def process_rainfall_data(data):
dataseries = []
# take first 10 results
for i, result in enumerate(data):
field_data = json_to_dataframe(result['hasMonthlyTotalRainfall']['hasDatapoint'])
field_data = convert_year_month_columns_to_date(field_data)
field_data["resultIndex"] = i
dataseries.append(field_data)
all_fields_data = pandas.concat(dataseries)
# plot series for all fields
box_plot_multiple_timeseries_by_freq(all_fields_data, ['month', 'year'], labels=['Monthly', 'Yearly'], title='Rainfall')
plot_multiple_timeseries_by_year(all_fields_data, title='Yearly rainfall per field')
# plot series for first field
monthly_rainfall_for_first_field = all_fields_data[all_fields_data.resultIndex == 0]
plot_timeseries(monthly_rainfall_for_first_field, title='3 years of rainfall for first field')
# pretty print first field
monthly_rainfall_for_first_field = monthly_rainfall_for_first_field[['year', 'month', 'value']].copy()
print('\nMonthly rainfall for first field')
print(monthly_rainfall_for_first_field.to_string(index=False))
def process_field_forecasts_temperature(field_finder_response):
# Get daily maximum temperatures
field_forecasts_url = get_api_url(field_finder_response, 'field-forecasts')
print(f"\nField Forecasts URL: {field_forecasts_url}")
forecast_result = get_data(field_forecasts_url)
daily_forecast_maximum_temperature = json_to_dataframe(forecast_result['hasForecastDailyMaximumTemperature']['hasDatapoint'])
daily_forecast_maximum_temperature = daily_forecast_maximum_temperature[['dateTime', 'value']].copy()
daily_forecast_maximum_temperature = convert_dateTime_columns_to_datetime(daily_forecast_maximum_temperature)
plot_timeseries(daily_forecast_maximum_temperature, title='Daily Maximum Temperature')
print('\nDaily Maximum Temperature')
print(daily_forecast_maximum_temperature.to_string(index=False))
def process_field_trends_rainfall(field_finder_response):
# Monthly Rainfall
field_trends_url = get_api_url(field_finder_response, 'field-trends')
print(f"\nField Trends URL: {field_trends_url}")
trends_result = get_data(field_trends_url)
monthly_rainfall = json_to_dataframe(trends_result['hasMonthlyTotalRainfall']['hasDatapoint'])
monthly_rainfall = monthly_rainfall[['year', 'month', 'value']].copy()
monthly_rainfall = convert_year_month_columns_to_date(monthly_rainfall)
plot_timeseries(monthly_rainfall, title='Monthly rainfall for last 3 years')
# the data can be output as csv by using to_csv function instead
print('\nMonthly rainfall for last 3 years')
print(monthly_rainfall.to_string(index=False))