Hi All, I am working on surface albedo data. Is there any function using which I can download the data as well as extract a time series and plot the graph.
Thank you
Hi, Have you seen the examples in the Toolbox gallery? e.g.
https://cds.climate.copernicus.eu/toolbox/doc/gallery/3_extract_time_series_and_plot_graph.html
Thanks,
Kevin
Hi Kevin Marsh
Yes I tried the one that you shared but I am getting the following error. Could you please help in resolving it.
Also, I wish to know if I want to download the data instead of getting figure as an output, should I put the command 'return data' in The end?
Traceback (most recent call last): File "/opt/cdstoolbox/cdscompute/cdscompute/cdshandlers/services/handler.py", line 59, in handle_request result
= cached(context.method, proc, context, context.args, context.kwargs) File "/opt/cdstoolbox/cdscompute/cdscompute/caching.py", line 108, in cached
result = proc(context, *context.args, **context.kwargs) File "/opt/cdstoolbox/cdscompute/cdscompute/services.py", line 124, in __call__ return p
(*args, **kwargs) File "/opt/cdstoolbox/cdscompute/cdscompute/services.py", line 60, in __call__ return self.proc(context, *args, **kwargs) File
"/home/cds/cdsservices/services/retrieve.py", line 200, in execute **retrieve_kwargs File "/opt/cdstoolbox/cdscompute/cdscompute/context.py",
line 301, in call return c.call(service, *args, **kwargs).value File "/opt/cdstoolbox/cdsworkflows/cdsworkflows/future.py", line 76, in value
raise self._result cdsworkflows.error.ClientError: None
Hi, The workflow is working for me
and to download the data you would add @ct.output.download() at the start and return 'data_daily' along with the figure:
import cdstoolbox as ct
# Defining an application layout with 3 input collumns locate above the outputs.
layout = {
'input_ncols': 3,
'output_align': 'bottom'
}
# Dictionary of selectable variables
variables = {
'Near-Surface Air Temperature': '2m_temperature',
'Eastward Near-Surface Wind': '10m_u_component_of_wind',
'Northward Near-Surface Wind': '10m_v_component_of_wind',
'Sea Level Pressure': 'mean_sea_level_pressure',
'Sea Surface Temperature': 'sea_surface_temperature',
}
# Define the main application function
@ct.application(
title='Extract a time series and plot graph',
layout=layout,
description='Plot the the time series of a user selected variable at a user selected location.'
)
@ct.input.dropdown('var', label='Variable', values=variables.keys(), description='Sample variables')
@ct.input.text('lon', label='Longitude', type=float, default=75., description='Decimal degrees')
@ct.input.text('lat', label='Latitude', type=float, default=43., description='Decimal degrees')
@ct.output.livefigure()
@ct.output.download()
def plot_time_series(var, lon, lat):
"""
Application main steps:
- set the application layout with 3 columns for the input and output at the bottom
- retrieve a variable over a defined time range
- select a location, defined by longitude and latitude coordinates
- compute the daily average
- show the result as a timeseries on an interactive chart
"""
# Retrieve the selected variable for a predefined time range at a spatial resolution of 3' x 3'.
data = ct.catalogue.retrieve(
'reanalysis-era5-single-levels',
{
'variable': variables[var],
'grid': ['3', '3'],
'product_type': 'reanalysis',
'year': [
'2008', '2009', '2010',
'2011', '2012', '2013',
'2014', '2015', '2016',
'2017'
],
'month': [
'01', '02', '03', '04', '05', '06',
'07', '08', '09', '10', '11', '12'
],
'day': [
'01', '02', '03', '04', '05', '06',
'07', '08', '09', '10', '11', '12',
'13', '14', '15', '16', '17', '18',
'19', '20', '21', '22', '23', '24',
'25', '26', '27', '28', '29', '30',
'31'
],
'time': ['00:00', '06:00', '12:00', '18:00'],
}
)
# Location selection
# Extract the closest point to selected lon/lat (no interpolation).
# If wrong number is set for latitude, the closest available one is chosen:
# e.g. if lat = 4000 -> lat = 90.
# If wrong number is set for longitude, first a wrap in [-180, 180] is made,
# then the closest one present is chosen:
# e.g. if lon = 200 -> lat = -160.
data_sel = ct.geo.extract_point(data, lon=lon, lat=lat)
# Daily mean on selection
data_daily = ct.climate.daily_mean(data_sel)
# Plot the daily data
fig = ct.chart.line(data_daily)
return fig, data_daily