{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"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",
"\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._load_FLUXNET_data()\n",
" self._load_MODIS_data()\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):\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:][144]\n",
" \n",
" if value != 'F':\n",
" date_list.append(date)\n",
" value_list.append(float(value)/ (24*60*60))\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 plot(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_FLUXNET.keys() or year not in self.data_MODIS.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_f = self.data_FLUXNET[year]['dates']\n",
" y_f = self.data_FLUXNET[year]['values']\n",
" \n",
" x_m = self.data_MODIS[year]['dates']\n",
" y_m = self.data_MODIS[year]['values']\n",
" \n",
" if len(years) > 1:\n",
" axs[count].plot(x_f, y_f, label='FLUXNET ' + str(year))\n",
" axs[count].plot(x_m, 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_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_f, y_f, label='FLUXNET ' + str(year))\n",
" axs.plot(x_m, 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_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",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5ff9ec5-2ccf-408f-98b5-1df96f2d5d26",
"metadata": {},
"outputs": [],
"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'FLUXNET years available: {oensingen.data_FLUXNET.keys()}')\n",
"print(f'MODIS years available: {oensingen.data_MODIS.keys()}')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ccfe8b7-b77c-4d27-abf8-af0f7c0db240",
"metadata": {},
"outputs": [],
"source": [
"# Call the plot functions with the years to be plotted\n",
"\n",
"oensingen.plot(2010,2011,2012)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b61f160b-1cdb-4131-8c8a-11d2c4b27b88",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}