fd-4ab0-8b20-cb8ae9df83ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.2, -0. , 0.2, 0.4, 0.6, 0.8, 1. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b14c42c9-fb03-4f47-8f89-9d77c51d1eb8' class='xr-section-summary-in' type='checkbox' checked><label for='section-b14c42c9-fb03-4f47-8f89-9d77c51d1eb8' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature sensitivity of MI</dd><dt><span>units :</span></dt><dd>eV</dd></dl></div></li></ul></div></div>"
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"obj_Eo_pdf = mi.trait_pdf(df, 'Eo', 7, [-0.2, 1.0])\n",
"plt.plot(obj_Eo_pdf.coord, obj_Eo_pdf.fitted(), '.-')\n",
"plt.title('Trait PDF: Eo')\n",
"plt.xlabel(util.attrs_label(df.Eo.attrs))\n",
"obj_Eo_pdf.coord"
]
},
{
"cell_type": "markdown",
"id": "c3e977cb",
"metadata": {},
"source": [
"### Generate a low-resolution trait space"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "03cb81c5",
"metadata": {},
"outputs": [
{
"data": {
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".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (Ac: 8, Eo: 7, Ao: 8)\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0399 0.0619 ... 0.3568 0.5528\n",
" * Eo (Eo) float64 -0.2 -0.0 0.2 0.4 0.6 0.8 1.0\n",
" * Ao (Ao) float64 0.0613 0.0962 0.1508 ... 0.5813 0.9114 1.429\n",
"Data variables:\n",
" trait_spc_active (Ac, Eo) float64 0.001347 0.004475 ... 1.041e-05\n",
" trait_spc_resting (Ao, Eo) float64 0.002386 0.007927 ... 0.000106 2.896e-05</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5079b818-9ad4-475a-9dc7-8c3e4c8bb498' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5079b818-9ad4-475a-9dc7-8c3e4c8bb498' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Ac</span>: 8</li><li><span class='xr-has-index'>Eo</span>: 7</li><li><span class='xr-has-index'>Ao</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5a03d4de-3dd0-4f60-8409-80c96fff87ed' class='xr-section-summary-in' type='checkbox' checked><label for='section-5a03d4de-3dd0-4f60-8409-80c96fff87ed' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ac</span></div><div class='xr-var-dims'>(Ac)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0258 0.0399 ... 0.3568 0.5528</div><input id='attrs-c64074bc-727e-4b27-8911-8c6d7b19887d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c64074bc-727e-4b27-8911-8c6d7b19887d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ec1c829-d9de-4dc9-81a5-7171b911a4cc' class='xr-var-data-in' type='checkbox'><label for='data-9ec1c829-d9de-4dc9-81a5-7171b911a4cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance (normalized by critical MI)</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div><div class='xr-var-data'><pre>array([0.0258, 0.0399, 0.0619, 0.0959, 0.1486, 0.2303, 0.3568, 0.5528])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Eo</span></div><div class='xr-var-dims'>(Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2 -0.0 0.2 0.4 0.6 0.8 1.0</div><input id='attrs-4ceda09a-b0de-44dc-a47e-64f4a60846a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ceda09a-b0de-44dc-a47e-64f4a60846a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eebadd2-3763-455a-8a25-4cac6696e1fb' class='xr-var-data-in' type='checkbox'><label for='data-9eebadd2-3763-455a-8a25-4cac6696e1fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature sensitivity of MI</dd><dt><span>units :</span></dt><dd>eV</dd></dl></div><div class='xr-var-data'><pre>array([-0.2, -0. , 0.2, 0.4, 0.6, 0.8, 1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ao</span></div><div class='xr-var-dims'>(Ao)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0613 0.0962 ... 0.9114 1.429</div><input id='attrs-679ea69f-8e2d-4f59-ba8e-652f4a01cea7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-679ea69f-8e2d-4f59-ba8e-652f4a01cea7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e93933b-756d-49c9-b04f-e01f5acaf9ac' class='xr-var-data-in' type='checkbox'><label for='data-6e93933b-756d-49c9-b04f-e01f5acaf9ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div><div class='xr-var-data'><pre>array([0.0613, 0.0962, 0.1508, 0.2364, 0.3707, 0.5813, 0.9114, 1.4291])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40cf4f4e-1f56-419d-8def-e3adc3f578de' class='xr-section-summary-in' type='checkbox' checked><label for='section-40cf4f4e-1f56-419d-8def-e3adc3f578de' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>trait_spc_active</span></div><div class='xr-var-dims'>(Ac, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001347 0.004475 ... 1.041e-05</div><input id='attrs-78bb95a4-1942-40f4-aff0-2ba4e8008784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78bb95a4-1942-40f4-aff0-2ba4e8008784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2da714e-07b9-4da0-a1cb-acbcd427d0d9' class='xr-var-data-in' type='checkbox'><label for='data-e2da714e-07b9-4da0-a1cb-acbcd427d0d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Trait density (active)</dd><dt><span>units :</span></dt><dd>eV 1/kPa</dd><dt><span>metabolic_baseline :</span></dt><dd>active</dd><dt><span>N_traits :</span></dt><dd>56</dd></dl></div><div class='xr-var-data'><pre>array([[1.34680503e-03, 4.47545908e-03, 9.02342586e-03, 1.10384049e-02,\n",
" 8.19298555e-03, 3.68959807e-03, 1.00813091e-03],\n",
" [1.06140909e-02, 3.52708287e-02, 7.11130864e-02, 8.69930173e-02,\n",
" 6.45684353e-02, 2.90775046e-02, 7.94502019e-03],\n",
" [1.23591079e-02, 4.10695540e-02, 8.28044832e-02, 1.01295165e-01,\n",
" 7.51838542e-02, 3.38580121e-02, 9.25122680e-03],\n",
" [7.08526930e-03, 2.35444865e-02, 4.74704214e-02, 5.80708193e-02,\n",
" 4.31016426e-02, 1.94102305e-02, 5.30357316e-03],\n",
" [2.56701395e-03, 8.53023685e-03, 1.71986736e-02, 2.10392290e-02,\n",
" 1.56158521e-02, 7.03238370e-03, 1.92150019e-03],\n",
" [6.36403263e-04, 2.11478032e-03, 4.26382256e-03, 5.21595685e-03,\n",
" 3.87141615e-03, 1.74343888e-03, 4.76370216e-04],\n",
" [1.11418788e-04, 3.70246781e-04, 7.46491996e-04, 9.13187634e-04,\n",
" 6.77791144e-04, 3.05233896e-04, 8.34008799e-05],\n",
" [1.39113912e-05, 4.62278214e-05, 9.32045879e-05, 1.14017669e-04,\n",
" 8.46268206e-05, 3.81105218e-05, 1.04131654e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trait_spc_resting</span></div><div class='xr-var-dims'>(Ao, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.002386 0.007927 ... 2.896e-05</div><input id='attrs-3ee73695-5674-415d-a087-e00e21814c16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ee73695-5674-415d-a087-e00e21814c16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52bc5486-165c-462d-b478-aaaa3ad03b4d' class='xr-var-data-in' type='checkbox'><label for='data-52bc5486-165c-462d-b478-aaaa3ad03b4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Trait density (resting)</dd><dt><span>units :</span></dt><dd>eV 1/kPa</dd><dt><span>metabolic_baseline :</span></dt><dd>resting</dd><dt><span>N_traits :</span></dt><dd>56</dd></dl></div><div class='xr-var-data'><pre>array([[2.38554272e-03, 7.92720445e-03, 1.59828390e-02, 1.95518920e-02,\n",
" 1.45119127e-02, 6.53523980e-03, 1.78566259e-03],\n",
" [9.38079224e-03, 3.11725535e-02, 6.28501391e-02, 7.68849099e-02,\n",
" 5.70659403e-02, 2.56988594e-02, 7.02185279e-03],\n",
" [1.10450327e-02, 3.67028565e-02, 7.40003425e-02, 9.05250131e-02,\n",
" 6.71899727e-02, 3.02580778e-02, 8.26759525e-03],\n",
" [7.35755842e-03, 2.44493085e-02, 4.92947247e-02, 6.03024992e-02,\n",
" 4.47580523e-02, 2.01561717e-02, 5.50739115e-03],\n",
" [3.26734585e-03, 1.08574533e-02, 2.18908101e-02, 2.67791445e-02,\n",
" 1.98761638e-02, 8.95095631e-03, 2.44572324e-03],\n",
" [1.02587285e-03, 3.40899528e-03, 6.87322029e-03, 8.40804698e-03,\n",
" 6.24066683e-03, 2.81039826e-03, 7.67901897e-04],\n",
" [2.33189580e-04, 7.74893471e-04, 1.56234113e-03, 1.91122022e-03,\n",
" 1.41855638e-03, 6.38827305e-04, 1.74550599e-04],\n",
" [3.86862337e-05, 1.28555101e-04, 2.59192945e-04, 3.17072110e-04,\n",
" 2.35339004e-04, 1.05981676e-04, 2.89580060e-05]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-863f08ba-e0d2-49d8-891a-bb49e51e6c94' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-863f08ba-e0d2-49d8-891a-bb49e51e6c94' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (Ac: 8, Eo: 7, Ao: 8)\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0399 0.0619 ... 0.3568 0.5528\n",
" * Eo (Eo) float64 -0.2 -0.0 0.2 0.4 0.6 0.8 1.0\n",
" * Ao (Ao) float64 0.0613 0.0962 0.1508 ... 0.5813 0.9114 1.429\n",
"Data variables:\n",
" trait_spc_active (Ac, Eo) float64 0.001347 0.004475 ... 1.041e-05\n",
" trait_spc_resting (Ao, Eo) float64 0.002386 0.007927 ... 0.000106 2.896e-05"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def gen_trait_space(obj_Ac_pdf, obj_Ao_pdf, obj_Eo_pdf):\n",
" dso = xr.Dataset()\n",
"\n",
" # active metabolism\n",
" dso['trait_spc_active'] = xr.DataArray(\n",
" obj_Ac_pdf.fitted(), dims=('Ac'), coords={'Ac': obj_Ac_pdf.coord}\n",
" ) * xr.DataArray(obj_Eo_pdf.fitted(), dims=('Eo'), coords={'Eo': obj_Eo_pdf.coord})\n",
" dso['trait_spc_active'] = dso.trait_spc_active / dso.trait_spc_active.sum()\n",
" np.testing.assert_almost_equal(dso.trait_spc_active.sum(), 1.0)\n",
"\n",
" dso.trait_spc_active.attrs['long_name'] = 'Trait density (active)'\n",
" dso.trait_spc_active.attrs['units'] = ' '.join([obj_Eo_pdf.coord.units, obj_Ac_pdf.coord.units])\n",
" dso.trait_spc_active.attrs['metabolic_baseline'] = 'active'\n",
" dso.trait_spc_active.attrs['N_traits'] = dso.trait_spc_active.size\n",
"\n",
" # resting metabolism\n",
" dso['trait_spc_resting'] = xr.DataArray(\n",
" obj_Ao_pdf.fitted(), dims=('Ao'), coords={'Ao': obj_Ao_pdf.coord}\n",
" ) * xr.DataArray(obj_Eo_pdf.fitted(), dims=('Eo'), coords={'Eo': obj_Eo_pdf.coord})\n",
" dso['trait_spc_resting'] = dso.trait_spc_resting / dso.trait_spc_resting.sum()\n",
" np.testing.assert_almost_equal(dso.trait_spc_resting.sum(), 1.0)\n",
"\n",
" dso.trait_spc_resting.attrs['long_name'] = 'Trait density (resting)'\n",
" dso.trait_spc_resting.attrs['units'] = ' '.join(\n",
" [obj_Eo_pdf.coord.units, obj_Ao_pdf.coord.units]\n",
" )\n",
" dso.trait_spc_resting.attrs['metabolic_baseline'] = 'resting'\n",
" dso.trait_spc_resting.attrs['N_traits'] = dso.trait_spc_resting.size\n",
"\n",
" return dso\n",
"\n",
"\n",
"dso = gen_trait_space(obj_Ac_pdf, obj_Ao_pdf, obj_Eo_pdf)\n",
"dso"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1aecd70e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dso.trait_spc_active.plot();"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5b709e46",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dso.trait_spc_resting.plot();"
]
},
{
"cell_type": "markdown",
"id": "76aa46db",
"metadata": {},
"source": [
"### Generate a higher resolution version of trait space"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "fed7587d",
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (Ac: 30, Eo: 30, Ao: 30)\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0287 0.0319 ... 0.4973 0.5528\n",
" * Eo (Eo) float64 -0.191 -0.1379 -0.0848 ... 1.242 1.295 1.348\n",
" * Ao (Ao) float64 0.0613 0.0684 0.0762 ... 1.15 1.282 1.429\n",
"Data variables:\n",
" trait_spc_active (Ac, Eo) float64 9.249e-05 0.0001328 ... 2.123e-08\n",
" trait_spc_resting (Ao, Eo) float64 0.0001659 0.0002381 ... 5.978e-08</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-48242766-a79e-4a72-8962-35335f2d06f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48242766-a79e-4a72-8962-35335f2d06f5' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Ac</span>: 30</li><li><span class='xr-has-index'>Eo</span>: 30</li><li><span class='xr-has-index'>Ao</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a1ea8f1c-522c-4d3c-b652-6bf3b846543c' class='xr-section-summary-in' type='checkbox' checked><label for='section-a1ea8f1c-522c-4d3c-b652-6bf3b846543c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ac</span></div><div class='xr-var-dims'>(Ac)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0258 0.0287 ... 0.4973 0.5528</div><input id='attrs-56adba32-711d-4a02-b049-d0b61fd56c78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56adba32-711d-4a02-b049-d0b61fd56c78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4350ac49-7e0e-45d1-a1f6-23c2ecae4a8a' class='xr-var-data-in' type='checkbox'><label for='data-4350ac49-7e0e-45d1-a1f6-23c2ecae4a8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance (normalized by critical MI)</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div><div class='xr-var-data'><pre>array([0.0258, 0.0287, 0.0319, 0.0354, 0.0393, 0.0437, 0.0486, 0.054 , 0.0601,\n",
" 0.0668, 0.0742, 0.0825, 0.0917, 0.1019, 0.1132, 0.1259, 0.1399, 0.1555,\n",
" 0.1728, 0.1921, 0.2135, 0.2373, 0.2638, 0.2932, 0.3259, 0.3622, 0.4026,\n",
" 0.4474, 0.4973, 0.5528])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Eo</span></div><div class='xr-var-dims'>(Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.191 -0.1379 ... 1.295 1.348</div><input id='attrs-3d5584cf-3329-4fca-b890-e1a0f7e93403' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d5584cf-3329-4fca-b890-e1a0f7e93403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3de63c01-8752-4903-875a-7a7f674d823e' class='xr-var-data-in' type='checkbox'><label for='data-3de63c01-8752-4903-875a-7a7f674d823e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature sensitivity of MI</dd><dt><span>units :</span></dt><dd>eV</dd></dl></div><div class='xr-var-data'><pre>array([-0.191 , -0.1379, -0.0848, -0.0318, 0.0213, 0.0744, 0.1275, 0.1806,\n",
" 0.2336, 0.2867, 0.3398, 0.3929, 0.446 , 0.499 , 0.5521, 0.6052,\n",
" 0.6583, 0.7114, 0.7644, 0.8175, 0.8706, 0.9237, 0.9768, 1.0298,\n",
" 1.0829, 1.136 , 1.1891, 1.2422, 1.2952, 1.3483])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ao</span></div><div class='xr-var-dims'>(Ao)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0613 0.0684 ... 1.282 1.429</div><input id='attrs-ae1974a4-8dfb-4d2c-8e8f-b49a845f9b80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae1974a4-8dfb-4d2c-8e8f-b49a845f9b80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ce9bff9-9fa8-427f-9298-e8e4df09fe85' class='xr-var-data-in' type='checkbox'><label for='data-6ce9bff9-9fa8-427f-9298-e8e4df09fe85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div><div class='xr-var-data'><pre>array([0.0613, 0.0684, 0.0762, 0.085 , 0.0947, 0.1056, 0.1177, 0.1311, 0.1462,\n",
" 0.163 , 0.1816, 0.2025, 0.2257, 0.2516, 0.2804, 0.3126, 0.3484, 0.3884,\n",
" 0.4329, 0.4826, 0.5379, 0.5996, 0.6684, 0.745 , 0.8304, 0.9257, 1.0318,\n",
" 1.1502, 1.2821, 1.4291])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ad93cd7-e934-47db-aa89-ada0f7191226' class='xr-section-summary-in' type='checkbox' checked><label for='section-8ad93cd7-e934-47db-aa89-ada0f7191226' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>trait_spc_active</span></div><div class='xr-var-dims'>(Ac, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.249e-05 0.0001328 ... 2.123e-08</div><input id='attrs-72d6eff5-6185-4eaa-a262-c8c8acbb23cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72d6eff5-6185-4eaa-a262-c8c8acbb23cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e145d3a-ee8a-4a44-95ba-408b073d4bc7' class='xr-var-data-in' type='checkbox'><label for='data-5e145d3a-ee8a-4a44-95ba-408b073d4bc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Trait density (active)</dd><dt><span>units :</span></dt><dd>eV 1/kPa</dd><dt><span>metabolic_baseline :</span></dt><dd>active</dd><dt><span>N_traits :</span></dt><dd>900</dd></dl></div><div class='xr-var-data'><pre>array([[9.24883428e-05, 1.32776078e-04, 1.84016329e-04, 2.46078026e-04,\n",
" 3.17867071e-04, 3.96389308e-04, 4.77201730e-04, 5.54607546e-04,\n",
" 6.22147647e-04, 6.73927854e-04, 7.04753168e-04, 7.11482715e-04,\n",
" 6.93418392e-04, 6.52520653e-04, 5.92736191e-04, 5.19795258e-04,\n",
" 4.40054908e-04, 3.59654178e-04, 2.83906433e-04, 2.16266701e-04,\n",
" 1.59040511e-04, 1.12909259e-04, 7.73846880e-05, 5.12432158e-05,\n",
" 3.27339215e-05, 2.01866095e-05, 1.20180059e-05, 6.90724933e-06,\n",
" 3.83687536e-06, 2.05535379e-06],\n",
" [2.25356714e-04, 3.23521644e-04, 4.48373428e-04, 5.99592702e-04,\n",
" 7.74513592e-04, 9.65840551e-04, 1.16274776e-03, 1.35135446e-03,\n",
" 1.51592239e-03, 1.64208983e-03, 1.71719866e-03, 1.73359584e-03,\n",
" 1.68958039e-03, 1.58992913e-03, 1.44425855e-03, 1.26653098e-03,\n",
" 1.07223597e-03, 8.76331886e-04, 6.91765243e-04, 5.26954549e-04,\n",
" 3.87517452e-04, 2.75114233e-04, 1.88555211e-04, 1.24859008e-04,\n",
" 7.97593378e-05, 4.91866093e-05, 2.92830236e-05, 1.68301752e-05,\n",
" 9.34891464e-06, 5.00806657e-06],\n",
" [3.93267639e-04, 5.64574229e-04, 7.82451768e-04, 1.04634294e-03,\n",
" 1.35159555e-03, 1.68547822e-03, 2.02909893e-03, 2.35823449e-03,\n",
" 2.64542026e-03, 2.86559374e-03, 2.99666537e-03, 3.02527993e-03,\n",
" 2.94846902e-03, 2.77456865e-03, 2.52036046e-03, 2.21020993e-03,\n",
"...\n",
" 1.29524925e-05, 1.05859927e-05, 8.35644801e-06, 6.36555297e-06,\n",
" 4.68116817e-06, 3.32334966e-06, 2.27772620e-06, 1.50828308e-06,\n",
" 9.63484024e-07, 5.94168828e-07, 3.53735703e-07, 2.03306664e-07,\n",
" 1.12933860e-07, 6.04968930e-08],\n",
" [1.62874936e-06, 2.33822930e-06, 3.24058654e-06, 4.33351291e-06,\n",
" 5.59774100e-06, 6.98054277e-06, 8.40367542e-06, 9.76681664e-06,\n",
" 1.09562195e-05, 1.18680855e-05, 1.24109292e-05, 1.25294386e-05,\n",
" 1.22113201e-05, 1.14910979e-05, 1.04382743e-05, 9.15376111e-06,\n",
" 7.74950797e-06, 6.33362536e-06, 4.99968329e-06, 3.80852592e-06,\n",
" 2.80075437e-06, 1.98836823e-06, 1.36276916e-06, 9.02409455e-07,\n",
" 5.76454849e-07, 3.55492663e-07, 2.11640937e-07, 1.21638875e-07,\n",
" 6.75686048e-08, 3.61954391e-08],\n",
" [9.55328721e-07, 1.37146798e-06, 1.90073775e-06, 2.54178417e-06,\n",
" 3.28330612e-06, 4.09437643e-06, 4.92910246e-06, 5.72864105e-06,\n",
" 6.42627489e-06, 6.96112196e-06, 7.27952217e-06, 7.34903287e-06,\n",
" 7.16244323e-06, 6.74000314e-06, 6.12247868e-06, 5.36905867e-06,\n",
" 4.54540625e-06, 3.71493267e-06, 2.93252059e-06, 2.23385763e-06,\n",
" 1.64275803e-06, 1.16626003e-06, 7.99320354e-07, 5.29300389e-07,\n",
" 3.38114560e-07, 2.08511119e-07, 1.24136144e-07, 7.13462201e-08,\n",
" 3.96317753e-08, 2.12301189e-08]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trait_spc_resting</span></div><div class='xr-var-dims'>(Ao, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001659 0.0002381 ... 5.978e-08</div><input id='attrs-d83656ff-9aa3-43d9-8f73-c908fbce77b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d83656ff-9aa3-43d9-8f73-c908fbce77b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cc3b58c-bdd5-4042-938e-0c827fe49736' class='xr-var-data-in' type='checkbox'><label for='data-0cc3b58c-bdd5-4042-938e-0c827fe49736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Trait density (resting)</dd><dt><span>units :</span></dt><dd>eV 1/kPa</dd><dt><span>metabolic_baseline :</span></dt><dd>resting</dd><dt><span>N_traits :</span></dt><dd>900</dd></dl></div><div class='xr-var-data'><pre>array([[1.65863909e-04, 2.38113893e-04, 3.30005564e-04, 4.41303867e-04,\n",
" 5.70046703e-04, 7.10864506e-04, 8.55789410e-04, 9.94605078e-04,\n",
" 1.11572807e-03, 1.20858808e-03, 1.26386863e-03, 1.27593706e-03,\n",
" 1.24354142e-03, 1.17019749e-03, 1.06298306e-03, 9.32174486e-04,\n",
" 7.89172180e-04, 6.44985585e-04, 5.09143416e-04, 3.87841746e-04,\n",
" 2.85215196e-04, 2.02485745e-04, 1.38777779e-04, 9.18969875e-05,\n",
" 5.87033566e-05, 3.62016429e-05, 2.15524831e-05, 1.23871110e-05,\n",
" 6.88085790e-06, 3.68596738e-06],\n",
" [2.83312057e-04, 4.06722218e-04, 5.63682333e-04, 7.53790908e-04,\n",
" 9.73696480e-04, 1.21422730e-03, 1.46177345e-03, 1.69888442e-03,\n",
" 1.90577455e-03, 2.06438867e-03, 2.15881336e-03, 2.17942743e-03,\n",
" 2.12409246e-03, 1.99881372e-03, 1.81568082e-03, 1.59224676e-03,\n",
" 1.34798459e-03, 1.10169955e-03, 8.69667606e-04, 6.62472286e-04,\n",
" 4.87175929e-04, 3.45865796e-04, 2.37046254e-04, 1.56969197e-04,\n",
" 1.00271173e-04, 6.18360076e-05, 3.68137852e-05, 2.11584180e-05,\n",
" 1.17531898e-05, 6.29599898e-06],\n",
" [4.09996236e-04, 5.88589770e-04, 8.15735260e-04, 1.09085168e-03,\n",
" 1.40908896e-03, 1.75717414e-03, 2.11541160e-03, 2.45854774e-03,\n",
" 2.75794966e-03, 2.98748875e-03, 3.12413584e-03, 3.15396758e-03,\n",
" 3.07388934e-03, 2.89259169e-03, 2.62757013e-03, 2.30422660e-03,\n",
"...\n",
" 3.16688396e-05, 2.58827485e-05, 2.04315124e-05, 1.55637747e-05,\n",
" 1.14454545e-05, 8.12558873e-06, 5.56903974e-06, 3.68775158e-06,\n",
" 2.35571809e-06, 1.45274257e-06, 8.64883667e-07, 4.97084721e-07,\n",
" 2.76123247e-07, 1.47914882e-07],\n",
" [4.26967713e-06, 6.12953988e-06, 8.49501991e-06, 1.13600665e-05,\n",
" 1.46741711e-05, 1.82991101e-05, 2.20297743e-05, 2.56031742e-05,\n",
" 2.87211285e-05, 3.11115354e-05, 3.25345702e-05, 3.28452363e-05,\n",
" 3.20113061e-05, 3.01232829e-05, 2.73633637e-05, 2.39960828e-05,\n",
" 2.03149102e-05, 1.66032515e-05, 1.31063956e-05, 9.98384183e-06,\n",
" 7.34202399e-06, 5.21239828e-06, 3.57242462e-06, 2.36561690e-06,\n",
" 1.51114477e-06, 9.31904522e-07, 5.54805109e-07, 3.18869639e-07,\n",
" 1.77127392e-07, 9.48843587e-08],\n",
" [2.68980719e-06, 3.86148177e-06, 5.35168466e-06, 7.15660399e-06,\n",
" 9.24442049e-06, 1.15280562e-05, 1.38782965e-05, 1.61294636e-05,\n",
" 1.80937095e-05, 1.95996158e-05, 2.04960980e-05, 2.06918111e-05,\n",
" 2.01664525e-05, 1.89770374e-05, 1.72383461e-05, 1.51170297e-05,\n",
" 1.27979680e-05, 1.04597008e-05, 8.25675476e-06, 6.28961129e-06,\n",
" 4.62532137e-06, 3.28370177e-06, 2.25055270e-06, 1.49028911e-06,\n",
" 9.51989564e-07, 5.87080335e-07, 3.49515601e-07, 2.00881196e-07,\n",
" 1.11586548e-07, 5.97751591e-08]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f84870a4-562a-434b-b3b1-fd06df37ef4d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f84870a4-562a-434b-b3b1-fd06df37ef4d' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (Ac: 30, Eo: 30, Ao: 30)\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0287 0.0319 ... 0.4973 0.5528\n",
" * Eo (Eo) float64 -0.191 -0.1379 -0.0848 ... 1.242 1.295 1.348\n",
" * Ao (Ao) float64 0.0613 0.0684 0.0762 ... 1.15 1.282 1.429\n",
"Data variables:\n",
" trait_spc_active (Ac, Eo) float64 9.249e-05 0.0001328 ... 2.123e-08\n",
" trait_spc_resting (Ao, Eo) float64 0.0001659 0.0002381 ... 5.978e-08"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dso_hires = gen_trait_space(\n",
" mi.trait_pdf(df, 'Ac', 30),\n",
" mi.trait_pdf(df, 'Ao', 30),\n",
" mi.trait_pdf(df, 'Eo', 30),\n",
")\n",
"dso_hires"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2c20ac02",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7fb8e8f43290>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dso_hires.trait_spc_active.plot()"
]
},
{
"cell_type": "markdown",
"id": "67888ed1",
"metadata": {},
"source": [
"## Solve for maximum metabolic temperature \n",
"\n",
"Use root finding technique to solve for $AT_{max}$ (the maximum temerature at which metabolism can be sustained) over all traits.\n",
"\n",
"First illustrate computation on a subset of traits."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "705a0965",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.022"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mi.dEodT_bar"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "12770ca4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 16 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(4, 4, figsize=(10, 10))\n",
"\n",
"PO2_atm = constants.XiO2 * constants.kPa_per_atm\n",
"\n",
"T = np.arange(-2.0, 40.0, 1.0)\n",
"\n",
"for i, Ac in enumerate([0.05, 0.1, 0.2, 0.4]):\n",
" for j, Eo in enumerate([-0.2, 0.0, 0.4, 1]):\n",
" ax = axs[i, j]\n",
" ATmax_ij = mi.compute_ATmax(PO2_atm, Ac, Eo, dEodT=mi.dEodT_bar)\n",
" ax.plot(T, mi.pO2_at_Phi_one(T, Ac, Eo, dEodT=mi.dEodT_bar), '-')\n",
" ax.plot(ATmax_ij, PO2_atm, 'o')\n",
" ax.axhline(PO2_atm, linewidth=1, color='k')\n",
" ax.set_title(f'Eo={Eo}, Ac={Ac}')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "3436b0ff",
"metadata": {},
"source": [
"Now compute ATmax over entire trait-space domain."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "f84e1e3a",
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (Ac: 8, Eo: 7, Ao: 8)\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0399 0.0619 ... 0.3568 0.5528\n",
" * Eo (Eo) float64 -0.2 -0.0 0.2 0.4 0.6 0.8 1.0\n",
" * Ao (Ao) float64 0.0613 0.0962 0.1508 ... 0.5813 0.9114 1.429\n",
"Data variables:\n",
" trait_spc_active (Ac, Eo) float64 0.001347 0.004475 ... 1.041e-05\n",
" trait_spc_resting (Ao, Eo) float64 0.002386 0.007927 ... 0.000106 2.896e-05\n",
" ATmax_active (Ac, Eo) float64 nan nan nan nan ... 33.55 31.08 29.07\n",
" ATmax_resting (Ao, Eo) float64 30.02 24.35 20.8 ... 38.56 35.77 33.43</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d4bce36f-dff6-49b9-83ba-2bd01b1e27b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d4bce36f-dff6-49b9-83ba-2bd01b1e27b2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Ac</span>: 8</li><li><span class='xr-has-index'>Eo</span>: 7</li><li><span class='xr-has-index'>Ao</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-72c99b74-6296-474c-b9d6-d5c0641dd789' class='xr-section-summary-in' type='checkbox' checked><label for='section-72c99b74-6296-474c-b9d6-d5c0641dd789' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ac</span></div><div class='xr-var-dims'>(Ac)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0258 0.0399 ... 0.3568 0.5528</div><input id='attrs-e67b7ef5-c10f-4be3-b40f-1a042c1321b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e67b7ef5-c10f-4be3-b40f-1a042c1321b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6760fcd3-14ed-4a94-a564-b5635d32e087' class='xr-var-data-in' type='checkbox'><label for='data-6760fcd3-14ed-4a94-a564-b5635d32e087' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance (normalized by critical MI)</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div><div class='xr-var-data'><pre>array([0.0258, 0.0399, 0.0619, 0.0959, 0.1486, 0.2303, 0.3568, 0.5528])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Eo</span></div><div class='xr-var-dims'>(Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2 -0.0 0.2 0.4 0.6 0.8 1.0</div><input id='attrs-437b5bbb-e241-43dd-91fc-53295eec3783' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-437b5bbb-e241-43dd-91fc-53295eec3783' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-399cfaec-b2a4-472b-b85e-5381e5722ad0' class='xr-var-data-in' type='checkbox'><label for='data-399cfaec-b2a4-472b-b85e-5381e5722ad0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature sensitivity of MI</dd><dt><span>units :</span></dt><dd>eV</dd></dl></div><div class='xr-var-data'><pre>array([-0.2, -0. , 0.2, 0.4, 0.6, 0.8, 1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Ao</span></div><div class='xr-var-dims'>(Ao)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0613 0.0962 ... 0.9114 1.429</div><input id='attrs-7bbfecee-1ee8-49a6-a48c-fce2ae48eccf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bbfecee-1ee8-49a6-a48c-fce2ae48eccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d19db651-40b3-4de1-99b3-d45e0785951c' class='xr-var-data-in' type='checkbox'><label for='data-d19db651-40b3-4de1-99b3-d45e0785951c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Hypoxic tolerance</dd><dt><span>units :</span></dt><dd>1/kPa</dd></dl></div><div class='xr-var-data'><pre>array([0.0613, 0.0962, 0.1508, 0.2364, 0.3707, 0.5813, 0.9114, 1.4291])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd0c226f-abc8-40b4-898d-5019c32a2414' class='xr-section-summary-in' type='checkbox' checked><label for='section-cd0c226f-abc8-40b4-898d-5019c32a2414' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>trait_spc_active</span></div><div class='xr-var-dims'>(Ac, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001347 0.004475 ... 1.041e-05</div><input id='attrs-600d95f3-9053-403e-bd9e-e07243fc32eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-600d95f3-9053-403e-bd9e-e07243fc32eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea525143-3fb9-4f8f-ae82-a30d4d92fc59' class='xr-var-data-in' type='checkbox'><label for='data-ea525143-3fb9-4f8f-ae82-a30d4d92fc59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Trait density (active)</dd><dt><span>units :</span></dt><dd>eV 1/kPa</dd><dt><span>metabolic_baseline :</span></dt><dd>active</dd><dt><span>N_traits :</span></dt><dd>56</dd></dl></div><div class='xr-var-data'><pre>array([[1.34680503e-03, 4.47545908e-03, 9.02342586e-03, 1.10384049e-02,\n",
" 8.19298555e-03, 3.68959807e-03, 1.00813091e-03],\n",
" [1.06140909e-02, 3.52708287e-02, 7.11130864e-02, 8.69930173e-02,\n",
" 6.45684353e-02, 2.90775046e-02, 7.94502019e-03],\n",
" [1.23591079e-02, 4.10695540e-02, 8.28044832e-02, 1.01295165e-01,\n",
" 7.51838542e-02, 3.38580121e-02, 9.25122680e-03],\n",
" [7.08526930e-03, 2.35444865e-02, 4.74704214e-02, 5.80708193e-02,\n",
" 4.31016426e-02, 1.94102305e-02, 5.30357316e-03],\n",
" [2.56701395e-03, 8.53023685e-03, 1.71986736e-02, 2.10392290e-02,\n",
" 1.56158521e-02, 7.03238370e-03, 1.92150019e-03],\n",
" [6.36403263e-04, 2.11478032e-03, 4.26382256e-03, 5.21595685e-03,\n",
" 3.87141615e-03, 1.74343888e-03, 4.76370216e-04],\n",
" [1.11418788e-04, 3.70246781e-04, 7.46491996e-04, 9.13187634e-04,\n",
" 6.77791144e-04, 3.05233896e-04, 8.34008799e-05],\n",
" [1.39113912e-05, 4.62278214e-05, 9.32045879e-05, 1.14017669e-04,\n",
" 8.46268206e-05, 3.81105218e-05, 1.04131654e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trait_spc_resting</span></div><div class='xr-var-dims'>(Ao, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.002386 0.007927 ... 2.896e-05</div><input id='attrs-aa359ce4-b5ee-47e6-893e-a566920d9c55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa359ce4-b5ee-47e6-893e-a566920d9c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27c46b44-4202-4e5f-b9f6-422a447912d9' class='xr-var-data-in' type='checkbox'><label for='data-27c46b44-4202-4e5f-b9f6-422a447912d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Trait density (resting)</dd><dt><span>units :</span></dt><dd>eV 1/kPa</dd><dt><span>metabolic_baseline :</span></dt><dd>resting</dd><dt><span>N_traits :</span></dt><dd>56</dd></dl></div><div class='xr-var-data'><pre>array([[2.38554272e-03, 7.92720445e-03, 1.59828390e-02, 1.95518920e-02,\n",
" 1.45119127e-02, 6.53523980e-03, 1.78566259e-03],\n",
" [9.38079224e-03, 3.11725535e-02, 6.28501391e-02, 7.68849099e-02,\n",
" 5.70659403e-02, 2.56988594e-02, 7.02185279e-03],\n",
" [1.10450327e-02, 3.67028565e-02, 7.40003425e-02, 9.05250131e-02,\n",
" 6.71899727e-02, 3.02580778e-02, 8.26759525e-03],\n",
" [7.35755842e-03, 2.44493085e-02, 4.92947247e-02, 6.03024992e-02,\n",
" 4.47580523e-02, 2.01561717e-02, 5.50739115e-03],\n",
" [3.26734585e-03, 1.08574533e-02, 2.18908101e-02, 2.67791445e-02,\n",
" 1.98761638e-02, 8.95095631e-03, 2.44572324e-03],\n",
" [1.02587285e-03, 3.40899528e-03, 6.87322029e-03, 8.40804698e-03,\n",
" 6.24066683e-03, 2.81039826e-03, 7.67901897e-04],\n",
" [2.33189580e-04, 7.74893471e-04, 1.56234113e-03, 1.91122022e-03,\n",
" 1.41855638e-03, 6.38827305e-04, 1.74550599e-04],\n",
" [3.86862337e-05, 1.28555101e-04, 2.59192945e-04, 3.17072110e-04,\n",
" 2.35339004e-04, 1.05981676e-04, 2.89580060e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ATmax_active</span></div><div class='xr-var-dims'>(Ac, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 33.55 31.08 29.07</div><input id='attrs-03ce9c17-5da6-4987-bec4-d4ffac42b855' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03ce9c17-5da6-4987-bec4-d4ffac42b855' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b183d1ac-e7dd-4a7b-b7fd-38a909480a1e' class='xr-var-data-in' type='checkbox'><label for='data-b183d1ac-e7dd-4a7b-b7fd-38a909480a1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>AT$_{max}$ (active)</dd><dt><span>units :</span></dt><dd>°C</dd><dt><span>metabolic_baseline :</span></dt><dd>active</dd><dt><span>N_traits :</span></dt><dd>56</dd><dt><span>note :</span></dt><dd>computed using dEodT = 0.022</dd></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, nan, nan,\n",
" 8.59712875, 10.26054055],\n",
" [ nan, nan, nan, 11.26387796, 12.8388557 ,\n",
" 13.4373032 , 13.76883614],\n",
" [30.18240604, 24.52676339, 20.95847016, 19.01339816, 17.93773339,\n",
" 17.29088493, 16.86852389],\n",
" [35.89425722, 30.56830096, 26.57428058, 23.78658247, 21.89162416,\n",
" 20.5876109 , 19.66246793],\n",
" [40.16719379, 34.94625356, 30.78025634, 27.61224875, 25.26557181,\n",
" 23.53322612, 22.23891874],\n",
" [43.75889101, 38.58814164, 34.31746958, 30.91612571, 28.26965939,\n",
" 26.22766245, 24.64625689],\n",
" [46.92768764, 41.78450184, 37.43921301, 33.87396619, 31.00840642,\n",
" 28.72815128, 26.91468975],\n",
" [49.80449098, 44.67726276, 40.27398609, 36.58412019, 33.54826733,\n",
" 31.07644863, 29.06971552]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ATmax_resting</span></div><div class='xr-var-dims'>(Ao, Eo)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.02 24.35 20.8 ... 35.77 33.43</div><input id='attrs-642f222a-9d42-4bfe-94c5-957eb797b261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-642f222a-9d42-4bfe-94c5-957eb797b261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab2d2f68-02fd-403c-a8a2-a9221a804e2e' class='xr-var-data-in' type='checkbox'><label for='data-ab2d2f68-02fd-403c-a8a2-a9221a804e2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>AT$_{max}$ (resting)</dd><dt><span>units :</span></dt><dd>°C</dd><dt><span>metabolic_baseline :</span></dt><dd>resting</dd><dt><span>N_traits :</span></dt><dd>56</dd><dt><span>note :</span></dt><dd>computed using dEodT = 0.022</dd></dl></div><div class='xr-var-data'><pre>array([[30.02255936, 24.35102069, 20.80150002, 18.88910821, 17.84023642,\n",
" 17.21234517, 16.8033633 ],\n",
" [35.92830891, 30.60348463, 26.60778668, 23.81644464, 21.91739391,\n",
" 20.60971175, 19.68154618],\n",
" [40.29664962, 35.07798669, 30.90772365, 27.73018451, 25.37157528,\n",
" 23.62728294, 22.32221041],\n",
" [43.95824223, 38.78961344, 34.51383485, 31.10118029, 28.43980052,\n",
" 26.38189519, 24.78529219],\n",
" [47.18912254, 42.04769691, 37.69680277, 34.11938739, 31.23732563,\n",
" 28.93874169, 27.1070405 ],\n",
" [50.11975966, 44.99386982, 40.58468483, 36.88229423, 33.82918215,\n",
" 31.33765969, 29.31072247],\n",
" [52.82556832, 47.70845059, 43.25154494, 39.4493493 , 36.25795703,\n",
" 33.60679284, 31.41402167],\n",
" [55.35680568, 50.2442091 , 45.74676543, 41.86187131, 38.55512774,\n",
" 35.76855984, 33.43227937]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce0f949c-7095-4c37-9fbe-3e70dd7dc49f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ce0f949c-7095-4c37-9fbe-3e70dd7dc49f' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (Ac: 8, Eo: 7, Ao: 8)\n",
"Coordinates:\n",
" * Ac (Ac) float64 0.0258 0.0399 0.0619 ... 0.3568 0.5528\n",
" * Eo (Eo) float64 -0.2 -0.0 0.2 0.4 0.6 0.8 1.0\n",
" * Ao (Ao) float64 0.0613 0.0962 0.1508 ... 0.5813 0.9114 1.429\n",
"Data variables:\n",
" trait_spc_active (Ac, Eo) float64 0.001347 0.004475 ... 1.041e-05\n",
" trait_spc_resting (Ao, Eo) float64 0.002386 0.007927 ... 0.000106 2.896e-05\n",
" ATmax_active (Ac, Eo) float64 nan nan nan nan ... 33.55 31.08 29.07\n",
" ATmax_resting (Ao, Eo) float64 30.02 24.35 20.8 ... 38.56 35.77 33.43"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PO2_atm = constants.XiO2 * constants.kPa_per_atm\n",
"\n",
"\n",
"def solve_ATmax_over_trait_space(trait_space):\n",
"\n",
" metabolic_baseline = trait_space.attrs['metabolic_baseline']\n",
" hypoxic_tol, temp_sens = trait_space.dims\n",
"\n",
" ATmax = xr.full_like(trait_space, fill_value=0.0)\n",
" ATmax.name = f'ATmax_{metabolic_baseline}'\n",
" ATmax.attrs['long_name'] = 'AT$_{max}$' + f' ({metabolic_baseline})'\n",
" ATmax.attrs['units'] = '°C'\n",
" ATmax.attrs['note'] = f'computed using dEodT = {mi.dEodT_bar}'\n",
"\n",
" for i, A_parm in enumerate(trait_space[hypoxic_tol].values):\n",
" for j, Eo in enumerate(trait_space[temp_sens].values):\n",
" ATmax_ij = mi.compute_ATmax(PO2_atm, Ac=A_parm, Eo=Eo, dEodT=mi.dEodT_bar)\n",
" ATmax[i, j] = ATmax_ij\n",
"\n",
" return ATmax\n",
"\n",
"\n",
"for v in ['trait_spc_active', 'trait_spc_resting']:\n",
" ATmax = solve_ATmax_over_trait_space(dso[v])\n",
" dso[ATmax.name] = ATmax\n",
"\n",
" ATmax = solve_ATmax_over_trait_space(dso_hires[v])\n",
" dso_hires[ATmax.name] = ATmax\n",
"\n",
"dso"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e6c5a7b7",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dso.ATmax_active.plot();"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "0ebdc15e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dso.ATmax_resting.plot();"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "000f469c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7fb8e8ceed10>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dso_hires.ATmax_resting.plot()"
]
},
{
"cell_type": "markdown",
"id": "8b01b575",
"metadata": {},
"source": [
"## Write out cached trait-space data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "aace30d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"overwriting \"trait-space\" key in \"sources\"\n",
"overwriting \"trait-space-hires\" key in \"sources\"\n"
]
}
],
"source": [
"clobber = True\n",
"\n",
"this_notebook = 'trait-space-joint-pdf.ipynb'\n",
"\n",
"curator = util.curator_local_assets()\n",
"\n",
"if clobber:\n",
" cache_file = 'data/cache/idealized-trait-space.zarr'\n",
" os.makedirs(os.path.dirname(cache_file), exist_ok=True)\n",
" dso.to_zarr(cache_file, mode='w', consolidated=True)\n",
"\n",
" curator.add_source(\n",
" key='trait-space',\n",
" urlpath=cache_file,\n",
" description=f'Idealized trait space generated by {this_notebook}',\n",
" driver='zarr',\n",
" overwrite=True,\n",
" )\n",
"\n",
" cache_file = 'data/cache/idealized-trait-space-hires.zarr'\n",
" os.makedirs(os.path.dirname(cache_file), exist_ok=True)\n",
" dso_hires.to_zarr(cache_file, mode='w', consolidated=True)\n",
"\n",
" curator.add_source(\n",
" key='trait-space-hires',\n",
" urlpath=cache_file,\n",
" description=f'Idealized trait space generated by {this_notebook}',\n",
" driver='zarr',\n",
" overwrite=True,\n",
" )\n",
"\n",
"cat = curator.open_catalog()\n",
"ds_cache = cat['trait-space'].to_dask()\n",
"xr.testing.assert_identical(dso, ds_cache)\n",
"\n",
"cat = curator.open_catalog()\n",
"ds_cache = cat['trait-space-hires'].to_dask()\n",
"xr.testing.assert_identical(dso_hires, ds_cache)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f4c225bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xarray.Dataset {\n",
"dimensions:\n",
"\tAc = 8 ;\n",
"\tEo = 7 ;\n",
"\tAo = 8 ;\n",
"\n",
"variables:\n",
"\tfloat64 Ac(Ac) ;\n",
"\t\tAc:long_name = Hypoxic tolerance (normalized by critical MI) ;\n",
"\t\tAc:units = 1/kPa ;\n",
"\tfloat64 Eo(Eo) ;\n",
"\t\tEo:long_name = Temperature sensitivity of MI ;\n",
"\t\tEo:units = eV ;\n",
"\tfloat64 trait_spc_active(Ac, Eo) ;\n",
"\t\ttrait_spc_active:long_name = Trait density (active) ;\n",
"\t\ttrait_spc_active:units = eV 1/kPa ;\n",
"\t\ttrait_spc_active:metabolic_baseline = active ;\n",
"\t\ttrait_spc_active:N_traits = 56 ;\n",
"\tfloat64 Ao(Ao) ;\n",
"\t\tAo:long_name = Hypoxic tolerance ;\n",
"\t\tAo:units = 1/kPa ;\n",
"\tfloat64 trait_spc_resting(Ao, Eo) ;\n",
"\t\ttrait_spc_resting:long_name = Trait density (resting) ;\n",
"\t\ttrait_spc_resting:units = eV 1/kPa ;\n",
"\t\ttrait_spc_resting:metabolic_baseline = resting ;\n",
"\t\ttrait_spc_resting:N_traits = 56 ;\n",
"\tfloat64 ATmax_active(Ac, Eo) ;\n",
"\t\tATmax_active:long_name = AT$_{max}$ (active) ;\n",
"\t\tATmax_active:units = °C ;\n",
"\t\tATmax_active:metabolic_baseline = active ;\n",
"\t\tATmax_active:N_traits = 56 ;\n",
"\t\tATmax_active:note = computed using dEodT = 0.022 ;\n",
"\tfloat64 ATmax_resting(Ao, Eo) ;\n",
"\t\tATmax_resting:long_name = AT$_{max}$ (resting) ;\n",
"\t\tATmax_resting:units = °C ;\n",
"\t\tATmax_resting:metabolic_baseline = resting ;\n",
"\t\tATmax_resting:N_traits = 56 ;\n",
"\t\tATmax_resting:note = computed using dEodT = 0.022 ;\n",
"\n",
"// global attributes:\n",
"}"
]
}
],
"source": [
"dso.info()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "42498bde",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xarray.Dataset {\n",
"dimensions:\n",
"\tAc = 30 ;\n",
"\tEo = 30 ;\n",
"\tAo = 30 ;\n",
"\n",
"variables:\n",
"\tfloat64 Ac(Ac) ;\n",
"\t\tAc:long_name = Hypoxic tolerance (normalized by critical MI) ;\n",
"\t\tAc:units = 1/kPa ;\n",
"\tfloat64 Eo(Eo) ;\n",
"\t\tEo:long_name = Temperature sensitivity of MI ;\n",
"\t\tEo:units = eV ;\n",
"\tfloat64 trait_spc_active(Ac, Eo) ;\n",
"\t\ttrait_spc_active:long_name = Trait density (active) ;\n",
"\t\ttrait_spc_active:units = eV 1/kPa ;\n",
"\t\ttrait_spc_active:metabolic_baseline = active ;\n",
"\t\ttrait_spc_active:N_traits = 900 ;\n",
"\tfloat64 Ao(Ao) ;\n",
"\t\tAo:long_name = Hypoxic tolerance ;\n",
"\t\tAo:units = 1/kPa ;\n",
"\tfloat64 trait_spc_resting(Ao, Eo) ;\n",
"\t\ttrait_spc_resting:long_name = Trait density (resting) ;\n",
"\t\ttrait_spc_resting:units = eV 1/kPa ;\n",
"\t\ttrait_spc_resting:metabolic_baseline = resting ;\n",
"\t\ttrait_spc_resting:N_traits = 900 ;\n",
"\tfloat64 ATmax_active(Ac, Eo) ;\n",
"\t\tATmax_active:long_name = AT$_{max}$ (active) ;\n",
"\t\tATmax_active:units = °C ;\n",
"\t\tATmax_active:metabolic_baseline = active ;\n",
"\t\tATmax_active:N_traits = 900 ;\n",
"\t\tATmax_active:note = computed using dEodT = 0.022 ;\n",
"\tfloat64 ATmax_resting(Ao, Eo) ;\n",
"\t\tATmax_resting:long_name = AT$_{max}$ (resting) ;\n",
"\t\tATmax_resting:units = °C ;\n",
"\t\tATmax_resting:metabolic_baseline = resting ;\n",
"\t\tATmax_resting:N_traits = 900 ;\n",
"\t\tATmax_resting:note = computed using dEodT = 0.022 ;\n",
"\n",
"// global attributes:\n",
"}"
]
}
],
"source": [
"dso_hires.info()"
]
}
],
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"display_name": "Python [conda env:jupyterhub-cmip6-201910]",
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