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import os
import xarray as xr
import pandas as pd
# Mapping of variable names to metadata (title, unit, and NetCDF variable key)
VARIABLE_MAPPING = {
'surface_downwelling_shortwave_radiation': ('Surface Downwelling Shortwave Radiation', 'W/m²', 'rsds'),
'moisture_in_upper_portion_of_soil_column': ('Moisture in Upper Portion of Soil Column', 'kg m-2', 'mrsos'),
'precipitation': ('Precipitation', 'kg m-2 s-1', 'pr'),
'near_surface_relative_humidity': ('Relative Humidity', '%', 'hurs'),
'evaporation_including_sublimation_and_transpiration': ('Evaporation (including sublimation and transpiration)', 'kg m-2 s-1', 'evspsbl'),
'total_runoff': ('Total Runoff', 'kg m-2 s-1', 'mrro'),
'daily_minimum_near_surface_air_temperature': ('Daily Minimum Near Surface Air Temperature', '°C', 'tasmin'),
'daily_maximum_near_surface_air_temperature': ('Daily Maximum Near Surface Air Temperature', '°C', 'tasmax'),
'near_surface_wind_speed': ('Near Surface Wind Speed', 'm/s', 'sfcWind'),
'near_surface_air_temperature': ('Near Surface Air Temperature', '°C', 'tas'),
}
def load_data(variable: str, ds: xr.Dataset, lat: float, lon: float) -> xr.DataArray:
"""
Load data for a given variable from the dataset at the nearest latitude and longitude.
Args:
variable (str): The variable to extract from the dataset.
ds (xr.Dataset): The xarray dataset containing climate data.
lat (float): Latitude for nearest data point.
lon (float): Longitude for nearest data point.
Returns:
xr.DataArray: The data array containing the variable values for the specified location.
"""
try:
data = ds[variable].sel(lat=lat, lon=lon, method="nearest")
# Convert temperature from Kelvin to Celsius for specific variables
if variable in ["tas", "tasmin", "tasmax"]:
data = data - 273.15
return data
except Exception as e:
print(f"Error loading {variable}: {e}")
return None
def get_forecast_datasets(climate_sub_files: list) -> dict:
"""
Get the forecast datasets by loading NetCDF files for each variable.
Args:
climate_sub_files (list): List of file paths to the NetCDF files.
Returns:
dict: Dictionary with variable names as keys and xarray datasets as values.
"""
datasets = {}
# Iterate over each file and check if the variable exists in the filename
for file_path in climate_sub_files:
filename = os.path.basename(file_path)
for long_name, (title, unit, var_key) in VARIABLE_MAPPING.items():
if var_key in filename: # Check for presence of variable in filename
if var_key in ["tas", "tasmax", "tasmin"]:
if f"_{var_key}_" in f"_{filename}_" or filename.endswith(f"_{var_key}.nc"):
datasets[long_name] = xr.open_dataset(file_path, engine="netcdf4")
else:
datasets[long_name] = xr.open_dataset(file_path, engine="netcdf4")
return datasets
def get_forecast_data(datasets: dict, lat: float, lon: float) -> pd.DataFrame:
"""
Extract climate data from the forecast datasets for a given location and convert to a DataFrame.
Args:
datasets (dict): Dictionary of datasets, one for each variable.
lat (float): Latitude of the location to extract data for.
lon (float): Longitude of the location to extract data for.
Returns:
pd.DataFrame: A DataFrame containing time series data for each variable.
"""
time_series_data = {'time': []}
# Iterate over the variable mapping to load and process data for each variable
for long_name, (title, unit, variable) in VARIABLE_MAPPING.items():
print(f"Processing {long_name} ({title}, {unit}, {variable})...")
# Load the data for the current variable
data = load_data(variable, datasets[long_name], lat, lon)
if data is not None:
print(f"Time values: {data.time.values[:5]}") # Preview first few time values
print(f"Data values: {data.values[:5]}") # Preview first few data values
# Add the time values to the 'time' list
time_series_data['time'] = data.time.values
# Format the column name with unit (e.g., "Precipitation (kg m-2 s-1)")
column_name = f"{title} ({unit})"
time_series_data[column_name] = data.values
# Convert the time series data into a pandas DataFrame
return pd.DataFrame(time_series_data)
# Define the directory to parse
folder_to_parse = "climate_data_pessimist/"
# Retrieve the subfolders and files to parse
climate_sub_folder = [os.path.join(folder_to_parse, e) for e in os.listdir(folder_to_parse) if os.path.isdir(os.path.join(folder_to_parse, e))]
climate_sub_files = [os.path.join(e, i) for e in climate_sub_folder for i in os.listdir(e) if i.endswith('.nc')]
# Load the forecast datasets
datasets = get_forecast_datasets(climate_sub_files)
# Get the forecast data for a specific latitude and longitude
lat, lon = 47.0, 5.0 # Example coordinates
final_df = get_forecast_data(datasets, lat, lon)
# Display the resulting DataFrame
print(final_df.head()) |