opec-tools/interactive_demo.txt at master · bcdev/opec-tools · GitHub

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# import dedicated interactive shell
import opec.benchmarking as benchmarking

# print a lot of useful information about the possible functions (only possible in IPython)
#help(benchmarking)

# load the source data file
data = benchmarking.load('resources/test.nc')

# show the reference variables
data.ref_vars()

# show the model variables
data.model_vars()

# calculate the statistics for some model variable with some reference variable
stats = benchmarking.calculate_statistics('chl', 'chl_ref', data)

# inspect statistics
stats['rmse']
stats['unbiased_rmse']
stats['p90']
stats['ref_p90']

# get all matchups in the data file
m = benchmarking.get_matchups(data)

# show number of matchups
len(m)

# inspect matchups
print(m[0])
print(m[1])
print(m[2])

# define valid matchups considered valid
valid_matchups = [m[0], m[1]]

# calculate the statistics only from matchups considered valid
stats2 = benchmarking.calculate_statistics_from_matchups(valid_matchups, 'chl', 'chl_ref', data)

# inspect statistics
stats2['rmse']
stats2['unbiased_rmse']
stats2['p90']
stats2['ref_p90']

# get data arrays from matchups
reference_data, model_data = benchmarking.extract_values_from_matchups(valid_matchups, data, 'chl', 'chl_ref')

# inspect reference data
reference_data

# inspect model data
model_data

# manipulate model data
model_data[0] = 0.5

stats3 = benchmarking.calculate_statistics_from_values(model_data, reference_data)

# inspect statistics
stats3['rmse']
stats3['unbiased_rmse']
stats3['p90']
stats3['ref_p90']

# write the benchmarking results to CSV, target diagram, and taylor diagram(s)
benchmarking.write_csv(stats3, 'chl', 'chl_ref', valid_matchups, data, './chl_stats.csv')
taylor_files = benchmarking.taylor_diagrams(stats3, './chl_stats_taylor.png')
target_file = benchmarking.target_diagram(stats3, './chl_stats_target.png')

# write a density plot of the matchups
density_file = benchmarking.density_plot('chl', 'chl_ref', model_data, reference_data, target_file='./chl_density.png', unit='mg/m^3')

# write an XHTML-report from stats3
benchmarking.write_xhtml_report([stats3], valid_matchups, data, './chl_report.xml', ['./chl_stats_taylor.png'], './chl_stats_target.png', ['./chl_density.png'])

# Following: examples using custom configurations

from opec.configuration import Configuration

config = Configuration(time_delta=86400, depth_delta=2.0, alpha=0.4, beta=0.4)

m = benchmarking.get_matchups(data, config)

stats4 = benchmarking.calculate_statistics_from_matchups(m, 'chl', 'chl_ref', data, config)
stats4['rmse']
stats4['unbiased_rmse']
stats4['p90']

benchmarking.write_csv(stats4, 'chl', 'chl_ref', m, data, './chl_stats_2.csv', config)
benchmarking.taylor_diagrams(stats4, './chl_stats.png', config)