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from config import 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
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(TIME_BEGINNING=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 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 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(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
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