Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
{
"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
}