273 lines
148 KiB
Plaintext
273 lines
148 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "9a43798c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d0ace207",
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"metadata": {},
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"source": [
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"## Corn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "6919bad1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ nan, 2022. , nan, nan, nan, nan, nan, nan,\n",
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" nan, nan, nan, nan, nan, 0. , nan, nan,\n",
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" nan, nan, 172.3, nan],\n",
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" [ nan, 2021. , nan, nan, nan, nan, nan, nan,\n",
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" nan, nan, nan, nan, nan, 0. , nan, nan,\n",
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" nan, nan, 176.7, nan],\n",
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" [ nan, 2020. , nan, nan, nan, nan, nan, nan,\n",
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" nan, nan, nan, nan, nan, 0. , nan, nan,\n",
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" nan, nan, 171.4, nan]])"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"csv=np.genfromtxt ('./data/corn.csv',\n",
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" skip_header=1,\n",
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" delimiter=\",\")\n",
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"csv[0:3]\n",
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"#csv[0:3].astype(float)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "e9cc14d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"corn_year=csv[:,1]\n",
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"corn_bu_per_acre=csv[:,-2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "afffad8b",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "ecdf1409",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x22de892fd60>"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.plot(corn_year,corn_bu_per_acre,\"o\",label=\"corn\")\n",
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"plt.xlabel(\"time (calendar year)\")\n",
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"plt.ylabel(\"Corn, bu/acre\")\n",
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"plt.title(\"US Agriculture yields, NASS (USDA)\")\n",
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"plt.legend()\n",
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"\n",
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"#plt.show()\n",
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"#plt.savefig(\"bear-quadratic.pdf\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "103d0ff6",
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"metadata": {},
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"source": [
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"## Potatoes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"id": "0c225547",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ nan, 2022., nan, nan, nan, nan, nan, nan, nan,\n",
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" nan, nan, nan, nan, 0., nan, nan, nan, nan,\n",
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" 438., nan],\n",
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" [ nan, 2021., nan, nan, nan, nan, nan, nan, nan,\n",
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" nan, nan, nan, nan, 0., nan, nan, nan, nan,\n",
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" 444., nan],\n",
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" [ nan, 2020., nan, nan, nan, nan, nan, nan, nan,\n",
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" nan, nan, nan, nan, 0., nan, nan, nan, nan,\n",
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" 461., nan]])"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"csv=np.genfromtxt ('./data/potatoes.csv',\n",
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" skip_header=1,\n",
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" delimiter=\",\")\n",
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"csv[0:3]\n",
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"#csv[0:3].astype(float)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "678ab55e",
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"metadata": {},
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"outputs": [],
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"source": [
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"potatoes_year=csv[:,1]\n",
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"potatoes_bu_per_acre=csv[:,-2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "469cb5bf",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.legend.Legend at 0x22de8b8dd60>"
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]
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(corn_year,corn_bu_per_acre,\"o\",label=\"Corn (BU/ACRE)\")\n",
|
||
|
|
"plt.plot(wheat_year,wheat_bu_per_acre,\"x\",label=\"Wheat (BU/ACRE)\")\n",
|
||
|
|
"plt.plot(potatoes_year,potatoes_bu_per_acre,\"x\",label=\"Potatoes (CWT / ACRE)\")\n",
|
||
|
|
"plt.xlabel(\"time (calendar year)\")\n",
|
||
|
|
"plt.ylabel(\"Average per acre production\")\n",
|
||
|
|
"plt.title(\"US Agriculture yields, NASS (USDA)\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"\n",
|
||
|
|
"#plt.show()\n",
|
||
|
|
"#plt.savefig(\"bear-quadratic.pdf\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "647c29a6",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"id": "622a1829",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x22de892fd60>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHFCAYAAAAUpjivAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB3G0lEQVR4nO3deViUVfsH8O+wL8IoqAykgrkl4q6ZS687UK6ZaWou6VumqbmlmZlaLqm5JamvvaalqWmFYfrD3C3FVJSKMFdcUghFBET2Ob8/fGdimO0ZmGEWvp/r4rrkmWeeOQfGeW7Ouc99ZEIIASIiIiIH5WTtBhARERFZEoMdIiIicmgMdoiIiMihMdghIiIih8Zgh4iIiBwagx0iIiJyaAx2iIiIyKEx2CEiIiKHxmCHiIiIHBqDHbJp8+bNg0wmw71793Q+HhYWhi5dumgcu3XrFsaPH4+GDRvC09MTfn5+aNq0KV577TXcunVL8mvHxMRAJpPB398f+fn55emGQao+Wsr169chk8mwefNm9bGTJ09i3rx5ePDggcVe19I2b94MmUyG69evW/S5Xbp00XqPmVOXLl0gk8kQGRmp9Zjqd/fxxx/rfK6U92hOTg6WLFmC5s2bw9fXFz4+PqhXrx4GDRqEY8eOaZx74cIFDB8+HE8++SQ8PDxQvXp1tGrVChMmTEBWVpak/nzwwQcIDQ2FUqmU1IePP/5Y63dRWFiI//znP2jbti38/Pzg5eWF4OBg9OvXD9HR0Vo/H9WXq6sr/P390bZtW0yZMgV//PGHwbZOnToVMpkMvXv31vn4pUuX4ObmhnPnzknqO9kuBjvkUP766y+0atUKBw4cwNSpU7Fv3z58/vnnGDJkCM6cOYNr165JvtbGjRsBAPfv38fu3bst1GLg3//+N+Li4ix2fV1OnjyJ+fPn23Ww06tXL8TFxSEwMNDaTTGL/fv34/DhwyY9x9h7tLi4GOHh4Vi4cCEGDhyIXbt24ZtvvsGUKVOQmZmJn376SX3u+fPn0bp1ayQlJeH9999HbGws1q9fj169emH//v24f/++0fbcuXMHS5cuxQcffAAnp7LfXoYPH46JEyeia9eu2Lp1K/bs2YP33nsPLi4u2L9/v9b5EydORFxcHI4dO4YtW7agf//+iImJQfPmzbFs2TKdr1FYWIitW7cCAGJjY3H79m2tcxo2bIhhw4ZhypQpZe4L2QhBZMPmzp0rAIi7d+/qfLxJkyaic+fO6u/ff/99AUBcu3ZN5/nFxcWSXjclJUW4uLiIbt26CQ8PD9GzZ0+T225MTk6O2a+pS3JysgAgNm3apD62bNkyAUAkJyeb9bUePXoklEqlWa9pCZs2bZLc/86dO2u8x8ytc+fOomHDhuLJJ58UrVu31vj5qX53y5Yt03qelPfo4cOHBQDx+eef63ztkv8fRowYIby9vUVWVpbOc6X8XmfMmCGeeOIJjesa6oMQ2u/Fa9euCQDi/fffN9pmQ9d+9OiRiIyMFADEvn37tB7ftWuXACB69eolAIiFCxfqfL2zZ88KAOLEiRN6+022jyM75FDS09Ph5OSEmjVr6nxc6l+bX3zxBYqKijBlyhQMGDAAhw4dwo0bN7TOe/DgAcaMGQM/Pz9UqVIFvXr1wrVr1yCTyTBv3jz1eaqpqnPnzmHgwIGoVq0a6tWrp/FYadu2bUP79u1RpUoVVKlSBS1atFD/JQ8AISEhGDVqlNbzjE27zJs3D2+//TYAoG7duuopgKNHjwKAVtv1vZ5qKujHH3/E6NGjUaNGDXh5eamnU77++mu0b98e3t7eqFKlCiIiInD+/Hm97QIeT0u4uLhg8eLFWo8dP34cMpkMu3bt0nj90lNRBw8eRPfu3eHr6wsvLy907NgRhw4dMvi6ACCEwNKlSxEcHAwPDw+0atUK//d//6d1nlKpxIIFC9CoUSN4enqiatWqaNasGVavXm30NfRxdXXFwoULER8fj6+//lrSc6S8R9PT0wFA7+hXyf8P6enp8PX1RZUqVXSea2yqtaCgABs3bsTQoUPLNapjSpsN8fT0xMaNG+Hq6qpzdGfjxo1wc3PDpk2bULt2bWzatAlCx77YrVu3RuPGjbF+/XoTekG2hsEOOZT27dtDqVRiwIAB2L9/v+Q8g9I+//xzBAYG4rnnnsPo0aOhVCo1cl6Axze9Pn36YNu2bZg5cyaio6PRrl07nbkXKgMGDED9+vWxa9cugx+e77//PoYNG4agoCBs3rwZ0dHRGDlypM6Ay1T//ve/MXHiRADAd999h7i4OMTFxaFVq1Zlut7o0aPh6uqKLVu24JtvvoGrqysWLVqEIUOGIDQ0FDt37sSWLVuQnZ2NZ599FklJSXqvFRISgr59+2L9+vUoLi7WeCwqKgpBQUF44YUX9D5/69atCA8Ph6+vL7744gvs3LkTfn5+iIiIMBrwzJ8/HzNnzkTPnj2xe/dujBs3Dq+99houXryocd7SpUsxb948DBkyBHv37sXXX3+NMWPGlHtKcPDgwWjdujXee+89FBYWGj1fynu0TZs2cHV1xVtvvYWvvvoKKSkpeq/Xvn17pKSkYNiwYTh27Bhyc3NNav8vv/yC9PR0dO3a1aTnlda4cWNUrVoV8+fPx4YNG8qUk6USFBSE1q1b4+TJkygqKlIf/+uvv/Djjz+iX79+qFGjBkaOHIkrV67g+PHjOq/TpUsX/N///Z/OYIjshJVHlogMMnUaS6lUirFjxwonJycBQMhkMtG4cWMxZcoUyVM2x48fFwDEO++8o75m3bp1RXBwsMZQ/t69ewUAsW7dOo3nL168WAAQc+fO1eqHrqF51WMq165dE87OzmLYsGEG2xkcHCxGjhypdbz0tIup01il267v9VRTQSNGjNA47+bNm8LFxUVMnDhR43h2drZQKBRi0KBBBvt15MgRAUBER0erj92+fVu4uLiI+fPna72+qg85OTnCz89P9OnTR+N6xcXFonnz5uLpp5/W+9yMjAzh4eEhXnjhBY3nnjhxQgDQ+Hn27t1btGjRwmAfTNG5c2fRpEkTIYQQBw8eFADEmjVrhBD6p2mkvkeFEGLjxo2iSpUqAoAAIAIDA8WIESPE8ePHNc7Ly8sT/fv3V5/n7OwsWrZsKWbPni3S0tKM9mPJkiUCgEhNTdU4buo0lhCP/29Vr15d3RZ/f3/x0ksviZiYGJOuLYQQgwcPFgDE33//rT72wQcfCAAiNjZWCPH4/5xMJhPDhw/XeY3PPvtMABAXLlww+DMg28WRHXIoMpkM69evx7Vr17B27Vq8+uqrKCwsxMqVK9GkSROt1Se6qKaKRo8erb7mqFGjcOPGDY3RAdW1Bg0apPH8IUOG6L32iy++aPT1Dxw4gOLiYrz55ptGz7UFpfu0f/9+FBUVYcSIESgqKlJ/eXh4oHPnzurpMn26dOmC5s2b49NPP1UfW79+PWQyGV5//XW9zzt58iTu37+PkSNHaryuUqlEZGQkzpw5g5ycHJ3PjYuLQ15eHoYNG6ZxvEOHDggODtY49vTTT+PXX3/F+PHjyzV6qEv37t0RHh6ODz74ANnZ2XrPk/oeVZ3z119/Ydu2bZg0aRJq166NrVu3onPnzhrTO+7u7oiOjkZSUhJWrlyJl19+GXfv3sXChQvRuHFjrRGu0u7cuQOZTIbq1auXtftqzz//PG7evIno6GhMnz4dTZo0we7du9G3b19MmDDBpGuJUqMxQgj11FXPnj0BPJ7O7dKlC7799ludv0/VtLiuJGayDwx2yKa5uLgAgNaUhkpRURFcXV21jgcHB2PcuHHYuHEjLl++jK+//hp5eXnqXBV9srOzsWvXLjz99NOoUaMGHjx4gAcPHuCFF16ATCbTyJlJT0+Hi4sL/Pz8NK4REBCg9/pSVg7dvXsXAFCrVi2j59qC0n36+++/AQBt27aFq6urxtfXX3+tt4xASZMmTcKhQ4dw8eJFFBYW4rPPPsPAgQOhUCj0Pkf1ugMHDtR63SVLlkAIoXdFkSpPRNf1Sx+bNWsWPv74Y5w6dQrPPfcc/P390b17d5w9e9Zov6RYsmQJ7t2
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(corn_year,corn_bu_per_acre,\"o\",label=\"corn\")\n",
|
||
|
|
"plt.xlabel(\"time (calendar year)\")\n",
|
||
|
|
"plt.ylabel(\"Corn, bu/acre\")\n",
|
||
|
|
"plt.title(\"US Agriculture yields, NASS (USDA)\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"\n",
|
||
|
|
"#plt.show()\n",
|
||
|
|
"#plt.savefig(\"bear-quadratic.pdf\")"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.13"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|