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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "44a7d641-fbb9-47d5-a54a-826a597d216f",
"metadata": {},
"outputs": [],
"source": [
"!pip install matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "dd9cf71b-e692-4c35-864a-428259e8bf7b",
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"from matplotlib.dates import DateFormatter, MonthLocator\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"class ProjectDataProcessor:\n",
" \"\"\"Process data for latent heat flux comparison between FLUXNET and MODIS dataset\"\"\"\n",
" \n",
" def __init__(self, FLUXNET_file_path, MODIS_file_path):\n",
" \n",
" self.FLUXNET_file_path = FLUXNET_file_path\n",
" self.MODIS_file_path = MODIS_file_path\n",
" \n",
" self.data_FLUXNET = {}\n",
" self.data_MODIS = {}\n",
" \n",
" self.data = {}\n",
" \n",
" self._load_FLUXNET_data()\n",
" self._load_MODIS_data()\n",
" \n",
" self._merge_datasets()\n",
" self._compute_delta()\n",
" \n",
" \n",
" def _load_FLUXNET_data(self):\n",
" \"\"\"Load latent heat flux data from FLUXNET dataset\"\"\"\n",
" \n",
" date_list = []\n",
" value_list = []\n",
" \n",
" with open(self.FLUXNET_file_path, 'r', encoding='utf-8') as f:\n",
" \n",
" first_line = f.readline().split(',')\n",
" ts_index = first_line.index('TIMESTAMP')\n",
" le_index = first_line.index('LE_F_MDS')\n",
" \n",
" for line in f:\n",
" line = line.split(',')\n",
" date = datetime.strptime(line[ts_index], '%Y%m%d')\n",
" value = line[le_index]\n",
" date_list.append(date)\n",
" value_list.append(float(value))\n",
" \n",
" for year in range(min(date_list).year, max(date_list).year + 1):\n",
" self.data_FLUXNET[year] = {'dates':[], 'values':[]}\n",
" \n",
" for date, value in zip(date_list, value_list):\n",
" self.data_FLUXNET[date.year]['dates'].append(date)\n",
" self.data_FLUXNET[date.year]['values'].append(value)\n",
" \n",
" \n",
" def _load_MODIS_data(self, pixel_number=145):\n",
" \"\"\"Load latent heat flux data from MODIS dataset\"\"\"\n",
" \n",
" date_list = []\n",
" value_list = []\n",
" \n",
" with open(self.MODIS_file_path, 'r', encoding='utf-8') as f:\n",
" \n",
" for line in f:\n",
" line = line.split(',')\n",
" date = datetime.strptime(line[2], 'A%Y%j')\n",
" value = line[5:][pixel_number - 1]\n",
" \n",
" if value != 'F':\n",
" date_list.append(date)\n",
" value_list.append((float(value) / (60*60*24)))\n",
" \n",
" for year in range(min(date_list).year, max(date_list).year + 1):\n",
" self.data_MODIS[year] = {'dates':[], 'values':[]}\n",
" \n",
" for date, value in zip(date_list, value_list):\n",
" self.data_MODIS[date.year]['dates'].append(date)\n",
" self.data_MODIS[date.year]['values'].append(value)\n",
" \n",
" \n",
" def _merge_datasets(self):\n",
" \"\"\"Merge datasets into one dataset with only shared values\"\"\"\n",
" \n",
" for year in range(1990, 2030):\n",
" if year in self.data_FLUXNET.keys() and year in self.data_MODIS.keys():\n",
" self.data[year] = {'dates':[], 'values':{'FLUXNET':[], 'MODIS':[]}}\n",
" \n",
" for year in self.data.keys():\n",
" \n",
" for date, value in zip(self.data_MODIS[year]['dates'], self.data_MODIS[year]['values']):\n",
" if date in self.data_FLUXNET[year]['dates']:\n",
" self.data[year]['dates'].append(date)\n",
" self.data[year]['values']['MODIS'].append(value)\n",
" \n",
" for date, value in zip(self.data_FLUXNET[year]['dates'], self.data_FLUXNET[year]['values']):\n",
" if date in self.data_MODIS[year]['dates']:\n",
" # Create 8-day composite period for FLUXNET based on daily data\n",
" first_day_index = self.data_FLUXNET[year]['dates'].index(date)\n",
" last_day_index = self.data_FLUXNET[year]['dates'].index(date) + 8\n",
" eight_day_range = self.data_FLUXNET[year]['values'][first_day_index:last_day_index]\n",
" eight_day_sum = sum(eight_day_range)\n",
" eight_day_average = eight_day_sum / len(eight_day_range)\n",
" self.data[year]['values']['FLUXNET'].append(eight_day_average)\n",
" \n",
" \n",
" def _compute_delta(self):\n",
" \"\"\"Compute absolute and relative deltas between datasets\"\"\"\n",
" \n",
" for year in self.data.keys():\n",
" self.data[year]['deltas_abs'] = {'FLUXNET':[], 'MODIS':[]}\n",
" self.data[year]['deltas_rel'] = {'FLUXNET':[], 'MODIS':[]}\n",
" for f_value, m_value in zip(self.data[year]['values']['FLUXNET'], self.data[year]['values']['MODIS']):\n",
" f_delta_abs = f_value - m_value\n",
" m_delta_abs = m_value - f_value\n",
" f_delta_rel = f_delta_abs / m_value\n",
" m_delta_rel = m_delta_abs / f_value\n",
" self.data[year]['deltas_abs']['FLUXNET'].append(f_delta_abs)\n",
" self.data[year]['deltas_abs']['MODIS'].append(m_delta_abs)\n",
" self.data[year]['deltas_rel']['FLUXNET'].append(f_delta_rel)\n",
" self.data[year]['deltas_rel']['MODIS'].append(m_delta_rel)\n",
" \n",
" \n",
" def plot_values(self, *years):\n",
" \"\"\"Plot latent heat flux data from FLUXNET and MODIS given any number of available years\"\"\"\n",
" \n",
" for year in years:\n",
" if year not in self.data.keys():\n",
" raise ValueError('Make sure all specified years are available in both datasets')\n",
" \n",
" fig, axs = plt.subplots(len(years), 1, figsize=(10, 5*len(years)))\n",
" \n",
" for count, year in enumerate(years):\n",
" x = self.data[year]['dates']\n",
" \n",
" y_f = self.data[year]['values']['FLUXNET']\n",
" y_m = self.data[year]['values']['MODIS']\n",
" \n",
" if len(years) > 1:\n",
" axs[count].plot(x, y_f, label='FLUXNET ' + str(year))\n",
" axs[count].plot(x, y_m, label='MODIS ' + str(year))\n",
" axs[count].set_xlim([datetime.strptime('1.1.'+str(year), '%d.%m.%Y'),\n",
" datetime.strptime('31.12.'+str(year), '%d.%m.%Y')])\n",
" axs[count].set_ylim(0, 200)\n",
" axs[count].set_ylabel('Latent Heat Flux [W m-2]')\n",
" axs[count].xaxis.set_major_locator(MonthLocator())\n",
" axs[count].xaxis.set_major_formatter(DateFormatter('%b'))\n",
" axs[count].set_title(year)\n",
" axs[count].legend()\n",
" \n",
" else:\n",
" axs.plot(x, y_f, label='FLUXNET ' + str(year))\n",
" axs.plot(x, y_m, label='MODIS ' + str(year))\n",
" axs.set_xlim([datetime.strptime('1.1.'+str(year), '%d.%m.%Y'),\n",
" datetime.strptime('31.12.'+str(year), '%d.%m.%Y')])\n",
" axs.set_ylim(0, 200)\n",
" axs.set_ylabel('Latent Heat Flux [W m-2]')\n",
" axs.xaxis.set_major_locator(MonthLocator())\n",
" axs.xaxis.set_major_formatter(DateFormatter('%b'))\n",
" axs.set_title(year)\n",
" axs.legend()\n",
" \n",
" plt.show()\n",
" \n",
" \n",
" def plot_deltas(self, *years, mode='rel', base='FLUXNET'):\n",
" \"\"\"Plot delta of latent heat flux data from FLUXNET and MODIS given any number of available years\"\"\"\n",
" \n",
" if mode == 'abs':\n",
" delta_type = 'deltas_abs'\n",
" ylabel = 'Absolute Difference in Latent Heat Flux [W m-2]'\n",
" ylim = (-100, 100)\n",
" elif mode == 'rel':\n",
" delta_type = 'deltas_rel'\n",
" ylabel = 'Relative Difference in Latent Heat Flux [%]'\n",
" ylim = (-200, 200)\n",
" else:\n",
" raise ValueError('Mode not valid')\n",
" \n",
" for year in years:\n",
" if year not in self.data.keys():\n",
" raise ValueError('Make sure all specified years are available in both datasets')\n",
" \n",
" fig, axs = plt.subplots(len(years), 1, figsize=(10, 5*len(years)))\n",
" \n",
" for count, year in enumerate(years):\n",
" x = self.data[year]['dates']\n",
" \n",
" y = np.array(self.data[year][delta_type][base])*100\n",
" \n",
" if len(years) > 1:\n",
" axs[count].bar(x, y, width=8, color=np.where(np.array(y)>0, 'tab:blue', 'tab:orange'))\n",
" axs[count].set_xlim([datetime.strptime('1.1.'+str(year), '%d.%m.%Y'),\n",
" datetime.strptime('31.12.'+str(year), '%d.%m.%Y')])\n",
" axs[count].set_ylim(ylim)\n",
" axs[count].set_ylabel(ylabel)\n",
" axs[count].xaxis.set_major_locator(MonthLocator())\n",
" axs[count].xaxis.set_major_formatter(DateFormatter('%b'))\n",
" axs[count].set_title(year)\n",
" \n",
" else:\n",
" axs.bar(x, y, width=8, color=np.where(np.array(y)>0, 'tab:blue', 'tab:orange'))\n",
" axs.set_xlim([datetime.strptime('1.1.'+str(year), '%d.%m.%Y'),\n",
" datetime.strptime('31.12.'+str(year), '%d.%m.%Y')])\n",
" axs.set_ylabel(ylabel)\n",
" axs.xaxis.set_major_locator(MonthLocator())\n",
" axs.xaxis.set_major_formatter(DateFormatter('%b'))\n",
" axs.set_title(year)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b5ff9ec5-2ccf-408f-98b5-1df96f2d5d26",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Years available: [2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014]\n"
]
}
],
"source": [
"FLUXNET_file_path = 'data/Oensingen_FLUXNET/FLX_CH-Oe2_FLUXNET2015_FULLSET_DD_2004-2014_1-4.csv'\n",
"MODIS_file_path = 'data/Oensingen_MODIS/LE_500m_filtered_scaled.csv'\n",
"\n",
"oensingen = ProjectDataProcessor(FLUXNET_file_path, MODIS_file_path)\n",
" \n",
"print(f'Years available: {list(oensingen.data.keys())}')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2ccfe8b7-b77c-4d27-abf8-af0f7c0db240",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Call the plot functions with the years to be plotted\n",
"\n",
"oensingen.plot_values(2010, 2011)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "58787137-5c82-4f17-9abc-4f46846d77e4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ZJGmD5DxvkmYy6DVs301yJE336H1doVX1yk5SSZK0QMzlekiLba2vQQu2U4ErgGcDb6OZ0uPyrkJJkrrR9Vk8zxJK3Rh0pYPfqaq3ALdX1SnA84CndBdLkiRJkwY9w3Z3e//LJHsAPwEe000kaWHxjMJ4cJoOSYvZoAXbsiSPBN4MLAc2Ad7SWSpJkiTdZ6Au0ao6qap+UVVfr6odq+oxwM0dZ5MkSRKDn2Gbzj/QrIAgaQMzjO7HxdB1bDetpPky6KCD6WTeUkiSJGlGD+QMW81bCmmMeFZEkjRu+q0l+n2mL8wCbNlJIknCwlmSevU7w3bgUFKshyTPAd4PLAFOqqp3jjiSJElSp/qtJXrtsIIMIskS4IPAs4DrgG8nWV5Vl402mSRJUnceyKCDUdgbWF1VV1XVb4DTgINGnEmSJKlTqVo4YweSHAI8p6r+rN1+GfCUqjpqynFHAEcAbLfddntde+1YnSjUhu64zdfz+Fu6yaGFb31/l2D9f5+6/n2dy/fQtWG85/wc0AySrKyqiantD2SU6NiqqmXAMoCJiYmFU5FK0oZmLoXIOBZ5UscGKtiSPA04Dnhc+5wAVVU7dhdtWtcD2/Zsb9O2SdKGx7Mu0gZj0DNsJwN/DawE7ukuTl/fBnZOsgNNoXYo8McjzCNJktS5QQu2W6rqS50mGUBVrUlyFHAuzbQeH62qVSOOJUkaZ56J1CIwaMH2tSTvBs4C7ppsrKrvdJJqFlX1ReCLw35dSZKkURm0YHtKe987aqGAZ8xvHEmS+vCMmTZAAxVsVbVf10EkSZI0vX5rif5JVf1zkjdMt7+q3tdNLEmSJE3qd4Zt4/Z+066DSJIkaXr91hL9SHv/1uHEkSSNFa8Xk8bCQltLVJIkaYNjwSZJkjTmFuVaopIkjTW7mrWeBjrDlmTLJCcn+VK7vVuSV3UbTZIkSTB4l+jHaZaDemy7/QPg9R3kkSRJ0hSDFmyPrqozgHuhWdOT0S4CL0mStMEYtGC7PcmjaJajIslTATvgJUmShmDQQQdvAJYDOyX5d2ApcEhnqSRJknSfQdcS/U6SpwOPBwJcWVV3d5pMkiRJwIAFW5IjgU9W1ap2+5FJDquqD3WaTlqMHM4vSVpPg17D9uqq+uXkRlX9Anh1J4kkSZK0jkELtiVJMrmRZAnwkG4iSZIkqdeggw7OAU5P8pF2+zVtmyRJkjo2aMH2tzRF2p+32+cBJ3WSSJIkSesYdJTovcCH25skSZKGaNBRok8DjgMe1z4nQFXVjt1FkyRJEgzeJXoy8NfASlySSpIkaagGHSV6S1V9qapurKqfTd66CJTkuCTXJ7m4vT23Z98xSVYnuTLJs7t4fUmSpHEz6Bm2ryV5N3AWcNdkY1V9p5NU8A9V9Z7ehiS7AYcCuwOPBb6SZJeq8oyfJEla1AYt2J7S3k/0tBXwjPmNM6uDgNOq6i7g6iSrgb2BbwwxgyRJ0tANOkp0v66DTHFUkj8FVgBvbFdW2Br4Zs8x17Vt95PkCOAIgO22267jqJIkSd0a6Bq2JFsmOTnJl9rt3ZK8aq4vmuQrSS6d5nYQzdQhOwF7AjcA713fr19Vy6pqoqomli5dOteYkiRJY2HQLtGPAx8Djm23fwCcTjN6dL1V1f6DHJfkRODz7eb1wLY9u7dp2yRJkha1QUeJPrqqzgDuBaiqNXQ0vUeSrXo2Xwhc2j5eDhya5KFJdgB2Bi7qIoMkSdI4GfQM2+1JHkUz0IAkTwVu6SjTu5Ls2b7WNTRLYlFVq5KcAVwGrAGOdISoJEnaEAxasL2B5gzXTkn+HVgKHNJFoKp62Sz73gG8o4vXlSRJGld9C7YkS4Cnt7fH0yxLdWVV3d1xNkmSJDHANWxtt+NhVbWmqlZV1aUWa5IkScMzaJfovyc5gWZk6O2TjR2udCBJkqTWoAXbnu3923rahr3SgSRJ0gZpXFc6kCRJUmskKx1IkiRpcINOnPtx4Fzgse32D4DXd5BHkiRJU4zdSgeSJEla16AF2zBXOpAkSVKPsVvpQJIkSeuatWBL8uKq+hfgF7jSgSRJ0kj06xI9pr0/05UOJEmSRqNfl+jPk3wZ2DHJ8qk7q+oPu4klSZKkSf0KtucCTwZOBd7bfRxJkiRN1a9gO7mqXpbkxKq6cCiJJEmStI5+17DtleSxwEuTPDLJFr23YQSUJEna0PU7w/ZPwPnAjsBKmhGik6ptlyRJUodmPcNWVcdX1X8BPlpVO1bVDj03izVJkqQh6DcP22ZVdStw7HRdoFX1886SSZIkCejfJfop4ECa7tCp7BKVJEkaglkLtqo6sL3fYThxJEmSNFXftUSTPBg4ANi1bboMOLeq1nQZTJIkSY1ZBx0k2RpYBbwReCywNfDfgVXtdB9zkuTFSVYluTfJxJR9xyRZneTKJM/uaX9O27Y6ydFzfW1JkqSFpt8ZtncAH66qf+xtTPI64H8DL5/j614KHAx8ZMrX3Q04FNidpkD8SpJd2t0fBJ4FXAd8O8nyqrpsjq8vSZK0YPQr2J5aVYdPbayq45NcOdcXrarLAZJM3XUQcFpV3QVcnWQ1sHe7b3VVXdU+77T2WAs2SZK06PVb6eDOWfbdMZ9BWlsDP+rZvq5tm6l9WkmOSLIiyYqbbrqpg5iSJEnD0+8M2+ZJDp6mPcBmsz0xyVeA355m17FVdfaA+eakqpYBywAmJiaqy9eSJEnqWr+C7ULg+TPs+/psT6yq/eeQ53pg257tbdo2ZmmXJEla1PrNw/aKYQVpLQc+leR9NIMOdgYuojmjt3OSHWgKtUOBPx5yNkmSpJHoOw9bF5K8EPgAsBT4QpKLq+rZVbUqyRk0gwnWAEdW1T3tc44CzgWW0KxtumoU2SVJkoYtVYv7Eq+JiYlasWLFqGNIkiT1lWRlVU1Mbe83SlSSJEkjNlDBluThSd6S5MR2e+ckB3YbTZIkSTD4GbaPAXcB+7Tb1wNv7ySRJEmS1jFowbZTVb0LuBugqu6gGbkpSZKkjg1asP0mycOAAkiyE80ZN0mSJHVs0Gk9jgPOAbZN8kngacDhHWWSJElSj4EKtqr6cpKVwFNpukL/qqpu7jSZJEmSgAELtiT/CnwKWF5Vt3cbSZIkSb0GvYbtPcDvAZcl+UySQ5Js1GEuSZIktQbtEr0QuDDJEuAZwKuBjwKbdZhNkiRJrMdaou0o0ecDfwQ8GTilq1CSJElaa9Br2M4A9qYZKXoCcGFV3dtlMEmSJDUGPcN2MnBYVd3TZRhJkiTd36wFW5JnVNVXgY2Bg5J1FzeoqrM6zCZJkiT6n2F7OvBVmmvXpirAgk2SJKljsxZsVfV37cO3VdXVvfuS7NBZKkmSJN1n0HnYzpym7TPzGUSSJEnT63cN267A7sDmSQ7u2bUZ4MS5kiRJQ9DvGrbHAwcCj2Dd69huo5k8V5IkSR3rdw3b2cDZSfapqm8MKZMkSZJ6DDoP23eTHEnTPXpfV2hVvbKTVJIkSbrPoIMOTgV+G3g2cCGwDU236JwkeXGSVUnuTTLR0759kjuTXNze/qln315Jvp9kdZLjM3VSOEmSpEVq0ILtd6rqLcDtVXUK8DzgKQ/gdS8FDga+Ps2+H1bVnu3ttT3tH6a5bm7n9vacB/D6kiRJC8agBdvd7f0vk+wBbA48Zq4vWlWXV9WVgx6fZCtgs6r6ZlUV8AngBXN9fUmSpIVk0IJtWZJHAm8BlgOXAe/qKNMOSb6b5MIkv9e2bQ1c13PMdW3btJIckWRFkhU33XRTRzElSZKGY6BBB1V1UvvwQmDHQZ6T5Cs0171NdWw7+nQ6NwDbVdXPkuwFfC7J7oO83pS8y4BlABMTE7W+z5ckSRon/SbOfcNs+6vqfbPs2399w1TVXcBd7eOVSX4I7AJcTzPQYdI2bZskSdKi169LdNM+t3mVZGmSJe3jHWkGF1xVVTcAtyZ5ajs69E+Bmc7SSZIkLSr9Js59axcvmuSFwAeApcAXklxcVc8Gfh94W5K7gXuB11bVz9un/QXwceBhwJfamyRJ0qKXZtBln4OSXWim1diyqvZI8gTgD6vq7V0HfKAmJiZqxYoVo44hSZLUV5KVVTUxtX3QUaInAsfQTu9RVZcAh85fPEmSJM1k0ILt4VV10ZS2NfMdRpIkSfc3aMF2c5KdgAJIcgjNFBySJEnq2KCLvx9JM6/ZrkmuB64GXtpZKkmSJN1n0IlzrwL2T7IxzVm5O2iuYbu2w2ySJEmiT5doks2SHJPkhCTPoinUXg6sBl4yjICSJEkbun5n2E4FfgF8A3g1cCwQ4IVVdXG30SRJkgT9C7Ydq+q/AiQ5ibVrff6682SSJEkC+o8SvXvyQVXdA1xnsSZJkjRc/c6wPTHJre3jAA9rtwNUVW3WaTpJkiT1XUt0ybCCSJIkaXqDTpwrSZKkEbFgkyRJGnMWbJIkSWPOgk2SJGnMWbBJkiSNOQs2SZKkMWfBJkmSNOYs2CRJksacBZskSdKYs2CTJEkacxZskiRJY24kBVuSdye5IsklST6b5BE9+45JsjrJlUme3dP+nLZtdZKjR5FbkiRpFEZ1hu08YI+qegLwA+AYgCS7AYcCuwPPAT6UZEmSJcAHgQOA3YDD2mMlSZIWvZEUbFX15apa025+E9imfXwQcFpV3VVVVwOrgb3b2+qquqqqfgOc1h4rSZK06D141AGAVwKnt4+3pingJl3XtgH8aEr7U2b6gkmOAI5oN+9Kcun8RB26RwM3jzrEgBZS1uks1PwLNTcs3OwLNfekhZx/oWZfqLknLdT8CzX346Zr7KxgS/IV4Len2XVsVZ3dHnMssAb45Hy+dlUtA5a1r7Giqibm8+sPy0LKvpCyTmeh5l+ouWHhZl+ouSct5PwLNftCzT1poeZfqLln0lnBVlX7z7Y/yeHAgcAzq6ra5uuBbXsO26ZtY5Z2SZKkRW1Uo0SfA/x34A+r6o6eXcuBQ5M8NMkOwM7ARcC3gZ2T7JDkITQDE5YPO7ckSdIojOoathOAhwLnJQH4ZlW9tqpWJTkDuIymq/TIqroHIMlRwLnAEuCjVbVqwNdaNu/ph2chZV9IWaezUPMv1NywcLMv1NyTFnL+hZp9oeaetFDzL9Tc08ra3khJkiSNI1c6kCRJGnMWbJIkSWNuURRsSX416gxzkeSeJBf33Laf5dgLkoxseHKSSvLPPdsPTnJTks+PKtP6SvKC9vvYddRZ+lkMP+9JC/X9Cf2zj/p9OZ2F9Hs+VZJjk6xqly28OMmM822OmyTbJDk7yX8m+WGS97eD5GY6/vVJHj7MjDPkqCTv7dl+U5LjRhhpID3/f65K8r0kb0yyKGqamSzqb24BuLOq9uy5XTPqQLO4HdgjycPa7WexnlOrJBn1RM2HAf/W3g+sXRpt2B7wz1sbrDn9no9akn1opnp6crts4f6sO2H62Eozeu4s4HNVtTOwC7AJ8I5ZnvZ6YOQFG3AXcHCSR486yHqa/P9zd5rPxwOAvxtxpk4tmoItySZJzk/ynSTfT3JQ2759ksuTnNhW4l/u+U9w7CTZK8mFSVYmOTfJVj27X9b+RXFpkr1HEO+LwPPax4cBn57ckWTvJN9I8t0k/5Hk8W374UmWJ/kqcP7wI9+XbxPgd4FX0UwLQ5J9k3w9yReSXJnknyb/QkvyqyTvTfI9YJ8RxZ7Lz/vrSfbsOe7fkjxxmKGn0/6sP9+zfUI7FyNJrkny1p737lidGZot+7iZ5fd8pp/9c5Nc0X7eHD/iM7hbATdX1V0AVXVzVf14ps/E9uzm+0f8mTjpGcCvq+pjbfZ7gL8GXplk4yTvaTNekuQvk7wOeCzwtSRfG2FuaGZkWEaTdx3t/59fbXOfn2S7JJsnubbns3LjJD9K8lvDDj6pqm6kWd3oqDSWJHl3km+32V/T8z39bfs5870k7xxV5rlYNAUb8GvghVX1ZGA/4L3tXz3QzOf2wbYS/yXwotFEvJ+HZW136GfbX/gPAIdU1V7AR1n3L7SHV9WewF+0+4btNJp58jYCngB8q2ffFcDvVdWTgP8J/H3PvifTfE9PH1rS+zsIOKeqfgD8LMlebfvewF8CuwE7AQe37RsD36qqJ1bVvw09bWMuP++TgcMBkuwCbFRV3xta4rm7uX3vfhh406jDLGAz/Z7fT/t79RHggPbzZumQMs7ky8C2SX6Q5ENJnr4APhMn7Q6s7G2oqluB/wf8GbA9sGd75vCTVXU88GNgv6rab8hZp/NB4KVJNp/S/gHglMncwPFVdQtwMTD5eX4gcG5V3T2ssNOpqqtopv16DM0fLLdU1X8D/hvw6jTzuB5A8x55SlU9EXjXyALPwai7qOZTgL9P8vvAvTRrkG7Z7ru6qi5uH6+kefOMgzvbDxsAkuwB7MHa+emWADf0HP9pgKr6epLNkjyiqn45rLBVdUma6+wOozn702tz4JQkOwMF9P61dV5V/Xw4KWd0GPD+9vFp7fbngYvaNzpJPk1zduIzwD3AmSPIeZ85/rz/BXhLkr+hWaf348NJ+4Cd1d6vZG3RrPU30+/5dHYFrqqqq9vtT7N2Deahq6pftQXm79H80X068HbG+DNxQPsCH6qqNQBj8Fl4P1V1a5JPAK8D7uzZtQ9r34+nsrbAOR34I+BrNGdyPzSkqIP6A+AJSQ5ptzenOXGzP/CxyQn7x/HfYjaLqWB7Kc1fiHtV1d1JrgE2avfd1XPcPcC4dokGWFVVM3XBTZ00bxST6C0H3kPzIfSonvb/BXytql7YFhkX9Oy7fVjhppNkC5oui/+apGg+9Av4AjP/TH89OWnziK3Xz7uq7khyHs1fkS8BZjzDMmRrWPeM/kZT9k++R+9h/D6X+mUfC7P8np/NAsgP93UlXgBckOT7wJGM/2ciNJO9H9LbkGQzYDvgmlEEmoN/BL4DfGyAY5fTnCDZguYz5qsd5hpIkh1pPj9upPm/9C+r6twpxzx7FNnmy2LqEt0cuLEt1vZjhtXux9yVwNI0F9+S5LeS7N6z/4/a9t+lOd17ywgyfhR4a1V9f0r75qy9KP7woSbq7xDg1Kp6XFVtX1XbAlfT/CW/d3uq/EE0P99RdX/OZC4/75OA44FvV9Uvuo03sGuB3dIsO/cI4JkjzrM+Fkr2mX7PH8T0+a8Edsza0el/NOzAvZI8vj1jPGlP4HLG/zMRmutzH57kT9s8S4D30pzhPhd4TdpBV22RA3AbsOnwo06vPdt0Bk134qT/oL0WkuakyP9tj/0VzZKR7wc+P+o/bpMsBf4JOKFdm/xc4M8nr6tLskuSjYHzgFekHZ3b82+xIIzbX7LrrX0T3EXTv/6v7V9lK2iu8VlQquo37Snc49trCR5M81fP5DJcv07yXZrur1eOKON1NMXAVO+i6aJ7M82Zq3FyGPB/prSdCfw5zYfOCcDv0Jze/+xwo81uLj/vqlqZ5FYG+0u5U5Pvz6r6UZpl5y6lKSK+O9pk/S3A7DP9nh9K8x/xOvmr6s4kfwGck+R2mvfCKG0CfKAtKtcAq2m6aJcxxp+JAFVVSV4IfCjJW2iK5C8C/4PmrM8uwCVJ7gZOpPnMWUbzs//xmFzHBk2ReVTP9l8CH2svsbgJeEXPvtNpLsHYd2jp1vWwJBfT/NuvoemyfV+77ySaS5++017LfhPwgqo6J82grBVJfsPaf6MFYcEvTZVmBNyJVTXKEUJagJLsC7ypqg4ccZR5leSxNN1Ku1bVvSPOsmDfnws5+6CSbNJeOxaaC8//s6r+YdS5BpHkApr374pRZ5GGYUF3iSZ5Lc1Fp28edRZpHLRdMt8Cjh2DYm3Bvj8Xcvb19Or2LMUqmm72j4w2jqSZLPgzbJIkSYvdgj7DJkmStCGwYJMkSRpzFmySJEljzoJNkiRpzFmwSZIkjTkLNkmSpDFnwSZJkjTmLNgkSZLGnAWbJEnSmLNgkyRJGnMWbJIkSWPOgk2SJGnMWbBJkiSNOQs2SZKkMWfBJkmSNOYs2CRJksacBZskSdKYs2CTtMFK8tAkJye5NsltSS5OckDP/mcmuSLJHUm+luRxPftekuQ/2n0XTPO1lyW5Msm9SQ4fznckabGyYJO0IXsw8CPg6cDmwJuBM5Jsn+TRwFnAW4AtgBXA6T3P/Tnwj8A7Z/ja3wP+AvhOJ8klbVAePOoAkjQqVXU7cFxP0+eTXA3sBTwKWFVV/wKQ5Djg5iS7VtUVVfWVtv3PZvjaH2z3/7q770DShsIzbJLUSrIlsAuwCtid5iwZcF9x98O2XZKGyoJNkoAkvwV8Ejilqq4ANgFumXLYLcCmw84mSRZskjZ4SR4EnAr8Bjiqbf4VsNmUQzcDbhtiNEkCLNgkbeCSBDgZ2BJ4UVXd3e5aBTyx57iNgZ3adkkaKgs2SRu6DwP/BXh+Vd3Z0/5ZYI8kL0qyEfA/gUva7lKSLGnbHww8KMlGbbcq7f6HtPsD/Fa7389cSXOSqhp1BkkaiXZetWuAu4A1PbteU1WfTLI/cALwOOBbwOFVdU373MOBj035kqdU1eHt/gtopgvptV9VXTCf34OkDYMFmyRJ0pjz9LwkSdKYG1nBlmTbdqmXy5KsSvJXbfsWSc5L8p/t/SPb9iQ5PsnqJJckefKoskuSJA3TKM+wrQHeWFW7AU8FjkyyG3A0cH5V7Qyc324DHADs3N6OoLlQWJIkadEbWcFWVTdU1Xfax7cBlwNbAwcBp7SHnQK8oH18EPCJanwTeESSrYabWpIkafjGYi3RJNsDT6IZhbVlVd3Q7voJzdxI0BRzP+p52nVt2w1MkeQImrNwbLzxxnvtuuuu3QSXJEmaRytXrry5qpZObR95wZZkE+BM4PVVdWszh2WjqirJeg9jraplwDKAiYmJWrFixXzFlSRJ6kySa6drH+ko0XaSyTOBT1bVWW3zTye7Otv7G9v264Fte56+TdsmSZK0qI1ylOjkcjCXV9X7enYtB17ePn45cHZP+5+2o0WfCtzS03UqSZK0aI2yS/RpwMuA7ye5uG37H8A7gTOSvAq4FnhJu++LwHOB1cAdwCuGmlaSJGlERlawVdW/0ayxN51nTnN8AUd2GkqSJGkMudKBJEnSmLNgkyRJGnMWbJIkSWPOgk2SJGnMWbBJkiSNOQs2SZKkMWfBJkmSNOYs2CRJksacBZskSdKYs2CTJEkacxZskiRJY86CTZIkaczNuvh7ki0G+Br3VtUv5yeOJEmSppq1YAN+3N4yyzFLgO3mLZEkSZLW0a9gu7yqnjTbAUm+O495JEmSNEW/a9j2GeBrDHKMJEmS5mjWM2xV9eve7SQbAX8CPAz4VFX9bOoxkiRJml/rO0r0/cBvgF8An5v3NJIkSbqfWQu2JJ9OslNP0xbAvwBnAo/sMpgkSZIa/QYdHAu8PckNwP8C3gN8FtgIOK7baJIkSYL+17BdBfxxkt8FTge+ADyvqu4ZRjhJkiT17xJ9ZJIjgd2AF9Ncu3ZukucPI5wkSZL6Dzr4HPBLoIBTq+pU4PnAk5L86wN98SQfTXJjkkt72o5Lcn2Si9vbc3v2HZNkdZIrkzz7gb6+JEnSQtDvGrZHAZ+hmcbjNQBVdSfwtiRbzcPrfxw4AfjElPZ/qKr39DYk2Q04FNgdeCzwlSS72D0rSZIWu34F298B5wD3AEf37qiqGx7oi1fV15NsP+DhBwGnVdVdwNVJVgN7A994oDkkSZLG2axdolV1ZlXtV1X7V9VXhhUKOCrJJW2X6eT0IVsDP+o55rq27X6SHJFkRZIVN910U9dZJUmSOtVv0MFx/b7AIMespw8DOwF7AjcA713fL1BVy6pqoqomli5dOs/xJEmShqtfl+ifJbl1lv2hua7suPkKVFU/ve+LJycCn283rwe27Tl0m7ZNkiRpUes3SvREYNNZbpu0x8ybKYMZXghMjiBdDhya5KFJdgB2Bi6az9eWJEkaR/0mzn1rly+e5NPAvsCjk1xHM8hh3yR70kwlcg1rR6euSnIGcBmwBjjSEaKSJGlDkKoadYZOTUxM1IoVK0YdQ5Ikqa8kK6tqYmp7vy5RSZIkjdicC7YkD5nPIJIkSZreQAVbkgt6J7hNsjfw7a5CSZIkaa1+03pM+t/AOUmOp5ms9gDgFZ2lkiRJ0n0GKtiq6twkrwXOA24GnlRVP+k0mSRJkoDBu0TfAnwA+H2aSXIvSPK8DnNJkiSpNWiX6KOAvavqTuAbSc4BTgK+0FkySZIkAYN3ib5+yva1wLO6CCRJkqR1DVSwJfkazcoD66iqZ8x7IkmSJK1j0C7RN/U83gh4Ec3yUJIkSerYoF2iK6c0/XsSF16XJEkagkG7RLfo2XwQsBeweSeJJEmStI5Bu0RX0lzDFpqu0KuBV3UVSpIkSWsN2iW6Q9dBJEmSNL1ZC7YkB8+2v6rOmt84kiRJmqrfGbbnz7KvAAs2SZKkjs1asFWVC7xLkiSN2KxriSb5eM/jl3eeRpIkSffTb/H3J/Y8/qsug0iSJGl6/Qq2+y1HJUmSpOHqN+hgmyTH08y/Nvn4PlX1us6SSZIkCehfsP1Nz+MV8/3iST4KHAjcWFV7tG1bAKcD2wPXAC+pql8kCfB+4LnAHcDhVfWd+c4kSZI0bvqNEj2l49f/OHAC8ImetqOB86vqnUmObrf/FjgA2Lm9PQX4cHsvSZK0qPW7hq1TVfV14OdTmg8CJgvFU4AX9LR/ohrfBB6RZKuhBJUkSRqhkRZsM9iyqm5oH/8E2LJ9vDXwo57jrmvb7ifJEUlWJFlx0003dZdUkiRpCAYq2JI8bZC2+VZVxRxGqlbVsqqaqKqJpUuXdpBMkiRpeAY9w/aBAdvmw08nuzrb+xvb9uuBbXuO26ZtkyRJWtT6Lf6+D/D/AUuTvKFn12bAko4yLQdeDryzvT+7p/2oJKfRDDa4pafrVJIkadHqN63HQ4BN2uM27Wm/FTjkgb54kk8D+wKPTnId8Hc0hdoZSV4FXAu8pD38izRTeqymmdbDdU4lSdIGIc1lYn0OSh5XVdcOIc+8m5iYqBUr5n0KOUmSpHmXZGVVTUxt73eGbdIdSd4N7A5sNNlYVc+Yp3ySJEmawaCDDj4JXAHsALyVZgWCb3eUSZIkST0GLdgeVVUnA3dX1YVV9UrAs2uSJElDMGiX6N3t/Q1Jngf8GNiim0iSJEnqNWjB9vYkmwNvpJl/bTPgrztLJUmSpPsMVLBV1efbh7cA+3UXR5KGY/ujv7Dez7nmnc/rIIkk9Tfo0lS7JDk/yaXt9hOSvLnbaJIkSYLBBx2cCBxDey1bVV0CHNpVKEmSJK01aMH28Kq6aErbmvkOI0mSpPsbtGC7OclOQAEkOQRwHU9JkqQhGHSU6JHAMmDXJNcDVwN/0lkqSZIk3WfQUaJXAfsn2Rh4UFXd1m0sSZIkTZq1YEvyhhnaAaiq93WQSZIkST36XcO2ac/tTVO2N+02miRJkqDPGbaqeuvk4yQv6N2WJEnScAw66ADaEaLSQuJs9pKkxWDQaT0kSZI0Iv0GHXyftWfWfifJJZO7gKqqJ3QZTpIkSf27RA8cSgpJkiTNqN+gg2uHFUSSJEnT8xo2SZKkMWfBJkmSNOYGmtYjyV9V1fv7tc2nJNcAtwH3AGuqaiLJFsDpwPbANcBLquoXXWXQhml9pwJxGhBJUtcGPcP28mnaDp/HHDPZr6r2rKqJdvto4Pyq2hk4v92WJEla1PpN63EY8MfADkmW9+zaFPh5l8FmcBCwb/v4FOAC4G9HkEMjMpeJcCVJWuj6dYn+B3AD8GjgvT3ttwGXTPuM+VPAl5MU8JGqWgZsWVU3tPt/Amw53ROTHAEcAbDddtt1HFMPhAWYJEn9DTKtx7XAPsOJs47frarrkzwGOC/JFVOyVVvM3U9b3C0DmJiYcEktSZK0oA10DVuSg5P8Z5Jbktya5LYkt3YZrKqub+9vBD4L7A38NMlWbaatgBu7zCBJkjQOBh108C7gD6tq86rarKo2rarNugqVZOMkm04+Bv4AuBRYztoBEC8Hzu4qgyRJ0rgYaFoP4KdVdXmnSda1JfDZJNBk/FRVnZPk28AZSV5F01X7kiFmkiRJGolBC7YVSU4HPgfcNdlYVWd1EaqqrgKeOE37z4BndvGakiRJ42rQgm0z4A6arslJBXRSsEmSJGmtgQq2qnpF10EkSZI0vUGXptoF+DDNPGh7JHkCzSCEt3eaTiM1lznSXKZJkqT5N+go0ROBY4C7AarqEuDQrkJJkiRprUELtodX1UVT2tbMdxhJkiTd36CDDm5OshPNQAOSHEKzZJUkSVpP63vJiZebaNCC7UiapZ52TXI9cDXw0s5SSZIk6T6DFmxVVfu3qw48qKpuS7JDl8EkSZLUGPQatjMBqur2qrqtbftMN5EkSZLUa9YzbEl2BXYHNk9ycM+uzYCNugwmSZKkRr8u0ccDBwKPAJ7f034b8OqOMkmSJKnHrAVbVZ0NnJ1kn6r6xpAySZIkqceggw6+m+RImu7R+7pCq+qVnaSSJEnSfQYt2E4FrgCeDbyNZkqPy7sKJUnjyLmzJI3KoAXb71TVi5McVFWnJPkU8H+7DCap4ZqukqRBp/W4u73/ZZI9gM2Bx3QTSZIkSb0GPcO2LMkjgTcDy4FNgLd0lkqSJEn3Gahgq6qT2odfB3YESPKirkJJWlzs1pWkB2bQLtHp/MO8pZAkSdKMHkjBlnlLIUmSpBkNeg3bdGreUkiS1BGnY9Fi0G8t0e8zfWEWYMtOEkkaOv9D23D4by0tTP3OsB04lBTrIclzgPcDS4CTquqdI44kSZLUqX5riV47rCCDSLIE+CDwLOA64NtJllfVZaNNJkmS1J0Hcg3bKOwNrK6qqwCSnAYcBFiwSdIGYi7TxEgLXaoWztiBJIcAz6mqP2u3XwY8paqOmnLcEcARANttt91e1147zYnC4zZfvxc/7pY5ZR78669nnjm9Rsffw4Zq3H6XtOGYy+fG+v7+DeOzaX2N2+fxOL6nx/F78P+5gSRZWVUTU9sfyLQeY6uqllXVRFVNLF26dNRxJEmSHpCBukSTPA04Dnhc+5wAVVU7dhdtWtcD2/Zsb9O2SZIkLVqDXsN2MvDXwErgnu7i9PVtYOckO9AUaocCfzzCPJIkSZ0btGC7paq+1GmSAVTVmiRHAefSTOvx0apaNeJY2tAtgmsmJEnjbdCC7WtJ3g2cBdw12VhV3+kk1Syq6ovAF4f9upIkSaMyaMH2lPa+d9RCAc+Y3zhaL57ZkSRpgzBQwVZV+3UdRJIkSdPrt5bon1TVPyd5w3T7q+p93cSSJPXlWXZpg9HvDNvG7f2mXQeRJEnS9PqtJfqR9v6tw4kjSZKkqRblSgeSJEmLiQWbJEnSmLNgkyRJGnODriW6JfD3wGOr6oAkuwH7VNXJnaaTJC0s6zty9bjNu8khLTKDnmH7OM1yUI9tt38AvL6DPJIkSZpi0ILt0VV1BnAvNGt6MtpF4CVJkjYYgy5NdXuSR9EsR0WSpwLO2DifnABTkiTNYNCC7Q3AcmCnJP8OLAUO6SzVMFggSVJ/XX9W+lksDWTQtUS/k+TpwOOBAFdW1d2dJpMkSRIw4DVsSY4ENqmqVVV1KbBJkr/oNpokSZJg8EEHr66qX05uVNUvgFd3kkiSJEnrGPQatiVJUlWTgw6WAA/pLpYkSZozrw1cdAYt2M4BTk/ykXb7NW2bJEmSOjZowfa3NEXan7fb5wEndZJIkiRJ6xh0lOi9wIfbmyRJkoZo0LVEnwYcBzyufU6Aqqodu4smSZIkGLxL9GTgr4GVuCSVJGkx84J9jaFBp/W4paq+VFU3VtXPJm9dBEpyXJLrk1zc3p7bs++YJKuTXJnk2V28viRJ0rgZ9Azb15K8GzgLuGuysaq+00kq+Ieqek9vQ5LdgEOB3YHHAl9JsktVecZPkiQtaoMWbE9p7yd62gp4xvzGmdVBwGlVdRdwdZLVwN7AN4aYQZIkDYNd0+sYdJTofl0HmeKoJH8KrADe2K6ssDXwzZ5jrmvb7ifJEcARANttt13HUSVJkro16FqiWyY5OcmX2u3dkrxqri+a5CtJLp3mdhDN1CE7AXsCNwDvXd+vX1XLqmqiqiaWLl0615iSJEljYdAu0Y8DHwOObbd/AJxOM3p0vVXV/oMcl+RE4PPt5vXAtj27t2nbJEnSuLOL8wEZdJToo6vqDOBegKpaQ0fTeyTZqmfzhcCl7ePlwKFJHppkB2Bn4KIuMkiSJI2TQc+w3Z7kUTQDDUjyVKCrUvldSfZsX+samiWxqKpVSc4ALgPWAEc6QlSSJG0IBi3Y3kBzhmunJP8OLAUO6SJQVb1sln3vAN7RxetKkiSNq74FW5IlwNPb2+NplqW6sqru7jibJEmSGOAatrbb8bCqWlNVq6rqUos1SZKk4Rm0S/Tfk5xAMzL09snGDlc6kCRJUmvQgm3P9v5tPW3DXulAkiRpgzSuKx1IkiSpNZKVDiRJkjS4QSfO/ThwLvDYdvsHwOs7yCNJkqQpxm6lA0mSJK1r0IJtmCsdSJIkqcfYrXQgSZKkdc1asCV5cVX9C/ALXOlAkiRpJPp1iR7T3p/pSgeSJEmj0a9L9OdJvgzsmGT51J1V9YfdxJIkSdKkfgXbc4EnA6cC7+0+jiRJkqbqV7CdXFUvS3JiVV04lESSJElaR79r2PZK8ljgpUkemWSL3tswAkqSJG3o+p1h+yfgfGBHYCXNCNFJ1bZLkiSpQ7OeYauq46vqvwAfraodq2qHnpvFmiRJ0hD0m4dts6q6FTh2ui7Qqvp5Z8kkSZIE9O8S/RRwIE136FR2iUqSJA3BrAVbVR3Y3u8wnDiSJEmaqu9aokkeDBwA7No2XQacW1VrugwmSZKkxqyDDpJsDawC3gg8Ftga+O/Aqna6jzlJ8uIkq5Lcm2Riyr5jkqxOcmWSZ/e0P6dtW53k6Lm+tiRJ0kLT7wzbO4APV9U/9jYmeR3wv4GXz/F1LwUOBj4y5evuBhwK7E5TIH4lyS7t7g8CzwKuA76dZHlVXTbH15ckSVow+hVsT62qw6c2VtXxSa6c64tW1eUASabuOgg4raruAq5OshrYu923uqquap93WnusBZskSVr0+q10cOcs++6YzyCtrYEf9Wxf17bN1D6tJEckWZFkxU033dRBTEmSpOHpd4Zt8yQHT9MeYLPZnpjkK8BvT7Pr2Ko6e8B8c1JVy4BlABMTE9Xla0mSJHWtX8F2IfD8GfZ9fbYnVtX+c8hzPbBtz/Y2bRuztEuSJC1q/eZhe8WwgrSWA59K8j6aQQc7AxfRnNHbOckONIXaocAfDzmbJEnSSPSdh60LSV4IfABYCnwhycVV9eyqWpXkDJrBBGuAI6vqnvY5RwHnAkto1jZdNYrskiRJw5aqxX2J18TERK1YsWLUMSRJkvpKsrKqJqa29xslKkmSpBEbqGBL8vAkb0lyYru9c5IDu40mSZIkGPwM28eAu4B92u3rgbd3kkiSJEnrGLRg26mq3gXcDVBVd9CM3JQkSVLHBi3YfpPkYUABJNmJ5oybJEmSOjbotB7HAecA2yb5JPA04PCOMkmSJKnHQAVbVX05yUrgqTRdoX9VVTd3mkySJEnAgAVbkn8FPgUsr6rbu40kSZKkXoNew/Ye4PeAy5J8JskhSTbqMJckSZJag3aJXghcmGQJ8Azg1cBHgc06zCZJkiTWYy3RdpTo84E/Ap4MnNJVKEmSJK016DVsZwB704wUPQG4sKru7TKYJEmSGoOeYTsZOKyq7ukyjCRJku5v1oItyTOq6qvAxsBBybqLG1TVWR1mkyRJEv3PsD0d+CrNtWtTFWDBJkmS1LFZC7aq+rv24duq6urefUl26CyVJEmS7jPoPGxnTtP2mfkMIkmSpOn1u4ZtV2B3YPMkB/fs2gxw4lxJkqQh6HcN2+OBA4FHsO51bLfRTJ4rSZKkjvW7hu1s4Owk+1TVN4aUSZIkST0GnYftu0mOpOkeva8rtKpe2UkqSZIk3WfQQQenAr8NPBu4ENiGpltUkiRJHRu0YPudqnoLcHtVnQI8D3jKXF80yYuTrEpyb5KJnvbtk9yZ5OL29k89+/ZK8v0kq5Mcn6mz+EqSJC1SgxZsd7f3v0yyB7A58JgH8LqXAgcDX59m3w+ras/29tqe9g/TDHTYub095wG8viRJ0oIxaMG2LMkjgbcAy4HLgHfN9UWr6vKqunLQ45NsBWxWVd+sqgI+Abxgrq8vSZK0kAw06KCqTmofXgjs2F0cAHZI8l3gVuDNVfV/ga2B63qOua5tm1aSI4AjALbbbrsOo0qSJHWv38S5b5htf1W9b5bnfoVmoMJUx7bThUznBmC7qvpZkr2AzyXZfbYMM+RaBiwDmJiYqPV9viRJ0jjpd4Zt07l+4arafw7PuQu4q328MskPgV2A62lGpk7apm2TJEla9PpNnPvWYQUBSLIU+HlV3ZNkR5rBBVdV1c+T3JrkqcC3gD8FPjDMbJIkSaMy0KCDJLskOT/Jpe32E5K8ea4vmuSFSa4D9gG+kOTcdtfvA5ckuZhmcfnXVtXP231/AZwErAZ+CHxprq8vSZK0kKQZdNnnoORC4G+Aj1TVk9q2S6tqj47zPWATExO1YsWKUceQJEnqK8nKqpqY2j7otB4Pr6qLprSteeCxJEmS1M+gBdvNSXYCCiDJITQjOiVJktSxQRd/P5Jmmoxdk1wPXA28tLNUkiRJus+gE+deBeyfZGOas3J3AIcC13aYTZIkSfTpEk2yWZJjkpyQ5Fk0hdrLaUZqvmQYASVJkjZ0/c6wnQr8AvgGzcLrxwIBXlhVF3cbTZIkSdC/YNuxqv4rQJKTWLt01K87TyZJkiSg/yjRuycfVNU9wHUWa5IkScPV7wzbE5Pc2j4O8LB2O0BV1WadppMkSVLftUSXDCuIJEmSpjfoxLmSJEkaEQs2SZKkMWfBJkmSNOYs2CRJksacBZskSdKYs2CTJEkacxZskiRJY86CTZIkacxZsEmSJI05CzZJkqQxZ8EmSZI05izYJEmSxtxICrYk705yRZJLknw2ySN69h2TZHWSK5M8u6f9OW3b6iRHjyK3JEnSKIzqDNt5wB5V9QTgB8AxAEl2Aw4FdgeeA3woyZIkS4APAgcAuwGHtcdKkiQteiMp2Krqy1W1pt38JrBN+/gg4LSququqrgZWA3u3t9VVdVVV/QY4rT1WkiRp0XvwqAMArwRObx9vTVPATbqubQP40ZT2p8z0BZMcARzRbt6V5NL5iTp0jwZuHnWIAS2krNNZqPkXam5YuNkXau5JCzn/Qs2+UHNPWqj5F2rux03X2FnBluQrwG9Ps+vYqjq7PeZYYA3wyfl87apaBixrX2NFVU3M59cfloWUfSFlnc5Czb9Qc8PCzb5Qc09ayPkXavaFmnvSQs2/UHPPpLOCrar2n21/ksOBA4FnVlW1zdcD2/Yctk3bxiztkiRJi9qoRok+B/jvwB9W1R09u5YDhyZ5aJIdgJ2Bi4BvAzsn2SHJQ2gGJiwfdm5JkqRRGNU1bCcADwXOSwLwzap6bVWtSnIGcBlNV+mRVXUPQJKjgHOBJcBHq2rVgK+1bN7TD89Cyr6Qsk5noeZfqLlh4WZfqLknLeT8CzX7Qs09aaHmX6i5p5W1vZGSJEkaR650IEmSNOYs2CRJksbcoijYkvxq1BnmIsk9SS7uuW0/y7EXJBnZ8OQkleSfe7YfnOSmJJ8fVab1leQF7fex66iz9LMYft6TFur7E/pnH/X7cjoL6fd8qiTHJlnVLlt4cZIZ59scN0m2SXJ2kv9M8sMk728Hyc10/OuTPHyYGWfIUUne27P9piTHjTDSQHr+/1yV5HtJ3phkUdQ0M1nU39wCcGdV7dlzu2bUgWZxO7BHkoe1289iPadWSTLqiZoPA/6tvR9YuzTasD3gn7c2WHP6PR+1JPvQTPX05HbZwv1Zd8L0sZVm9NxZwOeqamdgF2AT4B2zPO31wMgLNuAu4OAkjx51kPU0+f/n7jSfjwcAfzfiTJ1aNAVbkk2SnJ/kO0m+n+Sgtn37JJcnObGtxL/c85/g2EmyV5ILk6xMcm6SrXp2v6z9i+LSJHuPIN4Xgee1jw8DPj25I8neSb6R5LtJ/iPJ49v2w5MsT/JV4PzhR74v3ybA7wKvopkWhiT7Jvl6ki8kuTLJP03+hZbkV0nem+R7wD4jij2Xn/fXk+zZc9y/JXniMENPp/1Zf75n+4R2LkaSXJPkrT3v3bE6MzRb9nEzy+/5TD/75ya5ov28OX7EZ3C3Am6uqrsAqurmqvrxTJ+J7dnN94/4M3HSM4BfV9XH2uz3AH8NvDLJxkne02a8JMlfJnkd8Fjga0m+NsLc0MzIsIwm7zra/z+/2uY+P8l2STZPcm3PZ+XGSX6U5LeGHXxSVd1Is7rRUWksSfLuJN9us7+m53v62/Zz5ntJ3jmqzHOxaAo24NfAC6vqycB+wHvbv3qgmc/tg20l/kvgRaOJeD8Py9ru0M+2v/AfAA6pqr2Aj7LuX2gPr6o9gb9o9w3baTTz5G0EPAH4Vs++K4Dfq6onAf8T+PuefU+m+Z6ePrSk93cQcE5V/QD4WZK92va9gb8EdgN2Ag5u2zcGvlVVT6yqfxt62sZcft4nA4cDJNkF2Kiqvje0xHN3c/ve/TDwplGHWcBm+j2/n/b36iPAAe3nzdIhZZzJl4Ftk/wgyYeSPH0BfCZO2h1Y2dtQVbcC/w/4M2B7YM/2zOEnq+p44MfAflW135CzTueDwEuTbD6l/QPAKZO5geOr6hbgYmDy8/xA4NyquntYYadTVVfRTPv1GJo/WG6pqv8G/Dfg1WnmcT2A5j3ylKp6IvCukQWeg1F3Uc2nAH+f5PeBe2nWIN2y3Xd1VV3cPl5J8+YZB3e2HzYAJNkD2IO189MtAW7oOf7TAFX19SSbJXlEVf1yWGGr6pI019kdRnP2p9fmwClJdgYK6P1r67yq+vlwUs7oMOD97ePT2u3PAxe1b3SSfJrm7MRngHuAM0eQ8z5z/Hn/C/CWJH9Ds07vx4eT9gE7q71fydqiWetvpt/z6ewKXFVVV7fbn2btGsxDV1W/agvM36P5o/t04O2M8WfigPYFPlRVawDG4LPwfqrq1iSfAF4H3Nmzax/Wvh9PZW2BczrwR8DXaM7kfmhIUQf1B8ATkhzSbm9Oc+Jmf+BjkxP2j+O/xWwWU8H2Upq/EPeqqruTXANs1O67q+e4e4Bx7RINsKqqZuqCmzpp3igm0VsOvIfmQ+hRPe3/C/haVb2wLTIu6Nl3+7DCTSfJFjRdFv81SdF86BfwBWb+mf56ctLmEVuvn3dV3ZHkPJq/Il8CzHiGZcjWsO4Z/Y2m7J98j97D+H0u9cs+Fmb5PT+bBZAf7utKvAC4IMn3gSMZ/89EaCZ7P6S3IclmwHbANaMINAf/CHwH+NgAxy6nOUGyBc1nzFc7zDWQJDvSfH7cSPN/6V9W1blTjnn2KLLNl8XUJbo5cGNbrO3HDKvdj7krgaVpLr4lyW8l2b1n/x+17b9Lc7r3lhFk/Cjw1qr6/pT2zVl7UfzhQ03U3yHAqVX1uKravqq2Ba6m+Ut+7/ZU+YNofr6j6v6cyVx+3icBxwPfrqpfdBtvYNcCu6VZdu4RwDNHnGd9LJTsM/2eP4jp818J7Ji1o9P/aNiBeyV5fHvGeNKewOWM/2ciNNfnPjzJn7Z5lgDvpTnDfS7wmrSDrtoiB+A2YNPhR51ee7bpDJruxEn/QXstJM1Jkf/bHvsrmiUj3w98ftR/3CZZCvwTcEK7Nvm5wJ9PXleXZJckGwPnAa9IOzq3599iQRi3v2TXW/smuIumf/1f27/KVtBc47OgVNVv2lO4x7fXEjyY5q+eyWW4fp3kuzTdX68cUcbraIqBqd5F00X3ZpozV+PkMOD/TGk7E/hzmg+dE4DfoTm9/9nhRpvdXH7eVbUyya0M9pdypybfn1X1ozTLzl1KU0R8d7TJ+luA2Wf6PT+U5j/idfJX1Z1J/gI4J8ntNO+FUdoE+EBbVK4BVtN00S5jjD8TAaqqkrwQ+FCSt9AUyV8E/gfNWZ9dgEuS3A2cSPOZs4zmZ//jMbmODZoi86ie7b8EPtZeYnET8IqefafTXIKx79DSrethSS6m+bdfQ9Nl+75230k0lz59p72W/SbgBVV1TppBWSuS/Ia1/0YLwoJfmirNCLgTq2qUI4S0ACXZF3hTVR044ijzKsljabqVdq2qe0ecZcG+Pxdy9kEl2aS9diw0F57/Z1X9w6hzDSLJBTTv3xWjziINw4LuEk3yWpqLTt886izSOGi7ZL4FHDsGxdqCfX8u5Ozr6dXtWYpVNN3sHxltHEkzWfBn2CRJkha7BX2GTZIkaUNgwSZJkjTmLNgkSZLGnAWbJEnSmLNgkyRJGnP/Py9TB3Vl0zyxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Call the plot functions with the years to be plotted\n",
"# mode: 'rel' or 'abs'\n",
"# base: 'FLUXNET' or 'MODIS'\n",
"\n",
"oensingen.plot_deltas(2010, 2011, mode='rel', base='FLUXNET')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fbf83f6-3f20-4949-9b5d-97b277186c2d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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