In [1]:
import os
try:
import cPickle as pickle
except ImportError:
import pickle
run_name = '2015-08-17'
fname = os.path.join(run_name, 'config.pkl')
with open(fname, 'rb') as f:
config = pickle.load(f)
In [2]:
import numpy as np
from pandas import DataFrame, read_csv
from utilities import to_html, save_html, apply_skill
fname = '{}-all_obs.csv'.format(run_name)
all_obs = read_csv(os.path.join(run_name, fname), index_col='name')
def rename_cols(df):
columns = dict()
for station in df.columns:
mask = all_obs['station'].astype(str) == station
name = all_obs['station'][mask].index[0]
columns.update({station: name})
return df.rename(columns=columns)
In [3]:
from glob import glob
from pandas import Panel
from utilities import nc2df
def load_ncs(run_name):
fname = '{}-{}.nc'.format
ALL_OBS_DATA = nc2df(os.path.join(run_name,
fname(run_name, 'OBS_DATA')))
index = ALL_OBS_DATA.index
dfs = dict(OBS_DATA=ALL_OBS_DATA)
for fname in glob(os.path.join(run_name, "*.nc")):
if 'OBS_DATA' in fname:
continue
else:
model = fname.split('.')[0].split('-')[-1]
df = nc2df(fname)
# FIXME: Horrible work around duplicate times.
if len(df.index.values) != len(np.unique(df.index.values)):
kw = dict(subset='index', take_last=True)
df = df.reset_index().drop_duplicates(**kw).set_index('index')
kw = dict(method='time', limit=30)
df = df.reindex(index).interpolate(**kw).ix[index]
dfs.update({model: df})
return Panel.fromDict(dfs).swapaxes(0, 2)
In [4]:
from utilities import mean_bias
dfs = load_ncs(run_name)
df = apply_skill(dfs, mean_bias, remove_mean=False, filter_tides=False)
df = rename_cols(df)
skill_score = dict(mean_bias=df.copy())
# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')
html = to_html(df.T)
fname = os.path.join(run_name, 'mean_bias.html'.format(run_name))
save_html(fname, html)
html
Out[4]:
In [5]:
from utilities import rmse
dfs = load_ncs(run_name)
df = apply_skill(dfs, rmse, remove_mean=True, filter_tides=False)
df = rename_cols(df)
skill_score['rmse'] = df.copy()
# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')
html = to_html(df.T)
fname = os.path.join(run_name, 'rmse.html'.format(run_name))
save_html(fname, html)
html
Out[5]:
In [6]:
from utilities import r2
dfs = load_ncs(run_name)
df = apply_skill(dfs, r2, remove_mean=True, filter_tides=False)
df = rename_cols(df)
skill_score['r2'] = df.copy()
# Filter out stations with no valid comparison.
df.dropna(how='all', axis=1, inplace=True)
df = df.applymap('{:.2f}'.format).replace('nan', '--')
html = to_html(df.T)
fname = os.path.join(run_name, 'r2.html'.format(run_name))
save_html(fname, html)
html
Out[6]:
In [7]:
fname = os.path.join(run_name, 'skill_score.pkl')
with open(fname,'wb') as f:
pickle.dump(skill_score, f)
In [8]:
%matplotlib inline
import matplotlib.pyplot as plt
from utilities.taylor_diagram import TaylorDiagram
def make_taylor(samples):
fig = plt.figure(figsize=(9, 9))
dia = TaylorDiagram(samples['std']['OBS_DATA'],
fig=fig,
label="Observation")
colors = plt.matplotlib.cm.jet(np.linspace(0, 1, len(samples)))
# Add samples to Taylor diagram.
samples.drop('OBS_DATA', inplace=True)
for model, row in samples.iterrows():
dia.add_sample(row['std'], row['corr'], marker='s', ls='',
label=model)
# Add RMS contours, and label them.
contours = dia.add_contours(colors='0.5')
plt.clabel(contours, inline=1, fontsize=10)
# Add a figure legend.
kw = dict(prop=dict(size='small'), loc='upper right')
leg = fig.legend(dia.samplePoints,
[p.get_label() for p in dia.samplePoints],
numpoints=1, **kw)
return fig
In [9]:
dfs = load_ncs(run_name)
# Bin and interpolate all series to 1 hour.
freq = '30min'
for station, df in list(dfs.iteritems()):
df = df.resample(freq).interpolate().dropna(axis=1)
if 'OBS_DATA' in df:
samples = DataFrame.from_dict(dict(std=df.std(),
corr=df.corr()['OBS_DATA']))
else:
continue
samples[samples < 0] = np.NaN
samples.dropna(inplace=True)
if len(samples) <= 2: # 1 obs 1 model.
continue
fig = make_taylor(samples)
fig.savefig(os.path.join(run_name, '{}.png'.format(station)))
plt.close(fig)