File size: 9,024 Bytes
a16f583 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 |
from config import LOADING_TIME_BEGINNING, TIME_BEGINNING ,TIME_END , seasons, years_padded , SamplesCoordinates_Yearly, MatrixCoordinates_1mil_Yearly, DataYearly, SamplesCoordinates_Seasonally, MatrixCoordinates_1mil_Seasonally, DataSeasonally ,file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC
import pandas as pd
import numpy as np
def get_time_range(TIME_BEGINNING= TIME_BEGINNING, TIME_END=TIME_END, seasons=seasons, years_padded=years_padded):
# Define seasons list for matching
seasons_list = ['winter', 'spring', 'summer', 'autumn']
# Check if TIME_BEGINNING is a season
is_season = any(season in TIME_BEGINNING.lower() for season in seasons_list)
if is_season:
# Handle seasons case
start_idx = next(i for i, season in enumerate(seasons)
if TIME_BEGINNING.lower() in season.lower())
end_idx = next(i for i, season in enumerate(seasons)
if TIME_END.lower() in season.lower())
# Get the subset including both start and end
return seasons[start_idx:end_idx + 1]
else:
# Handle years case
start_idx = years_padded.index(TIME_BEGINNING)
end_idx = years_padded.index(TIME_END)
# Get the subset including both start and end
return years_padded[start_idx:end_idx + 1]
def process_paths_yearly(path, year, seen_years):
if 'Elevation' in path:
return path
elif 'MODIS_NPP' in path:
paths = []
# Add current year
if year not in seen_years:
seen_years.add(year)
paths.append(f"{path}/{year}")
# Add previous year
prev_year = str(int(year) - 1)
if prev_year not in seen_years:
seen_years.add(prev_year)
paths.append(f"{path}/{prev_year}")
return paths
else:
return f"{path}/{year}"
def create_path_arrays_yearly(SamplesCoordinates_Yearly, DataYearly, selected_years):
seen_years_samples = set()
seen_years_data = set()
samples_coordinates_array_path = [
processed_path
for idx, base_path in enumerate(SamplesCoordinates_Yearly)
for year in selected_years
if idx < len(SamplesCoordinates_Yearly)
if (processed_path := process_paths_yearly(base_path, year, seen_years_samples)) is not None
]
data_yearly_array_path = [
processed_path
for idx, base_path in enumerate(DataYearly)
for year in selected_years
if idx < len(DataYearly)
if (processed_path := process_paths_yearly(base_path, year, seen_years_data)) is not None
]
return samples_coordinates_array_path, data_yearly_array_path
def process_paths(path, season, seen_years):
if 'Elevation' in path:
return path
elif 'MODIS_NPP' in path:
year = season.split('_')[0][:4] # Get year from season
paths = []
# Add current year
if year not in seen_years:
seen_years.add(year)
paths.append(f"{path}/{year}")
# Add previous year
prev_year = str(int(year) - 1)
if prev_year not in seen_years:
seen_years.add(prev_year)
paths.append(f"{path}/{prev_year}")
return paths
else:
return f"{path}/{season}"
def create_path_arrays(SamplesCoordinates_Seasonally, DataSeasonally, selected_seasons):
seen_years_samples = set()
seen_years_data = set()
samples_coordinates_array_path = [
processed_path
for idx, base_path in enumerate(SamplesCoordinates_Seasonally)
for season in selected_seasons
if idx < len(SamplesCoordinates_Seasonally)
if (processed_path := process_paths(base_path, season, seen_years_samples)) is not None
]
data_seasons_array_path = [
processed_path
for idx, base_path in enumerate(DataSeasonally)
for season in selected_seasons
if idx < len(DataSeasonally)
if (processed_path := process_paths(base_path, season, seen_years_data)) is not None
]
return samples_coordinates_array_path, data_seasons_array_path
def separate_and_add_data(LOADING_TIME_BEGINNING=LOADING_TIME_BEGINNING, TIME_END=TIME_END, seasons=seasons, years_padded=years_padded,
SamplesCoordinates_Yearly=SamplesCoordinates_Yearly, DataYearly=DataYearly,
SamplesCoordinates_Seasonally=SamplesCoordinates_Seasonally, DataSeasonally=DataSeasonally):
# Define seasons list for matching
seasons_list = ['winter', 'spring', 'summer', 'autumn']
# Check if LOADING_TIME_BEGINNING is a season
is_season = any(season in LOADING_TIME_BEGINNING.lower() for season in seasons_list)
if is_season:
# Handle seasons case
start_idx = next(i for i, season in enumerate(seasons)
if LOADING_TIME_BEGINNING.lower() in season.lower())
end_idx = next(i for i, season in enumerate(seasons)
if TIME_END.lower() in season.lower())
# Get the seasonal range
selected_seasons = seasons[start_idx:end_idx + 1]
# Add seasonal data pairs
return create_path_arrays(SamplesCoordinates_Seasonally, DataSeasonally, selected_seasons)
else:
start_idx = years_padded.index(LOADING_TIME_BEGINNING)
end_idx = years_padded.index(TIME_END)
selected_years = years_padded[start_idx:end_idx + 1]
return create_path_arrays_yearly(SamplesCoordinates_Yearly, DataYearly, selected_years)
def add_season_column(dataframe):
seasons_months = {
'winter': [12, 1, 2],
'spring': [3, 4, 5],
'summer': [6, 7, 8],
'autumn': [9, 10, 11]
}
month_to_season = {
month: season
for season, months in seasons_months.items()
for month in months
}
dataframe['survey_date'] = pd.to_datetime(dataframe['survey_date'])
def get_season_year(row):
if pd.isna(row['survey_date']):
return None
month = row['survey_date'].month
year = row['survey_date'].year
if month == 12:
year += 1
season = month_to_season.get(month)
if season:
return f"{year}_{season}"
return None
valid_dates_mask = dataframe['survey_date'] >= '2000-01-01'
dataframe['season'] = None
dataframe.loc[valid_dates_mask, 'season'] = (
dataframe[valid_dates_mask].apply(get_season_year, axis=1)
)
return dataframe
def filter_dataframe(time_beginning, time_end, max_oc=150):
# Read and prepare data
df = pd.read_excel(file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC)
df = add_season_column(df)
# Convert columns to numeric
df['GPS_LONG'] = pd.to_numeric(df['GPS_LONG'], errors='coerce')
df['GPS_LAT'] = pd.to_numeric(df['GPS_LAT'], errors='coerce')
df['OC'] = pd.to_numeric(df['OC'], errors='coerce')
# Basic data quality mask
quality_mask = (
(df['OC'] <= max_oc) &
df['GPS_LONG'].notna() &
df['GPS_LAT'].notna() &
df['OC'].notna()
)
# Check if time_beginning contains a season
seasons = ['winter', 'spring', 'summer', 'autumn']
is_season = any(season in time_beginning.lower() for season in seasons)
if is_season:
# Create a list of all valid seasons between time_beginning and time_end
start_year, start_season = time_beginning.split('_')
end_year, end_season = time_end.split('_')
start_year = int(start_year)
end_year = int(end_year)
valid_seasons = []
current_year = start_year
season_order = ['winter', 'spring', 'summer', 'autumn']
start_idx = season_order.index(start_season)
end_idx = season_order.index(end_season)
while current_year <= end_year:
if current_year == start_year:
season_start = start_idx
else:
season_start = 0
if current_year == end_year:
season_end = end_idx
else:
season_end = len(season_order) - 1
for season in season_order[season_start:season_end + 1]:
valid_seasons.append(f"{current_year}_{season}")
current_year += 1
# Filter using the valid seasons list
filtered_df = df[
df['season'].isin(valid_seasons) &
quality_mask
]
else:
# Filter by year range
start_year = int(time_beginning)
end_year = int(time_end)
filtered_df = df[
(df['year'].between(start_year, end_year, inclusive='both')) &
quality_mask
]
print(f"Initial shape: {df.shape}")
print(f"Final filtered shape: {filtered_df.shape}")
if filtered_df.empty:
print("\nDebug information:")
print("NaN counts:", df[['GPS_LONG', 'GPS_LAT', 'OC', 'survey_date']].isna().sum())
print(f"OC range: {df['OC'].min()} to {df['OC'].max()}")
return filtered_df
|