def get_stats_on_spatial_predictions_4x5_2x25(res='4x5', ex_str='', target='Iodide',
use_annual_mean=True, filename=None,
folder=None, just_return_df=False,
var2template='Chance2014_STTxx2_I',
):
"""
Evaluate the spatial predictions between models at a resolution of 4x5 or 2x2.5
Parameters
-------
target (str): Name of the target variable (e.g. iodide)
res (str): horizontal resolution of dataset (e.g. 4x5)
var2template (str): variable to use a template for making new variables in ds
use_annual_mean (bool): use the annual mean of the variable
Returns
-------
Notes
-----
"""
# If filename or folder not given, then use defaults
if isinstance(filename, type(None)):
filename = 'Oi_prj_predicted_{}_{}.nc'.format(target, res)
if isinstance(folder, type(None)):
data_root = utils.get_file_locations('data_root')
folder = '{}/{}/outputs/'.format(data_root, target)
ds = xr.open_dataset(folder + filename)
# variables to consider
vars2plot = list(ds.data_vars)
# add LWI and surface area to array
ds = utils.add_LWI2array(ds=ds, var2template=var2template)
IS_WATER = ds['IS_WATER'].mean(dim='time')
# -- get general annual stats in a dataframe
df = pd.DataFrame()
for var_ in vars2plot:
ds_tmp = ds[var_].copy()
# take annual average
if use_annual_mean:
ds_tmp = ds_tmp.mean(dim='time')
# mask to only consider (100%) water boxes
arr = ds_tmp.values
arr = arr[(IS_WATER == True)]
# sve to dataframe
df[var_] = pd.Series(arr.flatten()).describe()
# Get area weighted mean
vals = []
for var_ in vars2plot:
ds_tmp = ds[var_]
# take annual average
if use_annual_mean:
ds_tmp = ds_tmp.mean(dim='time')
# mask to only consider (100%) water boxes
arr = np.ma.array(ds_tmp.values, mask=~(LWI == 0).T)
# also mask s_area
s_area_tmp = np.ma.array(s_area, mask=~(LWI == 0))
# save value
vals += [AC.get_2D_arr_weighted_by_X(arr, s_area=s_area_tmp.T)]
# Add area weighted mean to df
df = df.T
df['mean (weighted)'] = vals
df = df.T
# Save or just return the values
file_save = 'Oi_prj_annual_stats_global_ocean_{}{}.csv'.format(res, ex_str)
if just_return_df:
return df
df.T.to_csv(file_save)
def get_stats_on_spatial_predictions_0125x0125(use_annual_mean=True, target='Iodide',
RFR_dict=None, ex_str='',
just_return_df=False, folder=None,
filename=None, rm_Skagerrak_data=False,
debug=False):
"""
Evaluate the spatial predictions between models at 0.125x0.125
Parameters
-------
target (str): Name of the target variable (e.g. iodide)
debug (bool): print out debugging output?
rm_Skagerrak_data (bool): Remove specific data
(above argument is a iodide specific option - remove this)
just_return_df (bool): just return the data as dataframe
folder (str): folder where NetCDF of predicted data is located
ex_str (str): extra string to include in file name to save data
use_annual_mean (bool): use the annual mean of the variable for statistics
var2template (str): variable to use a template for making new variables in ds
Returns
-------
Notes
-----
"""
# ----
# Get spatial prediction data from NetCDF files saved already
res = '0.125x0.125'
if isinstance(filename, type(None)):
if rm_Skagerrak_data:
extr_file_str = '_No_Skagerrak'
else:
extr_file_str = ''
filename = 'Oi_prj_predicted_{}_{}{}.nc'.format(
target, res, extr_file_str)
if isinstance(folder, type(None)):
data_root = utils.get_file_locations('data_root')
folder = '{}/outputs/{}/'.format(data_root, target)
ds = xr.open_dataset(folder + filename)
# Variables to consider
vars2analyse = list(ds.data_vars)
# Add LWI and surface area to array
ds = utils.add_LWI2array(ds=ds, res=res, var2template='Chance2014_STTxx2_I')
# Set a name for output to saved as
file_save_str = 'Oi_prj_annual_stats_global_ocean_{}{}'.format(res, ex_str)
# ---- build an array with general statistics
df = pd.DataFrame()
# -- get general annual stats
# Take annual average over time (if using annual mean)
if use_annual_mean:
ds_tmp = ds.mean(dim='time')
for var_ in vars2analyse:
# mask to only consider (100%) water boxes
arr = ds_tmp[var_].values
arr = arr[(ds_tmp['IS_WATER'] == True)]
# save to dataframe
df[var_] = pd.Series(arr.flatten()).describe()
# Get area weighted mean too
vals = []
# Take annual average over time (if using annual mean) -
# Q: why does this need to be done twice separately?
if use_annual_mean:
ds_tmp = ds.mean(dim='time')
for var_ in vars2analyse:
# Mask to only consider (100%) water boxes
mask = ~(ds_tmp['IS_WATER'] == True)
arr = np.ma.array(ds_tmp[var_].values, mask=mask)
# Also mask surface area (s_area)
s_area_tmp = np.ma.array(ds_tmp['AREA'].values, mask=mask)
# Save value to list
vals += [AC.get_2D_arr_weighted_by_X(arr, s_area=s_area_tmp)]
# Add area weighted mean to df
df = df.T
df['mean (weighted)'] = vals
df = df.T
# just return the dataframe of global stats
if just_return_df:
return df
# save the values
df.T.to_csv(file_save_str+'.csv')
# ---- print out a more formatted version as a table for the paper
# remove variables
topmodels = get_top_models(RFR_dict=RFR_dict, vars2exclude=['DOC', 'Prod'])
params = [
'Chance2014_STTxx2_I', 'MacDonald2014_iodide', 'Ensemble_Monthly_mean'
]
# select just the models of interest
df = df[topmodels + params]
# rename the models
rename_titles = {u'Chance2014_STTxx2_I': 'Chance et al. (2014)',
u'MacDonald2014_iodide': 'MacDonald et al. (2014)',
'Ensemble_Monthly_mean': 'RFR(Ensemble)',
'Iodide': 'Obs.',
# u'Chance2014_Multivariate': 'Chance et al. (2014) (Multi)',
}
df.rename(columns=rename_titles, inplace=True)
# Sort the dataframe by the mean weighted vales
df = df.T
df.sort_values(by=['mean (weighted)'], ascending=False, inplace=True)
# rename columns (50% to median and ... )
cols2rename = {'50%': 'median', 'std': 'std. dev.', }
df.rename(columns=cols2rename, inplace=True)
# rename
df.rename(index=rename_titles, inplace=True)
# set column order
# Set the stats to use
first_columns = [
'mean (weighted)', 'std. dev.', '25%', 'median', '75%', 'max',
]
if debug:
print(df.head())
df = df[first_columns]
# save as CSV
df.round(1).to_csv(file_save_str+'_FOR_TABLE_'+'.csv')
# ---- Do some further analysis and save this to a text file
a = open(file_save_str+'_analysis.txt', 'w')
# Set a header
print('This file contains global analysis of {} data'.format(str), file=a)
print('\n', file=a)
# which files are being analysed?
print('---- Detail on the predicted fields', file=a)
models2compare = {
1: u'RFR(Ensemble)',
2: u'Chance et al. (2014)',
3: u'MacDonald et al. (2014)',
# 1: u'Ensemble_Monthly_mean',
# 2: u'Chance2014_STTxx2_I',
# 3:'MacDonald2014_iodide'
# 1: u'RFR(TEMP+DEPTH+SAL+NO3+DOC)',
# 2: u'RFR(TEMP+SAL+Prod)',
# 3: u'RFR(TEMP+DEPTH+SAL)',
}
debug = True
if debug:
print(df.head())
df_tmp = df.T[models2compare.values()]
# What are the core models
print('Core models being compared are:', file=a)
for key in models2compare.keys():
ptr_str = 'model {} - {}'
print(ptr_str.format(key, models2compare[key]), file=a)
print('\n', file=a)
# Now print analysis on predicted fields
# range in predicted model values
mean_ = df_tmp.T['mean (weighted)'].values.mean()
min_ = df_tmp.T['mean (weighted)'].values.min()
max_ = df_tmp.T['mean (weighted)'].values.max()
prt_str = 'avg predicted values = {:.5g} ({:.5g}-{:.5g})'
print(prt_str.format(mean_, min_, max_), file=a)
# range in predicted model values
range_ = max_-min_
prt_str = 'range of predicted avg values = {:.3g}'
print(prt_str.format(range_, min_, max_), file=a)
# % of range in predicted model values ( as an error of model choice... )
pcents_ = range_ / df_tmp.T['mean (weighted)'] * 100
min_ = pcents_.min()
max_ = pcents_.max()
prt_str = 'As a % this is = {:.3g} ({:.5g}-{:.5g})'
print(prt_str.format(pcents_.mean(), min_, max_), file=a)
a.close()
