def test_compare_single_level():
refdata = xr.open_dataset('GCCData/SpeciesConcTest.nc4')
refstr = 'GCC Test Data'
devdata = xr.open_dataset('GCHPData/SpeciesConcTest.nc4')
devstr = 'GCHP Test Data'
pdfname = "TestOutput/NewCompareSingleLevel.pdf"
try:
bmk.compare_single_level(refdata,
refstr,
devdata,
devstr,
pdfname=pdfname)
print(
"compare_single_level ran successfully, check output PDFs to visually verify"
)
except Exception as e:
print("compare_single_level failed")
raise e
if not os.path.exists('CorrectCompareSingleLevel.pdf'):
copyfile(pdfname, 'TestOutput/CorrectCompareSingleLevel.pdf')
def compare_data(refdata, devdata):
"""
Compares data from two different xarray datasets.
Args:
-----
refdata : xarray Dataset
The first Dataset to be compared.
devdata : xarray Dataset
The Dataset to be compared against refdata.
"""
# For each variable in refdata, but not non devdata, add an
# array of NaN values to refdata. Ditto for devdata. This will
# allow us to show that the variable is missing in either
# refdata or devdata.
[refdata, devdata] = util.add_missing_variables(refdata, devdata)
# Get the list of common variable names
quiet = not verbose
vardict = util.compare_varnames(refdata, devdata, quiet=quiet)
varlist_level = vardict["commonvars2D"] + vardict["commonvars3D"]
varlist_zonal = vardict["commonvars3D"]
# Restrict variables to those containing a given substring
if restrict_vars is not None:
varlist_level = [v for v in varlist_level if restrict_vars in v]
varlist_zonal = [v for v in varlist_zonal if restrict_vars in v]
# ==================================================================
# Generate the single level comparison plot
# ==================================================================
if create_single_level_plot:
bmk.compare_single_level(refdata,
refstr,
devdata,
devstr,
ilev=level_to_plot,
varlist=varlist_level,
pdfname=pdfname_level,
weightsdir=weightsdir,
verbose=verbose)
# ==================================================================
# Generate the zonal mean comparison plot
# ==================================================================
if create_zonal_mean_plot:
bmk.compare_zonal_mean(refdata,
refstr,
devdata,
devstr,
varlist=varlist_zonal,
pdfname=pdfname_zonal,
weightsdir=weightsdir,
verbose=verbose)
# ==================================================================
# Print totals for each quantity
# ==================================================================
if print_totals_and_diffs:
# Header
print("{} Ref={} Dev={} {}".format("Variable".ljust(22),
refstr.ljust(20), devstr.ljust(20),
"Dev-Ref"))
# Data
for v in varlist_level:
refsum = np.sum(refdata[v].values)
devsum = np.sum(devdata[v].values)
diff = devsum - refsum
print("{} : {} | {} | {} ".format(v.ljust(20),
str(refsum).ljust(22),
str(devsum).ljust(22), diff))
def compare_data(refdata, devdata):
'''
Compares data frwom two different xarray datasets.
Args:
-----
refdata : xarray Dataset
The first Dataset to be compared.
devdata : xarray Dataset
The Dataset to be compared against refdata.
Remarks:
--------
Uses the global variables defined above to specify
plotting options etc.
'''
# For each variable in refdata, but not non devdata, add an
# array of NaN values to refdata. Ditto for devdata. This will
# allow us to show that the variable is missing in either
# refdata or devdata.
[refdata, devdata] = bmk.add_missing_variables(refdata, devdata)
# Get the list of common variable names
quiet = not verbose
vardict = core.compare_varnames(refdata, devdata, quiet=quiet)
varlist_level = vardict['commonvars2D'] + vardict['commonvars3D']
varlist_zonal = vardict['commonvars3D']
# Restrict variables to those containing a given substring
if restrict_vars is not None:
varlist_level = [v for v in varlist_level if restrict_vars in v]
varlist_zonal = [v for v in varlist_zonal if restrict_vars in v]
# ==================================================================
# Generate the single level comparison plot
# ==================================================================
if create_single_level_plot:
# Create the plot
bmk.compare_single_level(refdata,
refstr,
devdata,
devstr,
ilev=level_to_plot,
varlist=varlist_level,
pdfname=pdfname_level,
weightsdir=weightsdir,
verbose=verbose)
# ==================================================================
# Generate the zonal mean comparison plot
# ==================================================================
if create_zonal_mean_plot:
bmk.compare_zonal_mean(refdata,
refstr,
devdata,
devstr,
varlist=varlist_zonal,
pdfname=pdfname_zonal,
weightsdir=weightsdir,
verbose=verbose)
# ==================================================================
# Print totals for each quantity
# ==================================================================
if print_totals_and_diffs:
print('{} Ref={} Dev={} {}'.format('Variable'.ljust(22),
refstr.ljust(20), devstr.ljust(20),
'Dev-Ref'))
for v in varlist_level:
refsum = np.sum(refdata[v].values)
devsum = np.sum(devdata[v].values)
diff = devsum - refsum
print('{} : {} | {} | {} '.format(v.ljust(20),
str(refsum).ljust(22),
str(devsum).ljust(22), diff))
def compare_data(config, data):
"""
Compares data from two different xarray datasets.
Args:
data : dict
Contains Ref and Dev data as xarray Dataset fields.
"""
# Get xarray datasets from the data object
refdata = data["ref"]
devdata = data["dev"]
# For each variable in refdata, but not non devdata, add an
# array of NaN values to refdata. Ditto for devdata. This will
# allow us to show that the variable is missing in either
# refdata or devdata.
[refdata, devdata] = util.add_missing_variables(refdata, devdata)
# Get the list of common variable names
verbose = config["options"]["verbose"]
quiet = not verbose
vardict = util.compare_varnames(refdata, devdata, quiet=quiet)
varlist_level = vardict["commonvars2D"] + vardict["commonvars3D"]
varlist_zonal = vardict["commonvars3D"]
# Restrict variables to those containing a given substring
restrict_vars = config["options"]["restrict_vars"]
if len(restrict_vars) > 0:
varlist_level = [v for v in varlist_level if v in restrict_vars]
varlist_zonal = [v for v in varlist_zonal if v in restrict_vars]
# ==================================================================
# Generate the single level comparison plot
# ==================================================================
if config["options"]["level_plot"]["create_plot"]:
print('... Creating single-level plots')
pdfname = os.path.join(config["paths"]["plots_dir"],
config["options"]["level_plot"]["pdfname"])
bmk.compare_single_level(
refdata,
config["data"]["ref"]["label"],
devdata,
config["data"]["dev"]["label"],
ilev=config["options"]["level_plot"]["level_to_plot"],
varlist=varlist_level,
pdfname=pdfname,
weightsdir=config["paths"]["weights_dir"],
verbose=verbose)
# ==================================================================
# Generate the zonal mean comparison plot
# ==================================================================
if config["options"]["zonal_mean"]["create_plot"]:
print('... Creating zonal mean plots')
pdfname = os.path.join(config["paths"]["plots_dir"],
config["options"]["zonal_mean"]["pdfname"])
bmk.compare_zonal_mean(refdata,
config["data"]["ref"]["label"],
devdata,
config["data"]["dev"]["label"],
varlist=varlist_zonal,
pdfname=pdfname,
weightsdir=config["paths"]["weights_dir"],
verbose=verbose)
# ==================================================================
# Print totals for each quantity
# ==================================================================
if config["options"]["totals_and_diffs"]["create_table"] or \
config["options"]["totals_and_diffs"]["print_to_screen"]:
print('... Printing totals and differences')
print_totals_and_diffs(config, refdata, devdata, varlist_level)