def validate(params,
timespan=('2009-01', '2009-12'), gpi=None, rescaling=None,
y_axis_range=None):
"""
This function is optimising the parameters vegetation water content
'm_veg', soil moisture 'm_soil' and, if specified, a third optional
parameter. The third optional parameter can eitehr be sand 'sand',
clay 'clay', fractional root mean square height 'f_rms',
stem volume 's_vol' or temperature 'temp'.
Parameters
----------
params : list of dicts
Model parameters. At least
four of the following parameters needs to be specified if an optional
parameter has been selected, otherwise all of them needs to be
specified: 'sand', 'clay', 'f_rms', 'temp', 's_vol'
timespan : tuple, optional
timespan to analyze
gpi : int, optional
Grid point index. If specified, it will read data from datapool.
rescaling : string, optional
rescaling method, one of 'min_max', 'linreg', 'mean_std' and 'lin_cdf_match'
Default: None
insitu is the reference to which is scaled
y_axis_range : tuple, optional
specify (min, max) of y axis
Returns
-------
df : pandas.DataFrame
Optimised soil moisture, vegetation water concent and, if specified,
optional optimised parameter.
"""
unit_dict = {'freq': 'GHz',
'sand': '',
'clay': '',
'temp': '$^\circ$C',
'eps': '',
'theta': '$^\circ$',
'f_rms': '',
'sig_bare': 'dB',
'm_soil': '%',
'm_veg': '%',
'm_soil_x0': '%',
'm_veg_x0': '%',
's_vol': '$m^3ha^{-1}$',
'sig_canopy': 'dB',
'sig_for': 'dB',
'sig_floor': 'dB',
'polarization': ''}
param_should = ['sand', 'clay', 'temp',
's_vol', 'f_rms',
'm_veg_x0', 'm_soil_x0']
for param in param_should:
assert param in params.keys()
if gpi is None:
ts_resam = pd.read_csv(os.path.join(os.path.split(os.path.abspath(__file__))[0],'data','2011528_2009.csv'), index_col=0,
parse_dates=True)[timespan[0]:timespan[1]]
gpi = 2011528
else:
ts_resam = read_resam(gpi)[timespan[0]:timespan[1]]
m_veg_x0 = params.pop('m_veg_x0')
m_soil_x0 = params.pop('m_soil_x0')
columns = ['m_veg', 'm_soil']
x0 = np.array([m_veg_x0, m_soil_x0])
df = pd.DataFrame(index=ts_resam.index, columns=columns)
df = df.fillna(np.nan)
# optimise m_soil and m_veg
for index, row in ts_resam.iterrows():
ascat_inc = np.array(row[['incf', 'incm', 'inca']].tolist())
ascat_sig = \
db2lin(np.array(row[['sigf', 'sigm', 'siga']].tolist()))
args = (ascat_inc, ascat_sig, params, '')
res = minimize(sig_sqr_diff, x0, args=args, method='Nelder-Mead')
if res['success'] == True:
df['m_veg'][index] = res['x'][0]
df['m_soil'][index] = res['x'][1]
str_static_p = \
', '.join("%s: %r" % t for t in locals().iteritems())
str_static_p += ",\nm_veg_x0 = {:.2f}, m_soil_x0 = {:.2f}".format(m_veg_x0, m_soil_x0)
ismn_file = os.path.join(os.path.split(os.path.abspath(__file__))[0],'data','ARM_ARM_Larned_sm_0.050000_0.050000_Water-Matric-Potential-Sensor-229L-W_20090101_20140527.stm')
ismn_data = ismn_readers.read_data(ismn_file)
insitu = pd.DataFrame(ismn_data.data['soil moisture']).rename(columns={'soil moisture': 'insitu'})
gldas = pd.read_csv(os.path.join(os.path.split(os.path.abspath(__file__))[0],'data', 'GLDAS_737602.csv'), parse_dates=True, index_col=0)
gldas.rename(columns={'086_L1': 'gldas'}, inplace=True)
gldas = pd.DataFrame(gldas['gldas']) / 100.0
ascat = pd.DataFrame(df['m_soil']).rename(columns={'m_soil': 'ascat'})
matched = temp_match.matching(ascat, insitu, gldas)
if rescaling is not None:
scaled = scaling.scale(matched, rescaling, reference_index=1)
else:
scaled = matched
metrics = OrderedDict()
metrics['bias'] = df_metrics.bias(scaled)
metrics['pearson'] = df_metrics.pearsonr(scaled)
metrics['spearman'] = df_metrics.spearmanr(scaled)
metrics['ubrmsd'] = df_metrics.rmsd(scaled)
metrics['std_ratio'] = df_std_ratio(scaled)
tcol_error = df_metrics.tcol_error(scaled)._asdict()
ts_title = "Soil moisture. "
if rescaling is not None:
ts_title = ' '.join([ts_title, 'Rescaling: %s.' % rescaling])
rmsd_title = 'unbiased RMSD'
else:
ts_title = ' '.join([ts_title, 'No rescaling.'])
rmsd_title = 'RMSD'
axes = scaled.plot(title=ts_title, figsize=(18, 8))
plt.legend()
# these are matplotlib.patch.Patch properties
props = dict(facecolor='white', alpha=0)
columns = ('ascat-insitu', 'ascat-gldas', 'insitu-gldas')
row_labels = ['bias', 'pearson R', 'spearman rho', rmsd_title, 'stddev ratio']
cell_text = []
for metric in metrics:
metric_values = metrics[metric]
if type(metric_values) == tuple:
metric_values = metric_values[0]
metric_values = metric_values._asdict()
cell_text.append(["%.2f" % metric_values['ascat_and_insitu'],
"%.2f" % metric_values['ascat_and_gldas'],
"%.2f" % metric_values['insitu_and_gldas']])
table = plt.table(
cellText=cell_text,
colLabels=columns,
colWidths=[0.1, 0.1, 0.1],
rowLabels=row_labels, loc='bottom',
bbox=(0.2, -0.5, 0.5, 0.3))
tcol_table = plt.table(
cellText=[["%.2f" % tcol_error['ascat'],
"%.2f" % tcol_error['gldas'],
"%.2f" % tcol_error['insitu']]],
colLabels=('ascat ', 'gldas ', 'insitu '),
colWidths=[0.1, 0.1, 0.1],
rowLabels=['Triple collocation error'], loc='bottom',
bbox=(0.2, -0.6, 0.5, 0.1))
plt.subplots_adjust(left=0.08, bottom=0.35, right=0.85)
plt.draw()
#
if y_axis_range is not None:
axes.set_ylim(y_axis_range)
params['m_veg_x0'] = m_veg_x0
params['m_soil_x0'] = m_soil_x0
infotext = []
for label in sorted(param_should):
infotext.append('%s = %s %s' % (label, params[label], unit_dict[label]))
infotext = '\n'.join(infotext)
# place a text box in upper left in axes coords
axes.text(1.03, 1, infotext, transform=axes.transAxes, fontsize=12,
verticalalignment='top', bbox=props)
axes = scatter_matrix(scaled)
axes.flat[0].figure.suptitle(ts_title)
# only draw 1:1 line if scaling was applied
for j, ax in enumerate(axes.flatten()):
if y_axis_range is not None:
ax.set_xlim(y_axis_range)
if np.remainder(j + 1, 3 + 1) != 1:
if y_axis_range is not None:
ax.set_ylim(y_axis_range)
min_x, max_x = ax.get_xlim()
min_y, max_y = ax.get_ylim()
# find minimum lower left coordinate and maximum upper right
min_ll = min([min_x, min_y])
max_ur = max([max_x, max_y])
ax.plot([min_ll, max_ur], [min_ll, max_ur], '--', c='0.6')
def optimise(params,
timespan=('2009-01', '2009-12'),
gpi=None,
rescaling=None):
"""
This function is optimising the parameters vegetation water content
'm_veg', soil moisture 'm_soil' and, if specified, a third optional
parameter. The third optional parameter can eitehr be sand 'sand',
clay 'clay', fractional root mean square height 'f_rms',
stem volume 's_vol' or temperature 'temp'.
Parameters
----------
params : list of dicts
Model parameters. At least
four of the following parameters needs to be specified if an optional
parameter has been selected, otherwise all of them needs to be
specified: 'sand', 'clay', 'f_rms', 'temp', 's_vol'
gpi : int, optional
Grid point index. If specified, it will read data from datapool.
Returns
-------
df : pandas.DataFrame
Optimised soil moisture, vegetation water concent and, if specified,
optional optimised parameter.
"""
if gpi is None:
ts_resam = pd.read_csv(os.path.join("data", "2011528_2009.csv"),
index_col=0,
parse_dates=True)[timespan[0]:timespan[1]]
gpi = 2011528
else:
ts_resam = read_resam(gpi)[timespan[0]:timespan[1]]
m_veg_x0 = params.pop('m_veg_x0')
m_soil_x0 = params.pop('m_soil_x0')
columns = ['m_veg', 'm_soil']
x0 = np.array([m_veg_x0, m_soil_x0])
df = pd.DataFrame(index=ts_resam.index, columns=columns)
df = df.fillna(np.nan)
# optimise m_soil and m_veg
for index, row in ts_resam.iterrows():
ascat_inc = np.array(row[['incf', 'incm', 'inca']].tolist())
ascat_sig = \
db2lin(np.array(row[['sigf', 'sigm', 'siga']].tolist()))
args = (ascat_inc, ascat_sig, params, '')
res = minimize(sig_sqr_diff, x0, args=args, method='Nelder-Mead')
if res['success'] == True:
df['m_veg'][index] = res['x'][0]
df['m_soil'][index] = res['x'][1]
str_static_p = \
', '.join("%s: %r" % t for t in locals().iteritems())
str_static_p += ",\nm_veg_x0 = {:.2f}, m_soil_x0 = {:.2f}".format(
m_veg_x0, m_soil_x0)
ismn_file = os.path.join(
'data',
'ARM_ARM_Larned_sm_0.050000_0.050000_Water-Matric-Potential-Sensor-229L-W_20090101_20140527.stm'
)
ismn_data = ismn_readers.read_data(ismn_file)
insitu = pd.DataFrame(ismn_data.data['soil moisture']).rename(
columns={'soil moisture': 'insitu'})
gldas = pd.read_csv(os.path.join('data', 'GLDAS_737602.csv'),
parse_dates=True,
index_col=0)
gldas.rename(columns={'086_L1': 'gldas'}, inplace=True)
gldas = pd.DataFrame(gldas['gldas'])
ascat = pd.DataFrame(df['m_soil']).rename(columns={'m_soil': 'ascat'})
matched = temp_match.matching(ascat, insitu, gldas)
if rescaling is not None:
scaled = scaling.scale(matched, rescaling, reference_index=1)
else:
scaled = matched
metrics = OrderedDict()
metrics['bias'] = df_metrics.bias(scaled)
metrics['pearson'] = df_metrics.pearsonr(scaled)
metrics['kendall'] = df_metrics.kendalltau(scaled)
metrics['ubrmsd'] = df_metrics.ubrmsd(scaled)
metrics['var_ratio'] = df_var_ratio(scaled)
tcol_error = df_metrics.tcol_error(scaled)._asdict()
ts_title = "Soil moisture. "
if rescaling is not None:
ts_title = ' '.join([ts_title, 'Rescaling: %s.' % rescaling])
else:
ts_title = ' '.join([ts_title, 'No rescaling.'])
axes = scaled.plot(subplots=True, title=ts_title, figsize=(18, 8))
# these are matplotlib.patch.Patch properties
props = dict(facecolor='white', alpha=0)
columns = ('ascat-insitu', 'ascat-gldas', 'insitu-gldas')
row_labels = [
'bias', 'pearson R', 'kendall tau', 'unbiased RMSD', 'variance ratio'
]
cell_text = []
for metric in metrics:
metric_values = metrics[metric]
if type(metric_values) == tuple:
metric_values = metric_values[0]
metric_values = metric_values._asdict()
cell_text.append([
"%.2f" % metric_values['ascat_and_insitu'],
"%.2f" % metric_values['ascat_and_gldas'],
"%.2f" % metric_values['insitu_and_gldas']
])
table = plt.table(cellText=cell_text,
colLabels=columns,
colWidths=[0.1, 0.1, 0.1],
rowLabels=row_labels,
loc='bottom',
bbox=(0.2, -1.25, 0.5, 0.8))
tcol_table = plt.table(cellText=[[
"%.2f" % tcol_error['ascat'],
"%.2f" % tcol_error['gldas'],
"%.2f" % tcol_error['insitu']
]],
colLabels=('ascat', 'gldas', 'insitu'),
colWidths=[0.1, 0.1, 0.1],
rowLabels=['Triple collocation error'],
loc='bottom',
bbox=(0.2, -1.65, 0.5, 0.3))
plt.subplots_adjust(left=0.08, bottom=0.35)
axes = scatter_matrix(scaled)
axes.flat[0].figure.suptitle(ts_title)
# only draw 1:1 line if scaling was applied
if rescaling is not None:
for j, ax in enumerate(axes.flatten()):
if np.remainder(j + 1, 3 + 1) != 1:
min_x, max_x = ax.get_xlim()
min_y, max_y = ax.get_ylim()
# find minimum lower left coordinate and maximum upper right
min_ll = min([min_x, min_y])
max_ur = max([max_x, max_y])
ax.plot([min_ll, max_ur], [min_ll, max_ur], '--', c='0.6')
return df
def optimise(params,
timespan=('2009-01', '2009-12'), gpi=None, rescaling=None):
"""
This function is optimising the parameters vegetation water content
'm_veg', soil moisture 'm_soil' and, if specified, a third optional
parameter. The third optional parameter can eitehr be sand 'sand',
clay 'clay', fractional root mean square height 'f_rms',
stem volume 's_vol' or temperature 'temp'.
Parameters
----------
params : list of dicts
Model parameters. At least
four of the following parameters needs to be specified if an optional
parameter has been selected, otherwise all of them needs to be
specified: 'sand', 'clay', 'f_rms', 'temp', 's_vol'
gpi : int, optional
Grid point index. If specified, it will read data from datapool.
Returns
-------
df : pandas.DataFrame
Optimised soil moisture, vegetation water concent and, if specified,
optional optimised parameter.
"""
if gpi is None:
ts_resam = pd.read_csv(os.path.join("data", "2011528_2009.csv"), index_col=0,
parse_dates=True)[timespan[0]:timespan[1]]
gpi = 2011528
else:
ts_resam = read_resam(gpi)[timespan[0]:timespan[1]]
m_veg_x0 = params.pop('m_veg_x0')
m_soil_x0 = params.pop('m_soil_x0')
columns = ['m_veg', 'm_soil']
x0 = np.array([m_veg_x0, m_soil_x0])
df = pd.DataFrame(index=ts_resam.index, columns=columns)
df = df.fillna(np.nan)
# optimise m_soil and m_veg
for index, row in ts_resam.iterrows():
ascat_inc = np.array(row[['incf', 'incm', 'inca']].tolist())
ascat_sig = \
db2lin(np.array(row[['sigf', 'sigm', 'siga']].tolist()))
args = (ascat_inc, ascat_sig, params, '')
res = minimize(sig_sqr_diff, x0, args=args, method='Nelder-Mead')
if res['success'] == True:
df['m_veg'][index] = res['x'][0]
df['m_soil'][index] = res['x'][1]
str_static_p = \
', '.join("%s: %r" % t for t in locals().iteritems())
str_static_p += ",\nm_veg_x0 = {:.2f}, m_soil_x0 = {:.2f}".format(m_veg_x0, m_soil_x0)
ismn_file = os.path.join('data', 'ARM_ARM_Larned_sm_0.050000_0.050000_Water-Matric-Potential-Sensor-229L-W_20090101_20140527.stm')
ismn_data = ismn_readers.read_data(ismn_file)
insitu = pd.DataFrame(ismn_data.data['soil moisture']).rename(columns={'soil moisture': 'insitu'})
gldas = pd.read_csv(os.path.join('data', 'GLDAS_737602.csv'), parse_dates=True, index_col=0)
gldas.rename(columns={'086_L1': 'gldas'}, inplace=True)
gldas = pd.DataFrame(gldas['gldas'])
ascat = pd.DataFrame(df['m_soil']).rename(columns={'m_soil': 'ascat'})
matched = temp_match.matching(ascat, insitu, gldas)
if rescaling is not None:
scaled = scaling.scale(matched, rescaling, reference_index=1)
else:
scaled = matched
metrics = OrderedDict()
metrics['bias'] = df_metrics.bias(scaled)
metrics['pearson'] = df_metrics.pearsonr(scaled)
metrics['kendall'] = df_metrics.kendalltau(scaled)
metrics['ubrmsd'] = df_metrics.ubrmsd(scaled)
metrics['var_ratio'] = df_var_ratio(scaled)
tcol_error = df_metrics.tcol_error(scaled)._asdict()
ts_title = "Soil moisture. "
if rescaling is not None:
ts_title = ' '.join([ts_title, 'Rescaling: %s.' % rescaling])
else:
ts_title = ' '.join([ts_title, 'No rescaling.'])
axes = scaled.plot(subplots=True, title=ts_title, figsize=(18, 8))
# these are matplotlib.patch.Patch properties
props = dict(facecolor='white', alpha=0)
columns = ('ascat-insitu', 'ascat-gldas', 'insitu-gldas')
row_labels = ['bias', 'pearson R', 'kendall tau', 'unbiased RMSD', 'variance ratio']
cell_text = []
for metric in metrics:
metric_values = metrics[metric]
if type(metric_values) == tuple:
metric_values = metric_values[0]
metric_values = metric_values._asdict()
cell_text.append(["%.2f" % metric_values['ascat_and_insitu'],
"%.2f" % metric_values['ascat_and_gldas'],
"%.2f" % metric_values['insitu_and_gldas']])
table = plt.table(
cellText=cell_text,
colLabels=columns,
colWidths=[0.1, 0.1, 0.1],
rowLabels=row_labels, loc='bottom',
bbox=(0.2, -1.25, 0.5, 0.8))
tcol_table = plt.table(
cellText=[["%.2f" % tcol_error['ascat'],
"%.2f" % tcol_error['gldas'],
"%.2f" % tcol_error['insitu']]],
colLabels=('ascat', 'gldas', 'insitu'),
colWidths=[0.1, 0.1, 0.1],
rowLabels=['Triple collocation error'], loc='bottom',
bbox=(0.2, -1.65, 0.5, 0.3))
plt.subplots_adjust(left=0.08, bottom=0.35)
axes = scatter_matrix(scaled)
axes.flat[0].figure.suptitle(ts_title)
# only draw 1:1 line if scaling was applied
if rescaling is not None:
for j, ax in enumerate(axes.flatten()):
if np.remainder(j + 1, 3 + 1) != 1:
min_x, max_x = ax.get_xlim()
min_y, max_y = ax.get_ylim()
# find minimum lower left coordinate and maximum upper right
min_ll = min([min_x, min_y])
max_ur = max([max_x, max_y])
ax.plot([min_ll, max_ur], [min_ll, max_ur], '--', c='0.6')
return df
