How to retrieve tco3 plots (jupyter)
This tutorial shows how to use a Jupyter notebook to retrieve data points of either tco3_zm plot or tco3_return plot in the JSON format and plot them. The Jupyter notebook can be downloaded from here.
System installations
If necessary, install required libraries or python packages
[1]:
### If needed, install additional modules:
#!pip3 install pandas
#!pip3 install matplotlib
### interactive plotting in jupyterlab requires node.js (pip does not install it!!):
#!apt update && apt install -y nodejs
#!pip3 install ipympl
#!jupyter labextension install @jupyter-widgets/jupyterlab-manager
#!jupyter labextension install jupyter-matplotlib
#!jupyter nbextension enable --py widgetsnbextension
### DON'T FORGET TO RESTART JupyterLab !!
# start jupyterlab:
# jupyter lab --ip=0.0.0.0
Import necessary packages
In this tutorial we need the following python packages:
json - to decode JSON data
matplotlib - to plot the data
numpy and pandas - to manipulate the data
os - to manipulate URL paths
requests - to communicate with the O3as API and retrieve data
[2]:
import json
import matplotlib.style as mplstyle
mplstyle.use('fast')
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import requests
Define global variables
we set a few variables: e.g. the base URL of O3AS API and the default figure size
[3]:
# for interactive plot, may change to widget, if installed
%matplotlib inline
# Set default size of plots
plt.rcParams['figure.figsize'] = [12, 8]
# A few variables needed for every REST API call:
url_o3api = "http://api.o3as.fedcloud.eu/api/v1/" # base URL of the O3as API
headers = {'Content-Type': 'application/json',
'Accept': 'application/json'}
# Define Reference Measurement and Reference Year
refMeasurement = 'SBUV_GSFC_merged-SAT-ozone'
refYear = 1980
Retrieve the list of models
We will analyse refC2 models, therefore we first request the list of corresponding models via the REST API
[4]:
# Use '/models' API Endpoint, i.e. append "models" to the base url_o3api:
url_o3api_tco3_zm_models = os.path.join(url_o3api, "models")
# request the list of modesl from O3as API, select "refC2" models:
tco3_zm_models = requests.request("GET",
url=url_o3api_tco3_zm_models,
params={'select': 'refc2'},
headers=headers).json()
print(tco3_zm_models)
['CCMI-1_ACCESS_ACCESS-CCM-refC2', 'CCMI-1_CCCma_CMAM-refC2', 'CCMI-1_CESM1-CAM4Chem_refC2_r1i1p1', 'CCMI-1_CESM1-CAM4Chem_refC2_r2i1p1', 'CCMI-1_CESM1-CAM4Chem_refC2_r3i1p1', 'CCMI-1_CESM1-WACCM_refC2_r1i1p1', 'CCMI-1_CESM1-WACCM_refC2_r2i1p1', 'CCMI-1_CESM1-WACCM_refC2_r3i1p1', 'CCMI-1_CHASER-MIROC-ESM-refC2', 'CCMI-1_CNRM-CERFACS_CNRM-CM5-3-refC2', 'CCMI-1_CNRM-CERFACS_MOCAGE-refC2', 'CCMI-1_ETH-PMOD_SOCOL3-refC2', 'CCMI-1_GSFC_GEOSCCM-refC2', 'CCMI-1_MESSy_EMAC-L90MA-refC2', 'CCMI-1_MOHC_HadGEM3-ES-refC2', 'CCMI-1_MRI_ESM1r1-refC2', 'CCMI-1_NIES_CCSRNIES-MIROC3.2-refC2', 'CCMI-1_NIWA_NIWA-UKCA-refC2', 'CCMI-1_U-CAMBRIDGE_UMUKCA-UCAM-refC2', 'CCMI-1_U-LAQUILA_CCM-refC2', 'CCMI-1_U-LEEDS_UMSLIMCAT-refC2']
We also add a reference measurement, refMeasurement (defined above in the Global Variables, e.g. SBUV_GSFC_merged-SAT-ozone), to the list of models to be plotted
[5]:
tco3_zm_models.append(refMeasurement)
Request points of the tco3_zm plot
we configure parameters of interest to get the plot. In the example we specify that we want a plot for:
Range of years: (1960, 2100)
month: September, October, November
South Hemisphere (SH, latitudes: -90, -60)
reference measurement: refMeasurement (e.g. SBUV_GSFC_merged-SAT-ozone)
reference year: refYear (e.g. 1980, see Global Variables)
[6]:
# initialize an empty dictionary
kwargs_tco3_zm = {}
# Build kwargs with tco3_zm parameters for O3as API.
# Keys have to correspond to expected by the API keys!
kwargs_tco3_zm = {
'begin' : 1960,
'end' : 2100,
'month' : '9,10,11',
'lat_min' : -90,
'lat_max' : -60,
'ref_meas': refMeasurement,
'ref_year': refYear
}
We want to retrieve “tco3_zm” plot: we use ‘/plots’ endpoint, plot type ‘tco3_zm’, therefore we append ‘plots/tco3_zm’ to the base URL of the O3as API
Then we retrieve tco3_zm plot data for the parameters of interest and the list of refC2 models defined above
[7]:
# Build the API URL, use /plots/tco3_zm endpoint:
url_o3api_plot_tco3_zm = os.path.join(url_o3api, "plots/tco3_zm")
# Request data
response = requests.request("POST",
url=url_o3api_plot_tco3_zm,
params=kwargs_tco3_zm,
headers=headers,
data=json.dumps(tco3_zm_models))
# print(response.request.url) # uncomment if you like to see the API call
# print(response.request.body) # uncomment if you like to see the API call Request Body
# Read the status_code. Normal response => 200
print(response.status_code)
tco3_zm_data = response.json()
print(tco3_zm_data[:5]) # print first five for cross-checking
200
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268.0345748901367, 271.59240875244143, 274.1342208862305, 278.1360504150391, 284.0222351074219, 288.8552017211914, 292.12681732177737, 294.62740020751954, 298.9747817993164, 302.2896011352539, 303.68848114013673, 304.14416961669923, 306.8114929199219, 307.885986328125, 309.46814880371096, 306.3762573242188, 306.3796752929688, 308.3333374023438, 306.14597167968753, 304.1287475585938, 302.96336059570314, 304.9953887939453, 305.5959197998047, 308.2728546142578, 309.0998504638672, 313.55296325683594, 313.0412628173828, 314.37506103515625, 318.391796875, 318.3229461669922, 321.5603729248047, 321.32123107910155, 323.6040252685547, 322.73389892578126, 323.77886657714845, 326.3802795410156, 326.79912109375, 331.80604858398436, 328.8478546142578, 331.20375061035156, 331.2844146728516, 337.35638122558595, 337.2773895263672, 337.6394073486328, 341.7844207763672, 337.78045043945315, 341.5189453125, 342.05064086914064, 343.88139953613285, 346.0853668212891, 348.1514434814453, 343.58464660644535, 343.0266967773438, 345.3072845458984, 342.8584014892578, 346.1903106689453, 346.3204620361328, 344.6767913818359, 344.90953369140624, 343.5954986572266, 344.3072143554688, 342.5970825195313, 344.10809020996095, 343.57060852050785, 344.4231201171875, 342.29920349121096, 343.61365356445316, 342.4048767089844]}, {'legalinfo': 'https://o3as.data.kit.edu/policies/terms-of-use.html', 'model': 'CCMI-1_CESM1-CAM4Chem_refC2_r3i1p1', 'plotstyle': {'color': 'darkorange', 'linestyle': 'dashed', 'marker': ''}, 'x': ['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025', '2026', '2027', '2028', '2029', '2030', '2031', '2032', '2033', '2034', '2035', '2036', '2037', '2038', '2039', '2040', '2041', '2042', '2043', '2044', '2045', '2046', '2047', '2048', '2049', '2050', '2051', '2052', '2053', '2054', '2055', '2056', '2057', '2058', '2059', '2060', '2061', '2062', '2063', '2064', '2065', '2066', '2067', '2068', '2069', '2070', '2071', '2072', '2073', '2074', '2075', '2076', '2077', '2078', '2079', '2080', '2081', '2082', '2083', '2084', '2085', '2086', '2087', '2088', '2089', '2090', '2091', '2092', '2093', '2094', '2095', '2096', '2097', '2098', '2099', '2100'], 'y': [365.4360290527344, 365.4360290527344, 366.3817901611328, 370.3562286376953, 370.0383636474609, 368.7543701171875, 363.65330505371094, 362.13975830078124, 362.7178039550781, 363.755908203125, 362.38750610351565, 360.49403991699216, 355.80242614746095, 350.2475311279297, 345.21844482421875, 343.0520751953125, 338.33714904785154, 333.865087890625, 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The received JSON data are converted into pandas Dataframe for an easier data manipulation
[8]:
tco3_zm_pd = pd.json_normalize(tco3_zm_data)
Finally, we plot the retrieved data:
[9]:
# set default linewidth:
linewidth_default = 2
tco3_zm_pd_plot = tco3_zm_pd.copy()
if not 'plotstyle.linewidth' in tco3_zm_pd_plot.columns:
tco3_zm_pd_plot['plotstyle.linewidth'] = linewidth_default
else:
tco3_zm_pd_plot['plotstyle.linewidth'] = tco3_zm_pd_plot['plotstyle.linewidth'].fillna(linewidth_default)
# plot every model
for index, c in tco3_zm_pd_plot.iterrows():
plt.plot(pd.to_datetime(c['x']), c['y'], label=c['model'],
color=c['plotstyle.color'], linestyle=c['plotstyle.linestyle'],
marker=c['plotstyle.marker'], linewidth=c['plotstyle.linewidth'])
# let's fill the area of (MMMean +/- 1 Std)
tco3_zm_mmmean_minus_std_pd = tco3_zm_pd_plot[tco3_zm_pd_plot['model']=='MMMean-Std']
tco3_zm_mmmean_plus_std_pd = tco3_zm_pd_plot[tco3_zm_pd_plot['model']=='MMMean+Std']
plt.fill_between(pd.to_datetime(tco3_zm_mmmean_minus_std_pd['x'].iloc[0]),
tco3_zm_mmmean_minus_std_pd['y'].iloc[0],
tco3_zm_mmmean_plus_std_pd['y'].iloc[0],
color='g', alpha=0.2);
# tune the plot
# show the legend
ax = plt.gca() # get axis instance
ax.legend(bbox_to_anchor=(1.0, 1.0))
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
# put titels for axes
plt.xlabel('Year', fontsize='x-large')
plt.ylabel('tco3_zm (DU)', fontsize='x-large')
[9]:
Text(0, 0.5, 'tco3_zm (DU)')
Request points of the tco3_return plot
Similar to the previous task, we configure parameters of interest to get the tco3_return plot. In the example we specify that we want a plot for:
month: September, October, November
South Hemisphere (SH, latitudes: -90, -60)
reference measurement: refMeasurement (e.g. SBUV_GSFC_merged-SAT-ozone, see Global Variables)
reference year: refYear (e.g. 1980, see Global Variables)
we don’t provide a specific range of years, like in the previous example, but use all available data
[10]:
# initialize an empty dictionary
kwargs_tco3_return = {}
# Build kwargs with tco3_zm parameters for O3as API.
# Keys have to correspond to expected by the API keys!
kwargs_tco3_return = {
'month' : '9,10,11',
'lat_min' : -90,
'lat_max' : -60,
'ref_meas': refMeasurement,
'ref_year': refYear
}
We want to retrieve “tco3_retrun” plot: we use ‘/plots’ endpoint, plot type ‘tco3_return’, therefore we append ‘plots/tco3_return’ to the base URL of the O3as API
Then we retrieve tco3_retrun plot data for the parameters of interest and the list of refC2 models defined above (this time we exclude the reference measurement)
[11]:
# Build the API URL, use /plots/tco3_return endpoint:
url_o3api_plot_tco3_return = os.path.join(url_o3api, "plots/tco3_return")
# Request data
response = requests.request("POST",
url=url_o3api_plot_tco3_return,
params=kwargs_tco3_return,
headers=headers,
data=json.dumps(tco3_zm_models))
# Read the status_code. Normal response => 200
print(response.status_code)
tco3_return_data = response.json()
print(tco3_return_data[:5]) # print first five for cross-checking
200
[{'legalinfo': 'https://o3as.data.kit.edu/policies/terms-of-use.html', 'model': 'CCMI-1_ACCESS_ACCESS-CCM-refC2', 'plotstyle': {'color': 'purple', 'linestyle': 'none', 'marker': 'o'}, 'x': ['Antarctic(Oct)', 'SH mid-lat', 'Tropics', 'NH mid-lat', 'Arctic(Mar)', 'Near global', 'Global', 'User region'], 'y': [2064, 2051, 2060, 2041, 2040, 2049, 2052, 2065]}, {'legalinfo': 'https://o3as.data.kit.edu/policies/terms-of-use.html', 'model': 'CCMI-1_CCCma_CMAM-refC2', 'plotstyle': {'color': 'red', 'linestyle': 'none', 'marker': 'x'}, 'x': ['Antarctic(Oct)', 'SH mid-lat', 'NH mid-lat', 'Arctic(Mar)', 'Near global', 'Global', 'User region'], 'y': [2087.0, 2049.0, 1986.0, 1986.0, 2045.0, 2048.0, 2083.0]}, {'legalinfo': 'https://o3as.data.kit.edu/policies/terms-of-use.html', 'model': 'CCMI-1_CESM1-CAM4Chem_refC2_r1i1p1', 'plotstyle': {'color': 'darkorange', 'linestyle': 'none', 'marker': 'd'}, 'x': ['Antarctic(Oct)', 'SH mid-lat', 'Tropics', 'NH mid-lat', 'Arctic(Mar)', 'Near global', 'Global', 'User region'], 'y': [2056, 2049, 2026, 1987, 2009, 2026, 2038, 2057]}, {'legalinfo': 'https://o3as.data.kit.edu/policies/terms-of-use.html', 'model': 'CCMI-1_CESM1-CAM4Chem_refC2_r2i1p1', 'plotstyle': {'color': 'darkorange', 'linestyle': 'none', 'marker': 'd'}, 'x': ['Antarctic(Oct)', 'SH mid-lat', 'Tropics', 'NH mid-lat', 'Arctic(Mar)', 'Near global', 'Global', 'User region'], 'y': [2057, 2038, 2022, 2006, 2017, 2026, 2036, 2058]}, {'legalinfo': 'https://o3as.data.kit.edu/policies/terms-of-use.html', 'model': 'CCMI-1_CESM1-CAM4Chem_refC2_r3i1p1', 'plotstyle': {'color': 'darkorange', 'linestyle': 'none', 'marker': 'd'}, 'x': ['Antarctic(Oct)', 'SH mid-lat', 'Tropics', 'NH mid-lat', 'Arctic(Mar)', 'Near global', 'Global', 'User region'], 'y': [2055, 2037, 2025, 2002, 2020, 2022, 2034, 2054]}]
The received JSON data can be converted into pandas Dataframe for an easier data manipulation
[12]:
tco3_return_pd = pd.json_normalize(tco3_return_data)
Finally, we plot the retrieved data:
[13]:
# set default markersize:
markersize_default = 6
tco3_return_pd_plot = tco3_return_pd.copy()
if not 'plotstyle.markersize' in tco3_return_pd_plot.columns:
tco3_return_pd_plot['plotstyle.markersize'] = markersize_default
else:
tco3_return_pd_plot['plotstyle.markersize'] = tco3_return_pd_plot['plotstyle.markersize'].fillna(markersize_default)
# plot every model
for index, c in tco3_return_pd_plot.iterrows():
plt.plot(c['x'], c['y'], label=c['model'],
color=c['plotstyle.color'], linestyle=c['plotstyle.linestyle'],
marker=c['plotstyle.marker'], markersize=c['plotstyle.markersize'])
# show the legend
ax = plt.gca() # get axis instance
ax.legend(bbox_to_anchor=(1.0, 1.0))
# put titels for axes
plt.xlabel('Region', fontsize='x-large')
plt.ylabel('tco3_zm (DU)', fontsize='x-large')
[13]:
Text(0, 0.5, 'tco3_zm (DU)')
